Google Patentsopen access

Artificial intelligence and/or virtual reality for activity optimization/ … — P Tech, Llc (US12380346B2)

ABSTRACT

Abstract

Optimizing and/or personalizing activities to a user through artificial intelligence and/or virtual reality.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. Non-Provisional application Ser. No. 18/317,523, filed May 15, 2023, which is a continuation of U.S. Non-Provisional application Ser. No. 16/118,025, filed Aug. 30, 2018, now U.S. Pat. No. 11,687,800, which claims the benefit of U.S. Provisional Application No. 62/552,096, filed Aug. 30, 2017, and U.S. Provisional Application No. 62/552,091, filed Aug. 30, 2017, and is a continuation of U.S. Non-Provisional application Ser. No. 17/395,177, filed Aug. 5, 2021, which is a continuation of U.S. Non-Provisional application Ser. No. 16/118,025, filed Aug. 30, 2018, now U.S. Pat. No. 11,687,800, which claims the benefit of U.S. Provisional Application No. 62/552,096, filed Aug. 30, 2017, and U.S. Provisional Application No. 62/552,091, filed Aug. 30, 2017, the entireties of which are hereby incorporated by reference.

FIELD OF THE DISCLOSURE

The field of the disclosure relates generally to artificial intelligence and virtual/augmented reality, and more specifically, to methods and systems for optimizing/personalizing user activities through artificial intelligence and/or virtual reality.

BACKGROUND

Artificial intelligence (AI) is slowly being incorporated into the medical field. AI systems and techniques can be used to improve health services—both at the physician and patient levels, used to improve the accuracy of medical diagnosis, manage treatments, provide real time monitoring of patients, and integrate the different health providers and health services together—all while decreasing the costs of medical services. However, some previous attempts to use artificial intelligence in medicine have failed. For example, IBM's Watson attempted to use artificial intelligence techniques for oncology therapy. Watson failed to help oncologic treatments because Watson was not a linear artificial intelligence dictated purely by logic and was unable to analyze variances in biologic functions at a variety of different levels.

BRIEF DESCRIPTION

In an aspect, a system includes a monitor device and an artificial intelligence (AI) system. The monitor device is configured to monitor one or more physical properties of a user. The AI system is configured to receive and analyze the monitored physical properties to generate one or more activity parameters optimized or personalized to the user. The AI system is configured to implement one or more artificial intelligence techniques such as predictive learning, machine learning, automated planning and scheduling, machine perception, computer vision, affective computing to generate one or more activity parameters optimized or personalized to a user.

In another aspect, a system includes a goggle device and a controller. The goggle device is configured to provide one or more images to a user of the system and perform at least one vision test on the user. The controller is configured to execute an algorithm for tracking at least one vision-related impairment of the user based on the vision test and/or enhancing the vision of the user based on the vision test.

In another aspect, a method of diagnosing diseases and assessing health is performed by retinal imaging or scanning. The pupil is dilated by, for example, dark glasses, and then the retina is imaged. In an embodiment, the image of the retina is evaluated by a computing device, a person, or both to glean information relating to not only health, but also emotional reactions, physiological reactions, etc.

In another aspect, optical imaging, especially of the retina, is used to obtain real-time feedback data, which can be analyzed by a computer, a person, or both to determine emotional response, pain, etc. This feedback data can be fed into a VR/AR program or otherwise used to determine a subject's emotional and/or physiological response to certain stimuli.

In still another aspect, optical imaging or scanning is used for continuous health monitoring. For example, continuous or regular imaging of the eye is used to track blood pressure. Reactions to a stimulus, such as exercise for example, could help doctors and could be utilized by a computer to automatically check for signs of disease and/or poor health in various areas. In an embodiment, findings are integrated with other systems, such as those used to collect medical data for example, to provide more accurate and/or comprehensive findings.

In yet another aspect, a retina is evaluated to allow a computer to make adjustments based on real-time user feedback. For example, if the person is playing a VR/AR game, a processor can use instantaneous feedback from the user to adjust difficulty, pace, etc.

In another aspect, alternate methods of measuring blood pressure and other health statistics are used for continual monitoring. In an exemplary and non-limiting embodiment, a wrist-wearable monitor or an earpiece monitor with a Doppler ultrasound imaging system is adapted to estimate blood pressure. In a preferred embodiment, continual retinal imaging and evaluation and health monitoring devices work in combination with a device to continuously measure blood pressure. In such an embodiment, a processor will preferably have access to any data gathered by retinal imaging and/or other health monitoring devices.

The features, functions, and advantages that have been discussed can be achieved independently in various embodiments or may be combined in yet other embodiments, further details of which can be seen with reference to the following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an exemplary system and data flow according to an embodiment.

FIG. 2 is a block diagram of an exemplary waking/alerting algorithm according to an embodiment.

FIG. 3 is a block diagram of an exemplary automated blood pressure measurement algorithm according to an embodiment.

FIG. 4 is a block diagram of an exemplary blood draw algorithm and system according to an embodiment.

FIG. 5 illustrates an exemplary architecture of a computing device configured to provide aspects of the systems and processes described herein via a software environment.

FIGS. 6 - 12 illustrate an exemplary virtual reality system according to an embodiment.

FIG. 13 is an image of an Amsler Grid showing a large scotoma encroaching on the central fixation.

FIG. 14 is an image of the peripheral vision of the human eye.

FIG. 15 is an image of scotoma affecting vision.

FIG. 16 is a modified image to correct for scotoma according to an embodiment.

FIG. 17 is an image of a histoplasmosis scar.

FIG. 18 is an image of vision distortion caused by the scar of FIG. 17 .

FIG. 19 is an image of adjusted vision distortion with a filter according to an embodiment.

FIG. 20 is a flowchart of an exemplary algorithm for filtering camera data according to an embodiment.

FIG. 21 is an example of conventional cellular phone apps that provide vision testing and/or Amsler grid progression testing.

FIG. 22 is an image of an exemplary goggle device (front and rear view), display, and mouse/pad according to an embodiment.

FIG. 23 is a block diagram of an exemplary goggle device controller method and system according to an embodiment.

FIG. 24 is a schematic illustration of a retinal evaluation system according to an embodiment.

FIG. 25 A is a schematic block diagram of an exemplary embodiment of the retinal evaluation system of FIG. 24 .

FIG. 25 B is a schematic block diagram of a retinal scanning system included in an alternative exemplary embodiment of the retinal evaluation system of FIG. 24 .

FIG. 26 is a schematic illustration of a stimulus response measurement system according to an embodiment.

FIG. 27 A is a schematic block diagram of the stimulus response measurement system of FIG. 26 .

FIG. 27 B is a schematic block diagram of an image creation system included in the embodiment of FIGS. 26 and 27 A .

FIG. 28 is a schematic illustration of an earpiece and computer for real-time measurement of blood pressure according to an embodiment.

FIG. 29 is a schematic block diagram of the earpiece and computer of FIG. 28 .

FIG. 30 A is a schematic illustration of a wearable blood pressure monitor with a traditional cuff according to an embodiment.

FIG. 30 B is a schematic block diagram of a wearable blood pressure monitor according to an embodiment.

