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Systems and methods using multidimensional language and vision models and maps … — Mofaip, Llc (US20230368878A1)

Mofaip, Llc · Google Patents

Google Patents · Patents · License: Open Access

patent, google patents, intellectual property, US20230368878A1, Mofaip, Llc, Matthew A. Molenda, en, 2023

ABSTRACT

Abstract

The disclosed embodiments relate to building and applying multidimensional language and vision models and maps to categorize, label and track anatomy and health and other data. Language models are used to accurately, precisely, and reproducibly describe and translate anatomy and health data into any coded, linguistic, or symbolic language. Vision-language models are used to describe, document, associate, categorize, diagnose, track, translate, map, and visualize anatomy and other health data such as morphology and symptoms and treatment recommendations. Language-vision models are used to describe, document, associate, categorize, diagnose, track, summarize, relate, translate, map, and visualize anatomy and other health data such as morphology and symptoms and treatment recommendations and regimens.

Description

CROSS REFERENCE TO RELATED APPLICATIONS

This continuation-in-part application claims priority to international application PCT/IB2022/000814, filed on Dec. 12, 2022, which claims the benefit of U.S. Provisional Application No. 63,265,216, filed on Dec. 10, 2021, U.S. Provisional Application No. 63/294,653, filed on Dec. 29, 2021, U.S. Provisional Application No. 63/267,269, filed on Jan. 28, 2022, U.S. Provisional Application No. 63/315,289, filed on Mar. 1, 2022, U.S. Provisional Application No. 63/269,516, filed on Mar. 17, 2022, U. S. Provisional Application No. 63/362,791, filed on Apr. 11, 2022, U.S. Provisional Application No. 63/364,393, filed on May 9, 2022, U.S. Provisional Application No. 63/364,764, filed on May 16, 2022, U.S. Provisional Application No. 63/365,026, filed on May 20, 2022, U.S. Provisional Application No. 63/365,373, filed on May 26, 2022, U.S. Provisional Application No. 63/366,107, filed on Jun. 9, 2022, U.S. Provisional Application No. 63/366,816, filed on Jun. 22, 2022, and U.S. Provisional Application No. 63/369,717, filed on Jul. 28, 2022, each of which is incorporated by reference in the entirety.

This continuation-in-part application also claims priority to international application PCT/IB2022/000777, filed on Dec. 12, 2022, which claims the benefit of U.S. Provisional Application No. 63/265,216, filed on Dec. 10, 2021, U.S. Provisional Application No. 63/315,289, filed on Mar. 1, 2022, U.S. Provisional Application No. 63/269,516, filed on Mar. 17, 2022, U.S. Provisional Application No. 63/362,791, filed on Apr. 11, 2022, U. S. Provisional Application No. 63/364,393, filed on May 9, 2022, U.S. Provisional Application No. 63/364,764, filed on May 16, 2022, U.S. Provisional Application No. 63/365,026, filed on May 20, 2022, U.S. Provisional Application No. 63/365,373, filed on May 26, 2022, U.S. Provisional Application No. 63/366,107, filed on Jun. 9, 2022, U.S. Provisional Application No. 63/369,717, filed on Jul. 28, 2022, U.S. Provisional Application No. 63/370,879, filed on Aug. 9, 2022, and U.S. Provisional Application No. 63/375,325, filed on Sep. 12, 2022, each of which is incorporated by reference in the entirety.

This continuation-in-part application also claims priority to international application PCT/IB2022/000793, filed on Dec. 12, 2022, which claims the benefit of U.S. Provisional Application No. 63,265,216, filed on Dec. 10, 2021, U.S. Provisional Application No. 63/315,289, filed on Mar. 1, 2022, U.S. Provisional Application No. 63/269,516, filed on Mar. 17, 2022, and U.S. Provisional Application No. 63/362,791, filed on Apr. 11, 2022, each of which is incorporated by reference in the entirety.

This continuation-in-part application also claims priority to international application PCT/IB2022/000813, filed on Dec. 12, 2022, which claims the benefit of U.S. Provisional Application No. 63,265,216, filed on Dec. 10, 2021, U.S. Provisional Application No. 63/315,289, filed on Mar. 1, 2022, U.S. Provisional Application No. 63/269,516, filed on Mar. 17, 2022, U.S. Provisional Application No. 63/362,791, filed on Apr. 11, 2022, and U.S. Provisional Application No. 63/369,717, filed on Jul. 28, 2022, each of which is incorporated by reference in the entirety.

