system

Abstract

The system according to the embodiment aims to understand the physical and mental state of the user based on the user's comments and wellness information, and generate an appropriate response. [Solution] A system according to an embodiment includes an analysis unit, a generation unit, a collection unit, an understanding unit, and a provision unit. The analysis unit analyzes a user's utterances. The generation unit generates a response based on the utterances analyzed by the analysis unit. The collection unit collects wellness information about the user. The understanding unit understands the user's physical and mental state based on the information collected by the collection unit. The provision unit provides the response generated by the generation unit to the user.

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 not adequately grasped the user's physical and mental state based on their comments and wellness information, and have not been able to generate responses, leaving room for improvement.

[0005] The system according to the embodiment aims to understand the physical and mental state of the user based on the user's comments and wellness information, and generate an appropriate response. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, a collection unit, an understanding unit, and a provision unit. The analysis unit analyzes a user's utterances. The generation unit generates a response based on the utterances analyzed by the analysis unit. The collection unit collects wellness information about the user. The understanding unit understands the user's physical and mental state based on the information collected by the collection unit. The provision unit provides the response generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the physical and mental state of the user based on the user's comments and wellness information, and generate an appropriate response. [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) A dialogue support system according to an embodiment of the present invention is a system that conducts dialogue through a personified user interface. This dialogue support system can make users feel closer to the system and reduce feelings of loneliness. The system assists users in stabilizing their mood and promoting positive thinking based on their personal wellness information and comments. For example, when a user accesses the service, a personified character appears and initiates a dialogue. The character analyzes the user's comments and behavior and provides appropriate responses, making the user feel closer to the system. Next, the system collects the user's wellness information, including the user's heart rate, sleep patterns, and exercise volume. This information is acquired from the user's device. Based on this information, the system assesses the user's physical and mental state and provides appropriate advice. Furthermore, the system analyzes the user's comments and assists users in stabilizing their mood and promoting positive thinking. For example, if a user says, "I've been feeling depressed lately," the system might offer advice such as, "That's tough. Why don't you try doing something fun?" The system can also detect signs of stress in the user's comments and suggest relaxation methods as needed. This system not only makes users feel closer to the user, but also reduces feelings of loneliness and supports their physical and mental health. This enables the dialogue support system to analyze users' comments, generate responses, collect wellness information, understand their physical and mental state, and provide responses.

[0029] A dialogue support system according to an embodiment includes an analysis unit, a generation unit, a collection unit, a comprehension unit, and a provision unit. The analysis unit analyzes a user's utterance. For example, the analysis unit converts the user's utterance into text data using voice analysis technology. The analysis unit can also analyze the content of the utterance using text analysis technology. The analysis unit can also infer emotions from the user's utterance using emotion analysis technology. For example, the analysis unit infers the user's emotions from the tone of voice and the content of the utterance. The generation unit generates a response based on the utterance analyzed by the analysis unit. For example, the generation unit generates an appropriate response using a text generation AI (e.g., LLM). The generation unit can also generate responses that include not only text but also images and audio using a multimodal generation AI. The generation unit can also adjust the tone and content of the response based on the user's emotions. The collection unit collects wellness information from the user. For example, the collection unit collects the user's heart rate using a heart rate sensor. The collection unit can also collect the user's sleep patterns using a sleep tracker. Furthermore, the collection unit can collect the user's exercise amount using a fitness tracker. The understanding unit understands the user's physical and mental state based on the information collected by the collection unit. For example, the understanding unit can analyze heart rate data to evaluate the user's stress level. The understanding unit can also analyze sleep pattern data to evaluate the user's fatigue level. The understanding unit can also analyze exercise amount data to evaluate the user's health condition. The providing unit provides the response generated by the generating unit to the user. For example, the providing unit can display the response as a text message. The providing unit can also play the response as a voice message. The providing unit can also display a response including an image or video. This enables the dialogue support system according to the embodiment to analyze user utterances, generate responses, collect wellness information, understand the user's physical and mental state, and provide responses.

[0030] The analysis unit can analyze the user's speech and detect signs of stress. The analysis unit can convert the user's speech into text data using, for example, voice analysis technology. For example, the analysis unit can convert speech data into text data using voice recognition software. The analysis unit can also analyze the content of the speech using text analysis technology. For example, the analysis unit can analyze the content of the speech using natural language processing technology and extract specific keywords or phrases. The analysis unit can also infer emotions from the user's speech using emotion analysis technology. For example, the analysis unit can infer the user's emotions from the tone of the voice and the content of the speech. This allows the analysis unit to detect signs of stress from the user's speech. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input speech data to a generation AI and have the generation AI convert the speech data into text data.

[0031] The generation unit can suggest relaxation methods based on the user's utterances. The generation unit can suggest appropriate relaxation methods using, for example, a text generation AI (e.g., LLM). For example, the generation unit can detect signs of stress from the user's utterances and suggest relaxation methods such as deep breathing or meditation. The generation unit can also use multimodal generation AI to suggest relaxation methods that include not only text but also images and audio. For example, the generation unit can suggest relaxing music or relaxation videos. Furthermore, the generation unit can adjust the tone and content of the relaxation methods based on the user's emotions. For example, if the user is feeling stressed, the generation unit can suggest relaxation methods in a calm tone, and if the user is relaxed, the generation unit can suggest relaxation methods in a bright tone. This allows the generation unit to suggest relaxation methods based on the user's utterances. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's utterance data into the generation AI and have the generation AI suggest relaxation methods.

[0032] The collection unit can collect wellness information on the user's heart rate, sleep pattern, and activity amount. The collection unit, for example, collects the user's heart rate using a heart rate sensor. For example, the collection unit can collect heart rate data using a smartwatch. The collection unit can also collect the user's sleep pattern using a sleep tracker. For example, the collection unit can collect sleep data using a smartphone app. The collection unit can also collect the user's activity amount using a fitness tracker. For example, the collection unit can collect step count data using a pedometer. This allows the collection unit to collect wellness information on the user's heart rate, sleep pattern, and activity amount. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input heart rate data to a generation AI and cause the generation AI to analyze the heart rate data.

[0033] The grasping unit can grasp the user's physical and mental state based on the collected wellness information. The grasping unit, for example, analyzes heart rate data to evaluate the user's stress level. For example, the grasping unit analyzes heart rate fluctuations to evaluate the stress level. The grasping unit can also analyze sleep pattern data to evaluate the user's fatigue level. For example, the grasping unit evaluates sleep quality and evaluates fatigue level. Furthermore, the grasping unit can analyze exercise amount data to evaluate the user's health state. For example, the grasping unit evaluates the frequency and intensity of exercise to evaluate the health state. This allows the grasping unit to grasp the user's physical and mental state based on the collected wellness information. Some or all of the above-mentioned processing in the grasping unit may be performed using AI, for example, or may be performed without using AI. For example, the grasping unit can input heart rate data to a generation AI and cause the generation AI to analyze the heart rate data.

[0034] The providing unit can provide the generated response to the user. The providing unit, for example, displays the response as a text message. For example, the providing unit displays the text message in a chat window. The providing unit can also play the response as a voice message. For example, the providing unit plays the voice message using voice synthesis technology. The providing unit can also display the response including an image or a video. For example, the providing unit displays relaxing music or a relaxation video. This allows the providing unit to provide the generated response to the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated response data to a generation AI and cause the generation AI to generate the response data.

