# US 12,004,859 B2 · Contactless automated and remote polygraph test > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,004,859 B2 - Document ID: US12004859B2 - Status: Granted - Kind: B2 - Application number: 18/375,520 - Filed: 2023-10-01 - Published: 2024-02-01 - Granted: 2024-06-11 - Earliest claimed: 2022-05-12 - Assignees: Valid.It Evaluation Solutions Ltd - Applicants: Valid.It Evaluation Solutions Ltd - Inventors: Yossi Penias - Web page: https://patentgrep.com/patent/US12004859B2 ## CPC - A61B 5/164: {Lie detection} - A61B 5/0022: {Monitoring a patient using a global network, e.g. telephone networks, internet} - A61B 5/004: {adapted for image acquisition of a particular organ or body part (A61B5/0082 takes precedence; arrangements for optical scanning A61B5/0062)} - A61B 5/0205: Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition - A61B 5/0533: Measuring galvanic skin response - A61B 5/117: Identification of persons (methods or arrangements for recognising patterns, e.g. fingerprints, G06F18/00, G06V40/00; identification of persons by analysing their voice or speech G10L17/00) - A61B 5/389: Electromyography [EMG] - A61B 5/7267: {involving training the classification device} - G06V 40/176: {Dynamic expression} - G16H 10/20: for electronic clinical trials or questionnaires - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - A61B 5/021: Measuring pressure in heart or blood vessels - A61B 5/02405: {Determining heart rate variability} - A61B 5/08: Measuring devices for evaluating the respiratory organs - A61B 5/0816: {Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00)} - A61B 5/1107: {Measuring contraction of parts of the body, e.g. organ or muscle} - A61B 5/1128: {using image analysis (A61B5/1127 takes precedence)} - A61B 5/165: {Evaluating the state of mind, e.g. depression, anxiety} - A61B 5/4266: {sweat secretion} - A61B 5/4803: {Speech analysis specially adapted for diagnostic purposes} - A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems} - A61B 2560/02: Operational features - A61B 2560/0443: Modular apparatus - G16H 40/63: for local operation - G16H 50/30: for calculating health indices; for individual health risk assessment - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract A computer-implemented method for conducting a polygraph test, the method comprising: monitoring physiological indices of a user during the steps of: providing the user with a plurality of pre-questions; and receiving answers from the user to the plurality of pre-questions; analyzing the answers to the pre-questions and physiological indices using machine learning and setting baselines for the monitored physiological indices; generating a plurality of test questions for the user based on the analysis; monitoring physiological indices of a user during the steps of: providing the user with the plurality of test questions; and receiving answers from the user to the test questions; comparing the baselines and the monitored physiological indices during the provision of answers to the test questions; and generating a test result to the test question. ## Claims ### Claim 1 A computer-implemented method for conducting a polygraph test, the method comprising: monitoring physiological indices of a user during the steps of: providing the user with a plurality of pre-questions; and receiving answers from the user to the plurality of pre-questions; analyzing the answers to the plurality of pre-questions and monitored physiological indices using machine learning and setting baselines for the monitored physiological indices; generating a plurality of test questions for the user based on the analysis; monitoring physiological indices of the user during the steps of: providing the user with the plurality of test questions; and receiving answers from the user to the plurality of test questions, wherein the plurality of test questions are dynamically modified during the provision of the test questions based on evaluation of received answers to the test questions using machine learning; comparing the baselines and the monitored physiological indices during the provision of the answers to the plurality of test questions; and generating a test result to the test questions, wherein said generation of said test result to said test questions comprises a quality assessment of said test result by calculating a ratio of non-detected data points for said monitored physiological indices to an overall number of theoretically monitored data points for said physiological indices monitored during said polygraph test. ### Claim 2 (depends on 1) A method according to claim 1, wherein the monitoring of physiological indices comprises measurement of cardio and respiratory factors. ### Claim 3 (depends on 1) A method according to claim 1, wherein the physiological indices are selected from a group consisting of: blood pressure, sweat, heart pulse, breathing, heart rate variation, respiratory rate, voice stress analysis and micro-reactions in facial muscles. ### Claim 4 (depends on 1) A method according to claim 1, wherein the baselines are continuously recalculated during the plurality of test questions based on dynamic monitoring of the physiological indices. ### Claim 5 (depends on 1) A method according to claim 1, wherein the baselines are adjusted to the answers of the user during the plurality of pre-questions and the plurality of test questions. ### Claim 6 (depends on 1) A method according to claim 1, wherein the plurality of pre-questions comprises questions on one or more of: personal data, credibility, personality traits and stimulation