Google Patentsopen access

… and method for extremely efficient image and pattern recognition and artificial … — Z Advanced Computing, Inc. (Zac) (US11074495B2)

ABSTRACT

Abstract

Specification covers new algorithms, methods, and systems for: Artificial Intelligence; the first application of General-AI (versus Specific, Vertical, or Narrow-AI) (as humans can do); addition of reasoning, inference, and cognitive layers/engines to learning module/engine/layer; soft computing; Information Principle; Stratification; Incremental Enlargement Principle; deep-level/detailed recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, tilted or partial-face, OCR, relationship, position, pattern, and object); Big Data analytics; machine learning; crowd-sourcing; classification; clustering; SVM; similarity measures; Enhanced Boltzmann Machines; Enhanced Convolutional Neural Networks; optimization; search engine; ranking; semantic web; context analysis; question-answering system; soft, fuzzy, or un-sharp boundaries/impreciseness/ambiguities/fuzziness in class or set, e.g., for language analysis; Natural Language Processing (NLP); Computing-with-Words (CWW); parsing; machine translation; music, sound, speech, or speaker recognition; video search and analysis (e.g. tracking); image annotation; image or color correction; data reliability; Z-Number; Z-Web; Z-Factor; rules engine; playing games; control system; autonomous vehicles or drones; self-diagnosis and self-repair robots; system diagnosis; medical diagnosis; genetics; drug discovery; biomedicine; data mining; event prediction; financial forecasting (e.g., for stocks); economics; risk assessment; fraud detection (e.g., for cryptocurrency); e-mail management; database management; indexing and join operation; memory management; data compression; event-centric social network; social behavior; and Image Ad and Referral Networks.

Description

RELATED APPLICATIONS

The current application is a CIP (Continuation-in-part) of another U.S. application Ser. No. 14/218,923, filed 18 Mar. 2014, called Zadeh-101-CIP, which is now allowed, which is a CIP (Continuation-in-part) of another U.S. application Ser. No. 13/781,303, filed Feb. 28, 2013, called ZAdvanced-1, now U.S. Pat. No. 8,873,813, issued on 28 Oct. 2014, which claims the benefit of and takes the priority of the earlier filing date of the following U.S. provisional application No. 61/701,789, filed Sep. 17, 2012, called ZAdvanced-1-prov. The application Ser. No. 14/218,923 also claims the benefit of and takes the priority of the earlier filing dates of the following U.S. provisional application Nos. 61/802,810, filed Mar. 18, 2013, called ZAdvanced-2-prov; and 61/832,816, filed Jun. 8, 2013, called ZAdvanced-3-prov; and 61/864,633, filed Aug. 11, 2013, called ZAdvanced-4-prov; and 61/871,860, filed Aug. 29, 2013, called ZAdvanced-5-prov. The application Ser. No. 14/218,923 is also a CIP (Continuation-in-part) of another U.S. application Ser. No. 14/201,974, filed 10 Mar. 2014, called Zadeh-101-Cont-4, now as U.S. Pat. No. 8,949,170, issued on 3 Feb. 2015, which is a Continuation of another U.S. application Ser. No. 13/953,047, filed Jul. 29, 2013, called Zadeh-101-Cont-3, now U.S. Pat. No. 8,694,459, issued on 8 Apr. 2014, which is also a Continuation of another application Ser. No. 13/621,135, filed Sep. 15, 2012, now issued as U.S. Pat. No. 8,515,890, on Aug. 20, 2013, which is also a Continuation of Ser. No. 13/621,164, filed Sep. 15, 2012, now issued as U.S. Pat. No. 8,463,735, which is a Continuation of another application, Ser. No. 13/423,758, filed Mar. 19, 2012, now issued as U.S. Pat. No. 8,311,973, which, in turn, claims the benefit of the U.S. provisional application No. 61/538,824, filed on Sep. 24, 2011. The current application incorporates by reference all of the applications and patents/provisionals mentioned above, including all their Appendices and attachments (Packages), and it claims benefits to and takes the priority of the earlier filing dates of all the provisional and utility applications or patents mentioned above. Please note that most of the Appendices and attachments (Packages) to the specifications for the above-mentioned applications and patents (such as U.S. Pat. No. 8,311,973) are available for public view, e.g., through Public Pair system at the USPTO web site, with some of their listings given below in the next section.

Attached Packages and Appendices to Prior Specifications (e.g., U.S. Pat. No. 8,311,973 and Zadeh-101-CIP):

In addition to the provisional cases above, the teachings of all 33 packages (the PDF files, named “Packages 1-33”) attached with some of the parent cases' filings (as Appendices) (such as U.S. Pat. No. 8,311,973 (i.e., Zadeh-101 docket)) are incorporated herein by reference to this current disclosure.

Furthermore, “Appendices 1-5” of Zadeh-101-CIP (i.e., Ser. No. 14/218,923) are incorporated herein by reference to this current disclosure.

To reduce the size of the appendices/disclosure, these Packages (Packages 1-33) and Appendices (Appendices 1-5) are not repeated here again, but they may be referred to/incorporated in, in the future from time to time in the current or the children/related applications, both in spec or claims, as our own previous teachings.

However, the new Appendices attached to this current application is now numbered after the appendices mentioned above, i.e., starting with Appendix 6, for this current application, to make it easier to refer to them in the future.

Please note that Appendices 1-5 (of Zadeh-101-CIP (i.e., Ser. No. 14/218,923)) are identified as:

Appendix 1: article about “Approximate Z-Number Evaluation based on Categorical Sets of Probability Distributions” (11 pages) Appendix 2: hand-written technical notes, formulations, algorithms, and derivations (5 pages) Appendix 3: presentation about “Approximate Z-Number Evaluation Based on Categorical Sets of Probability Distributions” (30 pages) Appendix 4: presentation with Figs. from B1 to B19 (19 pages) Appendix 5: presentation about “SVM Classifier” (22 pages)

Please note that Appendices 6-10 (of Zadeh-101-CIP-CIP (i.e., the current application)) are identified as:

Appendix 6: article/journal/technical/research/paper about “The Information Principle”, by Prof. Lotfi Zadeh, Information Sciences, submitted 16 May 2014, published 2015 (10 pages) Appendix 7: presentation/conference/talk/invited/keynote speaker/lecture about “Stratification, target set reachability, and incremental enlargement principle”, by Prof. Lotfi Zadeh, UC Berkeley, World Conference on Soft Computing, May 22, 2016 (14 pages, each page including 9 slides, for a total of 126 slides) (first version prepared on Feb. 8, 2016) Appendix 8: article about “Stratification, quantization, target set reachability, and incremental enlargement principle”, by Prof. Lotfi Zadeh, for Information Sciences, received 4 Jul. 2016 (17 pages) (first version prepared on Feb. 5, 2016) Appendix 9: This shows the usage of visual search terms for our image search engine (1 page), which is the first in the industry. It shows an example for shoes (component or parts matching, from various shoes), using ZAC/our technology and platform. For example, it shows the search for: “side look like shoe number 1, heel look like shoe number 2, and toe look like shoe number 3”, based on what the user is looking/searching for. In general, we can have a combination of conditions, e.g.: (R 1 AND R 2 AND . . . AND R n ), or any logical search terms or combinations or operators, e.g., [R 1 OR (R 2 AND R 3 )], which is very helpful for e-commerce or websites/e-stores. Appendix 10: “Brief Introduction to AI and Machine Learning”, for conventional tools and methods, sometimes used or referred to in this invention, for completeness and as support of the main invention, or just for the purpose of comparison with the conventional tools and methods.

