WO2017075387A1 - Cognitive telementoring system and method - Google Patents
Cognitive telementoring system and method Download PDFInfo
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- WO2017075387A1 WO2017075387A1 PCT/US2016/059371 US2016059371W WO2017075387A1 WO 2017075387 A1 WO2017075387 A1 WO 2017075387A1 US 2016059371 W US2016059371 W US 2016059371W WO 2017075387 A1 WO2017075387 A1 WO 2017075387A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
- G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B5/00—Electrically-operated educational appliances
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
Definitions
- the present invention relates to remote telementoring of individuals and groups of individuals performing or observing a variety of medical, industrial, government and commercial procedures and operations; and more particularly to the
- cognitive architectures and inference process algebras to automatically recognize command, communications and control peripherals associated with the procedures and operations, analyze and process data generated by the aforementioned
- peripherals and help interpret, communicate, store and
- a key feature of this invention is the ability to anticipate, in real time, deviations from standard protocols associated with similar procedures and operations .
- the flow of information between the mentor and mentee is also manually intensive especially when the mentor or mentee are using various instruments used to effectuate the procedure.
- the mentor uses audiovisual tools such as telestration to instruct the mentee where to place an instrument and how this instrument should be manipulated.
- audiovisual tools such as telestration
- the mentor observe real-time audiovisual feedback regarding the degree of accuracy that the mentee is achieving which in all disclosed systems requires intensive manual concentration and interpretation of audiovisual feedback data received by the mentor.
- these systems are incapable of anticipating, in real time, deviations from protocols associated with similar procedures and operations.
- artificial intelligence into telementoring systems and methods that utilizes mathematical techniques that emulate the cognitive processing abilities of the human brain including, but not limited to, symbolic cognitive architectures and inference process algebras.
- This form of cognitive intelligence enables telementoring systems to automatically recognize command, communications and control peripherals associated with the procedures and operations, analyze and process data generated by the aforementioned peripherals, and help interpret, communicate, store and correlate the collected data with multiple internal and external databases for the purpose of providing an easy to use, highly effective remote training system and method for mentors and mentees that is also capable of anticipating, in real time, deviations from protocols associated with similar procedures and operations and which is also less manually intensive than current systems and methods thereby reducing the possibility of human error during a telementoring process.
- a cognitive telementoring system and method used for, but not limited to, remote telementoring of individuals and groups of individuals performing or observing a variety of medical, industrial, government and commercial procedures and operations by utilizing cognitive processes and techniques such as, but not limited to, symbolic cognitive architectures and inference process algebras (e.g., "The SOAR Cognitive Architecture”, “$-Calculus of Bounded Rational Agents", "Using Emotions on Autonomous Agents.
- the provided cognitive telementoring system and method comprises a management interface that is integrated with data collection peripherals comprising audiovisual peripherals, non- audiovisual peripherals and biometric peripherals; a cognitive telementoring engine comprising a peripheral identification module, a telestration symbol and operational site tracking module, an instrument tracking module, a co-registration module, an operational analysis module, a bi-directional communications module, a procedure database and data storage bank.
- a management interface that is integrated with data collection peripherals comprising audiovisual peripherals, non- audiovisual peripherals and biometric peripherals
- a cognitive telementoring engine comprising a peripheral identification module, a telestration symbol and operational site tracking module, an instrument tracking module, a co-registration module, an operational analysis module, a bi-directional communications module, a procedure database and data storage bank.
- cognitive telementoring engine utilizes cognitive processes such as, but not limited to, symbolic cognitive architectures and inference process algebras winch are capable of dealing with incomplete and uncertain information; and in particular can anticipate abnormal conditions that fall outside known signature patterns.
- This form of artificial intelligence is a powerful tool that helps provide an easy to use, highly effective remote training system and method for mentors and mentees that is also capable of anticipating, in real time, deviations from protocols associated with similar procedures and operations and which is also less manually intensive than current systems and methods thereby reducing the possibility of human error during a
- telementoring engine utilizes a form of artificial intelligence that overcomes these limitations and is capable of dealing with incomplete and uncertain information; and in particular can anticipate, in real time, deviations from protocols associated with similar procedures and operations. These limitations in current systems often result in human error and ineffective telementoring results.
- Figure 1 is a block diagram of a cognitive telementoring system that utilizes cognitive artificial intelligence
- peripherals and help interpret, communicate, store and
- the primary function of this invention is the use of cognitive artificial intelligence techniques and processes to enable an easy to use, highly effective remote training system and method for mentors and mentees that is also capable of anticipating, in real time, deviations from protocols associated with similar procedures and operations and which is also less manually intensive than current systems and methods thereby reducing the possibility of human error during a telementoring process.
- Figure 1 is a block diagram of a cognitive telementoring system that utilizes cognitive artificial intelligence
- peripherals and help interpret, communicate, store and
- the cognitive telementoring engine 22 utilizes a form of artificial intelligence that overcomes these limitations and is capable of dealing with incomplete and uncertain information; and in particular can anticipate abnormal conditions that fall outside known signature patterns.
