System

Abstract

An object of a system according to an embodiment is to recognize an unconscious action of a user and to optimize the action by making the user conscious of the action.SOLUTION: A system according to an embodiment includes an action recognition part, an action consciousness part, and an action optimization part. The action recognition unit collects action data of a user and recognizes an unconscious action. The behavior awareness unit feeds back the unconscious behavior recognized by the behavior recognition unit to the user. The action optimization unit makes a proposal for optimizing the unconscious action fed back by the action awareness unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of not being able to adequately recognize users' unconscious behavior, make them conscious, and optimize it.

[0005] The system according to the embodiment aims to recognize the user's unconscious behavior, make it conscious, and optimize it. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavior recognition unit, a behavior awareness unit, and a behavior optimization unit. The behavior recognition unit collects behavioral data of a user and recognizes unconscious behavior. The behavior awareness unit provides feedback to the user on the unconscious behavior recognized by the behavior recognition unit. The behavior optimization unit makes suggestions for optimizing the unconscious behavior fed back by the behavior awareness unit. [Effects of the Invention]

[0007] The system according to the embodiment can recognize the user's unconscious behavior, make it conscious, and optimize it. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The behavior awareness system according to the embodiment of the present invention is a system that recognizes, makes conscious of, and optimizes a user's unconscious behavior, thereby improving the quality of the user's life.

[0029] A behavior awareness system according to an embodiment includes a behavior recognition unit, a behavior awareness unit, and a behavior optimization unit. The behavior recognition unit collects user behavior data and recognizes unconscious behavior. For example, the behavior recognition unit collects location information, click data, and sensor data to identify unconscious behavior. The behavior recognition unit also uses a generation AI to analyze the user behavior data and identify unconscious behavior. For example, the generation AI analyzes the user behavior data and identify unconscious behavior. The behavior recognition unit also uses sensor data to collect the user behavior data and identify unconscious behavior. For example, the sensor data collects user movement and location information to identify unconscious behavior. The behavior awareness unit provides feedback to the user about the unconscious behavior recognized by the behavior recognition unit. For example, the behavior awareness unit provides feedback using text notifications, audio alerts, or visual displays. The behavior awareness unit also uses a generation AI to provide feedback to the user about the unconscious behavior. For example, the generation AI provides feedback to the user in the form of, "You check your smartphone every morning before drinking coffee." The behavior awareness unit also uses a visual display to provide feedback on unconscious behavior to the user. For example, the visual display displays the user's behavioral patterns in a graph or chart, allowing the user to visually recognize unconscious behavior. The behavior optimization unit makes suggestions to optimize the unconscious behavior provided as feedback by the behavior awareness unit. For example, the behavior optimization unit uses a generation AI to make suggestions to optimize the user's behavior. For example, the generation AI makes a suggestion such as, "To make effective use of your morning time, try changing the time you check your smartphone to after drinking coffee." The behavior optimization unit also analyzes behavioral data and makes optimal suggestions to optimize the user's behavior. For example, the behavior optimization unit analyzes the user's behavioral data and suggests an optimal behavior pattern. The behavior optimization unit also uses a generation AI to optimize the user's behavior. For example, the generation AI analyzes the user's behavioral data and suggests an optimal behavior pattern. This allows the behavior awareness system according to the embodiment to improve the user's quality of life.For example, the output unit displays the proposed results to the user via a web application or a mobile application. If the user desires feedback in paper form, the output unit prints the results using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0030] When analyzing a user's behavioral data, the behavior recognition unit simultaneously analyzes the psychological factors behind the behavior, allowing it to identify the root causes of unconscious behavior. For example, when the generation AI analyzes a user's behavioral data, the behavior recognition unit refers to a psychological database and analyzes the motivation and emotional state of the behavior to identify the psychological factors behind the behavior. Furthermore, when analyzing the user's behavioral data, the behavior recognition unit collects and analyzes biometric data to identify psychological factors behind the behavior, such as stress and fatigue. For example, biometric data collects biological data such as heart rate and galvanic skin response, and identifies the psychological factors behind the behavior. Furthermore, when the generation AI analyzes the user's behavioral data, the behavior recognition unit refers to the user's past behavioral history and survey data and analyzes behavioral patterns to identify the psychological factors behind the behavior. This allows for more effective improvements by identifying the root causes of unconscious behavior.

[0031] When analyzing user behavior data, the behavior recognition unit can analyze the frequency and time periods of behavior in detail and extract specific patterns. For example, when the generation AI analyzes user behavior data, the behavior recognition unit uses time series data analysis technology to analyze the frequency and time periods of behavior in detail and extract specific patterns. Furthermore, when analyzing user behavior data, the behavior recognition unit uses a clustering algorithm to analyze the frequency and time periods of behavior in detail and extract specific patterns. For example, the clustering algorithm groups the user behavior data and extracts specific patterns. Furthermore, when the generation AI analyzes user behavior data, the behavior recognition unit generates and visually displays a heat map to analyze the frequency and time periods of behavior in detail and extract specific patterns. For example, a heat map visually displays the user behavior data and extracts specific patterns. This makes it easier to extract specific patterns by analyzing the frequency and time periods of behavior in detail.