Please note that Packages 1-33 (of U.S. Pat. No. 8,311,973) are also one of the inventor's (Prof. Lotfi Zadeh's) own previous technical teachings, and thus, they may be referred to (from time-to-time) for further details or explanations, by the reader, if needed.

Please note that Packages 1-25 had already been submitted (and filed) with our provisional application for one of the parent cases.

Packages 1-12 and 15-22 are marked accordingly at the bottom of each page or slide (as the identification). The other Packages (Packages 13-14 and 23-33) are identified here:

Package 13: 1 page, with 3 slides, starting with “ FIG. 1 . Membership function of A and probability density function of X” Package 14: 1 page, with 5 slides, starting with “ FIG. 1 . f-transformation and f-geometry. Note that fuzzy figures, as shown, are not hand drawn. They should be visualized as hand drawn figures.” Package 23: 2-page text, titled “The Concept of a Z-number—a New Direction in Computation, Lotfi A. Zadeh, Abstract” (dated Mar. 28, 2011) Package 24: 2-page text, titled “Prof. Lotfi Zadeh, The Z-mouse—a visual means of entry and retrieval of fuzzy data” Package 25: 12-page article, titled “Toward Extended Fuzzy Logic—A First Step, Abstract” Package 26: 2-page text, titled “Can mathematics deal with computational problems which are stated in a natural language?, Lotfi A. Zadeh, Sep. 30, 2011, Abstract” (Abstract dated Sep. 30, 2011) Package 27: 15 pages, with 131 slides, titled “Can Mathematics Deal with Computational Problems Which are Stated in a Natural Language?, Lotfi A. Zadeh” (dated Feb. 2, 2012) Package 28: 14 pages, with 123 slides, titled “Can Mathematics Deal with Computational Problems Which are Stated in a Natural Language?, Lotfi A. Zadeh” (dated Oct. 6, 2011) Package 29: 33 pages, with 289 slides, titled “Computing with Words—Principal Concepts and Ideas, Lotfi A. Zadeh” (dated Jan. 9, 2012) Package 30: 23 pages, with 205 slides, titled “Computing with Words—Principal Concepts and Ideas, Lotfi A. Zadeh” (dated May 10, 2011) Package 31: 3 pages, with 25 slides, titled “Computing with Words—Principal Concepts and Ideas, Lotfi A. Zadeh” (dated Nov. 29, 2011) Package 32: 9 pages, with 73 slides, titled “Z-NUMBERS-A NEW DIRECTION IN THE ANALYSIS OF UNCERTAIN AND IMPRECISE SYSTEMS, Lotfi A. Zadeh” (dated Jan. 20, 2012) Package 33: 15 pages, with 131 slides, titled “PRECISIATION OF MEANING—A KEY TO SEMANTIC COMPUTING, Lotfi A. Zadeh” (dated Jul. 22, 2011)

Please note that all the Packages and Appendices (prepared by one or more of the inventors here) were also identified by their PDF file names, as they were submitted to the USPTO electronically.

BACKGROUND OF THE INVENTION

Professor Lotfi A. Zadeh, one of the inventors of the current disclosure and some of the parent cases, is the “Father of Fuzzy Logic”. He first introduced the concept of Fuzzy Set and Fuzzy Theory in his famous paper, in 1965 (as a professor of University of California, at Berkeley). Since then, many people have worked on the Fuzzy Logic technology and science. Dr. Zadeh has also developed many other concepts related to Fuzzy Logic. He has invented Computation-with-Words (CWW or CW), e.g., for natural language processing (NLP) and analysis, as well as semantics of natural languages and computational theory of perceptions, for many diverse applications, which we address here, as well, as some of our new/innovative methods and systems are built based on those concepts/theories, as their novel/advanced extensions/additions/versions/extractions/branches/fields. One of his last revolutionary inventions is called Z-numbers, named after him (“Z” from Zadeh), which is one of the many subjects of the (many) current inventions. That is, some of the many embodiments of the current inventions are based on or related to Z-numbers. The concept of Z-numbers was first published in a recent paper, by Dr. Zadeh, called “A Note on Z-Numbers”, Information Sciences 181 (2011) 2923-2932.

However, in addition, there are many other embodiments in the current disclosure that deal with other important and innovative topics/subjects, e.g., related to General AI, versus Specific or Vertical or Narrow AI, machine learning, using/requiring only a small number of training samples (same as humans can do), learning one concept and use it in another context or environment (same as humans can do), addition of reasoning and cognitive layers to the learning module (same as humans can do), continuous learning and updating the learning machine continuously (same as humans can do), simultaneous learning and recognition (at the same time) (same as humans can do), and conflict and contradiction resolution (same as humans can do), with application, e.g., for image recognition, application for any pattern recognition, e.g., sound or voice, application for autonomous or driverless cars, application for security and biometrics, e.g., partial or covered or tilted or rotated face recognition, or emotion and feeling detections, application for playing games or strategic scenarios, application for fraud detection or verification/validation, e.g., for banking or cryptocurrency or tracking fund or certificates, application for medical imaging and medical diagnosis and medical procedures and drug developments and genetics, application for control systems and robotics, application for prediction, forecasting, and risk analysis, e.g., for weather forecasting, economy, oil price, interest rate, stock price, insurance premium, and social unrest indicators/parameters, and the like.

In the real world, uncertainty is a pervasive phenomenon. Much of the information on which decisions are based is uncertain. Humans have a remarkable capability to make rational decisions based on information which is uncertain, imprecise and/or incomplete. Formalization of this capability is one of the goals of these current inventions, in one embodiment.

Here are some of the publications on the related subjects, for some embodiments:

[1] R. Ash, Basic Probability Theory, Dover Publications, 2008. [2] J-C. Buisson, Nutri-Educ, a nutrition software application for balancing meals, using fuzzy arithmetic and heuristic search algorithms, Artificial Intelligence in Medicine 42, (3), (2008) 213-227. [3] E. Trillas, C. Moraga, S. Guadarrama, S. Cubillo and E. Castifieira, Computing with Antonyms, In: M. Nikravesh, J. Kacprzyk and L. A. Zadeh (Eds.), Forging New Frontiers: Fuzzy Pioneers I, Studies in Fuzziness and Soft Computing Vol 217, Springer-Verlag, Berlin Heidelberg 2007, pp. 133-153. [4] R. R. Yager, On measures of specificity, In: O. Kaynak, L. A. Zadeh, B. Turksen, I. J. Rudas (Eds.), Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications, Springer-Verlag, Berlin, 1998, pp. 94-113. [5] L. A. Zadeh, Calculus of fuzzy restrictions, In: L. A. Zadeh, K. S. Fu, K. Tanaka, and M. Shimura (Eds.), Fuzzy sets and Their Applications to Cognitive and Decision Processes, Academic Press, New York, 1975, pp. 1-39. [6] L. A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning,

Part I: Information Sciences 8 (1975) 199-249; Part II: Information Sciences 8 (1975) 301-357; Part III: Information Sciences 9 (1975) 43-80.