This continuation-in-part application also claims priority to international application PCT/US2022/81399, filed on Dec. 12, 2022, which claims the benefit of U.S. Provisional Application No. 63,265,216, filed on Dec. 10, 2021, U.S. Provisional Application No. 63/267,269, filed on Jan. 28, 2022, U.S. Provisional Application No. 63/315,289, filed on Mar. 1, 2022, U. S. Provisional Application No. 63/269,516, filed on Mar. 17, 2022, U.S. Provisional Application No. 63/362,791, filed on Apr. 11, 2022, U.S. Provisional Application No. 63/364,393, filed on May 9, 2022, U.S. Provisional Application No. 63/364,764, filed on May 16, 2022, U.S. Provisional Application No. 63/365,373, filed on May 26, 2022, U.S. Provisional Application No. 63/366,107, filed on Jun. 9, 2022, U.S. Provisional Application No. 63/369,469, filed on Jul. 26, 2022, U.S. Provisional Application No. 63/370,879, filed on Aug. 9, 2022, and U.S. Provisional Application No. 63/375,325, filed on Sep. 12, 2022, each of which is incorporated by reference in the entirety.

This continuation-in-part application also claims priority to international application PCT/IB2022/000823, filed on Dec. 29, 2022, which claims the benefit of U.S. Provisional Application No. 63/294,653, filed on Dec. 29, 2021, U.S. Provisional Application No. 63/269,516, filed on Mar. 17, 2022, U.S. Provisional Application No. 63/362,791, filed on Apr. 11, 2022, U.S. Provisional Application No. 63/364,393, filed on May 9, 2022, U. S. Provisional Application No. 63/364,764, filed on May 16, 2022, U.S. Provisional Application No. 63/365,026, filed on May 20, 2022, U.S. Provisional Application No. 63/366,816, filed on Jun. 22, 2022, U.S. Provisional Application No. 63/369,469, filed on Jul. 26, 2022, and U.S. Provisional Application No. 63/369,717, filed on Jul. 28, 2022, each of which is incorporated by reference in the entirety.

This continuation-in-part application also claims priority to international application PCT/US2023/066761, filed on May 9, 2023, which claims the benefit of U.S. Provisional Application No. 63/364,393, filed on May 9, 2022, United States Provisional Application No. 63/364,764, filed on May 16, 2022, U.S. Provisional Application No. 63/365,026, filed on May 20, 2022, and U.S. Provisional Application No. 63/365,373, filed on May 26, 2022, each of which is incorporated by reference in the entirety.

This continuation-in-part application also claims priority to international application PCT/US2023/066763, filed on May 9, 2023, which claims the benefit of U.S. Provisional Application No. 63/364,393, filed on May 9, 2022, and U.S. Provisional Application No. 63/364,764, filed on May 16, 2022, each of which is incorporated by reference in the entirety.

This continuation-in-part application also claims priority to international application PCT/US2023/067049, filed on May 16, 2023, which claims the benefit of U.S. Provisional Application No. 63/364,764, filed on May 16, 2022, U. S. Provisional Application No. 63/365,026, filed on May 20, 2022, and U. S. Provisional Application No. 63/365,373, filed on May 26, 2022, each of which is incorporated by reference in the entirety.

The continuation-in-part application also claims priority to U.S. Provisional Application No. 63/369,469, filed on Jul. 26, 2022, U.S. Provisional Application No. 63/369717, filed on Jul. 28, 2022, U.S. Provisional Application No. 63/370,879, filed on Aug. 9, 2022, U.S. Provisional Application No. 63/373,469, filed on Aug. 25, 2022, U.S. Provisional Application No. 63/375,325, filed on Sep. 12, 2022, U.S. Provisional Application No. 63/382,371, filed on Nov. 4, 2022, U.S. Provisional Application No. 63/482,693, filed on Feb. 1, 2023, U.S. Provisional Application No. 63/494,652, filed on Apr. 6, 2023, U.S. Provisional Application No. 63/470,546, filed on Jun. 2, 2023, and U.S. Provisional Application No. 63/521,020, filed on Jun. 14, 2023, each of which is incorporated by reference in the entirety.

FIELD

This application relates to medical systems, and more particularly, to graphical generation of medical records.