[0035] The analysis unit can analyze the user's past speech history and select the optimal analysis algorithm. For example, the analysis unit can analyze the user's past speech history and identify frequently used words and phrases. For example, the analysis unit can analyze past chat logs and extract words and phrases frequently used by the user. The analysis unit can also analyze the user's past speech patterns and select the most appropriate analysis method. For example, the analysis unit can analyze changes in the tone and content of the user's speech and select an appropriate analysis algorithm. Furthermore, the analysis unit can select an analysis algorithm related to a specific emotion or theme from the user's past speech history. For example, the analysis unit can analyze speech from a time when the user felt stressed and select an analysis algorithm related to stress. This allows the analysis unit to analyze the user's past speech history and select the optimal analysis algorithm. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's speech history data into a generation AI and have the generation AI analyze the speech history.

[0036] The analysis unit can filter comments based on the user's current living situation and areas of interest when analyzing comments. For example, if the user inputs their current living situation, the analysis unit filters the comments based on that information. For example, if the user inputs their work situation or home situation, the analysis unit filters the comments based on that information. The analysis unit can also prioritize analysis of related comments based on the user's areas of interest. For example, if the user inputs their hobbies or topics of interest, the analysis unit filters the comments based on that information. Furthermore, the analysis unit can eliminate unnecessary information based on the user's living situation and areas of interest to improve the accuracy of the analysis. For example, the analysis unit can eliminate comments that are not related to the user's current living situation and areas of interest to improve the accuracy of the analysis. This allows the analysis unit to filter comments based on the user's current living situation and areas of interest when analyzing comments. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's living situation data to a generation AI and have the generation AI analyze the living situation data.

[0037] When analyzing utterances, the analysis unit can prioritize analyzing highly relevant utterances by taking into account the user's geographical location information. For example, if the user is in a specific location, the analysis unit prioritizes analyzing utterances related to that location. For example, if the user is traveling, the analysis unit prioritizes analyzing utterances related to the travel. The analysis unit can also reflect region-specific information in the analysis based on the user's geographical location information. For example, if the user is in a specific city, the analysis unit prioritizes analyzing information related to that city. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing utterances related to the user's current location. For example, if the user is commuting, the analysis unit prioritizes analyzing utterances related to commuting. This allows the analysis unit to prioritize analyzing highly relevant utterances by taking into account the user's geographical location information when analyzing utterances. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's geographical location data into a generation AI and cause the generation AI to analyze the geographical location data.

[0038] The analysis unit can analyze the user's social media activity and analyze related comments when analyzing comments. The analysis unit, for example, analyzes the comments based on words and phrases frequently used by the user on social media. For example, the analysis unit can analyze the content of the user's social media posts and extract frequently used words and phrases. The analysis unit can also prioritize analysis of comments related to a specific theme from the user's social media activity. For example, if the user frequently posts about health, the analysis unit can prioritize analysis of health-related comments. The analysis unit can also analyze the user's social media activity pattern and select an optimal analysis method. For example, the analysis unit can analyze the user's posting frequency and the content of the comments and select an appropriate analysis algorithm. This allows the analysis unit to analyze the user's social media activity and analyze related comments when analyzing comments. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's social media data into a generation AI and have the generation AI analyze the social media data.

[0039] When generating a response, the generation unit can adjust the level of detail of the response based on the importance of the utterance. The generation unit generates an appropriate response using, for example, a text generation AI (e.g., LLM). For example, the generation unit evaluates the importance of the user's utterance and generates a detailed response for an important utterance. The generation unit can also generate a concise response for a general utterance. Furthermore, the generation unit can also generate a quick response for a utterance with high urgency. This allows the generation unit to adjust the level of detail of the response based on the importance of the utterance. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's utterance data into the generation AI and cause the generation AI to analyze the utterance data.

[0040] When generating a response, the generation unit can apply different response algorithms depending on the category of the utterance. The generation unit generates an appropriate response using, for example, a text generation AI (e.g., LLM). For example, if the user's utterance is related to health, the generation unit can apply a health-related response algorithm. Furthermore, if the user's utterance is related to work, the generation unit can also apply a work-related response algorithm. Furthermore, if the user's utterance is related to hobbies, the generation unit can also apply a hobby-related response algorithm. This allows the generation unit to apply different response algorithms depending on the category of the utterance. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's utterance data to the generation AI and cause the generation AI to analyze the utterance data.

[0041] When generating responses, the generation unit can determine the priority of responses based on the time of submission of the utterance. The generation unit generates appropriate responses using, for example, a text generation AI (e.g., LLM). For example, the generation unit prioritizes generating responses to utterances made recently by the user. The generation unit can also generate responses as needed for utterances made in the past by the user. Furthermore, the generation unit can generate responses at an appropriate time for utterances made by the user during a specific time period. This allows the generation unit to determine the priority of responses based on the time of submission of the utterance. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user utterance data to the generation AI and have the generation AI analyze the utterance data.

[0042] When generating responses, the generation unit can adjust the order of responses based on the relevance of the utterances. The generation unit generates appropriate responses using, for example, a text generation AI (e.g., LLM). For example, the generation unit prioritizes generating a response if the user's utterance is highly relevant. The generation unit can also postpone generating a response if the user's utterance is general. Furthermore, the generation unit can quickly generate a response if the user's utterance is urgent. This allows the generation unit to adjust the order of responses based on the relevance of the utterances. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's utterance data into the generation AI and have the generation AI analyze the utterance data.

[0043] The collection unit can analyze the user's past wellness information and select the optimal collection method. The collection unit, for example, selects the optimal collection method based on the user's past heart rate data. For example, the collection unit analyzes the past heart rate data and determines the optimal collection frequency and timing. The collection unit can also analyze the user's past sleep patterns and select the optimal collection method. For example, the collection unit analyzes past sleep data and determines the optimal collection method. Furthermore, the collection unit can also select the optimal collection method based on the user's past exercise data. For example, the collection unit analyzes past exercise data and determines the optimal collection method. In this way, the collection unit can analyze the user's past wellness information and select the optimal collection method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past wellness data to a generation AI and have the generation AI analyze the data.

[0044] The collection unit can filter the wellness information based on the user's current living situation and areas of interest when collecting the wellness information. For example, if the user inputs their current living situation, the collection unit filters the wellness information based on that information. For example, if the user inputs their work situation or home situation, the collection unit filters the wellness information based on that information. The collection unit can also prioritize collecting related wellness information based on the user's areas of interest. For example, if the user inputs their hobbies or topics of interest, the collection unit filters the wellness information based on that information. Furthermore, the collection unit can eliminate unnecessary information based on the user's living situation and areas of interest to improve the accuracy of the collection. For example, the collection unit eliminates information that is not related to the user's current living situation and areas of interest to improve the accuracy of the collection. This allows the collection unit to filter the wellness information based on the user's current living situation and areas of interest when collecting the wellness information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's living situation data to a generation AI and have the generation AI analyze the living situation data.

[0045] When collecting wellness information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collecting wellness information related to the location. For example, when the user is traveling, the collection unit prioritizes collecting wellness information related to the travel. The collection unit can also collect region-specific information based on the user's geographical location information. For example, when the user is in a specific city, the collection unit prioritizes collecting wellness information related to the city. Furthermore, when the user is traveling, the collection unit can prioritize collecting wellness information related to the user's current location. For example, when the user is commuting, the collection unit prioritizes collecting wellness information related to commuting. In this way, when collecting wellness information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input the user's geographical location data to a generation AI and cause the generation AI to analyze the geographical location data.

[0046] The collection unit can analyze the user's social media activities and collect related information when collecting wellness information. The collection unit can collect wellness information based on, for example, content frequently posted by the user on social media. For example, the collection unit can analyze the user's social media posts and extract frequently used words and phrases. The collection unit can also preferentially collect wellness information related to a specific theme from the user's social media activities. For example, if the user frequently posts about health, the collection unit can preferentially collect health-related wellness information. The collection unit can also analyze the user's social media activity patterns and select an optimal collection method. For example, the collection unit can analyze the user's posting frequency and the content of comments and select an appropriate collection algorithm. This allows the collection unit to analyze the user's social media activities and collect related information when collecting wellness information. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to analyze the social media data.