- Many embodiments of the present invention utilize symbolic cognitive architectures 21 and inference process algebras 19 (e.g., "Expressing Evolutionary Computation, Genetic Programming, Artificial Life, Autonomous Agents and DNA-Based Computing in $-Calculus - Revised Version"; "Sapience, Consciousness, and the Knowledge
- $-calculus has also been used in the DARPA Reactive Sensor Networks Project at ARL Penn. State university for empirical cost profiling (e.g., "Reactive Sensor Networks (RSN) " which is incorporated herein by reference in its entirety) with $-calculus expressing all variables as cost expressions related to the environment, multiple communication/interaction links, inference engines, modified structures, data, code and meta- code .
- RSN Reactive Sensor Networks
- a management interface 12 that is integrated with data collection peripherals 3 comprising audiovisual peripherals 9, non- audiovisual peripherals 10 and biometric peripherals 11; a cognitive telementoring engine 2 comprising a peripheral
- the cognitive telementoring engine 2 utilizes cognitive processes such as, but not limited to, symbolic cognitive architectures 21 and inference process algebras 19 which are capable of dealing with incomplete and uncertain information; and in particular can anticipate abnormal conditions that fall outside known signature patterns.
- This form of artificial intelligence comprises powerful techniques and processes that enable a telementoring system to anticipate, in real time, deviations from protocols associated with similar procedures and operations thereby reducing the possibility of human error during a telementoring process.
- the peripheral identification module 4 automatically identifies data collection peripherals 3 and their associated data input and output
- the cognitive telementoring engine 2 receives data inputs from data collection peripherals 3 and instructions from a remote user 16 which typically directs the onsite user 17 on how to utilize various instruments used during the procedure. These instructions could be audiovisual in nature and/or communicated through telestration symbols appearing on the onsite user 17 management interface 12. These instructions are inputted into the telestration symbol and operational tracking module which utilizes cognitive processes such as, but not limited to, symbolic cognitive architectures 21 and inference process algebras 19 to intelligently co-locate at least one inputted telestration symbol with at least one operational site and maintain said co-location if the operational site changes its location, morphology and combinations thereof. Data
- collection peripherals 3 also input instrument location data into an instrument tracking module 7 which defines the real time location of at least one instrument utilized during a
- the co-registration module 8 provides a real time coordinate transformation associated with at least one
- the operational analysis module 5 utilizes cognitive processes such as, but not limited to, symbolic cognitive architectures 21 and inference process algebras 19 to perform cognitive analysis of data collected during a telementoring procedure or operation and interpreting the results against a database of similar procedures and operations with the ability to anticipate
- the bi- directional communication module 13 enables real-time two-way audiovisual communications between the remote use and the onsite user 17 related to data generated and collected during a
- a data storage bank 15 records information and data generated and collected during a telementoring
- the cognitive telementoring engine 2 operates using an internal values system that is not only dependent on the data and metadata received by the data collection peripherals 3 but in addition depends upon metastates of the environment
- one of the cost functions used by the cognitive telementoring engine 2 could be "uncertainty" that the fused data and metadata do not provide an exact match to any signatures contained within the co-registration module 8.
- the cognitive telementoring engine 2 therefore works to minimize cost expressions such as
- peripherals and help interpret, communicate, store and
Abstract
Described is remote telementoring of individuals and groups of individuals performing or observing a variety of medical, industrial, government and commercial procedures and operations; and more particularly to the incorporation of cognitive artificial intelligence into a telementoring system and method that utilizes mathematical techniques which emulate the cognitive processing abilities of the human brain.
Description
DESCRIPTION OF INVENTION
COGNITIVE TELEMENTORING SYSTEM AND METHOD
Field of the Invention:
The present invention relates to remote telementoring of individuals and groups of individuals performing or observing a variety of medical, industrial, government and commercial procedures and operations; and more particularly to the
incorporation of cognitive artificial intelligence into a telementoring system and method that utilizes mathematical techniques which emulate the cognitive processing abilities of the human brain including, but not limited to, symbolic
cognitive architectures and inference process algebras, to automatically recognize command, communications and control peripherals associated with the procedures and operations, analyze and process data generated by the aforementioned
peripherals, and help interpret, communicate, store and
correlate the collected data with multiple internal and external databases for the purpose of providing an easy to use, highly effective remote training system and method for mentors and mentees that is less manually intensive than current systems and methods thereby reducing the possibility of human error during the telementoring process. A key feature of this invention is
the ability to anticipate, in real time, deviations from standard protocols associated with similar procedures and operations .
BACKGROUND OF THE INVENTION
Related Applications :
The present application is related to United States patent number 8,902,278, issued December 2, 2014, for SYSTEMS AND
METHODS FOR VISUALIZING AND MANAGING TELEPRESENCE DEVICES IN HEALTHCARE NETWORKS, by Pinter, Marco; Brallier, Greg; Ross, Scott, included by reference herein.
The present application is related to United States patent number 8,856,057, issued October 7, 2014, for COGNITIVE SECURITY SYSTEM AND METHOD, by James A. Ionson, included by reference herein .
The present application is related to United States patent number 8,494,829, issued July 23, 2013, for SENSOR FUSION AND PROBABILISTIC PARAMETER ESTIMATION METHOD AND APPARATUS, by Teixeira; Rodrigo E., included by reference herein.