BRIEF SUMMARY OF SELECTED EXAMPLES

In one embodiment, a method for generating a medical record, comprises: rendering, on a display, an anatomic representation; receiving an input, from an input device, having health data, wherein the input is text-based, visual-based, audio-based, and/or based on an interaction with the anatomic representation; processing the input to generate processed health data, the processed health data including a procedure, a diagnosis, a name of an anatomic site, and/or a description of an anatomic site; rendering, on the display, a marked anatomic site on the anatomic representation that is based on the processed health data, or an isolated anatomic representation having a marked anatomic site that is based on the processed health data; selectively rendering, on the display, a mirror image of the anatomic representation with the marked anatomic site or the isolated anatomic representation with the marked anatomic site based on a user preference or context; selecting one of a plurality of templates, each of the templates having one or more fields; populating one of the fields with the anatomic representation with the marked anatomic site or the isolated anatomic representation with the marked anatomic site to generate a populated selected template; and generating a health record including the populated selected template.

In certain embodiments, the anatomic representation includes a plurality of predefined anatomic sites. In certain embodiments, one of the predefined anatomic sites is located on and associated with an anatomic region with one or more subregions. In certain embodiments, the interaction with the anatomic representation includes generating a preview of the anatomic region with the one or more subregions associated with the one of the predefined anatomic sites. In certain embodiments, the input is a scanned image of a physical print of the anatomic representation with markups, wherein the markups include the health data. In certain embodiments, the physical print of the anatomic representation with markups includes orientation markers and processing the input includes detecting orientation markers and normalizing the axis based on the orientation markers. In certain embodiments, processing the input is performed using a language model, a vision-language model, and/or a language-vision model. In certain embodiments, the marked anatomic site is associated with the processed data. In certain embodiments, the description of the anatomic site includes a relationship between the marked anatomic site and another anatomic site, wherein the relationship includes a distance between the marked anatomic site and the another anatomic site, a spatial relationship between the marked anatomic site and the another anatomic site, and/or a data-based relationship between the marked anatomic site and the another anatomic site. In certain embodiments, populating one of the fields includes populating one or more additional fields of the fields with the processed health data. In certain embodiments, processing the input includes detecting a nontechnical term for the processed health data and converting the nontechnical term into a technical term. In certain embodiments, processing the input includes detecting a plurality of languages and translating the plurality of languages into the processed health data. In certain embodiments, the input includes a coded input and processing the input includes decoding the coded input into the processed health data. In certain embodiments, generating the health record includes formatting the health record into a database record suitable for an electronic health record database.

In one embodiment, a system for generating a medical record, comprises: a processer; a medium in communication with the processor, wherein the medium is tangible, non-transitory, and computer readable; processer-executable instructions stored on the medium, the processor-executable instructions defining a mapping platform including a data processing module, a knowledge base module, and a generation module; a display in communication with the medium; and an input device in communication with the medium and display; wherein the mapping platform is configured to: render, using the processor, an anatomic representation of a human on the display, receive, from the input device, an input having health data, wherein the input is text-based, visual-based, audio-based, and/or based on an interaction with the anatomic representation, process, using the data processing module, the input to generate processed health data, the processed health data including a procedure, a diagnosis, a name of an anatomic site, and/or a description of an anatomic site, render, using the processor, on the display, a marked anatomic site on the anatomic representation that is based on the processed health data, or an isolated anatomic representation having a marked anatomic site that is based on the processed health data, selectively rendering, using the processer, a mirror image of the anatomic representation with the marked anatomic site or the isolated anatomic representation with the marked anatomic site on the display based on a user preference; selecting one of a plurality of templates, from the knowledge base module, each of the templates having one or more fields; populating one of the fields, using the generation module, with the anatomic representation with the marked anatomic site or the isolated anatomic representation with the marked anatomic site to generate a populated selected template; and generating, using the generation module, a health record including the populated selected template. In certain embodiments, the marked anatomic site can be a pin within an anatomic site path or path group, or a highlighted anatomic site path or path group.

In certain embodiments, the system further comprises a printer configured to print a physical health record. In certain embodiments, the input device includes an image capturing device configured to scan a physical representation of the anatomic representation with markups, wherein the markups include the health data. In certain embodiments, the processor and the medium are located on one or more servers. In certain embodiments, the processor, the medium, and the display are located on a mobile phone, a tablet, a laptop, a computer, a microphone, a speaker, a headset, goggles, glasses, a contact lens, and/or an electronic device as non-limiting examples.