FIG. 31 is a schematic illustration of a wrist-wearable embodiment of the wearable blood pressure monitor of FIG. 30 B .

FIG. 32 is a flow chart of evaluating exercise through continuous measurements according to an embodiment.

FIG. 33 is a flow chart of a method of performing research studies using real-time measurements of the subjects of the study to determine reactions according to an embodiment.

FIG. 34 is a flow chart of a method of using real-time measurements of a content viewer to recommend content and select relevant ads according to an embodiment.

FIG. 35 is a flow chart of a method of adjusting a simulation based on real-time measurements of the user according to an embodiment.

FIG. 36 is a flow chart of a method of continuously monitoring health by taking real-time measurements of the user according to an embodiment.

Corresponding reference characters indicate corresponding parts throughout the drawings.

DETAILED DESCRIPTION

The systems and methods described herein, in an embodiment, enable the optimization and/or personalization of health-related tasks through artificial intelligence (AI). Aspects described herein also enable optimization and/or personalization in microclimate, robotics, management information systems, and the like.

FIG. 1 is a block diagram illustrating an exemplary system 100 for optimizing and/or personalizing health-related tasks, for example. The system 100 includes one or more patient monitor sensors 102 , an AI system 104 , a dietary database 106 , a pharmacy-controlled medication delivery subsystem 108 , an electronic medical records database 110 , a global expert system 112 , a patient-controlled medication delivery subsystem 114 , a call button 116 , a smart alert system 118 , one or more healthcare provider devices 120 , and one or more patient devices 122 . In an embodiment, system 100 enables automated planning and scheduling (e.g., AI planning) of strategies or action sequences for execution by healthcare providers and/or patients such that delivery of healthcare services is optimized (e.g., optimal for a healthcare provider and/or group of healthcare providers, optimal for the patient care and/or satisfaction, etc.) and/or personalized (e.g., personalized to needs/requirements of a healthcare provider and/or group of healthcare providers, personalized to needs/requirements of the patient, etc.).

The patient monitor sensors 102 , the dietary database 106 , the pharmacy-controlled medication delivery subsystem 108 , the electronic medical records database 110 , the global expert system 112 , and the patient-controlled medication delivery subsystem 114 are electrically and/or communicatively coupled to the AI system 104 . Additionally or alternatively, healthcare provider devices 120 and/or patient devices 122 are electrically and/or communicatively coupled to AI system 104 . The patient monitor sensors 102 , the AI system 104 , and the call button 116 are electrically and/or communicatively coupled to the smart alert system 118 . The smart alert system 118 is electrically and/or communicatively coupled to the healthcare provider devices 120 and the patient devices 122 . In an exemplary and non-limiting embodiment, the electrical and/or communicative couplings described herein are achieved via one or more communications networks capable of facilitating the exchange of data among various components of AI system 100 . For example, the one or more communications networks may include a wide area network (WAN) that is connectable to other telecommunications networks, including other WANs or portions of the Internet or an intranet, including local area networks (LANs) and/or personal area networks (PANs). The one or more communications networks may be any telecommunications network that facilitates the exchange of data, such as those that operate according to the IEEE 802.3 (e.g., Ethernet), the IEEE 802.11 (e.g., Wi-Fi™), and/or the IEEE 802.15 (e.g., Bluetooth®) protocols, for example. In another embodiment, the one or more communications networks are any medium that allows data to be physically transferred through serial or parallel communication channels (e.g., copper wire, optical fiber, computer bus, wireless communication channel, etc.).

The patient monitor sensors 102 are configured to sense physical properties associated with the patient. The patient monitor sensors 102 can be generally any type of biometric sensor that generates biometric data and may be positioned outside or inside the body of a patient. Exemplary sensors include, but are not limited to, contactless bed sensors such as the Murata SCA11H, activity trackers (e.g., wireless-enabled wearable devices available from Fitbit, Inc., etc.), smartwatches (e.g., the Apple® Watch available from Apple, Inc., etc.), smartphone computing devices, tablet computing devices, smart rings (e.g., MOTA® DOI SmartRing available from Mota Group, Inc., Token available from Tokenize Inc., etc.), smart glasses, smart contact lenses, video cameras, implants, retinal scanners, flexible sensors, surgical implants, medical implants, voice/sound input (e.g., microphones), accelerometers, goniometers, and like commercial or custom tracking devices with the ability to record and/or transmit patient metrics (e.g., distance walked or ran, calorie consumption, heartbeat, qu

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. Non-Provisional application Ser. No. 18/317,523, filed May 15, 2023, which is a continuation of U.S. Non-Provisional application Ser. No. 16/118,025, filed Aug. 30, 2018, now U.S. Pat. No. 11,687,800, which claims the benefit of U.S. Provisional Application No. 62/552,096, filed Aug. 30, 2017, and U.S. Provisional Application No. 62/552,091, filed Aug. 30, 2017, and is a continuation of U.S. Non-Provisional application Ser. No. 17/395,177, filed Aug. 5, 2021, which is a continuation of U.S. Non-Provisional application Ser. No. 16/118,025, filed Aug. 30, 2018, now U.S. Pat. No. 11,687,800, which claims the benefit of U.S. Provisional Application No. 62/552,096, filed Aug. 30, 2017, and U.S. Provisional Application No. 62/552,091, filed Aug. 30, 2017, the entireties of which are hereby incorporated by reference.

FIELD OF THE DISCLOSURE

The field of the disclosure relates generally to artificial intelligence and virtual/augmented reality, and more specifically, to methods and systems for optimizing/personalizing user activities through artificial intelligence and/or virtual reality.

BACKGROUND

Artificial intelligence (AI) is slowly being incorporated into the medical field. AI systems and techniques can be used to improve health services—both at the physician and patient levels, used to improve the accuracy of medical diagnosis, manage treatments, provide real time monitoring of patients, and integrate the different health providers and health services together—all while decreasing the costs of medical services. However, some previous attempts to use artificial intelligence in medicine have failed. For example, IBM's Watson attempted to use artificial intelligence techniques for oncology therapy. Watson failed to help oncologic treatments because Watson was not a linear artificial intelligence dictated purely by logic and was unable to analyze variances in biologic functions at a variety of different levels.

BRIEF DESCRIPTION

In an aspect, a system includes a monitor device and an artificial intelligence (AI) system. The monitor device is configured to monitor one or more physical properties of a user. The AI system is configured to receive and analyze the monitored physical properties to generate one or more activity parameters optimized or personalized to the user. The AI system is configured to implement one or more artificial intelligence techniques such as predictive learning, machine learning, automated planning and scheduling, machine perception, computer vision, affective computing to generate one or more activity parameters optimized or personalized to a user.

In another aspect, a system includes a goggle device and a controller. The goggle device is configured to provide one or more images to a user of the system and perform at least one vision test on the user. The controller is configured to execute an algorithm for tracking at least one vision-related impairment of the user based on the vision test and/or enhancing the vision of the user based on the vision test.

In another aspect, a method of diagnosing diseases and assessing health is performed by retinal imaging or scanning. The pupil is dilated by, for example, dark glasses, and then the retina is imaged. In an embodiment, the image of the retina is evaluated by a computing device, a person, or both to glean information relating to not only health, but also emotional reactions, physiological reactions, etc.