The present application is related to United States patent number 8,836,751, issued September 16, 2014, for TELEPRESENCE
SYSTEM WITH A USER INTEERFACE THAT DISPLAYS DIFFERENT
COMMUNICATIONS LINKS, by Ballantyne, James; Temby, Kelton;
Rosenthal, James; Roe, David B., included by reference herein.
The present application is related to United States patent number 8,069,420, issued November 29, 2011, for SYSTEM FOR
CONTROLLING THE COMMUNICATION OF MEDICAL IMAGING DATA, by
Roderick Plummer, included by reference herein.
The present application is related to United States patent number 7,907,166, issued March 15, 2011, for STEREO TELESTRATION FOR ROBOTIC SURGERY, by Lamprecht, Ben; Nowlin, William C;
Stern, John D., included by reference herein.
The present application is related to United States patent number 7,483,867, issued January 27, 2009, for PROCESSING DEVICE WITH INTUITIVE LEARNING CAPABILITY, by Ansari, Arif M; Ansari, Shiek; Sulaimann, Yusuf M., included by reference herein.
The present application is related to United States patent number 5,490,516, issued February 13, 1996, for METHOD AND SYSTEM TO ENHANCE MEDICAL SIGNALS FOR REAL-TIME ANALYSIS AND HIGH-RESOLUTION DISPLAY, by William H. Hudson, included by reference herein.
This application claims the benefit of U.S. Provisional Application No. 62/247,781, filed October 29, 2015 which is incorporated herein by reference in its entirety for all
purposes .
Other Publications:
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#20150062157, March 5, 2015.
Ehsan Kimiagar and Firth Whitwam, "Context Aware Command and Control System", USPTO Publication #20150066558, March 5, 2015.
James Paul Smurro; Anthony G. Reina and James Omer
L'Esperance, "System and Method for Surgical Telementoring and Training with Virtualized Telestration and Haptic Holograms, including MetaData Tagging, Encapsulation and Saving Multi-Modal Streaming Medical Imagery Together with Multi-Dimensional (4-D) Virtual Mesh and Multi-Sensory Annotation in Standard File
Formats used for Digital Imaging and Comnunications in Medicien (DICOM)", USPTO Publication #20140176661, June 26, 2014.
Wenyi Zhao et. al . , "Efficient 3-D Telestration for Local and Remote Robotic Proctering", USPTO Publication #20150025392, January 22, 2015.
Cosmin Boanca, et. al . , "Remote Video Management for
Intraoperative Consultation and Surgical Telepresence",
Telemedicine and e-Health, Volume 13, Number 5, 2007.
Rex L. Hazelet; Adam D. Mielke and David C. Holbrook,
"Dynamically Configurable Command and Control Systems and
Methods", USPTO Publication #20130036419, February 7, 2013.
Federico Castanedo, "A Review of Data Fusion Techniques", The Scientific World Journal, Vol 2013, Article ID 704504, 2013.
John E. Laird, "The SOAR Cognitive Architecture", MIT
Press, May 2012.
John E. Lard and Shiwali Mohan, "A Case Study of Knowledge Integration across Multiple Memories in SOAR", Biologically Inspired Cognitive Architectures, April 2014, 8, pp. 93-99.
Sigmund Frigstad and Bjorn Olstad, "Method and Apparatus for Knowledge based Diagnostic Imaging", USPTO Publication
#20050010098.
Peter Jackson, "Introduction to Expert Systems (3rd Edition) Hardcover", December 23, 1998.
Patrick Soon-Shiong, "Reasoning Engines", USPTO Publication #20140129504, May 2014. Nikolaos Anastasopoulos , "Systems and Methods for
Artificial Intelligence Decision Making in a Virtual
Environment", USPTO Publication #20140279800.
Nils Goerke, "EMOBOT: A Robot Control Architecture Based on Emotion-Like Internal Values", Mobile Robots, Moving
Intelligence (ed J.Buchli) . ARS/pIV, Germany, 75-94, 2006.
M. Salichs and M. Makfaz, "Using Emotions on Autonomous Agents. The Role of Happiness, Sadness and Fear" Adaptation in Artificial and Biological Systems (AISB'06), Bristol, England, 157-164, 2006. Eugene Eberbach, "Expressing Evolutionary Computation,
Genetic Programming, Artificial Life, Autonomous Agents and DNA- Based Computing in $-Calculus - Revised Version", November 2001.
Eugene Eberbach, "$-Calculus of Bounded Rational Agents: Flexible Optimization as Search under Bounded Resources in
Interactive Systems", Fundamentalnformaticae 68, 47-102, 2005.
Eugene Eberbach, "$-Calculus Bounded Rationality = Process Algebra + Anytime Algorithms", Applicable Mathematics: Its
Perspectives and Challenges, Narosa Publishing House, New Delhi, Mumbai, Calcutta, 532-539, 2001.
Eugene Eberbach and Shashi Phoha, "SAMON: Communication, Cooperation and Learning of Mobile Autonomous Robotic Agents, Proc. of the 11th IEEE. Conf. on Tools with Artificial
Intelligence ICTAI'99, Chicago, IL, 229-236, 1999.
Bradley J. Harnish, "Reactive Sensor Networks (RSN) " , AFRL- IF-RS-2003-245 Technical Report, Penn State University sponsored by DARPA and AFRL, 2003.