In one embodiment, a tangible, non-transitory, and computer-readable medium having processer-executable instructions stored thereon that when executed by a processor causes a method for generating a medical record, comprises: rendering, on a display, an anatomic representation; receiving an input, from an input device, having health data, wherein the input is text-based, visual-based, audio-based, and/or based on an interaction with the anatomic representation; processing the input to generate processed health data, the processed health data including a procedure, a diagnosis, a name of an anatomic site, and/or a description of an anatomic site; rendering, on the display, a marked anatomic site on the anatomic representation that is based on the processed health data, or an isolated anatomic representation having a marked anatomic site that is based on the processed health data; selectively rendering, on the display, a mirror image of the anatomic representation with the marked anatomic site or the isolated anatomic representation with the marked anatomic site based on a user preference; selecting one of a plurality of templates, each of the templates having one or more fields; populating one of the fields with the anatomic representation with the marked anatomic site or the isolated anatomic representation with the marked anatomic site to generate a populated selected template; and generating a health record including the populated selected template. In certain embodiments, the marked anatomic site can be a pin within an anatomic site path or path group, or a highlighted anatomic site path or path group, that can be marked physically (e.g., on paper) or digitally through a physical input device (e.g., a touchscreen) as non-limiting examples.

DESCRIPTION OF FIGURES

The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

FIG. 1 A illustrates an example embodiment of a system.

FIG. 1 B illustrates a non-limiting example embodiment of a method enabled by the system.

FIG. 1 C illustrates a non-limiting example embodiment of a method enabled by the system.

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CROSS REFERENCE TO RELATED APPLICATIONS

FIELD

This application relates to medical systems, and more particularly, to graphical generation of medical records.

BRIEF SUMMARY OF SELECTED EXAMPLES

DESCRIPTION OF FIGURES

FIG. 1 A illustrates an example embodiment of a system.

FIG. 1 B illustrates a non-limiting example embodiment of a method enabled by the system.

FIG. 1 C illustrates a non-limiting example embodiment of a method enabled by the system.

FIG. 1 D illustrates an example path with custom axes, segmentation, and relational instructions, directional planes.

FIG. 1 E illustrates an example path with associated data.

FIG. 1 F illustrates an example embodiment of a system.

FIG. 1 G is a screenshot of an example of hierarchical visualization overlay of one embodiment.

FIG. 1 H is a screenshot of an example of hierarchical visualization underlay of one embodiment.

FIG. 1 I is a cross-section diagram depicting an example representation of anatomic site paths in a single plane.

FIG. 1 J is the example representation shown in FIG. 1 I shows that custom axes are parallel and perpendicular to the cross-section of anatomic site paths in a single plane, axial mirroring, and auto-relation.

FIG. 1 K is an example screenshot of a plane with a rotated bounding box and custom defined axes.

FIG. 1 L is an example screenshot of a patient facial diagram displaying enhanced anatomic sites, hierarchical painting of anatomic sites, auto-relation of anatomic sites, hierarchical travel or hierarchical association of anatomic sites, and other capabilities.

FIG. 1 M is an example screenshot of a patient facial diagram displaying a translation with quick zoom multidimensional targeting and hierarchical diagnostic distribution painting options.

FIG. 1 N is an example screenshot showing map synchronization and pin-level data.

FIG. 1 O is an example screenshot of a two-dimensional model with customized axes and defining enhanced detail regions and automatic pin relationships.

FIG. 1 P is an example screenshot of a two-dimensional model with customized axes defining an enhanced detail plane, and customization of an axial boundary in the enhanced detail region.

FIG. 1 Q is an example screenshot of a marked anatomic site, namely pins on an anatomic site that are automatically related to each other.

FIG. 1 R is an example screenshot of a marked anatomic sites, namely hierarchical painting of morphologies with time point tracking.

FIG. 1 S is an example screenshot of a marked anatomic sites, hierarchical painting with associated treatment recommendations.

FIG. 1 T depicts an example printed output of targeted, isolated and combined visual previews with associated treatment recommendations.

FIG. 1 U depicts an example digital regimen map with associated treatment recommendations on combined and isolated visual previews.

FIG. 1 V depicts an example printed output with associated treatment recommendations with isolated visual previews and simplified, combined anatomic site descriptions.

FIG. 1 W is an example screenshot of an anatomic site name builder associated with a specific pin along with an isolated visual preview.

FIG. 1 X depicts an example progressive linguistic and visual sub-segmentation of a dynamic anatomic address.

FIG. 1 Y is an example partial screenshot of dynamic anatomic addresses with select patient characteristics.

FIG. 1 Z is an example partial screenshot of a dynamic anatomic addresses with alternative selected patient characteristics to dynamically filter the map.

FIG. 2 A is an example flowchart of a method enabled by the system.

FIG. 2 B is an example screenshot of an anatomic site code translator.

FIG. 2 C is an example screenshot of an exemplar anatomic site name to code translator.