In another aspect, optical imaging, especially of the retina, is used to obtain real-time feedback data, which can be analyzed by a computer, a person, or both to determine emotional response, pain, etc. This feedback data can be fed into a VR/AR program or otherwise used to determine a subject's emotional and/or physiological response to certain stimuli.

In still another aspect, optical imaging or scanning is used for continuous health monitoring. For example, continuous or regular imaging of the eye is used to track blood pressure. Reactions to a stimulus, such as exercise for example, could help doctors and could be utilized by a computer to automatically check for signs of disease and/or poor health in various areas. In an embodiment, findings are integrated with other systems, such as those used to collect medical data for example, to provide more accurate and/or comprehensive findings.

In yet another aspect, a retina is evaluated to allow a computer to make adjustments based on real-time user feedback. For example, if the person is playing a VR/AR game, a processor can use instantaneous feedback from the user to adjust difficulty, pace, etc.

In another aspect, alternate methods of measuring blood pressure and other health statistics are used for continual monitoring. In an exemplary and non-limiting embodiment, a wrist-wearable monitor or an earpiece monitor with a Doppler ultrasound imaging system is adapted to estimate blood pressure. In a preferred embodiment, continual retinal imaging and evaluation and health monitoring devices work in combination with a device to continuously measure blood pressure. In such an embodiment, a processor will preferably have access to any data gathered by retinal imaging and/or other health monitoring devices.

The features, functions, and advantages that have been discussed can be achieved independently in various embodiments or may be combined in yet other embodiments, further details of which can be seen with reference to the following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an exemplary system and data flow according to an embodiment.

FIG. 2 is a block diagram of an exemplary waking/alerting algorithm according to an embodiment.

FIG. 3 is a block diagram of an exemplary automated blood pressure measurement algorithm according to an embodiment.

FIG. 4 is a block diagram of an exemplary blood draw algorithm and system according to an embodiment.

FIG. 5 illustrates an exemplary architecture of a computing device configured to provide aspects of the systems and processes described herein via a software environment.

FIGS. 6 - 12 illustrate an exemplary virtual reality system according to an embodiment.

FIG. 13 is an image of an Amsler Grid showing a large scotoma encroaching on the central fixation.

FIG. 14 is an image of the peripheral vision of the human eye.

FIG. 15 is an image of scotoma affecting vision.

FIG. 16 is a modified image to correct for scotoma according to an embodiment.

FIG. 17 is an image of a histoplasmosis scar.

FIG. 18 is an image of vision distortion caused by the scar of FIG. 17 .

FIG. 19 is an image of adjusted vision distortion with a filter according to an embodiment.

FIG. 20 is a flowchart of an exemplary algorithm for filtering camera data according to an embodiment.

FIG. 21 is an example of conventional cellular phone apps that provide vision testing and/or Amsler grid progression testing.

FIG. 22 is an image of an exemplary goggle device (front and rear view), display, and mouse/pad according to an embodiment.

FIG. 23 is a block diagram of an exemplary goggle device controller method and system according to an embodiment.

FIG. 24 is a schematic illustration of a retinal evaluation system according to an embodiment.

FIG. 25 A is a schematic block diagram of an exemplary embodiment of the retinal evaluation system of FIG. 24 .

FIG. 25 B is a schematic block diagram of a retinal scanning system included in an alternative exemplary embodiment of the retinal evaluation system of FIG. 24 .

FIG. 26 is a schematic illustration of a stimulus response measurement system according to an embodiment.

FIG. 27 A is a schematic block diagram of the stimulus response measurement system of FIG. 26 .

FIG. 27 B is a schematic block diagram of an image creation system included in the embodiment of FIGS. 26 and 27 A .

FIG. 28 is a schematic illustration of an earpiece and computer for real-time measurement of blood pressure according to an embodiment.

FIG. 29 is a schematic block diagram of the earpiece and computer of FIG. 28 .

FIG. 30 A is a schematic illustration of a wearable blood pressure monitor with a traditional cuff according to an embodiment.

FIG. 30 B is a schematic block diagram of a wearable blood pressure monitor according to an embodiment.

FIG. 31 is a schematic illustration of a wrist-wearable embodiment of the wearable blood pressure monitor of FIG. 30 B .

FIG. 32 is a flow chart of evaluating exercise through continuous measurements according to an embodiment.

FIG. 33 is a flow chart of a method of performing research studies using real-time measurements of the subjects of the study to determine reactions according to an embodiment.

FIG. 34 is a flow chart of a method of using real-time measurements of a content viewer to recommend content and select relevant ads according to an embodiment.

FIG. 35 is a flow chart of a method of adjusting a simulation based on real-time measurements of the user according to an embodiment.

FIG. 36 is a flow chart of a method of continuously monitoring health by taking real-time measurements of the user according to an embodiment.

Corresponding reference characters indicate corresponding parts throughout the drawings.

DETAILED DESCRIPTION

The systems and methods described herein, in an embodiment, enable the optimization and/or personalization of health-related tasks through artificial intelligence (AI). Aspects described herein also enable optimization and/or personalization in microclimate, robotics, management information systems, and the like.

FIG. 1 is a block diagram illustrating an exemplary system 100 for optimizing and/or personalizing health-related tasks, for example. The system 100 includes one or more patient monitor sensors 102 , an AI system 104 , a dietary database 106 , a pharmacy-controlled medication delivery subsystem 108 , an electronic medical records database 110 , a global expert system 112 , a patient-controlled medication delivery subsystem 114 , a call button 116 , a smart alert system 118 , one or more healthcare provider devices 120 , and one or more patient devices 122 . In an embodiment, system 100 enables automated planning and scheduling (e.g., AI planning) of strategies or action sequences for execution by healthcare providers and/or patients such that delivery of healthcare services is optimized (e.g., optimal for a healthcare provider and/or group of healthcare providers, optimal for the patient care and/or satisfaction, etc.) and/or personalized (e.g., personalized to needs/requirements of a healthcare provider and/or group of healthcare providers, personalized to needs/requirements of the patient, etc.).

The patient monitor sensors 102 , the dietary database 106 , the pharmacy-controlled medication delivery subsystem 108 , the electronic medical records database 110 , the global expert system 112 , and the patient-controlled medication delivery subsystem 114 are electrically and/or communicatively coupled to the AI system 104 . Additionally or alternatively, healthcare provider devices 120 and/or patient devices 122 are electrically and/or communicatively coupled to AI system 104 . The patient monitor sensors 102 , the AI system 104 , and the call button 116 are electrically and/or communicatively coupled to the smart alert system 118 . The smart alert system 118 is electrically and/or communicatively coupled to the healthcare provider devices 120 and the patient devices 122 . In an exemplary and non-limiting embodiment, the electrical and/or communicative couplings described herein are achieved via one or more communications networks capable of facilitating the exchange of data among various components of AI system 100 . For example, the one or more communications networks may include a wide area network (WAN) that is connectable to other telecommunications networks, including other WANs or portions of the Internet or an intranet, including local area networks (LANs) and/or personal area networks (PANs). The one or more communications networks may be any telecommunications network that facilitates the exchange of data, such as those that operate according to the IEEE 802.3 (e.g., Ethernet), the IEEE 802.11 (e.g., Wi-Fi™), and/or the IEEE 802.15 (e.g., Bluetooth®) protocols, for example. In another embodiment, the one or more communications networks are any medium that allows data to be physically transferred through serial or parallel communication channels (e.g., copper wire, optical fiber, computer bus, wireless communication channel, etc.).