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http : //cogprints . org/2320/3/Gershenson-BBKS-Epigenetics .pdf .
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Hershey, PA, pp.64-105, 2006.
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http://www.arxiv.org/abs/1106.5917, June 29, 2011.
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Emotion-Like Internal Values", Mobile Robots, Moving
Intelligence (ed J.Buchli) . ARS/pIV, Germany, 75-94, 2006.
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Background Art Overview
A major deficiency with current telementoring systems such as those used for remote telestration of surgical procedures (e.g., U.S. Pat. No. 8,902,278, "Systems and methods for visualizing and managing telepresence devices in healthcare networks"; U.S. Pat. No. 8,836,751, "Tele-presence System with a User Interface that Displays Different Communication Links"; U.S. Pat. No. 8, 069,420, "System for Controlling the
Communication of Medical Imaging Data"; "Remote Video Management for Interoperative Consultation and Surgical Telepresence"; all incorporated herein in their entirety) is the labor intensive requirements for installation, operational usage, telementoring feedback and archiving of information related to the procedure. Furthermore, current telementoring systems and methods are unable to anticipate, in real time, deviations from protocols associated with similar procedures and operations. For example, all disclosed systems require manual identification of data collection peripherals used in the procedure. The operating characteristics of these data collection peripherals are then manually incorporated into the telementoring system before the procedure is initiated by the mentor and/or mentee. Once the procedure is initiated the flow of information between the mentor and mentee is also manually intensive especially when the
mentor or mentee are using various instruments used to effectuate the procedure. Specifically, in many procedures the mentor uses audiovisual tools such as telestration to instruct the mentee where to place an instrument and how this instrument should be manipulated. It's critical that the mentor observe real-time audiovisual feedback regarding the degree of accuracy that the mentee is achieving which in all disclosed systems requires intensive manual concentration and interpretation of audiovisual feedback data received by the mentor. Furthermore, these systems are incapable of anticipating, in real time, deviations from protocols associated with similar procedures and operations. These limitations in current systems often result in human error and ineffective telementoring results.
Therefore, there is a need to incorporate cognitive
artificial intelligence into telementoring systems and methods that utilizes mathematical techniques that emulate the cognitive processing abilities of the human brain including, but not limited to, symbolic cognitive architectures and inference process algebras. This form of cognitive intelligence enables telementoring systems to automatically recognize command, communications and control peripherals associated with the procedures and operations, analyze and process data generated by the aforementioned peripherals, and help interpret, communicate,
store and correlate the collected data with multiple internal and external databases for the purpose of providing an easy to use, highly effective remote training system and method for mentors and mentees that is also capable of anticipating, in real time, deviations from protocols associated with similar procedures and operations and which is also less manually intensive than current systems and methods thereby reducing the possibility of human error during a telementoring process.
Summary of the Invention
In accordance with the present invention, there is provided a cognitive telementoring system and method used for, but not limited to, remote telementoring of individuals and groups of individuals performing or observing a variety of medical, industrial, government and commercial procedures and operations by utilizing cognitive processes and techniques such as, but not limited to, symbolic cognitive architectures and inference process algebras (e.g., "The SOAR Cognitive Architecture", "$-Calculus of Bounded Rational Agents", "Using Emotions on Autonomous Agents. The role of Happiness, Sadness and Fear", "EMOBOT: A Robot Control Architecture Based on Emotion-Like Internal Values" which are incorporated herein by reference in their entirety) to autonomously recognize command,
communications and control peripherals associated with the procedures and operations, analyze and process data generated by the aforementioned peripherals, and help interpret, communicate, store and correlate the collected data with multiple internal and external databases for the purpose of providing an easy to use, highly effective remote training system and method for mentors and mentees that is also capable of anticipating, in real time, deviations from protocols associated with similar procedures and operations and which is also less manually intensive than current systems and methods thereby reducing the possibility of human error during a telementoring process.
The provided cognitive telementoring system and method comprises a management interface that is integrated with data collection peripherals comprising audiovisual peripherals, non- audiovisual peripherals and biometric peripherals; a cognitive telementoring engine comprising a peripheral identification module, a telestration symbol and operational site tracking module, an instrument tracking module, a co-registration module, an operational analysis module, a bi-directional communications module, a procedure database and data storage bank. The
cognitive telementoring engine utilizes cognitive processes such as, but not limited to, symbolic cognitive architectures and inference process algebras winch are capable of dealing with
incomplete and uncertain information; and in particular can anticipate abnormal conditions that fall outside known signature patterns. This form of artificial intelligence is a powerful tool that helps provide an easy to use, highly effective remote training system and method for mentors and mentees that is also capable of anticipating, in real time, deviations from protocols associated with similar procedures and operations and which is also less manually intensive than current systems and methods thereby reducing the possibility of human error during a
telementoring process.