[0047] When grasping the mental and physical state, the grasping unit can predict the current state by referring to past wellness information. The grasping unit predicts the current mental and physical state based on, for example, the user's past heart rate data. For example, the grasping unit analyzes past heart rate data to predict the current stress level. The grasping unit can also predict the current mental and physical state by referring to the user's past sleep patterns. For example, the grasping unit analyzes past sleep data to predict the current fatigue level. Furthermore, the grasping unit can predict the current mental and physical state based on the user's past exercise amount data. For example, the grasping unit analyzes past exercise data to predict the current health state. In this way, when grasping the mental and physical state, the grasping unit can predict the current state by referring to past wellness information. Some or all of the above-described processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit can input past wellness data to the generation AI and cause the generation AI to analyze the data.

[0048] The grasping unit can apply different grasping methods to each category of wellness information when grasping the mental and physical state. For example, the grasping unit applies a method of analyzing heart rate fluctuations to heart rate data. For example, the grasping unit analyzes heart rate fluctuations and evaluates stress levels. The grasping unit can also apply a method of evaluating sleep quality to sleep patterns. For example, the grasping unit evaluates sleep quality and evaluates fatigue levels. Furthermore, the grasping unit can apply a method of evaluating exercise frequency and intensity to exercise amount data. For example, the grasping unit evaluates exercise frequency and intensity and evaluates health conditions. This allows the grasping unit to apply different grasping methods to each category of wellness information when grasping the mental and physical state. Some or all of the above-mentioned processing in the grasping unit may be performed using AI, for example, or may be performed without using AI. For example, the grasping unit can input wellness data to a generation AI and cause the generation AI to analyze the data.

[0049] The grasping unit can analyze changes in the state based on the time of submission of the wellness information when grasping the mental and physical state. For example, the grasping unit analyzes changes in heart rate based on the time of submission of the user's heart rate data. For example, the grasping unit analyzes timestamps of the heart rate data and evaluates heart rate fluctuations. The grasping unit can also analyze changes in sleep quality based on the time of submission of the user's sleep pattern. For example, the grasping unit analyzes timestamps of the sleep data and evaluates fluctuations in sleep quality. Furthermore, the grasping unit can analyze changes in exercise frequency and intensity based on the time of submission of the user's exercise amount data. For example, the grasping unit analyzes timestamps of the exercise data and evaluates fluctuations in exercise frequency and intensity. In this way, the grasping unit can analyze changes in the state based on the time of submission of the wellness information when grasping the mental and physical state. Some or all of the above-described processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit can input wellness data to a generation AI and cause the generation AI to analyze the data.

[0050] The assessment unit can analyze the mental and physical state by referring to related market data. For example, the assessment unit compares the user's heart rate data with that of other users and analyzes it based on the market data. For example, the assessment unit compares the user's heart rate data with that of other users and evaluates the user's heart rate fluctuations. The assessment unit can also compare the user's sleep pattern with that of other users and analyze it based on the market data. For example, the assessment unit compares the user's sleep data with that of other users and evaluates the user's sleep quality. Furthermore, the assessment unit can compare the user's exercise data with that of other users and analyze it based on the market data. For example, the assessment unit compares the user's exercise data with that of other users and evaluates the frequency and intensity of the user's exercise. This allows the assessment unit to analyze the mental and physical state by referring to related market data. Some or all of the above-described processing in the assessment unit may be performed using AI, for example, or without AI. For example, the assessment unit may input market data to a generation AI and cause the generation AI to analyze the data.

[0051] When providing a response, the providing unit can select the optimal response method by referring to the user's past response history. The providing unit, for example, selects the optimal response method based on the user's past response history. For example, the providing unit analyzes past chat logs to identify the user's preferred response format. The providing unit can also analyze the user's past response patterns to select the optimal response method. For example, the providing unit analyzes the content of past responses to identify the user's preferred tone and style. Furthermore, the providing unit can select a response method related to a specific emotion or theme from the user's past response history. For example, the providing unit provides a calm response if the user is feeling stressed, and a cheerful response if the user is relaxed, based on the past response history. This allows the providing unit to select the optimal response method by referring to the user's past response history when providing a response. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may input past response history data into a generation AI and have the generation AI analyze the data.

[0052] The providing unit can customize the providing means based on the user's current living situation when providing a response. For example, if the user inputs their current living situation, the providing unit customizes the response means based on that information. For example, if the user inputs their work situation or home situation, the providing unit customizes the response means based on that information. The providing unit can also provide a relevant response means based on the user's living situation. For example, if the user inputs their health status, the providing unit provides a health-related response means based on that information. Furthermore, the providing unit can eliminate unnecessary information based on the user's living situation to improve the accuracy of the response. For example, the providing unit eliminates information that is not relevant to the user's current living situation to improve the accuracy of the response. This allows the providing unit to customize the providing means based on the user's current living situation when providing a response. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's living situation data to a generation AI and cause the generation AI to analyze the living situation data.

[0053] The providing unit can select the optimal response method by taking into consideration the user's geographical location information when providing a response. For example, if the user is in a specific location, the providing unit provides a response related to that location. For example, if the user is traveling, the providing unit provides a travel-related response. The providing unit can also provide a region-specific response based on the user's geographical location information. For example, if the user is in a specific city, the providing unit provides a response related to that city. Furthermore, if the user is traveling, the providing unit can provide a response related to the user's current location. For example, if the user is commuting, the providing unit provides a commute-related response. This allows the providing unit to select the optimal response method by taking into consideration the user's geographical location information when providing a response. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location data into a generating AI and cause the generating AI to analyze the geographical location data.

[0054] The providing unit can analyze the user's social media activity and suggest a means of response when providing a response. The providing unit can suggest a means of response based on, for example, words and phrases frequently used by the user on social media. For example, the providing unit can analyze the content of the user's social media posts and extract frequently used words and phrases. The providing unit can also suggest a means of response related to a specific theme from the user's social media activity. For example, if the user frequently posts about health, the providing unit can suggest a means of response related to health. The providing unit can also analyze the user's social media activity patterns and suggest an optimal means of response. For example, the providing unit can analyze the user's posting frequency and the content of comments and suggest an appropriate means of response. In this way, the providing unit can analyze the user's social media activity and suggest a means of response when providing a response. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to analyze the social media data.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] When analyzing a user's utterances, the analysis unit learns the user's past utterance patterns and can more accurately understand the intention of the utterances. For example, the analysis unit learns the meanings of specific phrases and words used by the user in the past and accurately understands the intention when the same phrases are used again. The analysis unit can also take into account the context of the user's utterances to more deeply understand the intention of the utterances. Furthermore, the analysis unit can analyze the tone and emotion of the user's utterances and can more accurately understand the intention of the utterances. This allows the analysis unit to more accurately analyze the user's utterances and generate appropriate responses.

[0057] The generation unit can take into account the user's past history of relaxation methods when suggesting relaxation methods based on the user's utterances. For example, the generation unit can record relaxation methods that the user has tried in the past and suggest the same method again if it was found to be effective. The generation unit can also evaluate the effectiveness of relaxation methods that the user has tried in the past and prioritize suggesting methods that were more effective. Furthermore, the generation unit can take into account the user's current mental and physical state and suggest the most appropriate relaxation method. This allows the generation unit to suggest more effective relaxation methods based on the user's utterances.

[0058] When collecting a user's wellness information, the collection unit can adjust the collection timing based on the user's lifestyle rhythm. For example, the collection unit records the user's usual wake-up time and bedtime, and collects the user's heart rate and sleep pattern according to those times. The collection unit can also collect the user's heart rate and exercise amount after exercise, taking into account the user's exercise habits. Furthermore, the collection unit can also collect the user's heart rate and blood sugar level after meals, taking into account the user's eating patterns. This enables the collection unit to collect wellness information that is tailored to the user's lifestyle rhythm.