The patient monitor sensors 102 are configured to sense physical properties associated with the patient. The patient monitor sensors 102 can be generally any type of biometric sensor that generates biometric data and may be positioned outside or inside the body of a patient. Exemplary sensors include, but are not limited to, contactless bed sensors such as the Murata SCA11H, activity trackers (e.g., wireless-enabled wearable devices available from Fitbit, Inc., etc.), smartwatches (e.g., the Apple® Watch available from Apple, Inc., etc.), smartphone computing devices, tablet computing devices, smart rings (e.g., MOTA® DOI SmartRing available from Mota Group, Inc., Token available from Tokenize Inc., etc.), smart glasses, smart contact lenses, video cameras, implants, retinal scanners, flexible sensors, surgical implants, medical implants, voice/sound input (e.g., microphones), accelerometers, goniometers, and like commercial or custom tracking devices with the ability to record and/or transmit patient metrics (e.g., distance walked or ran, calorie consumption, heartbeat, quality of sleep, movements, sleep patterns, blood pressure, pulse, sweat, skin resistance, etc.). Exemplary sensors further include, but are not limited to, existing sensors used in hospital monitoring systems, such as hospital records, pulse oximeters, retinal changes, implantable defibrillators, temperature, thermal gradients, changes in diet or food patterns (e.g., from dieticians), medication (e.g., from the pharmacy), test results from lab service (e.g., blood work and urinalysis) and the like. Additional exemplary sensors include, but are not limited to, devices configured to collect data relative to medications and/or exercise, such as motion patterns, eye movement, body temperature, core vs. peripheral breathing, shaking and/or tremors, heart rate, cardiac rhythms and/or arrhythmias, blood pressure, pulse, oximeters, respiration rate, diaphragm excursion, stride length, sleep patterns (e.g., EMGs, EEGs, etc.), oxygenation (e.g., pulse odometers), hair follicle movement and/or position change, lactic acidosis in muscles locally and/or systemically, sweat, thermal changes to skin and/or deep tissue, salinity or particles, blood flow, vasoconstriction, vasodilation, foot orthotic sensors on stride length, frequency, load, where load is applied, timing between steps, asymmetry in gait cadence and/or timing cadence, arm movement, pupillary and/or retinal response, retinal vascular changes, check movement (e.g., for retained air resistance, etc.), thermal gradients between one body part and another (e.g., quadriceps and chest or neck, etc.), blood flow between different body parts (e.g., neck and foot, etc.) such as measurements from laser flow sensors, ultrasonic sensors, acoustic sensors, electromagnetic field sensors, tension sensors, compression sensors, magnetic resonance imaging (MRI), positron emission tomography (PET), and the like. Further exemplary sensors include, but are not limited to, one or more aspects of a virtual reality system as further described herein. In an embodiment, a single patient monitor sensor 102 may provide a plurality of data points. In an embodiment, patient monitor sensors 102 transmit and/or provide data to other aspects of system 100 via wireless, radio frequency (RF), optical, and the like communications means. Further, if the sensor is implanted, the sensor can generate electricity by electromagnets, motion analysis and/or thermal changes. Moreover, the sensors are not limited to use with a patient and can be used in cellular testing, animal testing, and bacterial testing.

Accordingly, aspects of system 100 , through the one or more patient monitors 102 , enable patient properties such as biometric data, cellular data, biologic data, and non-biologic data to be collected and analyzed. This data can then be utilized by the AI system 104 to optimize and/or personalize health-related tasks, as described herein. The data collected can relate to any patient property such as body functions, organ function, cellular functions, and metabolic functions, for example. Moreover, aspects of system 100 are not limited to people and can be used for any biologic function. For example, aspects of system 100 can be used to analyze animal biologic functions and/or microbiologic functions. In all embodiments the capture of information could be done via wireless communication, or wired communication. The information could be uploaded to and stored in a central repository or processed on site.

The AI system 104 is configured to implement one or more artificial intelligence techniques (e.g., predictive learning, machine learning, automated planning and scheduling, machine perception, computer vision, affective computing, etc.) that optimize and/or personalize one or more aspects of monitoring, diagnosis, treatment, and prevention of disease, illness, injury, physical and/or mental impairments of the patient. In an embodiment, AI system 104 comprises processor-executable instructions embodied on a storage memory device of a computing device to provide predictive learning techniques via a software environment. For example, AI system 104 may be provided as processor-executable instructions that comprise a procedure, a function, a routine, a method, and/or a subprogram utilized independently or in conjunction with additional aspects of system 100 according to an exemplary embodiment of the disclosure. Additional details regarding AI system 104 are provided herein.

The dietary database 106 is configured to store an organized collection of data representing one or more of a dietary history (e.g., food and/or nutrient consumption levels, etc.) of the patient, dietary preferences of the patient, food and/or nutrient consumption levels of populations in a given geographic area (e.g., worldwide, in a geographic locality of the patient, etc.), food composition (e.g., USDA National Nutrient Database for Standard Reference, USDA Branded Food Products Database, etc.), dietary supplement labels (e.g., Dietary Supplement Label Database from the National Institutes of Health), and the like.

The pharmacy-controlled medication delivery subsystem 108 is configured to allow a pharmacy actor (e.g., pharmacist, pharmacy staff member, pharmacy automated system, etc.) to administer medication to the patient. In one aspect, the system 100 (e.g., AI system 104 ) sends data to the pharmacy actor via the pharmacy-controlled medication delivery subsystem regarding the medication. Such data can include information related to the medication's dosage, type, and administration for example. In addition, aspects of AI system 104 can be used with systems and methods of pharmaceutical delivery, such as those described in U.S. Pat. No. 9,750,612, the entire disclosure of which is hereby incorporated by reference.

The electronic medical records database 110 is configured to store an organized collection of data representing one or more of demographics, medical history, medication history, allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics (e.g., age, weight, etc.), billing information, and the like for the patient and/or an entire population.

The global expert system 112 is configured to emulate decision-making abilities of one or more human experts regarding one or more aspects of monitoring, diagnosis, treatment, and prevention of disease, illness, injury, physical and/or mental impairments of the patient. In an embodiment, global expert system 112 includes a knowledge base of facts and/or rules (e.g., global rule set) for each patient and an inference engine that applies the rules to known facts to deduce new facts, explain situations, and the like. In an embodiment, global expert system 112 comprises processor-executable instructions embodied on a storage memory device of a computing device to provide predictive learning techniques via a software environment. For example, global expert system 112 may be provided as processor-executable instructions that comprise a procedure, a function, a routine, a method, and/or a subprogram utilized independently or in conjunction with additional aspects of system 100 according to an exemplary embodiment of the disclosure. Additional details regarding global expert system 112 are provided herein.

