System

Abstract

An object of a system according to an embodiment is to propose a lifestyle suitable for an individual elderly person on the basis of lifestyle habit data of the elderly person.SOLUTION: A system according to an embodiment includes a lifestyle data collection unit and a lifestyle proposal unit. The lifestyle data collection unit collects data of daily life of the elderly person. The lifestyle proposal unit proposes a lifestyle suitable for the elderly person on the basis of the data collected by the lifestyle habit data collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide appropriate lifestyle suggestions based on the lifestyle habits of elderly people, and there is room for improvement.

[0005] The system according to the embodiment aims to propose a lifestyle suited to each elderly person based on the elderly person's lifestyle habit data. [Means for solving the problem]

[0006] The system according to the embodiment includes a lifestyle data collection unit and a lifestyle suggestion unit. The lifestyle data collection unit collects data on the daily life of an elderly person. The lifestyle suggestion unit suggests a lifestyle suited to the elderly person based on the data collected by the lifestyle data collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose a lifestyle suited to each elderly person based on the lifestyle habit data of the elderly person. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The lifestyle suggestion system according to the embodiment of the present invention is a system that learns the lifestyle habits of elderly people and suggests lifestyles, health management, and hobbies that are suited to each individual. As a result, the lifestyle suggestion system can improve the quality of life for elderly people and also contribute to resolving labor shortages.

[0029] The lifestyle proposal system according to the embodiment includes a lifestyle data collection unit and a lifestyle proposal unit. The lifestyle data collection unit collects data on the daily life of an elderly person. For example, it records the contents of meals. The lifestyle data collection unit can also record the frequency of exercise. The lifestyle data collection unit can also record a hospital visit schedule. The lifestyle proposal unit proposes a lifestyle suited to the elderly person based on the data collected by the lifestyle data collection unit. For example, the lifestyle proposal unit can propose an appropriate diet. The lifestyle proposal unit can also provide exercise advice. The lifestyle proposal unit can also propose a hospital visit schedule. This enables the lifestyle proposal system according to the embodiment to learn the lifestyle habits of the elderly person and propose individually customized lifestyles.

[0030] The lifestyle data collection unit can build a highly accurate lifestyle model by referencing past medical records and family health histories. For example, when the generation AI learns the lifestyle habits of an elderly person, the lifestyle data collection unit refers to past medical records to understand changes in health status. For example, it evaluates the current health status based on past medical history and treatment history. In this way, a highly accurate lifestyle model can be built by referencing past medical records and family health histories.

[0031] The lifestyle data collection unit can learn seasonal changes in lifestyle habits by taking into account seasonal and weather fluctuations. For example, the lifestyle data collection unit collects weather data and analyzes the correlation with lifestyle habits so that the generation AI can learn seasonal changes in lifestyle habits. For example, it can identify a tendency for physical activity to decrease in cold seasons. This makes it possible to learn lifestyle habits that take into account seasonal and weather fluctuations.

[0032] The lifestyle data collection unit can simultaneously collect voice data and image data and learn lifestyle habits using multimodal data. For example, the generation AI collects voice data and analyzes the content and tone of the conversation to learn lifestyle habits. For example, it can understand the content and frequency of meals from conversations during meals. This makes it possible to learn multimodal lifestyle habits using voice and image data.

[0033] The lifestyle habit data collection unit can compare the data with that of other elderly people and extract common patterns and differences. For example, the generation AI collects lifestyle habit data from multiple elderly people and extracts common patterns. For example, it compares the exercise habits of elderly people of the same age and understands general trends. This makes it possible to extract common patterns and differences by comparing the data with that of other elderly people.

[0034] The lifestyle suggestion unit can refer to the user's past lifestyle change history and learn which suggestions were successful and which were unsuccessful. For example, the generation AI in the lifestyle suggestion unit analyzes the user's past lifestyle change history and learns which suggestions were successful and which were unsuccessful. For example, new suggestions are made based on the success rate of past exercise plans. This allows the content of suggestions to be optimized based on the user's past lifestyle change history.

[0035] The lifestyle suggestion unit can take into account the user's social network and make suggestions that will make it easier to receive social support. For example, the generation AI analyzes the user's social network and makes suggestions that will make it easier to receive support from friends and family. For example, it suggests an exercise plan to do with family. This makes it possible to make suggestions that will make it easier to receive social support.