[0032] The behavioral awareness unit can simultaneously provide feedback on the psychological factors behind the behavior when providing feedback on unconscious behavior, allowing the user to understand the root cause of the behavior. For example, when the generation AI provides feedback on unconscious behavior, the behavioral awareness unit references a psychological database to simultaneously provide feedback on the psychological factors behind the behavior. Furthermore, when the generation AI provides feedback on unconscious behavior to the user, the behavioral awareness unit analyzes biometric data to simultaneously provide feedback on psychological factors behind the behavior, such as stress and fatigue. For example, the biometric data analyzes biological data such as heart rate and galvanic skin response to identify the psychological factors behind the behavior. Furthermore, when the generation AI provides feedback on unconscious behavior, the behavioral awareness unit references the user's past behavioral history and questionnaire data to simultaneously provide feedback on the psychological factors behind the behavior. This enables more effective improvements by understanding the root cause of the behavior.

[0033] The behavioral awareness unit, when providing feedback on unconscious behavior, can indicate the frequency and time period of the behavior in detail, allowing the user to recognize specific behavioral patterns. For example, the behavioral awareness unit uses time-series data analysis technology to indicate the frequency and time period of the behavior in detail when the generation AI provides feedback on unconscious behavior. The behavioral awareness unit also uses a clustering algorithm to indicate the frequency and time period of the behavior in detail when providing feedback on unconscious behavior to the user. For example, the clustering algorithm groups the user's behavioral data and extracts specific patterns. The behavioral awareness unit also generates and visually displays a heat map to indicate the frequency and time period of the behavior in detail when the generation AI provides feedback on unconscious behavior. For example, the heat map visually displays the user's behavioral data and extracts specific patterns. This makes it easier to recognize specific behavioral patterns by indicating the frequency and time period of the behavior in detail.

[0034] The behavioral awareness unit can compare behavioral data between different users and provide feedback on common unconscious behavior patterns. The behavioral awareness unit, for example, uses data mining technology to compare behavioral data between different users and provide feedback on common unconscious behavior patterns. The behavioral awareness unit also uses a clustering algorithm to compare behavioral data between users and provide feedback on common unconscious behavior patterns. For example, the clustering algorithm groups users' behavioral data and extracts common unconscious behavior patterns. The behavioral awareness unit also uses statistical analysis technology to compare behavioral data between different users and provide feedback on common unconscious behavior patterns. For example, the statistical analysis technology analyzes users' behavioral data and identifies common unconscious behavior patterns. As a result, providing feedback on common unconscious behavior patterns makes it easier for users to compare their behavior with other users.

[0035] When making behavioral optimization suggestions, the behavior optimization unit also takes into account the psychological factors behind the behavior and can propose fundamental improvement measures. For example, when the generation AI makes behavioral optimization suggestions, the behavior optimization unit refers to a psychological database to consider the psychological factors behind the behavior. In addition, when making behavioral optimization suggestions to the user, the behavior optimization unit analyzes biometric data to consider psychological factors behind the behavior, such as stress and fatigue. For example, biometric data analyzes biological data such as heart rate and galvanic skin response to identify the psychological factors behind the behavior. In addition, when the generation AI makes behavioral optimization suggestions, the behavior optimization unit refers to the user's past behavioral history and questionnaire data to consider the psychological factors behind the behavior. In this way, fundamental improvement measures can be proposed by taking psychological factors into account.

[0036] When making a behavior optimization suggestion, the behavior optimization unit can analyze the frequency and time period of a behavior in detail and suggest behavior changes at the optimal timing. For example, the behavior optimization unit uses time series data analysis technology to analyze the frequency and time period of a behavior in detail when the generation AI makes a behavior optimization suggestion. Furthermore, the behavior optimization unit uses a clustering algorithm to analyze the frequency and time period of a behavior in detail when making a behavior optimization suggestion to a user. For example, the clustering algorithm groups the user's behavior data and extracts specific patterns. Furthermore, the behavior optimization unit generates and visually displays a heat map to analyze the frequency and time period of a behavior in detail when the generation AI makes a behavior optimization suggestion. For example, the heat map visually displays the user's behavior data and extracts specific patterns. This allows for a detailed analysis of the frequency and time period of a behavior to suggest behavior changes at the optimal timing.

[0037] When making a behavioral optimization suggestion, the behavior optimization unit can make the suggestion visually and audibly using audio data or video data. For example, when the generation AI makes a behavioral optimization suggestion, the behavior optimization unit uses voice synthesis technology to make the suggestion audibly using audio data. Furthermore, when the behavior optimization unit makes a behavioral optimization suggestion to the user, the behavior optimization unit uses video generation technology to make the suggestion visually using video data. For example, the video generation technology displays the user's behavioral pattern as a video and visually suggests optimal behavioral changes. Furthermore, when the generation AI makes a behavioral optimization suggestion, the behavior optimization unit uses multimedia technology to make the suggestion visually and audibly using audio or video data. For example, the multimedia technology combines audio and video to suggest optimal behavioral changes to the user. This makes it easier for the user to understand the behavior optimization through visual and audible suggestions.