Most artificial intelligence methods focus on logical decision making and learning approaches based upon logical causes and effects related to past experiences and known
scenarios. There are numerous techniques incorporating logical decision-making and learned behavior through the use of preprogrammed databases and logical rules used by expert systems to enable autonomous decisions. All of these approaches are based upon logical reasoning rules such as deductive reasoning, abductive reasoning, cause-based reasoning, inductive reasoning, metaphorical mapping and fuzzy logic (e.g. "Introduction to Expert Systems", "Processing Device with Intuitive Learning Capability"; "Reasoning Engines"; "Systems and Methods for
Artificial Intelligence Decision Making in a Virtual
Environment", which are incorporated herein by reference in their entirety) . The aforementioned artificial intelligence methods, which are based upon logic-driven models, rules and algorithms have a major flaw in that they all break down when the collected data patterns fall outside of expected parameters and logical rules winch leads to an inability to anticipate future consequences of real-time actions. Therefore, in accordance with the present invention, the cognitive
telementoring engine utilizes a form of artificial intelligence that overcomes these limitations and is capable of dealing with incomplete and uncertain information; and in particular can anticipate, in real time, deviations from protocols associated with similar procedures and operations. These limitations in current systems often result in human error and ineffective telementoring results.
Brief Description of the Drawings
A complete understanding of the present invention may be obtained by reference to the accompanying drawings, when considered in conjunction with the subsequent, detailed
description, in which:
Figure 1 is a block diagram of a cognitive telementoring system that utilizes cognitive artificial intelligence
techniques and processes to enable telementoring systems to automatically recognize command, communications and control peripherals associated with the procedures and operations, analyze and process data generated by the aforementioned
peripherals, and help interpret, communicate, store and
correlate the collected data with internal and external
databases for the purpose of providing an easy to use, highly effective remote training system and method for mentors and mentees that is also capable of anticipating, in real time, deviations from protocols associated with similar procedures and operations and which is also less manually intensive than current systems and methods thereby reducing the possibility of human error during a telementoring process.
For purposes of clarity and brevity, like elements and components will bear the same designations and numbering
throughout the Figure.
Description of the Preferred Embodiment
To provide an overall understanding certain illustrative embodiments will be described; however, it will be understood by one skilled in the art of telementoring systems for medical and
other commercial applications; and skilled in the art of data collection, analysis and cognitive artificial intelligence that the system and method described can be adapted and modified to provide systems and methods for other suitable applications and that additions and modifications can be made without departing from the scope of the system and method described herein. The primary function of this invention is the use of cognitive artificial intelligence techniques and processes to enable an easy to use, highly effective remote training system and method for mentors and mentees that is also capable of anticipating, in real time, deviations from protocols associated with similar procedures and operations and which is also less manually intensive than current systems and methods thereby reducing the possibility of human error during a telementoring process.
Figure 1 is a block diagram of a cognitive telementoring system that utilizes cognitive artificial intelligence
techniques and processes to enable telementoring systems to automatically recognize command, communications and control peripherals associated with the procedures and operations, analyze and process data generated by the aforementioned
peripherals, and help interpret, communicate, store and
correlate the collected data with internal and external
databases for the purpose of providing an easy to use, highly
effective remote training system and method for mentors and mentees that is also capable of anticipating, in real time, deviations from protocols associated with similar procedures and operations and which is also less manually intensive than current systems and methods thereby reducing the possibility of human error during a telementoring process.
Most artificial intelligence methods focus on logical decision making and learning approaches based upon logical causes and effects related to past experiences and known
scenarios. There are numerous techniques incorporating logical decision-making and learned behavior through the use of preprogrammed databases and logical rules used by expert systems to enable autonomous decisions. All of these approaches are based upon logical reasoning rules such as deductive reasoning, abductive reasoning, cause-based reasoning, inductive reasoning, metaphorical mapping and fuzzy logic (e.g. "Introduction to Expert Systems", "Processing Device with Intuitive Learning Capability"; "Reasoning Engines"; "Systems and Methods for
Artificial Intelligence Decision Making in a Virtual
Environment", which are incorporated herein by reference in their entirety) . The aforementioned artificial intelligence methods, which are based upon logic-driven models, rules and algorithms have a major flaw in that they all break down when
the collected data patterns fall outside of expected parameters and logical rules which leads to numerous false positive and false negative interpretations.