FIG. 2 D is an example screenshot of another exemplar anatomic site name to code translator.

FIG. 3 A depicts an example flowchart of an embodiment of a method.

FIG. 3 B depicts an example pathology requisition form.

FIG. 3 C depicts an example timeline of dynamic anatomic site history.

FIG. 3 D depicts an example of a method enabled by the system for a collaborative logbook with different entities having different permissions.

FIG. 3 E is an example screenshot of a collaborative logbook example.

FIG. 3 F depicts an example automatically replotted anatomic site with associated diagnosis and diagnosis extension data blocks.

FIG. 3 G depicts an example automatically generated Mohs map.

FIG. 4 A is an example omnidirectional data model that illustrates the capabilities of the data block engine.

FIG. 4 B is an example screenshot of FIG. 1 N with portions translated to Chinese and added symbolically delimited and/or defined filename.

FIG. 4 C is an example screenshot showing options to customize the anatomic site name sequence configuration and the data block within a file name builder.

FIG. 4 D is an example screenshot of a modal view of a thumbnail image and its accompanying symbol delimited and symbol defined file name.

FIG. 4 E is an example screenshot of a coordinated anatomy data in correspondence with a color-coded legend, and symbolic definitions for the anatomic site group.

FIG. 4 F is an example legend of representative symbolic searches within a reSearch engine.

FIG. 4 G is an example legend of representative application examples to re-create pins on regions of interest.

FIG. 4 H is an example screenshot of an artificial intelligence collated patient history in an anatomic region of interest made possible by data blocks.

FIG. 5 A is an example flowchart of a method enabled by the system.

FIG. 5 B is an example representative annotated paper record with handwritten markings.

FIG. 5 C illustrates an example digital interpretation of only the handwritten markings of FIG. 5 B .

FIG. 5 D is an example generated electronic version of the paper record in FIG. 5 B .

FIG. 5 E in an example anatomic representation in paper form, namely an anatomic map with markups, e.g., handwritten annotations.

FIG. 5 .F illustrates an example digital interpretation of the handwritten markings overlayed on the digital photo of the paper form in FIG. 5 E .

FIG. 5 G is an example electronic record generated from the paper form in FIG. 5 E with automatic documentation and mapping of correct procedures, diagnoses, marked anatomic sites, notes, patient demographics, and billing codes.

FIG. 5 H is an example anatomic representation in paper form, namely an anatomic map in Chinese with hand-colored anatomic distributions.

FIG. 5 I is an example generated electronic version of the paper record in FIG. 5 H with detected color, area, intensity, and distribution in Chinese.

FIG. 5 J is an example generated electronic version depicted in FIG. 5 I translated to English.

FIG. 6 A is an example flowchart illustrating information management by a method enabled by the system.

FIG. 6 B is an example anatomic representation, namely a shadow chart.

FIG. 6 C is an enlarged view of a portion of the example shadow chart in FIG. 6 B .

FIG. 6 D is an enlarged view of an alternate portion of the example shadow chart in FIG. 6 B .

FIG. 6 E is an example captured image of an annotated and marked up version of the example shadow chart in FIG. 6 B .

FIG. 6 F is an enlarged view of a portion of the example capture from FIG. 6 E .

FIG. 6 G is an enlarged view of an alternate portion of the example capture from FIG. 6 E .

FIG. 6 H is an example screenshot showing FIG. 6 F converted to a digital record.

FIG. 6 I is an example screenshot showing FIG. 6 G converted to a digital record.

FIG. 6 J is an example printed shadow chart showing color annotations.

FIG. 6 K is an example screenshot showing FIG. 6 J converted to a digital record with correlating diagnoses based on color detection.

FIG. 6 L is an example screenshot showing FIG. 6 J converted to a digital record with correlating diagnoses based on color detection automatically translated to English.

FIG. 6 M is an example screenshot showing a visual alert on a shadow chart.

FIG. 7 A illustrates an example simplified block diagram of a method relevant to anatomy and morphology enabled by the system.

FIG. 7 B shows example morphology detections of the example photo from FIG. 10 D .

FIG. 7 C shows an example combined anatomic map and summary of detections of the example photo from FIG. 10 E , with English and Chinese correlates.

FIG. 7 D illustrates one embodiment of an automatically encoded diagnosis and diagnosis extensions.

FIG. 7 E is an example screenshot illustrating coded and symbolic translation of an anatomic site description.

FIG. 8 A illustrates an example block diagram of an omnidirectional neural network for ranges, collections, and categorizations of data.