[0038] The behavior optimization unit can compare behavioral data between different users and propose a common optimization pattern. The behavior optimization unit, for example, uses data mining technology to compare behavioral data between different users and propose a common optimization pattern. The behavior optimization unit also uses a clustering algorithm to compare behavioral data between users and propose a common optimization pattern. For example, the clustering algorithm groups users' behavioral data and extracts common optimization patterns. The behavior optimization unit also uses statistical analysis technology to compare behavioral data between different users and propose a common optimization pattern. For example, the statistical analysis technology analyzes users' behavioral data and identifies common optimization patterns. This makes it easier for users to compare their behavior with other users by proposing common optimization patterns.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] When collecting user behavior data, the behavior recognition unit simultaneously collects environmental data surrounding the user, and can analyze the association between the behavior and the environment. For example, the behavior recognition unit collects the user's location information and environmental data such as the ambient temperature, humidity, and noise level, and identifies the association between the behavior and the environment. When analyzing the user behavior data, the behavior recognition unit also collects environmental data such as the intensity of ambient light and color temperature, and identifies the association between the behavior and the environment. Furthermore, when collecting the user behavior data, the behavior recognition unit also collects environmental data such as the movements and conversations of people around the user, and identifies the association between the behavior and the environment. This enables more accurate behavior recognition by analyzing the association between the behavior and the environment.

[0041] When providing feedback on unconscious behavior, the behavioral awareness unit can compare the user's past behavioral history and indicate changes in behavior. For example, the behavioral awareness unit can compare the user's behavioral data from the past month with their current behavioral data and visually display changes in behavior in graphs or charts. The behavioral awareness unit can also compare the user's past behavioral history with their current behavioral data and provide feedback on changes in behavior via text notifications or audio alerts. Furthermore, the behavioral awareness unit can compare the user's past behavioral history with their current behavioral data and provide feedback on changes in behavior via visual displays. This makes it easier for users to recognize changes in their own behavior.

[0042] The behavior optimization unit can make customized suggestions based on the user's preferences and lifestyle in order to optimize the user's behavior. For example, the behavior optimization unit can suggest optimal meal plans and exercise plans based on the user's dietary preferences and exercise habits. The behavior optimization unit can also suggest optimal time management methods and relaxation methods based on the user's work schedule and hobbies. Furthermore, the behavior optimization unit can suggest optimal sleeping environments and stress management methods based on the user's sleep patterns and stress levels. This makes it possible to suggest optimal behaviors tailored to the user's lifestyle.

[0043] When analyzing user behavior data, the behavior recognition unit can analyze the frequency and time periods of behavior in detail and extract specific patterns. For example, when the generation AI analyzes user behavior data, the behavior recognition unit uses time series data analysis technology to analyze the frequency and time periods of behavior in detail and extract specific patterns. Furthermore, when analyzing user behavior data, the behavior recognition unit uses a clustering algorithm to analyze the frequency and time periods of behavior in detail and extract specific patterns. For example, the clustering algorithm groups the user behavior data and extracts specific patterns. Furthermore, when the generation AI analyzes user behavior data, the behavior recognition unit generates and visually displays a heat map to analyze the frequency and time periods of behavior in detail and extract specific patterns. For example, a heat map visually displays the user behavior data and extracts specific patterns. This makes it easier to extract specific patterns by analyzing the frequency and time periods of behavior in detail.

[0044] When providing feedback on unconscious behavior, the behavior awareness unit can simultaneously provide feedback on the psychological factors behind the behavior, allowing the user to understand the root causes of the behavior. For example, when the generation AI provides feedback on unconscious behavior, the behavior awareness unit references a psychological database to simultaneously provide feedback on the psychological factors behind the behavior. Furthermore, when providing feedback on unconscious behavior to the user, the behavior awareness unit analyzes biometric data to simultaneously provide feedback on psychological factors behind the behavior, such as stress and fatigue. For example, biometric data analyzes biological data such as heart rate and galvanic skin response to identify the psychological factors behind the behavior. Furthermore, when the generation AI provides feedback on unconscious behavior, the behavior awareness unit references the user's past behavioral history and survey data to simultaneously provide feedback on the psychological factors behind the behavior. This enables more effective improvements by understanding the root causes of the behavior.