[7] L. A. Zadeh, Fuzzy logic and the calculi of fuzzy rules and fuzzy graphs, Multiple-Valued Logic 1, (1996) 1-38. [8] L. A. Zadeh, From computing with numbers to computing with words—from manipulation of measurements to manipulation of perceptions, IEEE Transactions on Circuits and Systems 45, (1999) 105-119. [9] L. A. Zadeh, The Z-mouse—a visual means of entry and retrieval of fuzzy data, posted on BISC Forum, Jul. 30, 2010. A more detailed description may be found in Computing with Words-principal concepts and ideas, Colloquium PowerPoint presentation, University of Southern California, Los Angeles, Calif., Oct. 22, 2010.

RELATED APPLICATIONS

The current application is a CIP (Continuation-in-part) of another U.S. application Ser. No. 14/218,923, filed 18 Mar. 2014, called Zadeh-101-CIP, which is now allowed, which is a CIP (Continuation-in-part) of another U.S. application Ser. No. 13/781,303, filed Feb. 28, 2013, called ZAdvanced-1, now U.S. Pat. No. 8,873,813, issued on 28 Oct. 2014, which claims the benefit of and takes the priority of the earlier filing date of the following U.S. provisional application No. 61/701,789, filed Sep. 17, 2012, called ZAdvanced-1-prov. The application Ser. No. 14/218,923 also claims the benefit of and takes the priority of the earlier filing dates of the following U.S. provisional application Nos. 61/802,810, filed Mar. 18, 2013, called ZAdvanced-2-prov; and 61/832,816, filed Jun. 8, 2013, called ZAdvanced-3-prov; and 61/864,633, filed Aug. 11, 2013, called ZAdvanced-4-prov; and 61/871,860, filed Aug. 29, 2013, called ZAdvanced-5-prov. The application Ser. No. 14/218,923 is also a CIP (Continuation-in-part) of another U.S. application Ser. No. 14/201,974, filed 10 Mar. 2014, called Zadeh-101-Cont-4, now as U.S. Pat. No. 8,949,170, issued on 3 Feb. 2015, which is a Continuation of another U.S. application Ser. No. 13/953,047, filed Jul. 29, 2013, called Zadeh-101-Cont-3, now U.S. Pat. No. 8,694,459, issued on 8 Apr. 2014, which is also a Continuation of another application Ser. No. 13/621,135, filed Sep. 15, 2012, now issued as U.S. Pat. No. 8,515,890, on Aug. 20, 2013, which is also a Continuation of Ser. No. 13/621,164, filed Sep. 15, 2012, now issued as U.S. Pat. No. 8,463,735, which is a Continuation of another application, Ser. No. 13/423,758, filed Mar. 19, 2012, now issued as U.S. Pat. No. 8,311,973, which, in turn, claims the benefit of the U.S. provisional application No. 61/538,824, filed on Sep. 24, 2011. The current application incorporates by reference all of the applications and patents/provisionals mentioned above, including all their Appendices and attachments (Packages), and it claims benefits to and takes the priority of the earlier filing dates of all the provisional and utility applications or patents mentioned above. Please note that most of the Appendices and attachments (Packages) to the specifications for the above-mentioned applications and patents (such as U.S. Pat. No. 8,311,973) are available for public view, e.g., through Public Pair system at the USPTO web site, with some of their listings given below in the next section.

Attached Packages and Appendices to Prior Specifications (e.g., U.S. Pat. No. 8,311,973 and Zadeh-101-CIP):

In addition to the provisional cases above, the teachings of all 33 packages (the PDF files, named “Packages 1-33”) attached with some of the parent cases' filings (as Appendices) (such as U.S. Pat. No. 8,311,973 (i.e., Zadeh-101 docket)) are incorporated herein by reference to this current disclosure.

Furthermore, “Appendices 1-5” of Zadeh-101-CIP (i.e., Ser. No. 14/218,923) are incorporated herein by reference to this current disclosure.

To reduce the size of the appendices/disclosure, these Packages (Packages 1-33) and Appendices (Appendices 1-5) are not repeated here again, but they may be referred to/incorporated in, in the future from time to time in the current or the children/related applications, both in spec or claims, as our own previous teachings.

However, the new Appendices attached to this current application is now numbered after the appendices mentioned above, i.e., starting with Appendix 6, for this current application, to make it easier to refer to them in the future.

Please note that Appendices 1-5 (of Zadeh-101-CIP (i.e., Ser. No. 14/218,923)) are identified as:

Appendix 1: article about “Approximate Z-Number Evaluation based on Categorical Sets of Probability Distributions” (11 pages) Appendix 2: hand-written technical notes, formulations, algorithms, and derivations (5 pages) Appendix 3: presentation about “Approximate Z-Number Evaluation Based on Categorical Sets of Probability Distributions” (30 pages) Appendix 4: presentation with Figs. from B1 to B19 (19 pages) Appendix 5: presentation about “SVM Classifier” (22 pages)

Please note that Appendices 6-10 (of Zadeh-101-CIP-CIP (i.e., the current application)) are identified as:

Appendix 6: article/journal/technical/research/paper about “The Information Principle”, by Prof. Lotfi Zadeh, Information Sciences, submitted 16 May 2014, published 2015 (10 pages) Appendix 7: presentation/conference/talk/invited/keynote speaker/lecture about “Stratification, target set reachability, and incremental enlargement principle”, by Prof. Lotfi Zadeh, UC Berkeley, World Conference on Soft Computing, May 22, 2016 (14 pages, each page including 9 slides, for a total of 126 slides) (first version prepared on Feb. 8, 2016) Appendix 8: article about “Stratification, quantization, target set reachability, and incremental enlargement principle”, by Prof. Lotfi Zadeh, for Information Sciences, received 4 Jul. 2016 (17 pages) (first version prepared on Feb. 5, 2016) Appendix 9: This shows the usage of visual search terms for our image search engine (1 page), which is the first in the industry. It shows an example for shoes (component or parts matching, from various shoes), using ZAC/our technology and platform. For example, it shows the search for: “side look like shoe number 1, heel look like shoe number 2, and toe look like shoe number 3”, based on what the user is looking/searching for. In general, we can have a combination of conditions, e.g.: (R 1 AND R 2 AND . . . AND R n ), or any logical search terms or combinations or operators, e.g., [R 1 OR (R 2 AND R 3 )], which is very helpful for e-commerce or websites/e-stores. Appendix 10: “Brief Introduction to AI and Machine Learning”, for conventional tools and methods, sometimes used or referred to in this invention, for completeness and as support of the main invention, or just for the purpose of comparison with the conventional tools and methods.