of the user. ### Claim 7 (depends on 1) A method according to claim 1, wherein the physiological indices are monitored using video processing of the user's face. ### Claim 8 (depends on 1) A method according to claim 1, wherein the monitored physiological indices are analyzed using machine learning to identify deviations in the physiological indices compared to the baselines for the monitored physiological indices. ### Claim 9 (depends on 1) A method according to claim 1, wherein the plurality of test questions are dynamically adjusted using machine learning based on the received answers to the plurality of pre-questions. ### Claim 10 (depends on 9) A method according to claim 9, wherein the dynamical adjustment of the plurality of test questions enables the generation of the plurality of test questions with an increased discernment in the monitored physiological indices upon answering the plurality of test questions. ### Claim 11 (depends on 1) A method according to claim 1, wherein the plurality of test questions is dynamically reordered based on the received answers to the plurality of pre-questions. ### Claim 12 (depends on 1) A method according to claim 1, wherein the plurality of test questions comprises a test question to identify the user. ### Claim 13 (depends on 1) A method according to claim 1, wherein the polygraph test is conducted remotely in absence of a human examiner. ### Claim 14 (depends on 1) A method according to claim 1, wherein the comparison of the baselines and the monitored physiological indices during the provision of answers to the plurality of test questions allows identification of the physiological indices with large percentage deviation relative to the baselines in the generation of test results for the plurality of test questions. ### Claim 15 A system for conducting a polygraph test, the system comprising: a computing device; a memory; and a processor, the processor configured to: monitor physiological indices of a user during the steps of: providing the user with a plurality of pre-questions; and receiving answers from the user to the plurality of pre-questions; analyze the answers to the plurality of pre-questions and monitored physiological indices using machine learning and set baselines for the monitored physiological indices; generate a plurality of test questions for the user based on the analysis; monitor physiological indices of the user during the steps of: providing the user with the plurality of test questions; and receiving answers from the user to the plurality of test questions, wherein the plurality of test questions are dynamically modified during the provision of the test questions based on evaluation of received answers to the test questions using machine learning; compare the baselines and the monitored physiological indices during the provision of the answers to the plurality of test questions; and provide a test result to the test questions, wherein said test result to said test questions comprises a quality assessment of said test result by calculating a ratio of non-detected data points for said monitored physiological indices to an overall number of theoretically monitored data points for said physiological indices monitored during said polygraph test. ### Claim 16 (depends on 15) A system according to claim 15, wherein the monitoring of physiological indices comprises measurement of cardio and respiratory factors. ### Claim 17 (depends on 15) A system according to claim 15, wherein the physiological indices are selected from a group consisting of: blood pressure, sweat, heart pulse, breathing, heart rate variation, RR+, voice stress analysis and micro-reactions in facial muscles. ### Claim 18 (depends on 15) A system according to claim 15, wherein the baselines are continuously recalculated during the plurality of test questions based on dynamic monitoring of the physiological indices. ### Claim 19 (depends on 15) A system according to claim 15, wherein the baselines are adjusted to the answers of the user during the plurality of pre-questions and the plurality of test questions. ### Claim 20 A computer-implemented method for carrying out a polygraph test, the method comprising: measuring physiological parameters of a user during the steps of: sending to the user a first series of test questions; and collecting answers from the user to the first series of questions; analyzing the answers to the first series of test questions and measured physiological parameters using machine learning; calculating baselines for the measured physiological parameters; generating a second series of test questions for the user based on the analysis; measuring physiological parameters of the user during the steps of: sending to the user the second series of test questions; and collecting answers from the user to the second series of test questions, wherein the plurality of test questions are dynamically modified during the provision of the test questions based on evaluation of received answers to the test questions using machine learning; comparing the baselines and the measured physiological parameters during the collection of the answers to the second series of test questions; and providing a test result to the second series of test questions, wherein said test result to said second series of test questions comprises a quality assessment of said test result by calculating a ratio of non-detected data points for said monitored physiological parameters to an overall number of theoretically monitored data points for said physiological parameters monitored during said polygraph test.