FIG. 8 B shows a screenshot of one embodiment of interactive range categorizations and data collections used to describe and encode anatomy, diagnosis, and procedure.

FIG. 8 C is an example of a diagnosis and diagnosis extensions shown in Spanish with full translations and encodings before natural language processing.

FIG. 8 D shows a screenshot of an example of the visual ranges of anatomy under a given point or site.

FIG. 9 A illustrates example marked anatomic sites or âareas of interestâ represented by visible pins on an anatomic representation, namely an anatomic map.

FIG. 9 B illustrates an example invisible area of interest on an anatomic map.

FIG. 9 C illustrates the example areas of interest from FIG. 9 A reproduced at a different point in time.

FIG. 9 D illustrates an example area of interest in a void unmapped space.

FIG. 9 E illustrates an example relocation of the unmapped area of interest in FIG. 9 D to a mapped location.

FIG. 9 F illustrates an example reordering of the areas of interest from FIG. 9 A and shows a mirror view.

FIG. 9 G illustrates a non-limiting example of a method.

FIG. 9 H illustrates a non-limiting example of a method.

FIG. 9 I illustrates a non-limiting example of a method.

FIG. 9 J illustrates a non-limiting example of a method.

FIG. 9 K illustrates a non-limiting example of a method.

FIG. 9 L illustrates a non-limiting example of a method.

FIG. 9 M illustrates a non-limiting example of a method.

FIG. 9 N illustrates a non-limiting example of a method.

FIG. 9 O illustrates a non-limiting example of a method.

FIG. 9 P illustrates a non-limiting example of a method.

FIG. 9 Q illustrates a non-limiting example of a method.

FIG. 9 R illustrates a non-limiting example of a method.

FIG. 9 S illustrates a non-limiting example of a method.

FIG. 9 T illustrates a non-limiting example of a method.

FIG. 9 U illustrates a non-limiting example of a method.

FIG. 9 V illustrates a non-limiting example of a method.

FIG. 9 W illustrates a non-limiting example of a method.

FIG. 9 X illustrates a non-limiting example of a method.

FIG. 9 Y illustrates a non-limiting example of a method.

FIG. 10 A is an example anatomic representation, namely a three-dimensional model depicting a dynamic anatomic map with associated addresses.

FIG. 10 B is an example anatomic representation, namely a three-dimensional avatar visualization depicting patient characteristics.

FIG. 10 C is an example anatomic representation, namely a two-dimensional diagram of multiple anatomic perspectives depicting the same pin location.

FIG. 10 D is an example patient photograph.

FIG. 10 E is an example patient photograph overlayed with the accompanying hierarchical dynamic anatomic addressing method enabled by the system.

DETAILED DESCRIPTION OF SELECTED EXAMPLES

The following detailed description and the appended drawings describe and illustrate various examples of systems, methods, embodiments, engines, calculations, models, information systems, and algorithms that are stored on tangible, non-transitory, and computer-readable medium having processor-executable instructions stored thereon. The description and illustration of these examples can enable one skilled in the art to make and use various examples of multidimensional labeling, coordinated language model type modeling, relational capabilities of the systems, artificial intelligence, and generative capabilities. A non-limiting list of other capabilities described herein include tracking, translation, reproducibility, transformation, mirroring, aligning, targeting, extracting, detecting, describing, enhancing, calculating, communicating, overlaying, underlaying, encoding, searching, modifying, processing, reproducing, collating, and summarizing to name a few. They do not limit the scope of the claims in any manner.

References in the specification to âembodimentâ âone embodiment,â âcertain embodiments,â etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. The terms âfor example,â âe.g.,â âas one exampleâ, âas an exampleâ, âas one non-limiting exampleâ, etc. indicate one or more non-limiting examples, even when the ânon-limitingâ term is not expressly written in the description. When one component of a system or method is listed with âe.g.,â or âe.g., enabled byâ, this is intended to provide a non-limiting example or non-limiting examples, and it is contemplated that other components of the system may be substituted, added, subtracted, or otherwise modified by one skilled in the art. In the teachings herein, the system can form a method, and conversely the method can form a system. Also, an anatomic âvisualizationâ can be a type of an anatomic ârepresentation.â The terminology does not limit the scope of the claims in any manner.