[0036] The lifestyle suggestion unit can make a variety of suggestions by taking into account lifestyles from different cultural spheres and regions. For example, the generation AI collects lifestyle data from different cultural spheres and regions and makes a variety of suggestions. For example, it makes suggestions based on the Mediterranean diet or Asian health methods. This makes it possible to make a variety of suggestions by taking into account lifestyles from different cultural spheres and regions.

[0037] The lifestyle suggestion unit can take into account the user's hobbies and interests and make lifestyle suggestions linked to hobby activities. For example, the generation AI analyzes the user's hobbies and interests and makes lifestyle suggestions linked to them. For example, for a user whose hobby is gardening, an exercise plan incorporating gardening is proposed. This makes it possible to make lifestyle suggestions linked to hobby activities.

[0038] The health management unit can create an individually customized health management plan by referencing the user's genetic information and past medical history. For example, the health management unit uses a generative AI to analyze the user's genetic information and create a health management plan that takes genetic risk factors into account. For example, if there is a genetic risk of high blood pressure, the unit customizes diet and exercise suggestions. This makes it possible to create an individually customized health management plan based on the user's genetic information and past medical history.

[0039] The health management unit takes into account the user's living environment and can make health management suggestions suited to the environment. For example, the generation AI monitors the temperature and humidity of the user's home in real time and makes health management suggestions suited to the environment. For example, if the humidity is high, it will suggest appropriate hydration. This makes it possible to make health management suggestions that take the living environment into account.

[0040] The health management unit can compare the health data of other users to identify common health risks. For example, the generative AI collects health data from multiple users and identifies common health risks. For example, it compares data from users of the same age group to understand general health risks. This makes it possible to identify common health risks by comparing the health data of other users.

[0041] The health management unit collects the user's dietary data and exercise data in real time and can provide immediate health management suggestions. For example, the generation AI collects the user's dietary data in real time and provides immediate health management suggestions. For example, it analyzes the contents of meals and makes suggestions that take nutritional balance into consideration. This makes it possible to collect dietary and exercise data in real time and provide immediate health management suggestions.

[0042] The hobby suggestion unit can consider the user's physical limitations and suggest hobbies that can be enjoyed without strain. For example, the hobby suggestion unit uses a generation AI to analyze the user's physical limitations and suggest hobbies that can be enjoyed without strain. For example, for a user with low athletic ability, it can suggest hobbies that include light exercise. This makes it possible to suggest hobbies that can be enjoyed without strain, taking physical limitations into consideration.

[0043] The hobby suggestion unit can suggest a variety of hobbies by referring to hobby activities in different cultural spheres and regions. For example, the generation AI collects hobby activity data from different cultural spheres and regions and suggests a variety of hobbies. For example, it makes suggestions by referring to traditional Asian hobbies and European hobby activities. This makes it possible to suggest a variety of hobbies by referring to hobby activities in different cultural spheres and regions.

[0044] The hobby suggestion unit can take into account the hobbies of the user's friends and family and suggest common hobbies. For example, the generation AI analyzes the hobbies of the user's friends and family and suggests common hobbies. For example, it suggests hobby activities that the whole family can enjoy. This makes it possible to suggest common hobbies that take into account the hobbies of friends and family.

[0045] The conversation partner function unit can refer to the user's past conversation history and learn topics of interest and topics that the user wants to avoid. For example, the conversation partner function unit uses a generation AI to analyze the user's past conversation history and learn topics of interest and topics that the user wants to avoid. For example, it can suggest new conversations based on topics that were popular in the past. This makes it possible to learn topics of interest and topics that the user wants to avoid based on past conversation history.

[0046] The conversation partner function unit can provide highly relevant topics by taking into account the user's living environment and daily events. For example, the conversation partner function unit uses a generation AI to analyze the user's living environment and provide highly relevant topics. For example, it can provide topics about news and events in the area where the user lives. This makes it possible to provide highly relevant topics that take into account the living environment and daily events.

[0047] The conversation partner function unit can provide common topics by taking into account the topics of the user's friends and family. For example, the conversation partner function unit uses a generation AI to analyze the topics of the user's friends and family and provide common topics. For example, it can suggest topics that interest all family members. This makes it possible to provide common topics that take into account the topics of friends and family.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The lifestyle data collection unit collects data on the elderly person's daily life, such as dietary habits, exercise frequency, and hospital visit schedules. Step 2: The lifestyle suggestion unit proposes a lifestyle suited to the elderly based on the data collected by the lifestyle data collection unit, such as providing advice on appropriate eating habits, exercise, and suggesting a schedule for medical visits.