[0045] When making behavioral optimization suggestions, the behavior optimization unit also takes into account the psychological factors behind the behavior and can propose fundamental improvement measures. For example, when the generation AI makes behavioral optimization suggestions, the behavior optimization unit refers to a psychological database to consider the psychological factors behind the behavior. In addition, when making behavioral optimization suggestions to the user, the behavior optimization unit analyzes biometric data to consider psychological factors behind the behavior, such as stress and fatigue. For example, biometric data analyzes biological data such as heart rate and galvanic skin response to identify the psychological factors behind the behavior. In addition, when the generation AI makes behavioral optimization suggestions, the behavior optimization unit refers to the user's past behavioral history and survey data to consider the psychological factors behind the behavior. In this way, fundamental improvement measures can be proposed by taking psychological factors into account.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The behavior recognition unit collects user behavior data and recognizes unconscious behavior. For example, it collects location information, click data, and sensor data, and uses generation AI to identify unconscious behavior. Step 2: The behavioral awareness unit provides feedback to the user about the unconscious behavior recognized by the behavioral recognition unit. For example, feedback can be provided using text notifications, voice alerts, or visual displays, and specific behavioral patterns can be communicated to the user using a generation AI. Step 3: The behavior optimization unit makes suggestions to optimize the unconscious behaviors fed back by the behavior awareness unit. For example, it uses a generative AI to make suggestions to optimize the user's behavior, and analyzes behavioral data to propose optimal behavioral patterns.

[0048] (Example 2) The behavior awareness system according to the embodiment of the present invention is a system that recognizes, makes conscious of, and optimizes a user's unconscious behavior, thereby improving the quality of the user's life.

[0049] A behavior awareness system according to an embodiment includes a behavior recognition unit, a behavior awareness unit, and a behavior optimization unit. The behavior recognition unit collects user behavior data and recognizes unconscious behavior. For example, the behavior recognition unit collects location information, click data, and sensor data to identify unconscious behavior. The behavior recognition unit also uses a generation AI to analyze the user behavior data and identify unconscious behavior. For example, the generation AI analyzes the user behavior data and identify unconscious behavior. The behavior recognition unit also uses sensor data to collect the user behavior data and identify unconscious behavior. For example, the sensor data collects user movement and location information to identify unconscious behavior. The behavior awareness unit provides feedback to the user about the unconscious behavior recognized by the behavior recognition unit. For example, the behavior awareness unit provides feedback using text notifications, audio alerts, or visual displays. The behavior awareness unit also uses a generation AI to provide feedback to the user about the unconscious behavior. For example, the generation AI provides feedback to the user in the form of, "You check your smartphone every morning before drinking coffee." The behavior awareness unit also uses a visual display to provide feedback on unconscious behavior to the user. For example, the visual display displays the user's behavioral patterns in a graph or chart, allowing the user to visually recognize unconscious behavior. The behavior optimization unit makes suggestions to optimize the unconscious behavior provided as feedback by the behavior awareness unit. For example, the behavior optimization unit uses a generation AI to make suggestions to optimize the user's behavior. For example, the generation AI makes a suggestion such as, "To make effective use of your morning time, try changing the time you check your smartphone to after drinking coffee." The behavior optimization unit also analyzes behavioral data and makes optimal suggestions to optimize the user's behavior. For example, the behavior optimization unit analyzes the user's behavioral data and suggests an optimal behavior pattern. The behavior optimization unit also uses a generation AI to optimize the user's behavior. For example, the generation AI analyzes the user's behavioral data and suggests an optimal behavior pattern. This allows the behavior awareness system according to the embodiment to improve the user's quality of life.For example, the output unit displays the proposed results to the user via a web application or a mobile application. If the user desires feedback in paper form, the output unit prints the results using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0050] When analyzing a user's behavioral data, the behavior recognition unit simultaneously analyzes the psychological factors behind the behavior, allowing it to identify the root causes of unconscious behavior. For example, when the generation AI analyzes a user's behavioral data, the behavior recognition unit refers to a psychological database and analyzes the motivation and emotional state of the behavior to identify the psychological factors behind the behavior. Furthermore, when analyzing the user's behavioral data, the behavior recognition unit collects and analyzes biometric data to identify psychological factors behind the behavior, such as stress and fatigue. For example, biometric data collects biological data such as heart rate and galvanic skin response, and identifies the psychological factors behind the behavior. Furthermore, when the generation AI analyzes the user's behavioral data, the behavior recognition unit refers to the user's past behavioral history and survey data and analyzes behavioral patterns to identify the psychological factors behind the behavior. This allows for more effective improvements by identifying the root causes of unconscious behavior.

[0051] When analyzing user behavior data, the behavior recognition unit can analyze the frequency and time periods of behavior in detail and extract specific patterns. For example, when the generation AI analyzes user behavior data, the behavior recognition unit uses time series data analysis technology to analyze the frequency and time periods of behavior in detail and extract specific patterns. Furthermore, when analyzing user behavior data, the behavior recognition unit uses a clustering algorithm to analyze the frequency and time periods of behavior in detail and extract specific patterns. For example, the clustering algorithm groups the user behavior data and extracts specific patterns. Furthermore, when the generation AI analyzes user behavior data, the behavior recognition unit generates and visually displays a heat map to analyze the frequency and time periods of behavior in detail and extract specific patterns. For example, a heat map visually displays the user behavior data and extracts specific patterns. This makes it easier to extract specific patterns by analyzing the frequency and time periods of behavior in detail.