Please note that Packages 1-33 (of U.S. Pat. No. 8,311,973) are also one of the inventor's (Prof. Lotfi Zadeh's) own previous technical teachings, and thus, they may be referred to (from time-to-time) for further details or explanations, by the reader, if needed.

Please note that Packages 1-25 had already been submitted (and filed) with our provisional application for one of the parent cases.

Packages 1-12 and 15-22 are marked accordingly at the bottom of each page or slide (as the identification). The other Packages (Packages 13-14 and 23-33) are identified here:

Package 13: 1 page, with 3 slides, starting with “ FIG. 1 . Membership function of A and probability density function of X” Package 14: 1 page, with 5 slides, starting with “ FIG. 1 . f-transformation and f-geometry. Note that fuzzy figures, as shown, are not hand drawn. They should be visualized as hand drawn figures.” Package 23: 2-page text, titled “The Concept of a Z-number—a New Direction in Computation, Lotfi A. Zadeh, Abstract” (dated Mar. 28, 2011) Package 24: 2-page text, titled “Prof. Lotfi Zadeh, The Z-mouse—a visual means of entry and retrieval of fuzzy data” Package 25: 12-page article, titled “Toward Extended Fuzzy Logic—A First Step, Abstract” Package 26: 2-page text, titled “Can mathematics deal with computational problems which are stated in a natural language?, Lotfi A. Zadeh, Sep. 30, 2011, Abstract” (Abstract dated Sep. 30, 2011) Package 27: 15 pages, with 131 slides, titled “Can Mathematics Deal with Computational Problems Which are Stated in a Natural Language?, Lotfi A. Zadeh” (dated Feb. 2, 2012) Package 28: 14 pages, with 123 slides, titled “Can Mathematics Deal with Computational Problems Which are Stated in a Natural Language?, Lotfi A. Zadeh” (dated Oct. 6, 2011) Package 29: 33 pages, with 289 slides, titled “Computing with Words—Principal Concepts and Ideas, Lotfi A. Zadeh” (dated Jan. 9, 2012) Package 30: 23 pages, with 205 slides, titled “Computing with Words—Principal Concepts and Ideas, Lotfi A. Zadeh” (dated May 10, 2011) Package 31: 3 pages, with 25 slides, titled “Computing with Words—Principal Concepts and Ideas, Lotfi A. Zadeh” (dated Nov. 29, 2011) Package 32: 9 pages, with 73 slides, titled “Z-NUMBERS-A NEW DIRECTION IN THE ANALYSIS OF UNCERTAIN AND IMPRECISE SYSTEMS, Lotfi A. Zadeh” (dated Jan. 20, 2012) Package 33: 15 pages, with 131 slides, titled “PRECISIATION OF MEANING—A KEY TO SEMANTIC COMPUTING, Lotfi A. Zadeh” (dated Jul. 22, 2011)

Please note that all the Packages and Appendices (prepared by one or more of the inventors here) were also identified by their PDF file names, as they were submitted to the USPTO electronically.

BACKGROUND OF THE INVENTION

Professor Lotfi A. Zadeh, one of the inventors of the current disclosure and some of the parent cases, is the “Father of Fuzzy Logic”. He first introduced the concept of Fuzzy Set and Fuzzy Theory in his famous paper, in 1965 (as a professor of University of California, at Berkeley). Since then, many people have worked on the Fuzzy Logic technology and science. Dr. Zadeh has also developed many other concepts related to Fuzzy Logic. He has invented Computation-with-Words (CWW or CW), e.g., for natural language processing (NLP) and analysis, as well as semantics of natural languages and computational theory of perceptions, for many diverse applications, which we address here, as well, as some of our new/innovative methods and systems are built based on those concepts/theories, as their novel/advanced extensions/additions/versions/extractions/branches/fields. One of his last revolutionary inventions is called Z-numbers, named after him (“Z” from Zadeh), which is one of the many subjects of the (many) current inventions. That is, some of the many embodiments of the current inventions are based on or related to Z-numbers. The concept of Z-numbers was first published in a recent paper, by Dr. Zadeh, called “A Note on Z-Numbers”, Information Sciences 181 (2011) 2923-2932.

However, in addition, there are many other embodiments in the current disclosure that deal with other important and innovative topics/subjects, e.g., related to General AI, versus Specific or Vertical or Narrow AI, machine learning, using/requiring only a small number of training samples (same as humans can do), learning one concept and use it in another context or environment (same as humans can do), addition of reasoning and cognitive layers to the learning module (same as humans can do), continuous learning and updating the learning machine continuously (same as humans can do), simultaneous learning and recognition (at the same time) (same as humans can do), and conflict and contradiction resolution (same as humans can do), with application, e.g., for image recognition, application for any pattern recognition, e.g., sound or voice, application for autonomous or driverless cars, application for security and biometrics, e.g., partial or covered or tilted or rotated face recognition, or emotion and feeling detections, application for playing games or strategic scenarios, application for fraud detection or verification/validation, e.g., for banking or cryptocurrency or tracking fund or certificates, application for medical imaging and medical diagnosis and medical procedures and drug developments and genetics, application for control systems and robotics, application for prediction, forecasting, and risk analysis, e.g., for weather forecasting, economy, oil price, interest rate, stock price, insurance premium, and social unrest indicators/parameters, and the like.

In the real world, uncertainty is a pervasive phenomenon. Much of the information on which decisions are based is uncertain. Humans have a remarkable capability to make rational decisions based on information which is uncertain, imprecise and/or incomplete. Formalization of this capability is one of the goals of these current inventions, in one embodiment.

Here are some of the publications on the related subjects, for some embodiments:

[1] R. Ash, Basic Probability Theory, Dover Publications, 2008. [2] J-C. Buisson, Nutri-Educ, a nutrition software application for balancing meals, using fuzzy arithmetic and heuristic search algorithms, Artificial Intelligence in Medicine 42, (3), (2008) 213-227. [3] E. Trillas, C. Moraga, S. Guadarrama, S. Cubillo and E. Castifieira, Computing with Antonyms, In: M. Nikravesh, J. Kacprzyk and L. A. Zadeh (Eds.), Forging New Frontiers: Fuzzy Pioneers I, Studies in Fuzziness and Soft Computing Vol 217, Springer-Verlag, Berlin Heidelberg 2007, pp. 133-153. [4] R. R. Yager, On measures of specificity, In: O. Kaynak, L. A. Zadeh, B. Turksen, I. J. Rudas (Eds.), Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications, Springer-Verlag, Berlin, 1998, pp. 94-113. [5] L. A. Zadeh, Calculus of fuzzy restrictions, In: L. A. Zadeh, K. S. Fu, K. Tanaka, and M. Shimura (Eds.), Fuzzy sets and Their Applications to Cognitive and Decision Processes, Academic Press, New York, 1975, pp. 1-39. [6] L. A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning,

Part I: Information Sciences 8 (1975) 199-249; Part II: Information Sciences 8 (1975) 301-357; Part III: Information Sciences 9 (1975) 43-80.