[0059] The assessment unit can refer to the user's past health history when assessing the user's physical and mental condition based on the collected wellness information. For example, the assessment unit can refer to the user's past medical history and treatment history to more accurately assess the user's current physical and mental condition. The assessment unit can also refer to the user's past health checkup results to assess the user's current health condition. Furthermore, the assessment unit can refer to the health history of the user's family and evaluate the physical and mental condition taking genetic factors into consideration. This enables the assessment unit to more accurately assess the physical and mental condition based on the collected wellness information.

[0060] When providing the generated response to the user, the providing unit can adjust the providing method taking into account the characteristics of the user's device. For example, if the user is using a smartphone, the providing unit can display the response as a text message. Also, if the user is using a smart speaker, the providing unit can play the response as a voice message. Furthermore, if the user is using a smart watch, the providing unit can provide the response as a short text message or vibration. This allows the providing unit to provide a response tailored to the characteristics of the user's device.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The analysis unit analyzes the user's utterances. For example, the analysis unit converts the user's utterances into text data using voice analysis technology. It can also analyze the content of the utterances using text analysis technology and estimate emotions from the user's utterances using emotion analysis technology. The user's emotions are estimated from the tone of the voice and the content of the utterances. Step 2: The generator generates a response based on the utterances analyzed by the analyzer. For example, the generator uses text generation AI (e.g., LLM) to generate an appropriate response. It can also use multimodal generation AI to generate responses that include not only text but also images and audio. It can also adjust the tone and content of the response based on the user's emotions. Step 3: The collection unit collects the user's wellness information. For example, the collection unit may collect the user's heart rate using a heart rate sensor, collect the user's sleep pattern using a sleep tracker, or collect the user's exercise amount using a fitness tracker. Step 4: The determination unit determines the user's physical and mental state based on the information collected by the collection unit. For example, the determination unit can analyze the heart rate data to evaluate the user's stress level, analyze the sleep pattern data to evaluate the user's fatigue level, and analyze the exercise amount data to evaluate the user's health condition. Step 5: The providing unit provides the response generated by the generating unit to the user. For example, the providing unit may display the response as a text message, play the response as a voice message, or display a response including an image or video.

[0063] (Example 2) A dialogue support system according to an embodiment of the present invention is a system that conducts dialogue through a personified user interface. This dialogue support system can make users feel closer to the system and reduce feelings of loneliness. The system assists users in stabilizing their mood and promoting positive thinking based on their personal wellness information and comments. For example, when a user accesses the service, a personified character appears and initiates a dialogue. The character analyzes the user's comments and behavior and provides appropriate responses, making the user feel closer to the system. Next, the system collects the user's wellness information, including the user's heart rate, sleep patterns, and exercise volume. This information is acquired from the user's device. Based on this information, the system assesses the user's physical and mental state and provides appropriate advice. Furthermore, the system analyzes the user's comments and assists users in stabilizing their mood and promoting positive thinking. For example, if a user says, "I've been feeling depressed lately," the system might offer advice such as, "That's tough. Why don't you try doing something fun?" The system can also detect signs of stress in the user's comments and suggest relaxation methods as needed. This system not only makes users feel closer to the user, but also reduces feelings of loneliness and supports their physical and mental health. This enables the dialogue support system to analyze users' comments, generate responses, collect wellness information, understand their physical and mental state, and provide responses.

[0064] A dialogue support system according to an embodiment includes an analysis unit, a generation unit, a collection unit, a comprehension unit, and a provision unit. The analysis unit analyzes a user's utterance. For example, the analysis unit converts the user's utterance into text data using voice analysis technology. The analysis unit can also analyze the content of the utterance using text analysis technology. The analysis unit can also infer emotions from the user's utterance using emotion analysis technology. For example, the analysis unit infers the user's emotions from the tone of voice and the content of the utterance. The generation unit generates a response based on the utterance analyzed by the analysis unit. For example, the generation unit generates an appropriate response using a text generation AI (e.g., LLM). The generation unit can also generate responses that include not only text but also images and audio using a multimodal generation AI. The generation unit can also adjust the tone and content of the response based on the user's emotions. The collection unit collects wellness information from the user. For example, the collection unit collects the user's heart rate using a heart rate sensor. The collection unit can also collect the user's sleep patterns using a sleep tracker. Furthermore, the collection unit can collect the user's exercise amount using a fitness tracker. The understanding unit understands the user's physical and mental state based on the information collected by the collection unit. For example, the understanding unit can analyze heart rate data to evaluate the user's stress level. The understanding unit can also analyze sleep pattern data to evaluate the user's fatigue level. The understanding unit can also analyze exercise amount data to evaluate the user's health condition. The providing unit provides the response generated by the generating unit to the user. For example, the providing unit can display the response as a text message. The providing unit can also play the response as a voice message. The providing unit can also display a response including an image or video. This enables the dialogue support system according to the embodiment to analyze user utterances, generate responses, collect wellness information, understand the user's physical and mental state, and provide responses.

[0065] The analysis unit can analyze the user's speech and detect signs of stress. The analysis unit can convert the user's speech into text data using, for example, voice analysis technology. For example, the analysis unit can convert speech data into text data using voice recognition software. The analysis unit can also analyze the content of the speech using text analysis technology. For example, the analysis unit can analyze the content of the speech using natural language processing technology and extract specific keywords or phrases. The analysis unit can also infer emotions from the user's speech using emotion analysis technology. For example, the analysis unit can infer the user's emotions from the tone of the voice and the content of the speech. This allows the analysis unit to detect signs of stress from the user's speech. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input speech data to a generation AI and have the generation AI convert the speech data into text data.

[0066] The generation unit can suggest relaxation methods based on the user's utterances. The generation unit can suggest appropriate relaxation methods using, for example, a text generation AI (e.g., LLM). For example, the generation unit can detect signs of stress from the user's utterances and suggest relaxation methods such as deep breathing or meditation. The generation unit can also use multimodal generation AI to suggest relaxation methods that include not only text but also images and audio. For example, the generation unit can suggest relaxing music or relaxation videos. Furthermore, the generation unit can adjust the tone and content of the relaxation methods based on the user's emotions. For example, if the user is feeling stressed, the generation unit can suggest relaxation methods in a calm tone, and if the user is relaxed, the generation unit can suggest relaxation methods in a bright tone. This allows the generation unit to suggest relaxation methods based on the user's utterances. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's utterance data into the generation AI and have the generation AI suggest relaxation methods.

[0067] The collection unit can collect wellness information on the user's heart rate, sleep pattern, and activity amount. The collection unit, for example, collects the user's heart rate using a heart rate sensor. For example, the collection unit can collect heart rate data using a smartwatch. The collection unit can also collect the user's sleep pattern using a sleep tracker. For example, the collection unit can collect sleep data using a smartphone app. The collection unit can also collect the user's activity amount using a fitness tracker. For example, the collection unit can collect step count data using a pedometer. This allows the collection unit to collect wellness information on the user's heart rate, sleep pattern, and activity amount. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input heart rate data to a generation AI and cause the generation AI to analyze the heart rate data.

[0068] The grasping unit can grasp the user's physical and mental state based on the collected wellness information. The grasping unit, for example, analyzes heart rate data to evaluate the user's stress level. For example, the grasping unit analyzes heart rate fluctuations to evaluate the stress level. The grasping unit can also analyze sleep pattern data to evaluate the user's fatigue level. For example, the grasping unit evaluates sleep quality and evaluates fatigue level. Furthermore, the grasping unit can analyze exercise amount data to evaluate the user's health state. For example, the grasping unit evaluates the frequency and intensity of exercise to evaluate the health state. This allows the grasping unit to grasp the user's physical and mental state based on the collected wellness information. Some or all of the above-mentioned processing in the grasping unit may be performed using AI, for example, or may be performed without using AI. For example, the grasping unit can input heart rate data to a generation AI and cause the generation AI to analyze the heart rate data.