The patient-controlled medication delivery subsystem 114 is configured to allow the patient to administer his or her own medication. Exemplary routes of administration include, but are not limited to, oral, intravenous, epidural, inhaled, nasal, transcutaneous, and the like. Exemplary patient-controlled medication delivery systems include, but is not limited to, patient-controlled analgesia (PCA), an intravenous (IV) drip system, and the like.

The call button 116 is configured to enable the patient to alert the healthcare provider (e.g., doctor, nurse, staff member, etc.) of a need for aid.

The smart alert system 118 is configured to monitor and record physical properties associated with the patient (e.g., recent food, movement, sleep pattern, blood pressure, pulse, sweat, skin resistance, etc.) during a time period leading up to an activation of call button 116 by the patient, compile the monitored and recorded properties into an adaptive system, monitor the physical properties during a future time period, and proactively alert healthcare providers (e.g., via healthcare provider devices 120 ) when a similar set of property conditions are met. In this manner, smart alert system 118 is configured to alert healthcare providers, via healthcare provider devices 120 , before the patient presses call button 116 , for example. In an embodiment, smart alert system 118 comprises processor-executable instructions embodied on a storage memory device of a computing device to provide predictive learning techniques via a software environment. For example, smart alert system 118 may be provided as processor-executable instructions that comprise a procedure, a function, a routine, a method, and/or a subprogram utilized independently or in conjunction with additional aspects of system 100 according to an exemplary embodiment of the disclosure. Additional details regarding smart alert system 118 are provided herein.

The healthcare provider devices 120 are configured to provide access to AI system 104 and/or smart alert system 118 and/or provide alerts from smart alert system 118 to the healthcare providers. In an aspect, healthcare provider devices 120 are computing devices including, but not limited to, smartphone computing devices, smartwatches, tablet computing devices, desktop computing devices, and the like. Additionally or alternatively, healthcare provider devices 120 may include pagers, alarm clocks, buzzers, lights, printed notifications, and the like.

The patient devices 122 are configured to provide alerts from smart alert system 118 to the patient and/or provide access to AI system 104 by the patient. In an aspect, patient devices 122 are computing devices including, but not limited to, smartphone computing devices, activity monitoring devices, smartwatches, tablet computing devices, desktop computing devices, telpad computing devices (e.g., HC7-M Telpad available from PLDT Inc., etc.), and the like.

In an embodiment, medical devices are electrically and/or communicatively coupled to the AI system 104 and are configured to provide a medical treatment to a patient. For example, bone stimulators, neuro stimulators, and/or pain stimulators can be connected with the AI system 104 and controlled/operated by the AI system to delivery optimized and/or personalized patient treatment. In an embodiment, the medical device may be a robotic medical device such as those disclosed by U.S. Pat. No. 9,192,395, which is hereby incorporated by reference in its entirety. For example, aspects of system 100 (e.g., AI system 104 ) can direct a robotic medical device to deliver blood flow or pharmaceuticals to a specific location through minimally invasive approaches, such as by magnetic guidance. In an embodiment, the medical device may be an endotracheal tube such as those disclosed by U.S. Pat. Nos. 6,820,614 and 7,320,319, both of which are hereby incorporated by reference in their entirety.

In an embodiment, aspects of system 100 enable data for a specific patient to be compared relative to data (e.g., trends, etc.) for a group and/or subgroup of patients. Exemplary subgroups include, but are not limited to, age, gender, race, disease type, multiple disease types (e.g., ASA classification, etc.), and the like. For example, a 60-year-old patient with diabetes and hypertension differs from an 80-year-old patient with no disease-specific markers. Aspects of system 100 enable creating data trends for an individual, a subgroup (e.g., defined by healthcare provider to share and/or compare data, etc.) and a general group (e.g., age, sex, gender, country, location, etc.). For example, aspects of system 100 enable comparisons and identifications of variances on an individual basis, group basis, daily basis, nocturnal basis, day/night basis, based on when people eat and/or exercise and/or when people are exposed to different environmental conditions, such as sunlight. Moreover, this is just not limited to patient comparisons but can also include cellular functions and/or bacterial functions such as to optimize growth and/or inhibition.

In an embodiment, data collected by patient monitor sensors 102 is encrypted and/or is covered by regulatory (e.g., HIPPA, etc.) requirements. The data may be associated with the patient or the data may be anonymous and/or encrypted. Such data may include, but is not limited to, age, weight, gender, biometrics (e.g., macro, micro, cellular and/or mitochondrial), videos, and/or financials. A patient may choose to temporarily (e.g., during a hospital stay) and/or for a long term (e.g., at home) share data for use by aspects of system 100 or the data can be shared automatically with the system. For example, patient data may be collated to optimized medical treatments, workout regimens and/or timing, generic vs. specific drugs, neutraceutocals vs. over the counter drugs vs. no medication vs. workout time, and the like. In another embodiment, aspects of system 100 (e.g., AI system 104 ) utilize data collected by patient monitor sensors 102 to determine when a workout is most effective for a patient based on characteristics personal to the patient and/or a group to which the patient belongs and/or provides a best response for energy, endurance, and the like. In another embodiment, aspects of system 100 (e.g., AI system 104 ) utilize data collected by patient monitor sensors 102 to determine when is the best time for a patient to receive medication (i.e., not just if to take and dosage). In another embodiment, aspects of system 100 (e.g., AI system 104 ) utilize data collected by patient monitor sensors 102 to determine effects of food and/or physical activities on medication delivery. In another embodiment, aspects of system 100 (e.g., AI system 104 ) utilize data collected by patient monitor sensors 102 to determine whether a patient should workout and what is the best time to work out relative to medications and/or treatments. In aspect, these considerations are important for patients exhibiting multiple diseases, such as cancer and hypertension, diabetes and cardiovascular disease, and the like. In another embodiment, aspects of system 100 (e.g., AI system 104 ) predicts how patterns change over time (e.g., hourly, daily, monthly, etc.) for an individual and/or groups and optimizes efforts for schools, employers, families, churches, other social groups, and the like. The AI system 104 performs these determinations to optimize healthcare delivery for an individual patient instead of for healthcare provider staffing concerns, in an embodiment.

In another embodiment, aspects of system 100 (e.g., AI system 104 ) utilize data collected by patient monitor sensors 102 to determine an optimal and/or sub-optimal time for the user (e.g., patient) to study, cat, take medications, sleep, read for comprehension, concentrate, work, rest, socialize, call, text message, diet, cat, what to cat, and the like. For example, these determinations may be made on data sub-classified based on data points and may change as more data is obtained. In an embodiment, the user (e.g., patient) can actively control and turn on/off as desired.