Therefore, in accordance with the present invention, the cognitive telementoring engine 22 utilizes a form of artificial intelligence that overcomes these limitations and is capable of dealing with incomplete and uncertain information; and in particular can anticipate abnormal conditions that fall outside known signature patterns. Many embodiments of the present invention utilize symbolic cognitive architectures 21 and inference process algebras 19 (e.g., "Expressing Evolutionary Computation, Genetic Programming, Artificial Life, Autonomous Agents and DNA-Based Computing in $-Calculus - Revised Version"; "Sapience, Consciousness, and the Knowledge
Instinct. (Prolegomena to a Physical Theory)"; "Modeling Field
Theory of Higher Cognitive Functions"; "Implementing Human-Like Intuition Mechanism in Artificial Intelligence"; "Behavior-Based Knowledge Systems: An Epigenetic Path from Behaviour to
Knowledge"; "$-Calculus Bounded Rationality = Process Algebra + Anytime Algorithms"; "$-Calculus of Bounded Rational Agents: Flexible Optimization as Search under Bounded Resources in
Interactive Systems"; "Using Emotions on Autonomous Agents. The role of Happiness, Sadness and Fear"; "EMOBOT: A Robot Control
Architecture Based on Emotion-Like Internal Values"; "Modeling Field Theory of Higher Cognitive Functions"; "Implementing
Human-Like Intuition Mechanism in Artificial Intelligence";
"Behavior-Based Knowledge Systems: An Epigenetic Path from
Behaviour to Knowledge", "Towards a Human Behavior Model Based on Instinct" which are all incorporated herein by reference in their entirety) . Symbolic cognitive architectures 21 and inference process algebras 19 have built-in cost optimization mechanisms allowing them to deal with nondeterminism, incomplete and uncertain information. For example, $-calculus is a higher- order polyadic process algebra with a "cost" utility function, such as the probability that collected data has some has some kind of correlated or even un-correlated relationship with particular data patterns. Although these cognitive artificial intelligence techniques have never been utilized by
telementoring systems, they have been successfully applied to the Office of Naval research SAMON robotics testbed to derive GBML (Generic Behavior Message-passing Language) for behavior planning, control and communication of heterogeneous Autonomous Underwater Vehicles (AUV's) operating in hostile and
unpredictable environments (e.g., SAMON: Communication,
Cooperation and Learning of Mobile Autonomous Robotic Agents which is incorporated herein by reference in its entirety) . In addition, $-calculus has also been used in the DARPA Reactive
Sensor Networks Project at ARL Penn. State university for empirical cost profiling (e.g., "Reactive Sensor Networks (RSN) " which is incorporated herein by reference in its entirety) with $-calculus expressing all variables as cost expressions related to the environment, multiple communication/interaction links, inference engines, modified structures, data, code and meta- code . An innovative aspect of $-calculus techniques is that they integrate neural networks, symbolic cognitive, emotional and instinct-driven architectures, genetic
programming/algorithms, symbolic rule-based expert systems, logic, imperative and ob ect-oriented programming into a common framework. Another important feature of this disclosed
invention is its internal value system which is designed to operate in accordance with psychological terms that humans associate with "drives" and "emotions". These internal values do not actually realize real "drives" and "emotions", but the invention operates in such a way that it exhibits behavior that is governed by "drives" and "emotions" in a manner that
simulates the emotional, instinctive and logical thought
processes of humans and responds to dynamic changes in collected data just as highly trained humans might.
Key components of the disclosed invention is a management interface 12 that is integrated with data collection
peripherals 3 comprising audiovisual peripherals 9, non- audiovisual peripherals 10 and biometric peripherals 11; a cognitive telementoring engine 2 comprising a peripheral
identification module 4, a telestration symbol and operational site tracking module 6, an instrument tracking module 7, a co- registration module 8, an operational analysis module 5, a bi¬ directional communications module, an external data base and data storage bank 15. The cognitive telementoring engine 2 utilizes cognitive processes such as, but not limited to, symbolic cognitive architectures 21 and inference process algebras 19 which are capable of dealing with incomplete and uncertain information; and in particular can anticipate abnormal conditions that fall outside known signature patterns. This form of artificial intelligence comprises powerful techniques and processes that enable a telementoring system to anticipate, in real time, deviations from protocols associated with similar procedures and operations thereby reducing the possibility of human error during a telementoring process. The peripheral identification module 4 automatically identifies data collection peripherals 3 and their associated data input and output
protocols. The cognitive telementoring engine 2 receives data inputs from data collection peripherals 3 and instructions from a remote user 16 which typically directs the onsite user 17 on how to utilize various instruments used during the procedure.
These instructions could be audiovisual in nature and/or communicated through telestration symbols appearing on the onsite user 17 management interface 12. These instructions are inputted into the telestration symbol and operational tracking module which utilizes cognitive processes such as, but not limited to, symbolic cognitive architectures 21 and inference process algebras 19 to intelligently co-locate at least one inputted telestration symbol with at least one operational site and maintain said co-location if the operational site changes its location, morphology and combinations thereof. Data
collection peripherals 3 also input instrument location data into an instrument tracking module 7 which defines the real time location of at least one instrument utilized during a
telementoring process, procedure, operation and combinations thereof. The co-registration module 8 provides a real time coordinate transformation associated with at least one
instrument utilized in the telementoring procedure in which said transformation is correlated with the coordinate system defining the location of at least one telestration symbol that is co- located with at least one operational site. The operational analysis module 5 utilizes cognitive processes such as, but not limited to, symbolic cognitive architectures 21 and inference process algebras 19 to perform cognitive analysis of data collected during a telementoring procedure or operation and
interpreting the results against a database of similar procedures and operations with the ability to anticipate
deviations from protocols associated with similar procedures and operations contained within a procedure database 14. The bi- directional communication module 13 enables real-time two-way audiovisual communications between the remote use and the onsite user 17 related to data generated and collected during a
telementoring process, procedure, operation and combinations thereof. Finally, a data storage bank 15 records information and data generated and collected during a telementoring
procedure; and a procedure database 14 of information related to the current telementoring procedure but not generated by the current telementoring procedure.
The cognitive telementoring engine 2 operates using an internal values system that is not only dependent on the data and metadata received by the data collection peripherals 3 but in addition depends upon metastates of the environment
associated with unforeseen changes and/or conditions that lie outside the baseline signatures contained within the operational analysis module 5 and procedure database 14. These internal values are designed in accordance with psychological terms that we (human beings) associate with "drives" and "curiosity".