[0069] The providing unit can provide the generated response to the user. The providing unit, for example, displays the response as a text message. For example, the providing unit displays the text message in a chat window. The providing unit can also play the response as a voice message. For example, the providing unit plays the voice message using voice synthesis technology. The providing unit can also display the response including an image or a video. For example, the providing unit displays relaxing music or a relaxation video. This allows the providing unit to provide the generated response to the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated response data to a generation AI and cause the generation AI to generate the response data.

[0070] The analysis unit can estimate the user's emotions and adjust the analysis method of the utterances based on the estimated user emotions. The analysis unit can, for example, convert the user's utterances into text data using voice analysis technology. For example, the analysis unit can convert voice data into text data using voice recognition software. The analysis unit can also analyze the content of the utterances using text analysis technology. For example, the analysis unit can analyze the content of the utterances using natural language processing technology and extract specific keywords and phrases. The analysis unit can also estimate the emotion from the user's utterances using emotion analysis technology. For example, the analysis unit can estimate the user's emotion from the tone of the voice and the content of the utterances. This allows the analysis unit to estimate the user's emotions and adjust the analysis method of the utterances based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can simplify the analysis algorithm and quickly produce results. On the other hand, if the user is relaxed, the analysis unit can perform a detailed analysis and provide deeper insights. Furthermore, if the user is excited, the analysis unit can visually display the analysis results in an easy-to-understand manner. This allows the analysis unit to adjust the analysis method of the utterances based on the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and have the generation AI analyze the emotion data.

[0071] The analysis unit can analyze the user's past speech history and select the optimal analysis algorithm. For example, the analysis unit can analyze the user's past speech history and identify frequently used words and phrases. For example, the analysis unit can analyze past chat logs and extract words and phrases frequently used by the user. The analysis unit can also analyze the user's past speech patterns and select the most appropriate analysis method. For example, the analysis unit can analyze changes in the tone and content of the user's speech and select an appropriate analysis algorithm. Furthermore, the analysis unit can select an analysis algorithm related to a specific emotion or theme from the user's past speech history. For example, the analysis unit can analyze speech from a time when the user felt stressed and select an analysis algorithm related to stress. This allows the analysis unit to analyze the user's past speech history and select the optimal analysis algorithm. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's speech history data into a generation AI and have the generation AI analyze the speech history.

[0072] The analysis unit can filter comments based on the user's current living situation and areas of interest when analyzing comments. For example, if the user inputs their current living situation, the analysis unit filters the comments based on that information. For example, if the user inputs their work situation or home situation, the analysis unit filters the comments based on that information. The analysis unit can also prioritize analysis of related comments based on the user's areas of interest. For example, if the user inputs their hobbies or topics of interest, the analysis unit filters the comments based on that information. Furthermore, the analysis unit can eliminate unnecessary information based on the user's living situation and areas of interest to improve the accuracy of the analysis. For example, the analysis unit can eliminate comments that are not related to the user's current living situation and areas of interest to improve the accuracy of the analysis. This allows the analysis unit to filter comments based on the user's current living situation and areas of interest when analyzing comments. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's living situation data to a generation AI and have the generation AI analyze the living situation data.

[0073] The analysis unit can estimate the user's emotions and determine the priority of utterances to be analyzed based on the estimated user's emotions. The analysis unit can, for example, convert the user's utterances into text data using voice analysis technology. For example, the analysis unit can convert voice data into text data using voice recognition software. The analysis unit can also analyze the content of the utterances using text analysis technology. For example, the analysis unit can analyze the content of the utterances using natural language processing technology and extract specific keywords or phrases. The analysis unit can also estimate the user's emotions from the user's utterances using emotion analysis technology. For example, the analysis unit can estimate the user's emotions from the tone of the voice and the content of the utterances. This allows the analysis unit to estimate the user's emotions and determine the priority of utterances to be analyzed based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing utterances related to stress. Also, if the user is relaxed, the analysis unit can prioritize analyzing utterances related to relaxation. Furthermore, if the user is excited, the analysis unit can prioritize analyzing utterances related to excitement. This allows the analysis unit to prioritize utterances based on the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and have the generation AI analyze the emotion data.

[0074] When analyzing utterances, the analysis unit can prioritize analyzing highly relevant utterances by taking into account the user's geographical location information. For example, if the user is in a specific location, the analysis unit prioritizes analyzing utterances related to that location. For example, if the user is traveling, the analysis unit prioritizes analyzing utterances related to the travel. The analysis unit can also reflect region-specific information in the analysis based on the user's geographical location information. For example, if the user is in a specific city, the analysis unit prioritizes analyzing information related to that city. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing utterances related to the user's current location. For example, if the user is commuting, the analysis unit prioritizes analyzing utterances related to commuting. This allows the analysis unit to prioritize analyzing highly relevant utterances by taking into account the user's geographical location information when analyzing utterances. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's geographical location data into a generation AI and cause the generation AI to analyze the geographical location data.

[0075] The analysis unit can analyze the user's social media activity and analyze related comments when analyzing comments. The analysis unit, for example, analyzes the comments based on words and phrases frequently used by the user on social media. For example, the analysis unit can analyze the content of the user's social media posts and extract frequently used words and phrases. The analysis unit can also prioritize analysis of comments related to a specific theme from the user's social media activity. For example, if the user frequently posts about health, the analysis unit can prioritize analysis of health-related comments. The analysis unit can also analyze the user's social media activity pattern and select an optimal analysis method. For example, the analysis unit can analyze the user's posting frequency and the content of the comments and select an appropriate analysis algorithm. This allows the analysis unit to analyze the user's social media activity and analyze related comments when analyzing comments. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's social media data into a generation AI and have the generation AI analyze the social media data.

[0076] The generation unit can estimate the user's emotions and adjust the way the response is expressed based on the estimated user's emotions. The generation unit generates an appropriate response using, for example, a text generation AI (e.g., LLM). For example, the generation unit estimates the user's emotions and generates a response using a calm expression when the user is relaxed. The generation unit can also generate a response including encouraging words when the user is stressed. Furthermore, the generation unit can generate a response using an expression that shows empathy when the user is excited. This allows the generation unit to adjust the way the response is expressed based on the user's emotions. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI analyze the emotion data.

[0077] When generating a response, the generation unit can adjust the level of detail of the response based on the importance of the utterance. The generation unit generates an appropriate response using, for example, a text generation AI (e.g., LLM). For example, the generation unit evaluates the importance of the user's utterance and generates a detailed response for an important utterance. The generation unit can also generate a concise response for a general utterance. Furthermore, the generation unit can also generate a quick response for a utterance with high urgency. This allows the generation unit to adjust the level of detail of the response based on the importance of the utterance. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's utterance data into the generation AI and cause the generation AI to analyze the utterance data.

[0078] When generating a response, the generation unit can apply different response algorithms depending on the category of the utterance. The generation unit generates an appropriate response using, for example, a text generation AI (e.g., LLM). For example, if the user's utterance is related to health, the generation unit can apply a health-related response algorithm. Furthermore, if the user's utterance is related to work, the generation unit can also apply a work-related response algorithm. Furthermore, if the user's utterance is related to hobbies, the generation unit can also apply a hobby-related response algorithm. This allows the generation unit to apply different response algorithms depending on the category of the utterance. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's utterance data to the generation AI and cause the generation AI to analyze the utterance data.