Traditionally, there has been no way to use language models, language-vision models, vision-language models, and/or other data models, to precisely, accurately, and reproducibly describe, visualize, track, and target anatomic sites or other health findings. Numerous ontologies exist, but there is not a single model that unifies language, encodings, and vision across multidimensional space and through time for anatomy and health data. Disjointed record systems that use different languages or human generated language as free text often lack precision and reproducibility, and present numerous issues solved by the teachings herein. The relationships between two or more points or sites are established and described through a combination physical proximities or distances (such as coordinates, overlays, or underlays on an image, map, avatar, or illustration), data-based proximities (such as in a hierarchical or relational database stored on a server), semantic/linguistic comparisons (such as two anatomic sites that have the same or similar linguistic, coded, or symbolic name elements), customized axes (on individual or grouped paths in a map file, avatar, diagram, or image, for example), and directional planes (independent path or path groups that supply separate directional information and custom axes regardless of the customized axes on the anatomic site paths and path groups, as one example). The magnitude of distance between points or areas in one or more axes can also be described with linguistic, coded, and symbolic, and calculated (such as numerical measurements) language through a processor and plurality of logical comparisons from one or more databases stored in the physical medium. Path or path groups stored within or extrapolated from a multimedia file stored in the physical medium also contain segmentation instructions stored within their metadata, and those segmentation instructions communicate with the database and a processor in certain embodiments, to provide enhanced descriptions and visualization of paths or path groups with progressive segmentation (e.g., representation), regressive segmentation, progressive coordination, regressive coordination, and/or enhanced relational descriptions through any combination of language models, vision-language models, language-vision models, and/or Dimensionally Extended 9-Intersection Models enabled by a coordinated language model engine comprising of databases stored on physical storage mediums on physical servers that are in communication with physical processor, output devices, displays, paper or other printable media, and captured or stored multimedia files within a physical storage medium.

FIG. 1 A illustrates an example embodiment of a system 985 . The system 985 includes a processor 986 , a medium 995 , a display 987 , an input device 988 , and/or an output device 989 .

The processor 986 can be in communication with the medium 995 . The processor 986 can include any type of general or specific purpose processor. In certain embodiments, the processor 986 can include multiple processors. As non-limiting examples, the processor 986 can include one or more general-purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and processors based on a multi-core processor architecture. The processor and the memory can be located on the same device, e.g., a server, a mobile phone, a tablet, a laptop, a computer, a headset, an electronic device, etc.

The medium 995 is a tangible, physical, non-transitory computer readable medium 995 with processor-executable instructions stored thereon. The medium 995 can be located on the same device as the processor 986 or a separate device. In certain embodiments, the processor 986 and the medium 995 are located on one or more servers. The medium 995 can be one or more memories and of any type suitable to the local application environment and can be implemented using any suitable volatile or nonvolatile data storage technology such as a semiconductor-based memory device, a magnetic memory device and system, an optical memory device and system, fixed memory, and removable memory. For example, the medium 995 can comprise of any combination of random-access memory (RAM), read only memory (ROM), static storage such as a magnetic or optical disk, hard disk drive (HDD), solid state drive (SSD), or any other type of non-transitory machine or computer readable media. The tangible medium 995 also enables various network types, such as neural networks and/or artificial neural networks; and models such as data models, language models, language-vision models, vision-language models, coordinated language models, and/or other models; engines such as search engines, research engines, anatomy-data engines, non-anatomy data engines, generation engines, dissection and categorization engines, coordinated language model engines, encoding engines, summarization engines, data-blocking engines, analysis engines, record generation engines, translations engines, morphology mapping engines, form processing engines, mapping engines, coding engines, search engines, and/or visualization engines; and/or capabilities such as artificial intelligence, machine learning, augmented reality, virtual reality, spatial computing, and/or computer vision; when the medium 995 is in communication with the processor 986 and/or other components of the system 985 , as non-limiting examples enabled by the teachings herein.

The processor-executable instructions define a mapping platform and when executed by the processor 986 , can render a graphical user interface (GUI) 991 on the display 987 thereby enabling a user to interface with the mapping platform. The GUI 991 can include one or more interfaces, also called âscreenshotsâ with interactable objects, e.g., anatomic representations, that will be further described in further detail below. In certain embodiments, the GUI 991 can also be combined with or part of other components of the system such as an output device 989 , input device 988 , and/or medium 995 .