[0052] The behavior recognition unit can use the emotion estimation function to analyze the emotional state of the user during an action and identify the association between the emotion and the action. For example, the behavior recognition unit uses the emotion estimation function to estimate an emotion in real time using facial expression recognition technology to analyze the emotional state of the user during an action. The behavior recognition unit also uses voice analysis technology to estimate an emotion from the tone and pitch of the voice to analyze the emotional state of the user during an action. For example, the voice analysis technology analyzes the tone and pitch of the user's voice to estimate the emotion. The behavior recognition unit also uses the emotion estimation function to collect biometric data such as heart rate and galvanic skin response to estimate the emotion in order to analyze the emotional state of the user during an action. For example, the heart rate and galvanic skin response are used as indicators of the user's emotional state. This allows for a deeper understanding of the action by identifying the association between the emotion and the action.

[0053] The behavioral awareness unit can simultaneously provide feedback on the psychological factors behind the behavior when providing feedback on unconscious behavior, allowing the user to understand the root cause of the behavior. For example, when the generation AI provides feedback on unconscious behavior, the behavioral awareness unit references a psychological database to simultaneously provide feedback on the psychological factors behind the behavior. Furthermore, when the generation AI provides feedback on unconscious behavior to the user, the behavioral awareness unit analyzes biometric data to simultaneously provide feedback on psychological factors behind the behavior, such as stress and fatigue. For example, the biometric data analyzes biological data such as heart rate and galvanic skin response to identify the psychological factors behind the behavior. Furthermore, when the generation AI provides feedback on unconscious behavior, the behavioral awareness unit references the user's past behavioral history and questionnaire data to simultaneously provide feedback on the psychological factors behind the behavior. This enables more effective improvements by understanding the root cause of the behavior.

[0054] The behavioral awareness unit, when providing feedback on unconscious behavior, can indicate the frequency and time period of the behavior in detail, allowing the user to recognize specific behavioral patterns. For example, the behavioral awareness unit uses time-series data analysis technology to indicate the frequency and time period of the behavior in detail when the generation AI provides feedback on unconscious behavior. The behavioral awareness unit also uses a clustering algorithm to indicate the frequency and time period of the behavior in detail when providing feedback on unconscious behavior to the user. For example, the clustering algorithm groups the user's behavioral data and extracts specific patterns. The behavioral awareness unit also generates and visually displays a heat map to indicate the frequency and time period of the behavior in detail when the generation AI provides feedback on unconscious behavior. For example, the heat map visually displays the user's behavioral data and extracts specific patterns. This makes it easier to recognize specific behavioral patterns by indicating the frequency and time period of the behavior in detail.

[0055] The behavior awareness unit uses the emotion estimation function to provide feedback on the user's emotional state during the behavior, allowing the user to recognize the association between emotion and behavior. The behavior awareness unit uses, for example, facial expression recognition technology to provide feedback on the user's emotional state during the behavior using the emotion estimation function. The behavior awareness unit also uses voice analysis technology to provide feedback on the user's emotional state during the behavior. For example, the voice analysis technology analyzes the tone and pitch of the user's voice to estimate the emotion. The behavior awareness unit also uses the emotion estimation function to collect and analyze biological data such as heart rate and galvanic skin response to provide feedback on the user's emotional state during the behavior. For example, the heart rate and galvanic skin response are used as indicators of the user's emotional state. This allows the user to recognize the association between emotion and behavior, thereby deepening understanding of the behavior.

[0056] The behavioral awareness unit can compare behavioral data between different users and provide feedback on common unconscious behavior patterns. The behavioral awareness unit, for example, uses data mining technology to compare behavioral data between different users and provide feedback on common unconscious behavior patterns. The behavioral awareness unit also uses a clustering algorithm to compare behavioral data between users and provide feedback on common unconscious behavior patterns. For example, the clustering algorithm groups users' behavioral data and extracts common unconscious behavior patterns. The behavioral awareness unit also uses statistical analysis technology to compare behavioral data between different users and provide feedback on common unconscious behavior patterns. For example, the statistical analysis technology analyzes users' behavioral data and identifies common unconscious behavior patterns. As a result, providing feedback on common unconscious behavior patterns makes it easier for users to compare their behavior with other users.

[0057] The behavior awareness unit can use the emotion estimation function to provide real-time feedback on changes in emotions when the user performs a specific behavior, thereby promoting awareness of the behavior. The behavior awareness unit, for example, uses facial expression recognition technology to provide real-time feedback on changes in emotions when the user performs a specific behavior using the emotion estimation function. The behavior awareness unit also uses voice analysis technology to provide real-time feedback on changes in emotions when the user performs a specific behavior. For example, the voice analysis technology analyzes the tone and pitch of the user's voice to estimate emotions. The behavior awareness unit also uses the emotion estimation function to collect and analyze biometric data to provide real-time feedback on changes in emotions when the user performs a specific behavior. For example, the biometric data includes data such as heart rate and electrodermal response, and feeds back changes in emotions in real time. This promotes behavior awareness by providing real-time feedback on changes in emotions.