[0079] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated user emotions. The generation unit generates an appropriate response using, for example, a text generation AI (e.g., LLM). For example, the generation unit estimates the user's emotions and generates a short, to-the-point response when the user is in a hurry. The generation unit can also generate a longer response with detailed explanations when the user is relaxed. Furthermore, the generation unit can generate a response with visually stimulating effects when the user is excited. This allows the generation unit to adjust the length of the response based on the user's emotions. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI analyze the emotion data.

[0080] When generating responses, the generation unit can determine the priority of responses based on the time of submission of the utterance. The generation unit generates appropriate responses using, for example, a text generation AI (e.g., LLM). For example, the generation unit prioritizes generating responses to utterances made recently by the user. The generation unit can also generate responses as needed for utterances made in the past by the user. Furthermore, the generation unit can generate responses at an appropriate time for utterances made by the user during a specific time period. This allows the generation unit to determine the priority of responses based on the time of submission of the utterance. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user utterance data to the generation AI and have the generation AI analyze the utterance data.

[0081] When generating responses, the generation unit can adjust the order of responses based on the relevance of the utterances. The generation unit generates appropriate responses using, for example, a text generation AI (e.g., LLM). For example, the generation unit prioritizes generating a response if the user's utterance is highly relevant. The generation unit can also postpone generating a response if the user's utterance is general. Furthermore, the generation unit can quickly generate a response if the user's utterance is urgent. This allows the generation unit to adjust the order of responses based on the relevance of the utterances. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's utterance data into the generation AI and have the generation AI analyze the utterance data.

[0082] The collection unit can estimate the user's emotions and adjust the timing of collecting wellness information based on the estimated user's emotions. The collection unit, for example, collects the user's heart rate using a heart rate sensor. For example, the collection unit can collect heart rate data using a smartwatch. The collection unit can also collect the user's sleep patterns using a sleep tracker. For example, the collection unit can collect sleep data using a smartphone app. The collection unit can also collect the user's exercise amount using a fitness tracker. For example, the collection unit can collect step count data using a pedometer. This allows the collection unit to collect wellness information including the user's heart rate, sleep patterns, and exercise amount. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input heart rate data to a generation AI and cause the generation AI to analyze the heart rate data.

[0083] The collection unit can analyze the user's past wellness information and select the optimal collection method. The collection unit can select the optimal collection method based on the user's past heart rate data, for example. For example, the collection unit can analyze the past heart rate data and determine the optimal collection frequency and timing. The collection unit can also analyze the user's past sleep patterns and select the optimal collection method. For example, the collection unit can analyze the past sleep data and determine the optimal collection method. Furthermore, the collection unit can select the optimal collection method based on the user's past exercise data. For example, the collection unit can analyze the past exercise data and determine the optimal collection method. In this way, the collection unit can analyze the user's past wellness information and select the optimal collection method. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the past wellness data to a generation AI and have the generation AI analyze the data.

[0084] The collection unit can filter the wellness information based on the user's current living situation and areas of interest when collecting the wellness information. For example, if the user inputs their current living situation, the collection unit filters the wellness information based on that information. For example, if the user inputs their work situation or home situation, the collection unit filters the wellness information based on that information. The collection unit can also prioritize collecting related wellness information based on the user's areas of interest. For example, if the user inputs their hobbies or topics of interest, the collection unit filters the wellness information based on that information. Furthermore, the collection unit can eliminate unnecessary information based on the user's living situation and areas of interest to improve the accuracy of the collection. For example, the collection unit eliminates information that is not related to the user's current living situation and areas of interest to improve the accuracy of the collection. This allows the collection unit to filter the wellness information based on the user's current living situation and areas of interest when collecting the wellness information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's living situation data to a generation AI and have the generation AI analyze the living situation data.

[0085] The collection unit can estimate the user's emotions and prioritize wellness information to be collected based on the estimated user emotions. The collection unit, for example, collects the user's heart rate using a heart rate sensor. For example, the collection unit can collect heart rate data using a smartwatch. The collection unit can also collect the user's sleep patterns using a sleep tracker. For example, the collection unit can collect sleep data using a smartphone app. The collection unit can also collect the user's exercise amount using a fitness tracker. For example, the collection unit can collect step count data using a pedometer. This allows the collection unit to collect wellness information including the user's heart rate, sleep patterns, and exercise amount. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input heart rate data to a generation AI and cause the generation AI to analyze the heart rate data.

[0086] When collecting wellness information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collecting wellness information related to the location. For example, when the user is traveling, the collection unit prioritizes collecting wellness information related to the travel. The collection unit can also collect region-specific information based on the user's geographical location information. For example, when the user is in a specific city, the collection unit prioritizes collecting wellness information related to the city. Furthermore, when the user is traveling, the collection unit can prioritize collecting wellness information related to the user's current location. For example, when the user is commuting, the collection unit prioritizes collecting wellness information related to commuting. In this way, when collecting wellness information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input the user's geographical location data to a generation AI and cause the generation AI to analyze the geographical location data.

[0087] The collection unit can analyze the user's social media activities and collect related information when collecting wellness information. The collection unit can collect wellness information based on, for example, content frequently posted by the user on social media. For example, the collection unit can analyze the user's social media posts and extract frequently used words and phrases. The collection unit can also preferentially collect wellness information related to a specific theme from the user's social media activities. For example, if the user frequently posts about health, the collection unit can preferentially collect health-related wellness information. The collection unit can also analyze the user's social media activity patterns and select an optimal collection method. For example, the collection unit can analyze the user's posting frequency and the content of comments and select an appropriate collection algorithm. This allows the collection unit to analyze the user's social media activities and collect related information when collecting wellness information. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to analyze the social media data.

[0088] The grasping unit can estimate the user's emotions and adjust the method of grasping the mental and physical state based on the estimated user's emotions. The grasping unit, for example, analyzes heart rate data to evaluate the user's stress level. For example, the grasping unit analyzes heart rate fluctuations to evaluate the stress level. The grasping unit can also analyze sleep pattern data to evaluate the user's fatigue level. For example, the grasping unit evaluates sleep quality and evaluates fatigue level. Furthermore, the grasping unit can analyze exercise amount data to evaluate the user's health state. For example, the grasping unit evaluates the frequency and intensity of exercise to evaluate the health state. This allows the grasping unit to grasp the user's mental and physical state based on the collected wellness information. Some or all of the above-mentioned processing in the grasping unit may be performed using AI, for example, or may be performed without using AI. For example, the grasping unit can input heart rate data to a generation AI and cause the generation AI to analyze the heart rate data.

[0089] When grasping the mental and physical state, the grasping unit can predict the current state by referring to past wellness information. The grasping unit predicts the current mental and physical state based on, for example, the user's past heart rate data. For example, the grasping unit analyzes past heart rate data to predict the current stress level. The grasping unit can also predict the current mental and physical state by referring to the user's past sleep patterns. For example, the grasping unit analyzes past sleep data to predict the current fatigue level. Furthermore, the grasping unit can predict the current mental and physical state based on the user's past exercise amount data. For example, the grasping unit analyzes past exercise data to predict the current health state. In this way, when grasping the mental and physical state, the grasping unit can predict the current state by referring to past wellness information. Some or all of the above-described processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit can input past wellness data to the generation AI and cause the generation AI to analyze the data.

[0090] The grasping unit can apply different grasping methods to each category of wellness information when grasping the mental and physical state. For example, the grasping unit applies a method of analyzing heart rate fluctuations to heart rate data. For example, the grasping unit analyzes heart rate fluctuations and evaluates stress levels. The grasping unit can also apply a method of evaluating sleep quality to sleep patterns. For example, the grasping unit evaluates sleep quality and evaluates fatigue levels. Furthermore, the grasping unit can apply a method of evaluating exercise frequency and intensity to exercise amount data. For example, the grasping unit evaluates exercise frequency and intensity and evaluates health conditions. This allows the grasping unit to apply different grasping methods to each category of wellness information when grasping the mental and physical state. Some or all of the above-mentioned processing in the grasping unit may be performed using AI, for example, or may be performed without using AI. For example, the grasping unit can input wellness data to a generation AI and cause the generation AI to analyze the data.