[7] L. A. Zadeh, Fuzzy logic and the calculi of fuzzy rules and fuzzy graphs, Multiple-Valued Logic 1, (1996) 1-38. [8] L. A. Zadeh, From computing with numbers to computing with words—from manipulation of measurements to manipulation of perceptions, IEEE Transactions on Circuits and Systems 45, (1999) 105-119. [9] L. A. Zadeh, The Z-mouse—a visual means of entry and retrieval of fuzzy data, posted on BISC Forum, Jul. 30, 2010. A more detailed description may be found in Computing with Words-principal concepts and ideas, Colloquium PowerPoint presentation, University of Southern California, Los Angeles, Calif., Oct. 22, 2010.

As one of the applications mentioned here in this disclosure, for comparisons, some of the search engines or question-answering engines in the market (in the recent years) are (or were): Google®, Yahoo®, Autonomy, IBM®, Fast Search, Powerset® (by Xerox® PARC and bought by Microsoft®), Microsoft® Bing, Wolfram®, AskJeeves, Collarity, Vivisimo®, Endeca®, Media River, Hakia®, Ask.com®, AltaVista, Excite, Go Network, HotBot®, Lycos®, Northern Light, and Like.com.

Other references on some of the related subjects are:

[1] A. R. Aronson, B. E. Jacobs, J. Minker, A note on fuzzy deduction, J. ACM27 (4) (1980), 599-603. [2] A. Bardossy, L. Duckstein, Fuzzy Rule-based Modelling with Application to Geophysical, Biological and Engineering Systems, CRC Press, 1995. [3] T. Berners-Lee, J. Hendler, O. Lassila, The semantic web, Scientific American 284 (5) (2001), 34-43. [4] S. Brin, L. Page, The anatomy of a large-scale hypertextual web search engine, Computer Networks 30 (1-7) (1998), 107-117. [5] W. J. H. J. Bronnenberg, M. C. Bunt, S. P. J. Lendsbergen, R. H. J. Scha, W. J. Schoenmakers, E. P. C. van Utteren, The question answering system PHLIQA1, in: L. Bola (Ed.), Natural Language Question Answering Systems, Macmillan, 1980. [6] L. S. Coles, Techniques for information retrieval using an inferential question-answering system with natural language input, SRI Report, 1972. [7] A. Di Nola, S. Sessa, W. Pedrycz, W. 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However, none of the prior art teaches the features mentioned in our invention disclosure.

There are a lot of research going on today, focusing on the search engine, analytics, Big Data processing, natural language processing, economy forecasting, dealing with reliability and certainty, medical diagnosis, pattern recognition, object recognition, biometrics, security analysis, risk analysis, fraud detection, satellite image analysis, machine generated data, machine learning, training samples, and the like.

For example, see the article by Technology Review, published by MIT, “Digging deeper in search web”, Jan. 29, 2009, by Kate Greene, or search engine by GOOGLE®, MICROSOFT® (BING®), or YAHOO®, or APPLE® SIRI, or WOLFRAM® ALPHA computational knowledge engine, or AMAZON engine, or FACEBOOK® engine, or ORACLE® database, or YANDEX® search engine in Russia, or PICASA® (GOOGLE®) web albums, or YOUTUBE® (GOOGLE®) engine, or ALIBABA (Chinese supplier connection), or SPLUNK® (for Big Data), or MICROSTRATEGY® (for business intelligence), or QUID (or KAGGLE, ZESTFINANCE, APIXIO, DATAMEER, BLUEKAL GNIP, RETAILNEXT, or RECOMMIND) (for Big Data), or paper by Viola-Jones, Viola et al., at Conference on Computer Vision and Pattern Recognition, 2001, titled “Rapid object detection using a boosted cascade of simple features”, from Mitsubishi and Compaq research labs, or paper by Alex Pentland et al., February 2000, at Computer, IEEE, titled “Face recognition for smart environments”, or GOOGLE® official blog publication, May 16, 2012, titled “Introducing the knowledge graph: things, not strings”, or the article by Technology Review, published by MIT, “The future of search”, Jul. 16, 2007, by Kate Greene, or the article by Technology Review, published by MIT, “Microsoft searches for group advantage”, Jan. 30, 2009, by Robert Lemos, or the article by Technology Review, published by MIT, “WOLFRAM ALPHA and GOOGLE face off”, May 5, 2009, by David Talbot, or the paper by Devarakonda et al., at International Journal of Software Engineering (USE), Vol. 2, Issue 1, 2011, titled “Next generation search engines for information retrieval”, or paper by Nair-Hinton, titled “Implicit mixtures of restricted Boltzmann machines”, NIPS, pp. 1145-1152, 2009, or paper by Nair, V. and Hinton, G. E., titled “3-D Object recognition with deep belief nets”, published in Advances in Neural Information Processing Systems 22, (Y. Bengio, D. Schuurmans, J. lafferty, C. K. I. Williams, and A. Culotta (Eds.)), pp 1339-1347. Other research groups include those headed by Andrew Ng, Yoshua Bengio, Fei Fei Li, Ashutosh Saxena, LeCun, Michael I. Jordan, Zoubin Ghahramani, and others in companies and universities around the world.

However, none of the prior art teaches the features mentioned in our invention disclosure, even in combination.

SUMMARY OF THE INVENTION

For one embodiment: Decisions are based on information. To be useful, information must be reliable. Basically, the concept of a Z-number relates to the issue of reliability of information. A Z-number, Z, has two components, Z=(A,B). The first component, A, is a restriction (constraint) on the values which a real-valued uncertain variable, X, is allowed to take. The second component, B, is a measure of reliability (certainty) of the first component. Typically, A and B are described in a natural language. Example: (about 45 minutes, very sure). An important issue relates to computation with Z-numbers. Examples are: What is the sum of (about 45 minutes, very sure) and (about 30 minutes, sure)? What is the square root of (approximately 100, likely)? Computation with Z-numbers falls within the province of Computing with Words (CW or CWW). In this disclosure, the concept of a Z-number is introduced and methods of computation with Z-numbers are shown. The concept of a Z-number has many applications, especially in the realms of economics, decision analysis, risk assessment, prediction, anticipation, rule-based characterization of imprecise functions and relations, and biomedicine. Different methods, applications, and systems are discussed. Other Fuzzy inventions and concepts are also discussed. Many non-Fuzzy-related inventions and concepts are also discussed.

For other embodiments: Specification also covers new algorithms, methods, and systems for artificial intelligence, soft computing, and deep/detailed learning/recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, facial, OCR (text), background, relationship, position, pattern, and object), large number of images (“Big Data”) analytics, machine learning, training schemes, crowd-sourcing (using experts or humans), feature space, clustering, classification, similarity measures, optimization, search engine, ranking, question-answering system, soft (fuzzy or unsharp) boundaries/impreciseness/ambiguities/fuzziness in language, Natural Language Processing (NLP), Computing-with-Words (CWW), parsing, machine translation, sound and speech recognition, video search and analysis (e.g. tracking), image annotation, geometrical abstraction, image correction, semantic web, context analysis, data reliability (e.g., using Z-number (e.g., “About 45 minutes; Very sure”)), rules engine, control system, autonomous vehicle (e.g., self-parking), self-diagnosis and self-repair robots, system diagnosis, medical diagnosis, biomedicine, data mining, event prediction, financial forecasting, economics, risk assessment, e-mail management, database management, indexing and join operation, memory management, and data compression.