In another embodiment, aspects of system 100 (e.g., AI system 104 ) determines how user actions can be modified by diurnal patterns and how to optimize environment, food, medications, local events, and the like and to predict and/or optimize body function and/or activity. In another embodiment, aspects of system 100 (e.g., AI system 104 ) determine when is the best time for a surgery or procedure, when to take medications, when to cat food, and the like. In an embodiment, a patient verbalizes discomfort (e.g., “I feel sick,” “I have a headache,” etc.) and aspects of system 100 (e.g., AI system 104 ) modifies recommendations on when to study, read, exercise, take medications, dosage levels, level of activity (e.g., how strenuous), and the like. In an embodiment, aspects of system 100 (e.g., AI system 104 ) communicate to an employer and/or healthcare provider how much activity, stress, medications, and the like is appropriate for an individual/patient. In an embodiment, aspects of system 100 (e.g., AI system 104 ) give direct feedback to the users/patients themselves on when, where, and how to complete various activities to obtain an optimal effect. In an embodiment, a user/patient can obtain an image of himself (e.g., a “selfie”) to see facial movements or activity to determine health-related parameters and/or how active to be. In another embodiment, aspects of system 100 (e.g., AI system 104 ) utilize information from a reference (e.g., the Old Farmer's Almanac, horoscopes, etc.) in the intelligence mix to determine trends, such as diurnal (e.g., best time during day), and the like.

In another embodiment, patient monitor sensors 102 modify midstream so if the patient slept poorly, is under stress, is slower responding to questions, and the like, aspects of system 100 (e.g., AI system 104 ) change the patient's activity pattern for that day but not subsequent days. In this manner, aspects of the disclosure are not just comparing to a group but also with an individual's variation patterns note and modified on a daily, hourly, and the like basis. For example, if the individual is hung over he or she will be slower and won't perform as well during that day. The same concept applies in a hospital setting, school setting, and the like. For example, knowing status of employees (e.g., hung over, sleepy, etc.) affects how the individual is treated and the employer can staff a shift based on their abilities, problems, and the like.

In another embodiment, aspects of system 100 (e.g., AI system 104 ) determine if a patient needs a pain medication and if/when they need anxiolytics, anti-inflammatoires, or just someone to talk to and/or music to pacify. For example, aspects of system 100 (e.g., patient monitor sensors 102 and/or AI system 104 ) determine these patterns by eye movement, temperature, sweating, core vs. peripheral movement, sweating palms vs. general sweating, heart rate changes, rate of breathing, how deep breathing is, shaking, tremors, tone of voice and the like. These “tells” (e.g., like in poker) may vary between patients but learning their response outside a hospital setting helps inside the hospital setting and/or after surgery and the like. In an embodiment, knowledge of these “tells” by aspects of system 100 also help healthcare providers (e.g., nurses) respond.

In another embodiment, aspects of system 100 (e.g., AI system 104 ) can be used to regulate or control medical devices were a treatment is varied based on body motion, activity, diet, nutrition, sunlight, and/or environmental conditions. Such medical devices may include, for example, neuromuscular stimulators, pain stimulators and/or pacemakers that deliver an electrical flow (broadly, treatment) to the patient. For example, internal pacemakers simply try to regulate the heart rate to a known condition using electrical flow. However, pacemakers, generally, are set to regulate the heart rate of a patient to a set rate to treat a heart condition (e.g., atrial-fibrillation or ventricular fibrillation or when the heart as asystolically or has multiple heartbeats in a shorter period of time). Aspects of system 100 can monitor the patient and vary or adjust the heat rate the pacemaker regulates the heart of the patient at. For example, AI system 104 can identify when a patient is under a high degree of stress, such as by analyzing data from a patient monitor sensor 102 , and control the pacemaker to adjust or alter the heart rate based on the amount of stress. A change in a patient's stress level may be due to a fear, apprehension, or exercise. Moreover, AI system 104 may change the heart rate for other conditions such as when a patient is eating, moving, or resting. Accordingly, instead of a constant heart rate set by the pacemaker, the AI system 104 can regulate the heart rate imposed by the pacemaker based on the needs of the patient.

In another embodiment, aspects of system 100 (e.g., AI system 104 ) are not just limited to patients and can be used in other areas such as for animals, living cells, bacteria, cell growth, cell culture, tissue culture, and other aspects of microbiology. In addition, the aspects system 100 can be used for cellular growth, cellular mechanics mitochondrial mechanics, bacterial growth, and bacterial functions. For example, aspects of system 100 can be used for cellular growth in 3 D printing applications.

In accordance with one or more embodiments:

Aspects of system 100 (e.g., patient monitor sensors 102 and/or AI system 104 ) monitor physical properties of the patient, such as patient movement and sleep patterns, and shift drug delivery, blood pressure measurements, and blood draws, food delivery, and the like up/back to a predetermined amount of time so they are performed at an optimal time in the patient's sleep schedule. Aspects of system 100 (e.g., AI system 104 ) combine sleep patterns for multiple patients to create an optimal room order for a phlebotomist, dietician, nurse, food deliverer, and the like to follow to minimize patient disturbances. When a patient is checked in, a local copy of the global rules set (e.g., global expert system 112 ) is created. Aspects of system 100 (e.g., AI system 104 ) integrate patient-specific information in the patient's rule set. Aspects of system 100 (e.g., AI system 104 ) add adaptive rules based on patient behaviors. When the patient presses the bed-side call button 116 the signal is sent to a nurse either via a traditional call system or through smart alert system 118 . Aspects of system 100 (e.g., AI system 104 , smart alert system 118 , etc.) would then record the conditions when the button was pressed (e.g., recent food, movement, sleep patterns, BP, pulse, sweat, etc.). Aspects of system 100 (e.g., AI system 104 , smart alert system 118 , etc.) uses this information to compile an adaptive system that could set alerts before the patient presses the call button 116 in the future. For example, when a similar set of conditions are met or the conditions are within a threshold the alert would be set. In one or more embodiments, after the hospital staff is notified, there is a feedback mechanism for the patient and/or the hospital staff that would indicate if the alert was a false alert. Reduced call button presses could indicate that the AI generated rules are having a positive result. Aspects of system 100 (e.g., AI system 104 , smart alert system 118 , etc.) would then use this scoring system to intelligently modify the created rule set. Other known AI weighting algorithms could be used. The AI System 104 could also ensure the nurse responds to the call button 116 . Aspects of system 100 include safety measures to ensure that the alerts generated by AI system 104 and/or smart alert system 118 cannot bypass the global/expert system rules in global expert system 112 . Rules generated by AI system 104 and/or smart alert system 118 are compiled and integrated into global expert system 112 . Aspects of system 100 (e.g., AI system 104 ) monitor the food and/or water intake patterns of the patient so that changes in diet, portion size, and the like can be monitored. Aspects of system 100 (e.g., smart alert system 118 ) queue the patient's information by severity of alert (e.g., higher priority given to more critical cases). For example, a patient with critical blood pressure levels would get a higher rating than a patient who had all normal readings. A hospital medical records system is integrated into system 100 so that all information in a patient's records could be used as an input to system 100 .

A goal of AI is the creation of an intelligent computer system. These intelligent systems can be used to optimize systems and methods for healthcare delivery to provide better care and increase patient satisfaction. At a high level, AI has been broken into strong AI, which believes machines can be sentient, and weak AI, which does not. Although embodiments described herein focus on weak AI, they can also be implemented with a strong AI system in accordance with one or more aspects of the disclosure. While the embodiments disclosed herein are related to healthcare, it is understood that aspects of system 100 may also apply to non-medical applications, such as but not limited to industrial systems, commercial systems, automotive systems, aerospace system and/or entertainment systems.