The mapping platform enabled by the <figure-callout id="985" l

CLAIMS

Claims ( 20 )

What is claimed is:

1 . A method for generating a medical record, comprising:

rendering, on a display, an anatomic representation; receiving an input, from an input device, having health data, wherein the input is text-based, visual-based, audio-based, and/or based on an interaction with the anatomic representation; processing the input to generate processed health data, the processed health data including a procedure, a diagnosis, a name of an anatomic site, and/or a description of an anatomic site; rendering, on the display,

a marked anatomic site on the anatomic representation that is based on the processed health data, or

an isolated anatomic representation having a marked anatomic site that is based on the processed health data;

selectively rendering, on the display, a mirror image of the anatomic representation with the marked anatomic site or the isolated anatomic representation with the marked anatomic site based on a user preference; selecting one of a plurality of templates, each of the templates having one or more fields; populating one of the fields with the anatomic representation with the marked anatomic site or the isolated anatomic representation with the marked anatomic site to generate a populated selected template; and generating a health record including the populated selected template.

2 . The method of claim 1 , wherein the anatomic representation includes a plurality of predefined anatomic sites.

3 . The method of claim 2 , wherein one of the predefined anatomic sites is located on and associated with an anatomic region with one or more subregions.

4 . The method of claim 3 , wherein the interaction with the anatomic representation includes generating a preview of the anatomic region with the one or more subregions associated with the one of the predefined anatomic sites.

5 . The method of claim 1 , wherein the input is a scanned image of a physical print of the anatomic representation with markups, wherein the markups include the health data.

6 . The method of claim 5 , wherein the physical print of the anatomic representation with markups includes orientation markers and processing the input includes detecting orientation markers and normalizing the axis based on the orientation markers.

7 . The method of claim 1 , wherein processing the input is performed using a language model, a vision-language model, and/or a language-vision model.

8 . The method of claim 1 , wherein the marked anatomic site is associated with the processed health data.

9 . The method of claim 8 , wherein the description of the anatomic site includes a relationship between the marked anatomic site and another anatomic site, wherein the relationship includes a distance between the marked anatomic site and the another anatomic site, a spatial relationship between the marked anatomic site and the another anatomic site, and/or a data-based relationship between the marked anatomic site and the another anatomic site.

10 . The method of claim 1 , wherein populating one of the fields includes populating one or more additional fields of the fields with the processed health data.

11 . The method of claim 1 , wherein processing the input includes detecting a nontechnical term for processed health data and converting the nontechnical term into a technical term.

12 . The method of claim 1 , wherein processing the input includes detecting a plurality of languages and translating the plurality of languages into the processed health data.

13 . The method of claim 1 , wherein the input includes a coded input and processing the input includes decoding the coded input into the processed health data.

14 . The method of claim 1 , wherein generating the health record includes formatting the health record into a database record suitable for an electronic health record database.

15 . A system for generating a medical record, comprising:

a processor; a medium in communication with the processor, wherein the medium is tangible, non-transitory, and computer readable; processor-executable instructions stored on the medium, the processor-executable instructions defining a mapping platform including a data processing module, a knowledge base module, and/or a generation module; a display in communication with the medium; and an input device in communication with the medium and display; wherein the mapping platform is configured to:

render, using the processor, an anatomic representation of a human on the display,

receive, from the input device, an input having health data, wherein the input is text-based, visual-based, audio-based, and/or based on an interaction with the anatomic representation,

process, using the data processing module, the input to generate processed health data, the processed health data including a procedure, a diagnosis, a name of an anatomic site, and/or a description of an anatomic site,

render, using the processor, on the display,

a marked anatomic site on the anatomic representation that is based on the processed health data, or

an isolated anatomic representation having a marked anatomic site that is based on the processed health data,

selectively rendering, using the processer, a mirror image of the anatomic representation with the marked anatomic site or the isolated anatomic representation with the marked anatomic site on the display based on a user preference; selecting one of a plurality of templates, from the knowledge base module, each of the templates having one or more fields; populating one of the fields, using the generation module, with the anatomic representation with the marked anatomic site or the isolated anatomic representation with the marked anatomic site to generate a populated selected template; and

generating, using the generation module, a health record including the populated selected template.

16 . The system of claim 15 , further comprising a printer configured to print a physical health record.

17 . The system of claim 15 , wherein the input device includes an image capturing device configured to scan a physical representation with markups of the anatomic representation, wherein the markups include the health data.

18 . The system of claim 15 , wherein the processor and the medium are located on one or more servers.

19 . The system of claim 15 , wherein the processor, the medium, and the display are located on a mobile phone, a tablet, a laptop, a computer, and/or electronic device.

20 . A tangible, non-transitory, and computer-readable medium having processer-executable instructions stored thereon that when executed by a processor causes a method for generating a medical record, comprising:

a marked anatomic site on the anatomic representation that is based on the processed health data, or

an isolated anatomic representation having a marked anatomic site that is based on the processed health data;

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