[0058] When making behavioral optimization suggestions, the behavior optimization unit also takes into account the psychological factors behind the behavior and can propose fundamental improvement measures. For example, when the generation AI makes behavioral optimization suggestions, the behavior optimization unit refers to a psychological database to consider the psychological factors behind the behavior. In addition, when making behavioral optimization suggestions to the user, the behavior optimization unit analyzes biometric data to consider psychological factors behind the behavior, such as stress and fatigue. For example, biometric data analyzes biological data such as heart rate and galvanic skin response to identify the psychological factors behind the behavior. In addition, when the generation AI makes behavioral optimization suggestions, the behavior optimization unit refers to the user's past behavioral history and questionnaire data to consider the psychological factors behind the behavior. In this way, fundamental improvement measures can be proposed by taking psychological factors into account.

[0059] When making a behavior optimization suggestion, the behavior optimization unit can analyze the frequency and time period of a behavior in detail and suggest behavior changes at the optimal timing. For example, the behavior optimization unit uses time series data analysis technology to analyze the frequency and time period of a behavior in detail when the generation AI makes a behavior optimization suggestion. Furthermore, the behavior optimization unit uses a clustering algorithm to analyze the frequency and time period of a behavior in detail when making a behavior optimization suggestion to a user. For example, the clustering algorithm groups the user's behavior data and extracts specific patterns. Furthermore, the behavior optimization unit generates and visually displays a heat map to analyze the frequency and time period of a behavior in detail when the generation AI makes a behavior optimization suggestion. For example, the heat map visually displays the user's behavior data and extracts specific patterns. This allows for a detailed analysis of the frequency and time period of a behavior to suggest behavior changes at the optimal timing.

[0060] The behavior optimization unit uses the emotion estimation function to make optimization suggestions that take into account the user's emotional state, thereby encouraging emotionally positive behavioral changes. The behavior optimization unit, for example, uses facial expression recognition technology to make optimization suggestions that take into account the user's emotional state using the emotion estimation function. The behavior optimization unit also uses voice analysis technology to make optimization suggestions that take into account the user's emotional state. For example, the voice analysis technology analyzes the tone and pitch of the user's voice to infer emotions. The behavior optimization unit also uses the emotion estimation function to collect and analyze biometric data such as heart rate and galvanic skin response to make optimization suggestions that take into account the user's emotional state. For example, the heart rate and galvanic skin response are used as indicators of the user's emotional state. In this way, by taking the user's emotional state into account, it is possible to encourage emotionally positive behavioral changes.

[0061] When making a behavioral optimization suggestion, the behavior optimization unit can make the suggestion visually and audibly using audio data or video data. For example, when the generation AI makes a behavioral optimization suggestion, the behavior optimization unit uses voice synthesis technology to make the suggestion audibly using audio data. Furthermore, when the behavior optimization unit makes a behavioral optimization suggestion to the user, the behavior optimization unit uses video generation technology to make the suggestion visually using video data. For example, the video generation technology displays the user's behavioral pattern as a video and visually suggests optimal behavioral changes. Furthermore, when the generation AI makes a behavioral optimization suggestion, the behavior optimization unit uses multimedia technology to make the suggestion visually and audibly using audio or video data. For example, the multimedia technology combines audio and video to suggest optimal behavioral changes to the user. This makes it easier for the user to understand the behavior optimization through visual and audible suggestions.

[0062] The behavior optimization unit can compare behavioral data between different users and propose a common optimization pattern. The behavior optimization unit, for example, uses data mining technology to compare behavioral data between different users and propose a common optimization pattern. The behavior optimization unit also uses a clustering algorithm to compare behavioral data between users and propose a common optimization pattern. For example, the clustering algorithm groups users' behavioral data and extracts common optimization patterns. The behavior optimization unit also uses statistical analysis technology to compare behavioral data between different users and propose a common optimization pattern. For example, the statistical analysis technology analyzes users' behavioral data and identifies common optimization patterns. This makes it easier for users to compare their behavior with other users by proposing common optimization patterns.