[0091] The grasping unit can estimate the user's emotions and adjust the importance of the user's mental and physical state based on the estimated user's emotions. The grasping unit, for example, analyzes heart rate data to evaluate the user's stress level. For example, the grasping unit analyzes heart rate fluctuations to evaluate the stress level. The grasping unit can also analyze sleep pattern data to evaluate the user's fatigue level. For example, the grasping unit evaluates sleep quality and evaluates fatigue level. Furthermore, the grasping unit can analyze exercise amount data to evaluate the user's health state. For example, the grasping unit evaluates the frequency and intensity of exercise to evaluate the health state. This allows the grasping unit to grasp the user's mental and physical state based on the collected wellness information. Some or all of the above-mentioned processing in the grasping unit may be performed using AI, for example, or may be performed without using AI. For example, the grasping unit can input heart rate data to a generation AI and cause the generation AI to analyze the heart rate data.

[0092] The grasping unit can analyze changes in the state based on the time of submission of the wellness information when grasping the mental and physical state. For example, the grasping unit analyzes changes in heart rate based on the time of submission of the user's heart rate data. For example, the grasping unit analyzes timestamps of the heart rate data and evaluates heart rate fluctuations. The grasping unit can also analyze changes in sleep quality based on the time of submission of the user's sleep pattern. For example, the grasping unit analyzes timestamps of the sleep data and evaluates fluctuations in sleep quality. Furthermore, the grasping unit can analyze changes in exercise frequency and intensity based on the time of submission of the user's exercise amount data. For example, the grasping unit analyzes timestamps of the exercise data and evaluates fluctuations in exercise frequency and intensity. In this way, the grasping unit can analyze changes in the state based on the time of submission of the wellness information when grasping the mental and physical state. Some or all of the above-described processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit can input wellness data to a generation AI and cause the generation AI to analyze the data.

[0093] The assessment unit can analyze the mental and physical state by referring to related market data. For example, the assessment unit compares the user's heart rate data with that of other users and analyzes it based on the market data. For example, the assessment unit compares the user's heart rate data with that of other users and evaluates the user's heart rate fluctuations. The assessment unit can also compare the user's sleep pattern with that of other users and analyze it based on the market data. For example, the assessment unit compares the user's sleep data with that of other users and evaluates the user's sleep quality. Furthermore, the assessment unit can compare the user's exercise data with that of other users and analyze it based on the market data. For example, the assessment unit compares the user's exercise data with that of other users and evaluates the frequency and intensity of the user's exercise. This allows the assessment unit to analyze the mental and physical state by referring to related market data. Some or all of the above-described processing in the assessment unit may be performed using AI, for example, or without AI. For example, the assessment unit may input market data to a generation AI and cause the generation AI to analyze the data.

[0094] The providing unit can estimate the user's emotions and adjust the way in which a response is provided based on the estimated user's emotions. The providing unit generates an appropriate response using, for example, a text generation AI (e.g., LLM). For example, the providing unit can estimate the user's emotions and provide a response in a calm voice if the user is feeling stressed. The providing unit can also provide a response in a cheerful voice if the user is relaxed. Furthermore, the providing unit can provide a response in a voice that shows empathy if the user is excited. This allows the providing unit to adjust the way in which a response is provided based on the user's emotions. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to analyze the emotion data.

[0095] When providing a response, the providing unit can select the optimal response method by referring to the user's past response history. The providing unit, for example, selects the optimal response method based on the user's past response history. For example, the providing unit analyzes past chat logs to identify the user's preferred response format. The providing unit can also analyze the user's past response patterns to select the optimal response method. For example, the providing unit analyzes the content of past responses to identify the user's preferred tone and style. Furthermore, the providing unit can select a response method related to a specific emotion or theme from the user's past response history. For example, the providing unit provides a calm response if the user is feeling stressed, and a cheerful response if the user is relaxed, based on the past response history. This allows the providing unit to select the optimal response method by referring to the user's past response history when providing a response. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may input past response history data into a generation AI and have the generation AI analyze the data.

[0096] The providing unit can customize the providing means based on the user's current living situation when providing a response. For example, if the user inputs their current living situation, the providing unit customizes the response means based on that information. For example, if the user inputs their work situation or home situation, the providing unit customizes the response means based on that information. The providing unit can also provide a relevant response means based on the user's living situation. For example, if the user inputs their health status, the providing unit provides a health-related response means based on that information. Furthermore, the providing unit can eliminate unnecessary information based on the user's living situation to improve the accuracy of the response. For example, the providing unit eliminates information that is not relevant to the user's current living situation to improve the accuracy of the response. This allows the providing unit to customize the providing means based on the user's current living situation when providing a response. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's living situation data to a generation AI and cause the generation AI to analyze the living situation data.

[0097] The providing unit can estimate the user's emotions and determine the priority of providing responses based on the estimated user's emotions. For example, the providing unit estimates the user's emotions and, if the user is feeling stressed, prioritizes providing responses related to stress. For example, the providing unit analyzes the user's emotion data and, if the user's stress level is high, prioritizes providing responses that help reduce stress. The providing unit can also prioritize providing responses related to relaxation if the user is relaxed. For example, the providing unit analyzes the user's emotion data and, if the user is relaxed, prioritizes providing responses that promote relaxation. Furthermore, the providing unit can also prioritize providing responses related to excitement if the user is excited. For example, the providing unit analyzes the user's emotion data and, if the user is excited, prioritizes providing responses that reduce excitement. This allows the providing unit to determine the priority of providing responses based on the user's emotions. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input the user's emotion data to a generation AI and cause the generation AI to analyze the emotion data.

[0098] The providing unit can select the optimal response method by taking into consideration the user's geographical location information when providing a response. For example, if the user is in a specific location, the providing unit provides a response related to that location. For example, if the user is traveling, the providing unit provides a travel-related response. The providing unit can also provide a region-specific response based on the user's geographical location information. For example, if the user is in a specific city, the providing unit provides a response related to that city. Furthermore, if the user is traveling, the providing unit can provide a response related to the user's current location. For example, if the user is commuting, the providing unit provides a commute-related response. This allows the providing unit to select the optimal response method by taking into consideration the user's geographical location information when providing a response. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location data into a generating AI and cause the generating AI to analyze the geographical location data.