These internal values do not actually realize real "drives" and
"curiosity", but the cognitive telementoring engine 2 is designed in such a way that it exhibits behavior that emulates how highly trained and experienced human operators would use logical reasoning combined with intuition and instinct to analyze and interpret in real time the effectiveness and
efficiency of the telementoring process. For example, one of the cost functions used by the cognitive telementoring engine 2 could be "uncertainty" that the fused data and metadata do not provide an exact match to any signatures contained within the co-registration module 8. The cognitive telementoring engine 2 therefore works to minimize cost expressions such as
"uncertainty", "suspicion" and/or "fear" in a manner that simulates the cognitive processing abilities of multiple highly trained human operators given the same conditions. This form of cognitive intelligence enables telementoring systems to
automatically recognize command, communications and control peripherals associated with the procedures and operations, analyze and process data generated by the aforementioned
peripherals, and help interpret, communicate, store and
correlate the collected data with multiple internal and external databases for the purpose of providing an easy to use, highly effective remote training system and method for mentors and mentees that is also capable of anticipating, in real time, deviations from protocols associated with similar procedures and
operations and which is also less manually intensive than current systems and methods thereby reducing the possibility of human error during a telementoring process.
Since other modifications and changes varied to fit particular operating requirements and environments will be apparent to those skilled in the art, the invention is not considered limited to the example chosen for purposes of disclosure, and covers all changes and modifications which do not constitute departures from the true spirit and scope of thi invention .
Having thus described the invention, what is desired to be protected by Letters Patent is presented in the subsequently appended claims.
Claims
1. A cognitive telementoring system and method for, but not limited to, medical, industrial, government and commercial markets through use of cognitive artificial intelligence
techniques and processes, comprising: means for executing autonomous recognition of data collection peripherals as well as cognitive analysis of the data they generate through the use of mathematical
techniques that emulate the cognitive processing powers of the human brain including, but not limited to, inference process algebras and symbolic cognitive architectures; means for collecting and transmitting audiovisual, non-audiovisual, biometric, and combinations thereof data collected during a telementoring procedure; means for autonomously identifying data collection peripherals and their associated data input and output protocols ; means for providing to the cognitive telementoring system data that has been generated during procedures and operations similar to a procedure or operation being telementored;
means for performing cognitive analysis of data generating during a telementoring procedure through the utilization of mathematical techniques that emulate the cognitive processing abilities of the human brain
including, but not limited to symbolic cognitive
architectures and inference process algebras; and
interpreting the results against a database of similar procedures with the ability to anticipate deviations from protocols associated with similar procedures contained within the database; means for utilizing cognitive processes such as, but not limited to symbolic cognitive architectures and
inference process algebras to intelligently co-locate at least one inputted telestration symbol with at least one operational site and maintain said co-location if the operational site changes its location, morphology and combinations thereof; means for collecting data which defines the real time location of at least one instrument utilized during a telementoring process, procedure, operation and
combinations thereof; and means for autonomously performing real time coordinate transformations associated with at last one instrument
utilized in a telementoring process, procedure, operation, and combinations thereof, in which said transformations are correlated with coordinate systems used to define the location of at least one telestration symbol that is co- located with at least one operational site.
2. The cognitive telementoring system and method in accordance with claim 1, wherein said means for executing autonomous recognition of data collection peripherals as well as cognitive analysis of the data they generate through the use of
mathematical techniques that emulate the cognitive processing powers of the human brain including, but not limited to, inference process algebras and symbolic cognitive architectures comprises a software, firmware, hardware and combinations thereof cognitive telementoring engine.
3. The cognitive telementoring system and method in accordance with claim 1, wherein said means for collecting and transmitting audiovisual, non-audiovisual, biometric, and combinations thereof data collected during a telementoring procedure
comprises data collection peripherals.
4. The cognitive telementoring system and method in accordance with claim 1, wherein said means for autonomously identifying data collection peripherals and their associated data input and output protocols comprises a software, firmware, hardware and
combinations thereof peripheral identification module.
5. The cognitive telementoring system and method in accordance with claim 1, wherein said means for providing to the cognitive telementoring system data that has been generated during
procedures and operations similar to a procedure or operation being telementored comprises a software, firmware, hardware and combinations thereof procedure database.
6. The cognitive telementoring system and method in accordance with claim 1, wherein said means for performing cognitive analysis of data generating during a telementoring procedure through the utilization of mathematical techniques that emulate the cognitive processing abilities of the human brain including, but not limited to symbolic cognitive architectures and
inference process algebras; and interpreting the results against a database of similar procedures with the ability to anticipate deviations from protocols associated with similar procedures contained within the database comprises a software, firmware, hardware and combinations thereof operational analysis module.
7. The cognitive telementoring system and method in accordance with claim 1, wherein said means for utilizing cognitive
processes such as, but not limited to symbolic cognitive
architectures and inference process algebras to intelligently co-locate at least one inputted telestration symbol with at
least one operational site and maintain said co-location if the operational site changes its location, morphology and
combinations thereof comprises a software, firmware, hardware and combinations thereof telestration symbol and operational site tracking module.
8. The cognitive telementoring system and method in accordance with claim 1, wherein said means for collecting data which defines the real time location of at least one instrument utilized during a telementoring process, procedure, operation and combinations thereof comprises a software, firmware, hardware and combinations thereof instrument tracking module.