[0063] The behavior optimization unit uses the emotion estimation function to monitor changes in emotions in real time when the user performs a specific behavior and continuously suggest optimal behavioral changes. The behavior optimization unit, for example, uses facial expression recognition technology to monitor changes in emotions in real time when the user performs a specific behavior using the emotion estimation function. The behavior optimization unit also uses voice analysis technology to monitor changes in emotions in real time when the user performs a specific behavior. For example, the voice analysis technology analyzes the tone and pitch of the user's voice to estimate emotions. The behavior optimization unit also uses the emotion estimation function to collect and analyze biometric data to monitor changes in emotions in real time when the user performs a specific behavior. For example, the biometric data collects data such as heart rate and galvanic skin response to monitor changes in emotions in real time. This allows changes in emotions to be monitored in real time and continuously suggests optimal behavioral changes, thereby promoting improvement of the user's behavior.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] When collecting user behavior data, the behavior recognition unit simultaneously collects environmental data surrounding the user, and can analyze the association between the behavior and the environment. For example, the behavior recognition unit collects the user's location information and environmental data such as the ambient temperature, humidity, and noise level, and identifies the association between the behavior and the environment. When analyzing the user behavior data, the behavior recognition unit also collects environmental data such as the intensity of ambient light and color temperature, and identifies the association between the behavior and the environment. Furthermore, when collecting the user behavior data, the behavior recognition unit also collects environmental data such as the movements and conversations of people around the user, and identifies the association between the behavior and the environment. This enables more accurate behavior recognition by analyzing the association between the behavior and the environment.

[0066] When providing feedback on unconscious behavior, the behavioral awareness unit can compare the user's past behavioral history and indicate changes in behavior. For example, the behavioral awareness unit can compare the user's behavioral data from the past month with their current behavioral data and visually display changes in behavior in graphs or charts. The behavioral awareness unit can also compare the user's past behavioral history with their current behavioral data and provide feedback on changes in behavior via text notifications or audio alerts. Furthermore, the behavioral awareness unit can compare the user's past behavioral history with their current behavioral data and provide feedback on changes in behavior via visual displays. This makes it easier for users to recognize changes in their own behavior.

[0067] The behavior optimization unit can make customized suggestions based on the user's preferences and lifestyle in order to optimize the user's behavior. For example, the behavior optimization unit can suggest optimal meal plans and exercise plans based on the user's dietary preferences and exercise habits. The behavior optimization unit can also suggest optimal time management methods and relaxation methods based on the user's work schedule and hobbies. Furthermore, the behavior optimization unit can suggest optimal sleeping environments and stress management methods based on the user's sleep patterns and stress levels. This makes it possible to suggest optimal behaviors tailored to the user's lifestyle.

[0068] The behavior recognition unit can use the emotion estimation function to analyze the emotional state of the user during an action and identify the association between the emotion and the action. For example, the behavior recognition unit uses the emotion estimation function to estimate an emotion in real time using facial expression recognition technology to analyze the emotional state of the user during an action. The behavior recognition unit also uses voice analysis technology to estimate an emotion from the tone and pitch of the voice to analyze the emotional state of the user during an action. For example, the voice analysis technology analyzes the tone and pitch of the user's voice to estimate the emotion. The behavior recognition unit also uses the emotion estimation function to collect biometric data such as heart rate and galvanic skin response to estimate the emotion in order to analyze the emotional state of the user during an action. For example, the heart rate and galvanic skin response are used as indicators of the user's emotional state. This allows for a deeper understanding of the action by identifying the association between the emotion and the action.

[0069] The behavior awareness unit uses the emotion estimation function to provide feedback on the user's emotional state during the behavior, allowing the user to recognize the association between emotion and behavior. For example, the behavior awareness unit uses facial expression recognition technology to provide feedback on the user's emotional state during the behavior using the emotion estimation function. The behavior awareness unit also uses voice analysis technology to provide feedback on the user's emotional state during the behavior. For example, the voice analysis technology analyzes the tone and pitch of the user's voice to estimate the emotion. The behavior awareness unit also uses the emotion estimation function to collect and analyze biometric data such as heart rate and galvanic skin response to provide feedback on the user's emotional state during the behavior. For example, the heart rate and galvanic skin response are used as indicators of the user's emotional state. This allows the user to recognize the association between emotion and behavior, thereby deepening understanding of the behavior.

[0070] The behavior optimization unit uses the emotion estimation function to make optimization suggestions that take into account the user's emotional state, thereby encouraging emotionally positive behavioral changes. For example, the behavior optimization unit uses facial expression recognition technology to make optimization suggestions that take into account the user's emotional state using the emotion estimation function. The behavior optimization unit also uses voice analysis technology to make optimization suggestions that take into account the user's emotional state. For example, the voice analysis technology analyzes the tone and pitch of the user's voice to infer emotions. The behavior optimization unit also uses the emotion estimation function to collect and analyze biometric data such as heart rate and galvanic skin response to make optimization suggestions that take into account the user's emotional state. For example, the heart rate and galvanic skin response are used as indicators of the user's emotional state. In this way, it is possible to encourage emotionally positive behavioral changes by taking into account the user's emotional state.

[0071] When analyzing user behavior data, the behavior recognition unit can analyze the frequency and time periods of behavior in detail and extract specific patterns. For example, when the generation AI analyzes user behavior data, the behavior recognition unit uses time series data analysis technology to analyze the frequency and time periods of behavior in detail and extract specific patterns. Furthermore, when analyzing user behavior data, the behavior recognition unit uses a clustering algorithm to analyze the frequency and time periods of behavior in detail and extract specific patterns. For example, the clustering algorithm groups the user behavior data and extracts specific patterns. Furthermore, when the generation AI analyzes user behavior data, the behavior recognition unit generates and visually displays a heat map to analyze the frequency and time periods of behavior in detail and extract specific patterns. For example, a heat map visually displays the user behavior data and extracts specific patterns. This makes it easier to extract specific patterns by analyzing the frequency and time periods of behavior in detail.