Other topics/inventions covered are, e.g.:

Method and System for Identification or Verification for an Object, a Person, or their Attributes System and Method for Image Recognition and Matching for Targeted Advertisement System and Method for Analyzing Ambiguities in Language for Natural Language Processing Application of Z-Webs and Z-factors to Analytics, Search Engine, Learning, Recognition, Natural Language, and Other Utilities Method and System for Approximate Z-Number Evaluation based on Categorical Sets of Probability Distributions Image and Video Recognition and Application to Social Network and Image and Video Repositories System and Method for Image Recognition for Event-Centric Social Networks System and Method for Image Recognition for Image Ad Network System and Method for Increasing Efficiency of Support Vector Machine Classifiers

Other topics/inventions covered are, e.g.:

Information Principle Stratification Incremental Enlargement Principle Deep/detailed Machine Learning and training schemes Image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, facial (e.g. using eigenface), monument and landmark, OCR, background, partial object, relationship, position, pattern, texture, and object) Basis functions Image and video auto-annotation Focus window Modified/Enhanced Boltzmann Machines Feature space translation Geometrical abstraction Image correction Semantic web Context analysis Data reliability Correlation layer Clustering Classification Support Vector Machines Similarity measures Optimization Z-number Z-factor Z-web Rules engine Control system Robotics Search engine Ranking Question-answering system Soft boundaries & Fuzziness in language Natural Language Processing (NLP) System diagnosis Medical diagnosis Big Data analytics Event prediction Financial forecasting Computing with Words (CWW) Parsing Soft boundaries & Fuzziness in clustering & classification Soft boundaries & Fuzziness in recognition Machine translation Risk assessment e-mail management Database management Indexing and join operation Memory management Sound and speech recognition Video search & analysis (e.g. tracking) Data compression Crowd sourcing (e.g., with experts or SMEs) Event-centric social networking (based on image) Energy Transportation Distribution of materials Optimization Scheduling

We have also introduced the first Image Ad Network, powered by our next generation image search engine.

We have introduced our novel “ZAC™ Image Recognition Platform”, which applies learning based on General-AI algorithms. This way, we need much smaller number of training samples to train (the same as humans do), e.g., for evaluating or analyzing a 3-D object/image, e.g., a complex object, such as a shoe, from any direction or angle. To our knowledge, nobody has solved this problem, yet. This is the “Holy Grail” of image recognition. Having/requiring much smaller number of training samples to train is also the “Holy Grail” of AI and machine learning. So, here, we have achieved 2 major scientific and technical milestones/breakthroughs that others have failed to obtain. (These results had been originally reported in our parent cases, as well.)

In addition, to our knowledge, this is the first successful example of application of General-AI algorithms, systems, and methods in any field, application, industry, university, research, paper, experiment, demo, or usage.

With other methods in the industry/universities, e.g., Deep Learning or Convolutional Neural Networks or Deep Reinforcement Learning (maximizing a cumulative reward function) or variations of Neural Networks (e.g., Capsule Networks, recently introduced by Prof. Hinton, Sara Sabour, and Nicholas Frosst, from Google and U. of Toronto), these cannot be done at all, even with much larger number of training samples and much larger CPU/GPU computing time/power and much longer training time periods.

So, we have a significant advantage over the other methods in the industry/universities, as these tasks cannot be done by other methods at all.

Even for the conventional/much easier/very specific tasks, where the other AI methods are applicable/useful, we still have a huge advantage over them, by some orders of magnitude, in terms of cost, efficiency, size, training time, computing/resource requirements, battery lifetime, flexibility, and detection/recognition/prediction accuracy.

These shortcomings/failures/limitations of the other methods/systems/algorithms/results in the AI/machine learning industry/universities have been expressed/confirmed by various AI/machine learning people/researchers. For example, Prof. Hinton, a Google Fellow and a pioneer in AI from U. of Toronto, in an interview (GIGAOM, Jan. 16, 2017), stated that, “One problem we still haven't solved is getting neural nets to generalize well from small amounts of data, and I suspect that this may require radical changes in the types of neuron we use”. In addition, in another interview (Axios, Sep. 15, 2017), he strongly cast doubts about AI's current methodologies, and said that, “My view is throw it all away and start again”. Similarly, Mr. Suleyman (the head of Applied AI, now at DeepMind/Google) stated in an interview at TechCrunch (Dec. 5, 2016) that he thinks that the “general AI is still a long way off”.

So, to our knowledge, beyond the futuristic movies, wish-lists, science fiction novels, and generic non-scientific or non-technical articles (which have no basis/reliance/foundation on theory or experiment or proper/complete teachings), nobody has been successful in the application/usage/demonstration of General-AI, yet, in the AI industry or academia around the world. Thus, our demo/ZAC General-AI Image Recognition Software Platform here is a very significant breakthrough in the field/science of AI and machine learning technology. (These results had been originally reported in our parent cases, as well.)

Please note that General-AI is also called/referred to as General Artificial Intelligence (GAI), or Artificial General Intelligence (AGI), or General-Purpose AI, or Strong Artificial Intelligence (AI), or True AI, or as we call it, Thinking-AI, or Reasoning-AI, or Cognition-A, or Flexible-AI, or Full-Coverage-AI, or Comprehensive-AI, which can perform tasks that was never specifically trained for, e.g., in different context/environment, to recycle/re-use the experience and knowledge, using reasoning and cognition layers, usually in a completely different or unexpected or very new situation/condition/environment (same as what a human can do). Accordingly, we have shown here in this disclosure a new/novel/revolutionary architecture, system, method, algorithm, theory, and technique, to implement General-AI, e.g., for 3-D image/object recognition from any directions and other applications discussed here.

Our technology here (based on General-AI) is in contrast to (versus) Specific AI (or Vertical or Functional or Narrow or Weak AI) (or as we have coined the phrase, “Dumb-AI”), because, e.g., a Specific AI machine trained for face recognition cannot do any other tasks, e.g., finger-print recognition or medical imaging recognition. That is, the Specific AI machine cannot carry over/learn from any experience or knowledge that it has gained from one domain (face recognition) into another/new domain (finger-print or medical imaging), which it has not seen before (or was not trained for before). So, Specific AI has a very limited scope/“intelligence”/functionality/usage/re-usability/flexibility/usefulness.

Please note that the conventional/current state-of-the-art technologies in the industry/academia (e.g., Convolutional Neural Nets or Deep Learning) are based on the Specific AI, which has some major/serious theoretical/practical limits. For example, it cannot perform a 3-D image/object recognition from all directions, or cannot carry over/learn from any experience or knowledge in another domain, or requires extremely large number of training samples (which may not be available at all, or is impractical, or is too expensive, or takes too long to gather or train), or requires extremely large neural network (which cannot converge in the training stage, due to too much degree of freedom, or tends to memorize (rather than learn) the patterns (which is not good for out-of-sample recognition accuracy)), or requires extremely large computing power (which is impractical, or is too expensive, or is not available, or still cannot converge in the training stage). So, they have serious theoretical/practical limitations.