9. The cognitive telementoring system and method in accordance with claim 1, wherein said means for autonomously performing real time coordinate transformations associated with at last one instrument utilized in a telementoring process, procedure, operation, and combinations thereof, in which said
transformations are correlated with coordinate systems used to define the location of at least one telestration symbol that is co-located with at least one operational site comprises a software, hardware, firmware co-registration module.
10. A cognitive telementoring system for, but not limited to, medical, industrial, government and commercial markets through use of cognitive artificial intelligence techniques and
processes, comprising: a software, firmware, hardware and combinations thereof cognitive telementoring engine, for executing autonomous recognition of data collection peripherals as well as cognitive analysis of the data they generate through the use of mathematical techniques that emulate the cognitive processing powers of the human brain including, but not limited to, inference process algebras and symbolic cognitive architectures; data collection peripherals, for collecting and transmitting audiovisual, non-audiovisual, biometric, and combinations thereof data collected during a telementoring procedure ; a software, firmware, hardware and combinations thereof peripheral identification module, for autonomously identifying data collection peripherals and their
associated data input and output protocols; a software, firmware, hardware and combinations thereof procedure database, for providing to the cognitive
telementoring system data that has been generated during procedures and operations similar to a procedure or
operation being telementored;
a software, firmware, hardware and combinations thereof operational analysis module, for performing cognitive analysis of data generating during a
telementoring procedure through the utilization of
mathematical techniques that emulate the cognitive
processing abilities of the human brain including, but not limited to symbolic cognitive architectures and inference process algebras; and interpreting the results against a database of similar procedures with the ability to
anticipate deviations from protocols associated with similar procedures contained within the database; a software, firmware, hardware and combinations thereof telestration symbol and operational site tracking module, for utilizing cognitive processes such as, but not limited to symbolic cognitive architectures and inference process algebras to intelligently co-locate at least one inputted telestration symbol with at least one operational site and maintain said co-location if the operational site changes its location, morphology and combinations thereof; a software, firmware, hardware and combinations thereof instrument tracking module, for collecting data which defines the real time location of at least one instrument utilized during a telementoring process,
procedure, operation and combinations thereof; and a software, hardware, firmware co-registration module, for autonomously performing real time coordinate
transformations associated with at last one instrument utilized in a telementoring process, procedure, operation, and combinations thereof, in which said transformations are correlated with coordinate systems used to define the location of at least one telestration symbol that is co- located with at least one operational site.
11. The cognitive telementoring system as recited in claim 10, further comprising: a set of inference process algebras, for analyzing data generated during the a telementoring procedure in a manner that emulates the cognitive processing powers of the human brain.
12. The cognitive telementoring system as recited in claim 10, further comprising: a set of symbolic cognitive architectures, for
analyzing data generated during a telementoring procedure in a manner that emulates the cognitive processing powers of the human brain.
13. The cognitive telementoring system as recited in claim 10, further comprising: a software, firmware, hardware and combinations thereof bi-directional communication module, for
autonomously configuring bi-directional communication protocols necessary to communicate data generated and collected during a telementoring process, procedure, operation and combinations thereof.
14. The cognitive telementoring system as recited in claim 10, further comprising: a software, firmware, hardware and combinations thereof data storage bank, for storing information
collected during the telementoring process, procedure, operation and combinations thereof.
15. A cognitive telementoring method for, but not limited to, medical, industrial, government and commercial markets through use of cognitive artificial intelligence techniques and
processes, comprising the steps of:
Executing autonomous recognition of data collection peripherals as well as cognitive analysis of the data they generate through the use of mathematical techniques that emulate the cognitive processing powers of the human brain
including, but not limited to, inference process algebras and symbolic cognitive architectures;
Collecting and transmitting audiovisual, non- audiovisual, biometric, and combinations thereof data collected during a telementoring procedure;
Autonomously identifying data collection peripherals and their associated data input and output protocols;
Providing to the cognitive telementoring system data that has been generated during procedures and operations similar to a procedure or operation being telementored;
Performing cognitive analysis of data generating during a telementoring procedure through the utilization of mathematical techniques that emulate the cognitive
processing abilities of the human brain including, but not limited to symbolic cognitive architectures and inference process algebras; and interpreting the results against a database of similar procedures with the ability to
anticipate deviations from protocols associated with similar procedures contained within the database;
Utilizing cognitive processes such as, but not limited to symbolic cognitive architectures and inference process algebras to intelligently co-locate at least one inputted
telestration symbol with at least one operational site and maintain said co-location if the operational site changes its location, morphology and combinations thereof;
Collecting data which defines the real time location of at least one instrument utilized during a telementoring process, procedure, operation and combinations thereof; and
Autonomously performing real time coordinate
transformations associated with at last one instrument utilized in a telementoring process, procedure, operation, and combinations thereof, in which said transformations are correlated with coordinate systems used to define the location of at least one telestration symbol that is co- located with at least one operational site.
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| US201562247781P | 2015-10-29 | 2015-10-29 | |
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| CN109002887A (en) * | 2018-08-10 | 2018-12-14 | 华北理工大学 | The heuristic curiosity cognitive development system of biology and its operation method |
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