[0072] When providing feedback on unconscious behavior, the behavior awareness unit can simultaneously provide feedback on the psychological factors behind the behavior, allowing the user to understand the root causes of the behavior. For example, when the generation AI provides feedback on unconscious behavior, the behavior awareness unit references a psychological database to simultaneously provide feedback on the psychological factors behind the behavior. Furthermore, when providing feedback on unconscious behavior to the user, the behavior awareness unit analyzes biometric data to simultaneously provide feedback on psychological factors behind the behavior, such as stress and fatigue. For example, biometric data analyzes biological data such as heart rate and galvanic skin response to identify the psychological factors behind the behavior. Furthermore, when the generation AI provides feedback on unconscious behavior, the behavior awareness unit references the user's past behavioral history and survey data to simultaneously provide feedback on the psychological factors behind the behavior. This enables more effective improvements by understanding the root causes of the behavior.

[0073] When making behavioral optimization suggestions, the behavior optimization unit also takes into account the psychological factors behind the behavior and can propose fundamental improvement measures. For example, when the generation AI makes behavioral optimization suggestions, the behavior optimization unit refers to a psychological database to consider the psychological factors behind the behavior. In addition, when making behavioral optimization suggestions to the user, the behavior optimization unit analyzes biometric data to consider psychological factors behind the behavior, such as stress and fatigue. For example, biometric data analyzes biological data such as heart rate and galvanic skin response to identify the psychological factors behind the behavior. In addition, when the generation AI makes behavioral optimization suggestions, the behavior optimization unit refers to the user's past behavioral history and survey data to consider the psychological factors behind the behavior. In this way, fundamental improvement measures can be proposed by taking psychological factors into account.

[0074] The behavior optimization unit uses the emotion estimation function to monitor changes in emotions in real time when the user performs a specific behavior and continuously suggest optimal behavioral changes. For example, the behavior optimization unit uses facial expression recognition technology to monitor changes in emotions in real time when the user performs a specific behavior using the emotion estimation function. The behavior optimization unit also uses voice analysis technology to monitor changes in emotions in real time when the user performs a specific behavior. For example, the voice analysis technology analyzes the tone and pitch of the user's voice to estimate emotions. The behavior optimization unit also uses the emotion estimation function to collect and analyze biometric data to monitor changes in emotions in real time when the user performs a specific behavior. For example, the biometric data includes data such as heart rate and galvanic skin response, and emotional changes are monitored in real time. This allows for monitoring changes in emotions in real time and continuously suggests optimal behavioral changes, thereby promoting behavioral improvement for the user.

[0075] The processing flow of the second embodiment will be briefly explained below.

[0076] Step 1: The behavior recognition unit collects user behavior data and recognizes unconscious behavior. For example, it collects location information, click data, and sensor data, and uses generation AI to identify unconscious behavior. Step 2: The behavioral awareness unit provides feedback to the user about the unconscious behavior recognized by the behavioral recognition unit. For example, feedback can be provided using text notifications, voice alerts, or visual displays, and specific behavioral patterns can be communicated to the user using a generation AI. Step 3: The behavior optimization unit makes suggestions to optimize the unconscious behaviors fed back by the behavior awareness unit. For example, it uses a generative AI to make suggestions to optimize the user's behavior, and analyzes behavioral data to propose optimal behavioral patterns.

[0077] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0078] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0079] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0080] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0081] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0082] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0083] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0084] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0085] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0086] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0087] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0088] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0089] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0090] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0091] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0095] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0096] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0111] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0118] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0127] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0128] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0129] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0130] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0131] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0132] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0133] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0134] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0135] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0136] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0137] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0138] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0139] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0140] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0141] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0142] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0143] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0144] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a behavior recognition unit that collects user behavior data and recognizes unconscious behavior; a behavior awareness unit that provides feedback to a user about the unconscious behavior recognized by the behavior recognition unit; a behavior optimization unit that makes a proposal to optimize the unconscious behavior fed back by the behavior awareness unit. A system characterized by:

2. The behavior recognition unit When analyzing the user's behavioral data, the psychological factors behind the behavior are also analyzed at the same time, and the root cause of the unconscious behavior is identified.

2. The system of claim 1.

3. The behavior recognition unit When analyzing the user's behavioral data, the frequency and time period of the behavior are analyzed in detail to extract specific patterns.

2. The system of claim 1.

4. The behavior recognition unit Analyzing the emotional state of the user during the action and identifying the relationship between the emotion and the action.

2. The system of claim 1.

5. The behavior awareness unit When providing feedback on the unconscious behavior, the psychological factors behind the behavior are also provided as feedback, allowing the user to understand the root cause of the behavior.

2. The system of claim 1.

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