In addition, in Specific AI, if a new class of objects is added/introduced/found to the universe of all objects (e.g., a new animal/species is discovered), the training has to be done from scratch. Otherwise, training on just the last object will bias the whole learning machine, which is not good/accurate for recognition later on. Thus, all weights/biases or parameters in the learning machine must be erased completely, and the whole learning, with the new class added/mixed randomly with previous ones, must be repeated again from scratch, with all parameters erased and re-done/calculated again. So, the solution is not cumulative, or scalable, or practical, at all, e.g., for daily learning or continuous learning, as is the case for most practical situations, or as how the humans or most animals do/learn/recognize. So, they have serious theoretical/practical limitations.

Furthermore, for Specific AI, the learning phase cannot be mixed with the training phase. That is, they are not simultaneous, in the same period of time. So, during the training phase, the machine is useless or idle for all practical purposes, as it cannot recognize anything properly at that time. This is not how humans learn/recognize on a daily basis. So, they have serious theoretical/practical limitations.

General-AI solves/overcomes all of the above problems, as shown/discussed here in this disclosure. So, it has a huge advantage, for many reasons, as stated here, over Specific-AI.

It is also noteworthy that using smaller CPU/GPU power enables easier integration in mobile devices and wearables and IoT and telephones and watches, as an example, which, otherwise, drains the battery very quickly, and thus, requires much bigger battery or frequent recharging, which is not practical for most situations at all.

The industries/applications for our inventions are, e.g.:

Mobile devices (e.g., phones, wearable devices, eyeglasses, tablets) Smart devices & connected/Internet appliances The Internet of Things (IoT), as the network of physical devices, vehicles, home appliances, wearables, mobile devices, stationary devices, wireless or cellular devices, BlueTooth or WiFi devices, and the like, embedded with electronics, software, sensors, actuators, mechanical parts, switches, and/or connectivity, which enables these objects to connect and exchange data/commands/info/trigger events. Natural Language Processing Photo albums & web sites containing pictures Video libraries & web sites Image and video search & summarization & directory & archiving & storage Image & video Big Data analytics Smart Camera Smart Scanning Device Social networks Dating sites Tourism Real estate Manufacturing Biometrics Security Satellite or aerial images Medical Financial forecasting Robotics vision & control Control systems & optimization Autonomous vehicles

We have the following usage examples: object/face recognition; rules engines & control modules; Computation with Words & soft boundaries; classification & search; information web; data search & organizer & data mining & marketing data analysis; search for similar-looking locations or monuments; search for similar-looking properties; defect analysis; fingerprint, iris, and face recognition; Face/emotion/expression recognition, monitoring, tracking; recognition & information extraction, for security & map; diagnosis, using images & rules engines; and Pattern and data analysis & prediction; image ad network; smart cameras and phones; mobile and wearable devices; searchable albums and videos; marketing analytics; social network analytics; dating sites; security; tracking and monitoring; medical records and diagnosis and analysis, based on images; real estate and tourism, based on building, structures, and landmarks; maps and location services and security/intelligence, based on satellite or aerial images; big data analytics, deep image recognition and search platform; deep/detailed machine learning; object recognition (e.g., shoe, bag, clothing, watch, earring, tattoo, pants, hat, cap, jacket, tie, medal, wrist band, necklace, pin, decorative objects, fashion accessories, ring, food, appliances, equipment, tools, machines, cars, electrical devices, electronic devices, office supplies, office objects, factory objects, and the like).

Here, we also introduce Z-webs, including Z-factors and Z-nodes, for the understanding of relationships between objects, subjects, abstract ideas, concepts, or the like, including face, car, images, people, emotions, mood, text, natural language, voice, music, video, locations, formulas, facts, historical data, landmarks, personalities, ownership, family, friends, love, happiness, social behavior, voting behavior, and the like, to be used for many applications in our life, including on the search engine, analytics, Big Data processing, natural language processing, economy forecasting, face recognition, dealing with reliability and certainty, medical diagnosis, pattern recognition, object recognition, biometrics, security analysis, risk analysis, fraud detection, satellite image analysis, machine generated data analysis, machine learning, training samples, extracting data or patterns (from the video, images, text, or music, and the like), editing video or images, and the like. Z-factors include reliability factor, confidence factor, expertise factor, bias factor, truth factor, trust factor, validity factor, “trustworthiness of speaker”, “sureness of speaker”, “statement helpfulness”, “expertise of speaker”, “speaker's truthfulness”, “perception of speaker (or source of information)”, “apparent confidence of speaker”, “broadness of statement”, and the like, which is associated with each Z-node in the Z-web.

For one embodiment/example, e.g., we have “Usually, people wear short sleeve and short pants in Summer.”, as a rule number N given by an SME, e.g., human expert. The word “short” is a fuzzy parameter for both instances above. The sentence above is actually expressed as a Z-number, as described before, invented recently by Prof. Lotfi Zadeh, one of our inventors here. The collection of these rules can simplify the recognition of objects in the images, with higher accuracy and speed, e.g., as a hint, e.g., during Summer vacation, the pictures taken probably contain shirts with short sleeves, as a clue to discover or confirm or examine the objects in the pictures, e.g., to recognize or examine the existence of shirts with short sleeves, in the given pictures, taken during the Summer vacation. Having other rules, added in, makes the recognition faster and more accurate, as they can be in the web of relationships connecting concepts together, e.g., using our concept of Z-web, described before, or using semantic web. For example, the relationship between 4th of July and Summer vacation, as well as trip to Florida, plus shirt and short sleeve, in the image or photo, can all be connected through the Z-web, as nodes of the web, with Z numbers or probabilities in between on connecting branches, between each 2 parameters or concepts or nodes, as described before in this disclosure and in our prior parent applications.

In addition, there are many other embodiments in the current disclosure that deal with other important and innovative topics/subjects, e.g., related to General AI, versus Specific or Vertical or Narrow AI, machine learning, using/requiring only a small number of training samples (same as humans can do), learning one concept and use it in another context or environment (same as humans can do), addition of reasoning and cognitive layers to the learning module (same as humans can do), continuous learning and updating the learning machine continuously (same as humans can do), simultaneous learning and recognition (at the same time) (same as humans can do), and conflict and contradiction resolution (same as humans can do), with application, e.g., for image recognition, application for any pattern recognition, e.g., sound or voice, application for autonomous or driverless cars, application for security and biometrics, e.g., partial or covered or tilted or rotated face recognition, or emotion and feeling detections, application for playing games or strategic scenarios, application for fraud detection or verification/validation, e.g., for banking or cryptocurrency or tracking fund or certificates, application for medical imaging and medical diagnosis and medical procedures and drug developments and genetics, application for control systems and robotics, application for prediction, forecasting, and risk analysis, e.g., for weather forecasting, economy, oil price, interest rate, stock price, insurance premium, and social unrest indicators/parameters, and the like. (These results had been originally reported in our parent cases, as well.)