Data analysis by the AI system 104 can include pure algorithms or individual or panels that review and comment at specific data analysis points (“opinion” data). The “opinion” data could be included for further analysis or bifurcated into a column with and without expert (e.g., humanistic) data analysis and evaluate conclusions. Human analysis could be individual specialist or pooled group specialists or different specialists like oncologist then a statistician then economist then ethics expert. Each can add analysis at certain critical points, and then reanalyze the data for conclusions. The AI system 104 may then analyze the human conclusion and compare them to its own. This adds a biological factor to analysis and not pure analysis from data.

In all embodiments there may be an advantage to combining known types of AI such as expert systems, genetic algorithms, deep learning, and convolutional neural networks (CNN) to implement a unique approach to the system. Convolutional neural networks and deep learning can be very useful in image recognition, such as recognizing a cat. There are cases such as robotic surgery, medical diagnosis, or reviewing medical journals which may not lend itself to traditional AI methods. For example, when using peer reviewed journals to assist in diagnosing a medical condition it may be necessary to perform an interim analysis of the data to ensure that all the conditions and symptoms of the patient are being considered or articles which not applicable are being excluded. Because interim analysis of traditional CNN is not something that can be easily done due to the encoding of the data. Because of this it could be preferable to break the CNN into multiple CNN with an expert system or evaluation by experts at each stage. The interim could be done could be done by a single user, or a system could be setup where the results are done by peer review where multiple users review. In a system with multiple users reviewing the interim of final results, it could be done through a website interface where in exchange for the reviewing of the data the users were given access to the peer reviewed articles or the input data at no charge.

Another embodiment of the AI system 104 may be constructed in a way to question data points and how it affects the entire algorithm. For example, one looks at entire chaining of information to end up with a conclusion. For example, if one looks at a research article, the conclusions of a research article are often based upon the references within the article. However, if one of these references is erroneous, it would be necessary to remove this data and through machine learning change the ultimate algorithm so that the conclusion is changed based on changing or altering one of the reference or data points. The operator could change this data format or this reference as and mark it as an invalid or questionable point. Grouping AI algorithms could also be used to do this. This method of interim analysis could also be used to allow and experts to review and weight the results for search results that may not have a black and white or definitive answer. The expert, or an expert system algorithm, could be used to weight the output of the AI system 104 or search results. This could be used in internet search engines, drug databases, or any algorithm that produces none definitive results.

For example, if one changes the data point/reference of how a black male would function relative to a total knee replacement versus elderly white female. The AI system 104 would allow for changes to one of the data points in terms of functional return or risks of keloid formation and the impact on how this affects stiffness of the joint, range of motion, and function. It may have an impact on the algorithm for sensing the ligament balance within the joint or how one would allow bone resection via MAKO robotic system to move the knee. The AI system 104 would allow the user to alter that based on the risks of scar tissue forming and what would the scar risk be for elderly white female versus younger black male versus a patient with sickle-cell anemia versus patient that would have very elastic soft tissue. Current systems do not allow this change in concepts on the fly based on individual data. This could be inputted manually by the operator or it could allow multiple variables to say if the patient has sickle-cell, Ehlers-Danlos, or keloid formation. These changes of the data could affect the incision approach, robotic mechanism for tissue resection, tissue repair, and the amount of bone to be removed for a total knee replacement to optimize function. This would also link to sensors and postoperative function/rehabilitation so one could enhance the rehabilitation/recovery. If this patient needs more aggressive therapy to work on flexion or to deal with keloid/hyperelasticity of the tissue or how one could improve scar formation and function.

Embodiments described herein may be implemented using global expert system 112 , which utilizes the knowledge from one or more experts in the algorithm executed thereby. A system of rules and data is required prior to the running of global expert system 112 . Global expert system 112 can be implemented such that in the introduction of new knowledge is rebuilt into the code, or the code can dynamically update to include new knowledge generated by global expert system 112 and/or AI system 104 . In an embodiment, updates to the code of global expert system 112 are validated with respect to regulatory requirements before implementation.

In an embodiment, AI system 104 implements one or more genetic algorithms. Genetic algorithms use the principles of natural selection and evolution to produce several solutions to a given problem. In an exemplary approach, AI system 104 randomly creates a population of solutions of a problem. The AI system 104 then evaluates and scores each solution using criteria determined by the specific application. The AI system 104 selects the top results, based on the score, and uses them to “reproduce” to create solutions which are a combination of the two selected solutions. These offspring go through mutations and AI system 104 repeats these steps or a portion of the steps until a suitable solution is found. Additionally or alternatively, AI system 104 utilizes other known AI techniques such as neural networks, reinforcement learning, and the like.

In an aspect, AI system 104 uses a combination of known AI techniques. In an embodiment, AI system 104 uses an expert system (e.g., global expert system 112 ) as a global ruleset that has a local copy of the rules created for each patient who is checked into system 100 . AI system 104 uses adaptive rules to modify the local rule set for each individual patient. Instead of a complete local copy, only the modified rules could be kept locally at a computing device executing processor-executable instructions for implementing AI system 104 and/or global expert system 112 to reduce the required memory needed. In an embodiment, a safety control is included so that rules and/or alerts generated by AI system 104 and/or global expert system 112 (e.g., the inference engine) cannot bypass one or more (or a group) of the global or expert system rules. Rules generated by AI system 104 or rules from the predictive rules are compiled and/or integrated into the global or expert rule set in accordance with one or more embodiments.

One embodiment of using system 100 in a hospital environment includes maximizing a patient's ability to rest at night by scheduling certain procedures and activities around the patient's sleep patterns. During a normal sleep pattern, a person goes through different cycles of sleep, including light sleep, deep sleep, and REM. A patient's sleep patterns, heart rate, and movements at night are monitored using patient monitor sensors 102 (e.g., Fitbit® activity tracker, Apple® Watch, smartphone computing device, an electronic ring, or any commercial or custom tracker with the ability to record and or transmit patient's movements and sleep patterns). Additionally or alternatively, system 100 utilizes patient monitor sensors 102 in the form of existing sensors used in hospital monitoring systems, hospital records, pulse oximeter, retinal changes, temperature, or thermal gradients, changes in diet or food patterns from dieticians, and mediation from the pharmacy. The data collected from patient monitor sensors 102 is then uploaded (e.g., via a communications network) to AI system 104 in real time for analysis. Additionally or alternatively, the data collected from patient monitor sensors 102 is manually uploaded to AI system 104 for analysis. The AI system 104 then uses this information to ensure that the patient is in the correct sleep cycle when they must be woken up for procedures, such as by execution of a waking/alerting algorithm 200 ( FIG. 2 ). For example, system 100 monitors all patients on a certain a floor and creates an optimized map or order of blood draws for the phlebotomist to minimize the patients being disturbed from deep sleep. This map could be printed along with the ordered bloodwork or could be sent wirelessly to healthcare provider device 120 (e.g., a tablet or smartphone computing device) and updated in real time. The same algorithm could be used by multiple departments in the hospital so that medication delivery, food, etc. is optimally scheduled.

For patients who are taking medications at night outside of the hospital the waking/alerting algorithm 200 may be implemented by one or more patient devices 122 (e.g., a smartwatch, smartphone computing device, tablet computing device, or other monitor) to wake the user at an optimal time in the sleep cycle to take medication. In an embodiment, system 100 is also used to determine the optimal time of the day to take a medicine for an individual user based on daily activity level, sleep patterns, metabolism, and like factors.