[0099] The providing unit can analyze the user's social media activity and suggest a means of response when providing a response. The providing unit can suggest a means of response based on, for example, words and phrases frequently used by the user on social media. For example, the providing unit can analyze the content of the user's social media posts and extract frequently used words and phrases. The providing unit can also suggest a means of response related to a specific theme from the user's social media activity. For example, if the user frequently posts about health, the providing unit can suggest a means of response related to health. The providing unit can also analyze the user's social media activity patterns and suggest an optimal means of response. For example, the providing unit can analyze the user's posting frequency and the content of comments and suggest an appropriate means of response. In this way, the providing unit can analyze the user's social media activity and suggest a means of response when providing a response. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to analyze the social media data. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, collection unit, understanding unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 and analyzes the user's utterances. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a response based on the analyzed utterances. The collection unit, for example, collects the user's wellness information using a sensor of the smart device 14. The understanding unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and understands the user's physical and mental state based on the collected information. The provision unit, for example, is realized by the control unit 46A of the smart device 14 and provides the generated response to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, collection unit, understanding unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 and analyzes the user's utterances. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a response based on the analyzed utterances. The collection unit, for example, collects the user's wellness information using a sensor of the smart glasses 214. The understanding unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and understands the user's mental and physical state based on the collected information. The provision unit, for example, is realized by the control unit 46A of the smart glasses 214 and provides the generated response to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, collection unit, understanding unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 and analyzes the user's utterances. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a response based on the analyzed utterances. The collection unit collects the user's wellness information using, for example, a sensor of the headset type terminal 314. The understanding unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and understands the user's physical and mental state based on the collected information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the generated response to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, collection unit, understanding unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 and analyzes the user's utterances. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a response based on the analyzed utterances. The collection unit, for example, collects wellness information of the user using a sensor of the robot 414. The understanding unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and understands the user's physical and mental state based on the collected information. The provision unit, for example, is realized by the control unit 46A of the robot 414 and provides the generated response to the user.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] When analyzing a user's utterances, the analysis unit learns the user's past utterance patterns and can more accurately understand the intention of the utterances. For example, the analysis unit learns the meanings of specific phrases and words used by the user in the past and accurately understands the intention when the same phrases are used again. The analysis unit can also take into account the context of the user's utterances to more deeply understand the intention of the utterances. Furthermore, the analysis unit can analyze the tone and emotion of the user's utterances and can more accurately understand the intention of the utterances. This allows the analysis unit to more accurately analyze the user's utterances and generate appropriate responses.

[0102] The generation unit can take into account the user's past history of relaxation methods when suggesting relaxation methods based on the user's utterances. For example, the generation unit can record relaxation methods that the user has tried in the past and suggest the same method again if it was found to be effective. The generation unit can also evaluate the effectiveness of relaxation methods that the user has tried in the past and prioritize suggesting methods that were more effective. Furthermore, the generation unit can take into account the user's current mental and physical state and suggest the most appropriate relaxation method. This allows the generation unit to suggest more effective relaxation methods based on the user's utterances.

[0103] When collecting a user's wellness information, the collection unit can adjust the collection timing based on the user's lifestyle rhythm. For example, the collection unit records the user's usual wake-up time and bedtime, and collects the user's heart rate and sleep pattern according to those times. The collection unit can also collect the user's heart rate and exercise amount after exercise, taking into account the user's exercise habits. Furthermore, the collection unit can also collect the user's heart rate and blood sugar level after meals, taking into account the user's eating patterns. This enables the collection unit to collect wellness information that is tailored to the user's lifestyle rhythm.

[0104] The assessment unit can refer to the user's past health history when assessing the user's physical and mental condition based on the collected wellness information. For example, the assessment unit can refer to the user's past medical history and treatment history to more accurately assess the user's current physical and mental condition. The assessment unit can also refer to the user's past health checkup results to assess the user's current health condition. Furthermore, the assessment unit can refer to the health history of the user's family and evaluate the physical and mental condition taking genetic factors into consideration. This enables the assessment unit to more accurately assess the physical and mental condition based on the collected wellness information.

[0105] When providing the generated response to the user, the providing unit can adjust the providing method taking into account the characteristics of the user's device. For example, if the user is using a smartphone, the providing unit can display the response as a text message. Also, if the user is using a smart speaker, the providing unit can play the response as a voice message. Furthermore, if the user is using a smart watch, the providing unit can provide the response as a short text message or vibration. This allows the providing unit to provide a response tailored to the characteristics of the user's device.

[0106] The analysis unit can estimate the user's emotions and adjust the analysis method for utterances based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can simplify the analysis algorithm and quickly produce results. Alternatively, if the user is relaxed, the analysis unit can perform a detailed analysis and provide deeper insights. Furthermore, if the user is excited, the analysis unit can display the analysis results in an easy-to-understand visual format. This allows the analysis unit to adjust the analysis method for utterances based on the user's emotions.

[0107] The generation unit can estimate the user's emotion and adjust the way the response is expressed based on the estimated user's emotion. For example, the generation unit generates a response using a calm expression when the user is relaxed. The generation unit can also generate a response including encouraging words when the user is feeling stressed. Furthermore, the generation unit can generate a response using an expression that shows empathy when the user is excited. This allows the generation unit to adjust the way the response is expressed based on the user's emotion.

[0108] The collection unit can estimate the user's emotions and adjust the timing of collecting wellness information based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit can frequently collect heart rate and blood pressure data. When the user is relaxed, the collection unit can also collect data on sleep patterns and exercise volume. Furthermore, when the user is excited, the collection unit can also collect heart rate and breathing rate data. This allows the collection unit to adjust the timing of collecting wellness information based on the user's emotions.

[0109] The assessment unit can estimate the user's emotions and adjust the method of assessing the mental and physical state based on the estimated user emotions. For example, if the user is feeling stressed, the assessment unit can evaluate the mental and physical state by placing emphasis on heart rate and blood pressure data. Also, if the user is relaxed, the assessment unit can evaluate the mental and physical state by placing emphasis on sleep pattern and exercise amount data. Furthermore, if the user is excited, the assessment unit can evaluate the mental and physical state by placing emphasis on heart rate and respiratory rate data. This allows the assessment unit to adjust the method of assessing the mental and physical state based on the user's emotions.

[0110] The providing unit can estimate the user's emotion and adjust the response providing method based on the estimated user's emotion. For example, the providing unit can provide a response in a calm voice when the user is feeling stressed. The providing unit can also provide a response in a cheerful voice when the user is relaxed. Furthermore, the providing unit can also provide a response in a voice that shows empathy when the user is excited. This allows the providing unit to adjust the response providing method based on the user's emotion.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The analysis unit analyzes the user's utterances. For example, the analysis unit converts the user's utterances into text data using voice analysis technology. It can also analyze the content of the utterances using text analysis technology and estimate emotions from the user's utterances using emotion analysis technology. The user's emotions are estimated from the tone of the voice and the content of the utterances. Step 2: The generator generates a response based on the utterances analyzed by the analyzer. For example, the generator uses text generation AI (e.g., LLM) to generate an appropriate response. It can also use multimodal generation AI to generate responses that include not only text but also images and audio. It can also adjust the tone and content of the response based on the user's emotions. Step 3: The collection unit collects the user's wellness information. For example, the collection unit may collect the user's heart rate using a heart rate sensor, collect the user's sleep pattern using a sleep tracker, or collect the user's exercise amount using a fitness tracker. Step 4: The determination unit determines the user's physical and mental state based on the information collected by the collection unit. For example, the determination unit can analyze the heart rate data to evaluate the user's stress level, analyze the sleep pattern data to evaluate the user's fatigue level, and analyze the exercise amount data to evaluate the user's health condition. Step 5: The providing unit provides the response generated by the generating unit to the user. For example, the providing unit may display the response as a text message, play the response as a voice message, or display a response including an image or video.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0161] 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.

[0162] 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.

[0163] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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. an analysis unit that analyzes user comments; a generation unit that generates a response based on the utterance analyzed by the analysis unit; a collection unit that collects wellness information of a user; a recognition unit that recognizes the mental and physical state of the user based on the information collected by the collection unit; a providing unit that provides the response generated by the generating unit to a user. A system characterized by:

2. The analysis unit Analyzing user speech to detect signs of stress The system of claim 1 .

3. The generation unit Suggest relaxation methods based on user comments The system of claim 1 .

4. The collecting unit Collect wellness information about your heart rate, sleep patterns, and activity levels The system of claim 1 .

5. The grasping unit is Understand the user's physical and mental state based on collected wellness information The system of claim 1 .

6. The providing unit Providing the generated response to the user The system of claim 1 .

7. The analysis unit Estimate the user's emotions and adjust the analysis method of the speech based on the estimated user emotions. The system of claim 1 .

8. The analysis unit Analyze the user's past comment history and select an analysis algorithm The system of claim 1 .

Images