In one embodiment, we present a brief description of the basics of stratified programming (SP). SP is a computational system in which the objects of computation are in the main, nested strata of data centering on a target set, T. SP has a potential for significant applications in many fields, among them, robotics, optimal control, planning, multiobjective optimization, exploration, search, and Big Data. In spirit, SP has some similarity to dynamic programing (DP), but conceptually it is much easier to understand and much easier to implement. An interesting question which relates to neuro science is: Is the human brain employ stratification to store information? It will be natural to represent a concept such as a chair as a collection of strata with one or more strata representing a type of chair.

Underlining of our approach is a model, call it FSM. FSM is a finite state system. The importance of FSM as a model varies from use of digitalization (granulation, quantization) to almost any kind of system that can be approximated by a finite state system. The most important part is the concept of reachability of a target set in minimum number of steps. The objective of minimum number of steps serves as a basis for verification of the step of FSM state space. A concept which plays a key role in our approach is the target set reachability. Reachability involves moving (transitioning) FSM from a state w to a state in target state, T, in a minimum number of steps. To this end, the state space, W, is stratified through the use of what is called the incremental enlargement principle. Reachability is also related to the concept of accessibility.

For the current inventions, we can combine/attach/integrate/connect any and all the systems and methods (or embodiments or steps or sub-components or algorithms or techniques or examples) of our own prior applications/teachings/spec/appendices/Figs., which we have priority claim for, as mentioned in the current spec/application, to provide very efficient and fast algorithms for image processing, learning machines, NLP, pattern recognition, classification, SVM, deep/detailed analysis/discovery, and the like, for all the applications and usages mentioned here in this disclosure, with all tools, systems, and methods provided here.

BRIEF DESCRIPTION OF THE DRAWINGS

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CLAIMS

Claims ( 20 )

The invention claimed is:

1. A system for image recognition in an image recognition platform, said system comprising: an interface which receives an image; said interface receives a location of interest; a neural network implemented by a computer with a processor; wherein said neural network comprises a visual layer and a first hidden layer; wherein said visual layer is located below said first hidden layer; wherein said neural network receives said image and said location of interest; wherein said image is connected to said visual layer; a parameter layer; wherein said parameter layer is added to said neural network; a representation layer; wherein said representation layer is added to said neural network; wherein said parameter layer has information for coordinates, width, height, orientation, or type of shape for said location of interest; wherein said representation layer represents a value, values, or range of values that said parameter layer has for said location of interest; wherein said representation layer has a weighted link to a second hidden layer, connected horizontally from side of said neural network; wherein said second hidden layer is located between said visual layer and said first hidden layer; wherein said second hidden layer is located above said visual layer; wherein said second hidden layer is located below said first hidden layer; a correlation layer; wherein said correlation layer is located above said first hidden layer; the computer trains the neural network to correlate said location of interest with said image.

2. The system for image recognition in an image recognition platform, as recited in claim 1 , wherein said representation layer is connected to said correlation layer in both directions.

3. The system for image recognition in an image recognition platform, as recited in claim 1 , wherein said parameter layer is connected to said correlation layer in both directions.

4. The system for image recognition in an image recognition platform, as recited in claim 1 , wherein said correlation layer correlates said representation layer with said image.

5. The system for image recognition in an image recognition platform, as recited in claim 1 , wherein said correlation layer correlates said parameter layer with said image.

6. The system for image recognition in an image recognition platform, as recited in claim 1 , wherein said correlation layer correlates said location of interest with said image, using said representation layer.

7. The system for image recognition in an image recognition platform, as recited in claim 1 , wherein said correlation layer correlates said location of interest with said image, using said parameter layer.

8. The system for image recognition in an image recognition platform, as recited in claim 1 , wherein said correlation layer reconstructs, in reverse mode, after training.

9. The system for image recognition in an image recognition platform, as recited in claim 1 , wherein said system comprises or applies one or more of following: softmax, cross entropy, sigmoid cross entropy, contrastive, Eucledean distance, sum of squares of difference, multinomial logistic, infogain, generalization of multinomial logistic, or hinge or margin loss layer, unit, or comparison module.

10. The system for image recognition in an image recognition platform, as recited in claim 1 , wherein said system comprises or applies one of following between said representation layer and said second hidden layer: softmax, cross entropy, sigmoid cross entropy, contrastive, Eucledean distance, sum of squares of difference, multinomial logistic, infogain, generalization of multinomial logistic, or hinge or margin loss layer, unit, or comparison module.

11. The system for image recognition in an image recognition platform, as recited in claim 1 , wherein said neural network is not fully connected.

12. The system for image recognition in an image recognition platform, as recited in claim 1 , wherein said connection between said representation layer and said second hidden layer is not fully connected.

13. The system for image recognition in an image recognition platform, as recited in claim 1 , wherein said neural network comprises convolutional neural network connectivity format.

14. The system for image recognition in an image recognition platform, as recited in claim 1 , wherein said representation layer is expressed in Carthesian coordinates.

15. The system for image recognition in an image recognition platform, as recited in claim 1 , wherein said representation layer is expressed in polar or angular coordinates.

16. The system for image recognition in an image recognition platform, as recited in claim 1 , wherein said parameter layer is expressed in Fuzzy values.

17. The system for image recognition in an image recognition platform, as recited in claim 1 , wherein said location of interest is a part of an object represented by said image.

18. The system for image recognition in an image recognition platform, as recited in claim 1 , wherein said location of interest is represented as a coarse value or Fuzzy value.

19. The system for image recognition in an image recognition platform, as recited in claim 1 , wherein said system is used or applied recursively in said image recognition platform, to find or distinguish or detect or recognize various objects and their components.

20. A system for image recognition in an image recognition platform, said system comprising: an interface which receives an image; said interface receives a location of interest; a neural network implemented by a computer with a processor; wherein said neural network comprises a visual layer and a first hidden layer; wherein said visual layer is located below said first hidden layer; wherein said neural network receives said image and said location of interest; wherein said image is connected to said visual layer; a representation layer; wherein said representation layer is added to said neural network; wherein said representation layer represents a value, values, or range of values of information corresponding to coordinates, width, height, orientation, or type of shape for said location of interest; wherein said representation layer has a weighted link to a second hidden layer, connected horizontally from side of said neural network; wherein said second hidden layer is located between said visual layer and said first hidden layer; wherein said second hidden layer is located above said visual layer; wherein said second hidden layer is located below said first hidden layer; a correlation layer; wherein said correlation layer is located above said first hidden layer; the computer trains the neural network to correlate said location of interest with said image.

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System and method for extremely efficient image and pattern recognition and artificial intelligence platform

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US10726273B2

( en )

*

2017-05-01

2020-07-28

Symbol Technologies, Llc

Method and apparatus for shelf feature and object placement detection from shelf images

WO2018204342A1

( en )

2017-05-01

2018-11-08

Symbol Technologies, Llc

Product status detection system

CN110603533A

( en )

2017-05-01

2019-12-20

讯宝科技有限责任公司

Method and apparatus for object state detection

US10591918B2

( en )

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