FIG. 2 illustrates an exemplary embodiment of the waking/alerting algorithm 200 . In an embodiment, the waking/alerting algorithm 200 comprises processor-executable instructions embodied on a storage memory device of a computing device to provide waking/alerting techniques for medication delivery via a software environment. For example, the waking/alerting algorithm 200 may be provided as processor-executable instructions that comprise a procedure, a function, a routine, a method, and/or a subprogram utilized independently or in conjunction with additional aspects of system 100 according to an exemplary embodiment of the disclosure. In an embodiment, the waking/alerting algorithm 200 is executed by a computing device, such as one or more of a computing device implementing AI system 104 , patient monitor sensors 102 , and patient devices 122 in accordance with one or more embodiments of the disclosure.

At 202 , the patient or healthcare provider enters the medication schedule. For example, the patient may enter the medication schedule via patient monitor sensors 102 , patient device 122 , and/or patient-controlled mediation delivery subsystem 114 and the healthcare provider may enter the medication schedule via healthcare provider device 120 .

At 204 , the computing device determines whether a dose is required at the current time according to the entered schedule. When a dose is determined to be not r

CLAIMS

Claims ( 18 )

What is claimed is:

1. A system, comprising:

an exoskeleton configured to be worn on a user, the exoskeleton including at least one of form fitting cuffs or inflatable bladder type cuffs or supports for fitting the exoskeleton to the user;

a plurality of sensors each configured to sense one or more physical properties of a user and to generate at least one signal representative thereof, wherein the one or more sensed physical properties includes a physiological response of the user, and wherein at least one of the plurality of sensors is configured to wirelessly transmit the at least one signal;

one or more processors;

one or more non-transitory computer-readable media storing:

an artificial intelligence (AI) system configured to generate a regimen of activity parameters optimized or personalized to the user using machine learning, the activity parameters each associated with an action taken on behalf of the user, wherein at least one of the activity parameters comprises stimuli for causing the physiological response sensed by the sensors; and

instructions that, when executed by the one or more processors, configure the system to perform operations, the operations comprising:

receiving, by the AI system, data associated with the one or more physical properties via the signal from the plurality of sensors; and

processing the received data with the AI system to monitor the one or more physical properties including the physiological response of the user and to generate the regimen based thereon, wherein the generated regimen specifies movement of the exoskeleton; and

an immersive technology system configured to generate an immersive environment in which the user performs the regimen, wherein the immersive environment presents the stimuli to the user causing the physiological response sensed by the sensors.

2. The system of claim 1 , wherein the exoskeleton is configured to be worn on one or more of a trunk, arm, hand, finger, leg, foot, toe, head, or neck of the user.

3. The system of claim 1 , wherein the exoskeleton is configured to amplify strength of the user.

4. The system of claim 1 , wherein the exoskeleton comprises at least one of a servomotor, a robotic control, and a hydraulic piston for driving movement of the exoskeleton.

5. The system of claim 1 , wherein the exoskeleton further comprises structural supports for fitting the exoskeleton to the user.

6. The system of claim 1 , wherein the instructions, when executed by the one or more processors, further configure the system to perform operations, the operations comprising:

providing the regimen as an output of the AI system to the immersive technology system, wherein the immersive environment presents stimuli to the user causing the physiological response sensed by the sensors.

7. The system of claim 1 , further comprising a portable computing device in communication with the one or more processors and the sensors, the portable computing device configured to receive the signals from the sensors and to send the data associated with the one or more physical properties of the user to the one or more processors executing the AI system, wherein the portable computing device is configured to receive the regimen output by the AI system and present the regimen to the user via the portable computing device.

8. The system of claim 1 , wherein the AI system is configured to store information relating to the monitored physical properties in the non-transitory computer-readable media.

9. The system of claim 1 , wherein processing the received data comprises analyzing, by the AI system, the monitored physical properties to predict a biologic function of the user and/or determine a user's response to the activity parameters of the regimen.

10. The system of claim 1 , wherein the AI system implements one or more of predictive learning, automated planning and scheduling, machine perception, computer vision and affective computing to generate said activity parameters of the regimen optimized or personalized to the user.

11. The system of claim 1 , wherein the one or more physical properties of the user comprises at least one of the following: an activity level of the user, biometric data, cellular data, biologic data, and non-biologic data.

12. The system of claim 1 , wherein the regimen comprises physical therapy performed using the exoskeleton, and wherein an immersive technology system generates an immersive environment in which the user performs the physical therapy regimen with assistance from the exoskeleton.

13. The system of claim 1 , wherein at least one of the plurality of sensors comprises a camera coupled to an immersive technology system for tracking motion of the user to monitor compliance with the regimen.

14. The method of claim 1 , wherein executing the instructions stored on the one or more non-transitory computer-readable media to configure the AI system to perform operations comprises implementing one or more of predictive learning, automated planning and scheduling, machine perception, computer vision and affective computing to generate said activity parameters of the regimen optimized or personalized to the user.

15. A method of generating a regimen of activity parameters optimized or personalized to a user, comprising:

fitting a user with an exoskeleton configured to be worn on the user, the exoskeleton including at least one of form fitting cuffs or inflatable bladder type cuffs or supports for fitting the exoskeleton to the user;

receiving, by an artificial intelligence (AI) system, data associated with one or more physical properties of the user, wherein the data is received via a signal from a plurality of sensors each configured to sense the one or more physical properties of the user and to generate the signal representative thereof, wherein the one or more physical properties sensed by the sensors comprises a physiological response of the user, and wherein at least one of the plurality of sensors is configured to wirelessly transmit the at least one signal; and

executing, by one or more processors, instructions stored on one or more non-transitory computer-readable media to configure the AI system to perform operations using machine learning, the operations comprising:

processing the received data with the AI system to monitor the one or more physical properties including the physiological response of the user and to generate the regimen based thereon, wherein the regimen specifies movement of the exoskeleton; and

providing the regimen as an output of the AI system to an immersive technology system, the immersive technology system generating an immersive environment in which the user performs the regimen, the activity parameters of the regimen each associated with an action taken on behalf of the user, wherein at least one of the activity parameters comprises stimuli for causing the physiological response sensed by the sensors, wherein the immersive environment presents a stimulus to the user causing the physiological response sensed by the sensors.

16. The method of claim 15 , wherein processing the received data comprises analyzing, by the AI system, the monitored physical properties to predict a biologic function of the user and/or determine a user's response to the activity parameters of the regimen.

17. The method of claim 15 , wherein the regimen comprises physical therapy performed using the exoskeleton, and further comprising generating, by the immersive technology system, the immersive environment in which the user performs the physical therapy regimen with assistance from the exoskeleton.

18. The method of claim 15 , wherein at least one of the plurality of sensors comprises a camera coupled to the immersive technology system, and further comprising tracking motion of the user with the camera to monitor compliance with the regimen.

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Artificial intelligence and/or virtual reality for activity optimization/personalization

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2017-08-30

2021-08-05

Artificial intelligence and/or virtual reality for activity optimization/personalization

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