# US 2021/0279547 A1 · Electronic device for high-precision behavior profiling for transplanting with humans' intelligence into artificial intelligence and operating method thereof > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0279547 A1 - Document ID: US20210279547A1 - Status: Published application - Kind: A1 - Application number: 17/141,841 - Filed: 2021-01-05 - Published: 2021-09-09 - Earliest claimed: 2020-03-09 - Assignees: Korea Advanced Institute of Science and Technology - Applicants: Korea Advanced Institute Of Science And Technology - Inventors: Sang Wan LEE; Dongjae KIM; Jae Hoon SHIN - Web page: https://patentgrep.com/patent/US20210279547A1 ## CPC - G06Q 10/063: Operations research, analysis or management - G06F 3/015: {Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection} - G06F 17/15: Correlation function computation {including computation of convolution operations (arithmetic circuits for sum of products per se, e.g. multiply-accumulators G06F7/5443; digital filters, e.g. FIR, IIR, adaptive filters H03H17/00)} - G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques} - G06F 18/24137: {Distances to cluster centroïds} - G06N 3/004: Artificial life, i.e. computing arrangements simulating life - G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 20/00: Machine learning - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Provided are an electronic device for precision behavior profiling for transplanting humans' intelligence into AI and an operating method thereof, which may be configured to theoretically design at least one environmental factor, fit a first level model from human's processing data for a task based on the environmental factor, fit a second level model from processing data of the first level model for the task based on the environmental factor, and determine the second level model as a transplant model for humans' intelligence based on a correlation between the first level model and the second level model through profiling for the first level model and the second level model. According to various embodiments, the human's processing data may include at least any one of behavioral data or a brain signal generated while the human processes the task. ## Claims ### Claim 1 An operating method of an electronic device comprising: fitting a first level model based on human's processing data for a task; fitting a second level model based on processing data of the first level model for the task; and determining the second level model as a transplant model for the humans' intelligence through profiling for the first level model and the second level model. ### Claim 2 (depends on 1) The operating method of claim 1, wherein the human's processing data comprises at least one of behavioral data or a brain signal generated while the human processes the task. ### Claim 3 (depends on 1) The operating method of claim 1, wherein the determining of the transplant model comprises: detecting a correlation between the first level model and the second level model; and determining whether to determine the second level model as the transplant model based on the correlation. ### Claim 4 (depends on 3) The operating method of claim 3, further comprising theoretically designing at least one environmental factor, wherein the fitting of the first level model comprises fitting the first level model from the human's processing data based on the environmental factor, and wherein the fitting of the second level model comprises fitting the second level model from the processing data of the first level model based on the environmental factor. ### Claim 5 (depends on 4) The operating method of claim 4, wherein the fitting of the first level model comprises learning the first level model based on the human's processing data, thereby at least any one of a behavior profile or at least one parameter of the first level model is detected based on the environmental factor by the fitting. ### Claim 6 (depends on 5) The operating method of claim 5, wherein the fitting the second level model comprises learning the second level model based on the processing data of the first level model, thereby at least any one of a behavior profile or at least one parameter of the second level model is detected based on the environmental factor by the fitting. ### Claim 7 (depends on 6) The operating method of claim 6, wherein the detecting of the correlation comprises: at least one of detecting a profile correlation by comparing a behavior profile of the first level model and a behavior profile of the second level model or detecting a parameter correlation by comparing the parameter of the first level model and the parameter of the second level model; and detecting the correlation based on at least any one of the profile correlation or the parameter correlation. ### Claim 8 (depends on 3) The operating method of claim 3, wherein the determining whether to determine the second level model as the transplant model comprises determining the second level model as the transplant model when the correlation is greater than a present threshold value. ### Claim 9 (depends on 4) The operating method of claim 4, wherein the environmental factor comprises at least any one of state-transition uncertainty, state-space complexity, a novelty, a state prediction error or a reward prediction error. ### Claim 10 An electronic device comprising: a memory; and a processor connected to the memory and configured to execute at least one instruction stored in the memory, wherein the processor is configured to: fit a first level model based on human's processing data for a task, fit a second level model based on processing data of the first level model for the task, and determine the second level model as a transplant model for the humans' intelligence through profiling for the first level model and the second level mode. ### Claim 11 (depends on 10) The electronic device of claim 10, wherein the human's processing data comprises at least one of behavioral data or a brain signal generated while the human processes the task. ### Claim 12 (depends on 10) The electronic device of claim 10, wherein the processor is configured to: detect a correlation between the first level model and the second level model; and determine whether to determine the second level model as the transplant model based on the correlation. ### Claim 13 (depends on 12) The electronic device of claim 12, wherein the processor is configured to: theoretically design at least one environmental factor, fit the first level model from the human's processing data based on the environmental factor, and fit the second level model comprises fitting the second level model from the processing data of the first level model based on the environmental factor. ### Claim 14 (depends on 13) The electronic device of claim 13, wherein the processor is configured to learn the first level model based on the human's processing data, thereby at least any one of a behavior profile or at least one parameter of the first level model is detected based on the environmental factor by the fitting. ### Claim 15 (depends on 14) The electronic device of claim 14, wherein the processor is configured to learn the second level model based on the processing data of the first level model, thereby at least any one of a behavior profile or at least one parameter of the second level model is detected based on the environmental factor by the fitting. ### Claim 16 (depends on 15) The electronic device of claim 15, wherein the processor is configured to: detect a profile correlation by comparing a behavior profile of the first level model and a behavior profile of the second level model, detect a parameter correlation by comparing the parameter of the first level model and the parameter of the second level model, and detect the correlation based on at least any one of the profile correlation or the parameter correlation. ### Claim 17 (depends on 12) The electronic device of claim 12, wherein the processor is configured to determine the second level model as the transplant model when the correlation is greater than a present threshold value. ### Claim 18 (depends on 13) The electronic device of claim 13, wherein the environmental factor comprises at least any one of state-transition uncertainty, state-space complexity, a novelty, a state prediction error or a reward prediction error. ### Claim 19 A computer program coupled to a computing device and stored in a recording medium readable by the computing device, the computer program executes: fitting a first level model based on human's processing data for a task; fitting a second level model based on processing data of the first level model for the task; and determining the second level model as a transplant model for the humans' intelligence through profiling for the first level model and the second level model. ### Claim 20 (depends on 19) The computer program of claim 19, wherein the determining of the transplant model comprises: detecting a correlation between the first level model and the second level model; and determining whether to determine the second level model as the transplant model based on the correlation.