[0051] (Example 2) The lifestyle suggestion system according to the embodiment of the present invention is a system that learns the lifestyle habits of elderly people and suggests lifestyles, health management, and hobbies that are suited to each individual. As a result, the lifestyle suggestion system can improve the quality of life for elderly people and also contribute to resolving labor shortages.

[0052] The lifestyle proposal system according to the embodiment includes a lifestyle data collection unit and a lifestyle proposal unit. The lifestyle data collection unit collects data on the daily life of an elderly person. For example, it records the contents of meals. The lifestyle data collection unit can also record the frequency of exercise. The lifestyle data collection unit can also record a hospital visit schedule. The lifestyle proposal unit proposes a lifestyle suited to the elderly person based on the data collected by the lifestyle data collection unit. For example, the lifestyle proposal unit can propose an appropriate diet. The lifestyle proposal unit can also provide exercise advice. The lifestyle proposal unit can also propose a hospital visit schedule. This enables the lifestyle proposal system according to the embodiment to learn the lifestyle habits of the elderly person and propose individually customized lifestyles.

[0053] The lifestyle data collection unit uses the emotion estimation function to analyze the user's emotional state in real time and collect data according to emotional fluctuations. For example, when the generation AI collects lifestyle data on an elderly person, the lifestyle data collection unit analyzes the user's facial expressions and voice in real time to detect emotional fluctuations. For example, it analyzes facial expressions and conversational tone while eating and collects data according to emotional fluctuations. This makes it possible to collect data according to the user's emotional state.

[0054] The lifestyle data collection unit can build a highly accurate lifestyle model by referencing past medical records and family health histories. For example, when the generation AI learns the lifestyle habits of an elderly person, the lifestyle data collection unit refers to past medical records to understand changes in health status. For example, it evaluates the current health status based on past medical history and treatment history. In this way, a highly accurate lifestyle model can be built by referencing past medical records and family health histories.

[0055] The lifestyle data collection unit can learn seasonal changes in lifestyle habits by taking into account seasonal and weather fluctuations. For example, the lifestyle data collection unit collects weather data and analyzes the correlation with lifestyle habits so that the generation AI can learn seasonal changes in lifestyle habits. For example, it can identify a tendency for physical activity to decrease in cold seasons. This makes it possible to learn lifestyle habits that take into account seasonal and weather fluctuations.

[0056] The lifestyle data collection unit can simultaneously collect voice data and image data and learn lifestyle habits using multimodal data. For example, the generation AI collects voice data and analyzes the content and tone of the conversation to learn lifestyle habits. For example, it can understand the content and frequency of meals from conversations during meals. This makes it possible to learn multimodal lifestyle habits using voice and image data.

[0057] The lifestyle habit data collection unit can compare the data with that of other elderly people and extract common patterns and differences. For example, the generation AI collects lifestyle habit data from multiple elderly people and extracts common patterns. For example, it compares the exercise habits of elderly people of the same age and understands general trends. This makes it possible to extract common patterns and differences by comparing the data with that of other elderly people.

[0058] The lifestyle suggestion unit can use the emotion estimation function to analyze the user's emotional response to the suggested content in real time and adjust the suggested content. For example, when the generation AI makes a lifestyle suggestion, the lifestyle suggestion unit analyzes the user's emotional response in real time and adjusts the suggested content. For example, the type and frequency of exercise can be adjusted based on the user's emotional response to the suggested exercise plan. This allows the suggested content to be adjusted based on the user's emotional response.

[0059] The lifestyle suggestion unit can refer to the user's past lifestyle change history and learn which suggestions were successful and which were unsuccessful. For example, the generation AI in the lifestyle suggestion unit analyzes the user's past lifestyle change history and learns which suggestions were successful and which were unsuccessful. For example, new suggestions are made based on the success rate of past exercise plans. This allows the content of suggestions to be optimized based on the user's past lifestyle change history.

[0060] The lifestyle suggestion unit can take into account the user's social network and make suggestions that will make it easier to receive social support. For example, the generation AI analyzes the user's social network and makes suggestions that will make it easier to receive support from friends and family. For example, it suggests an exercise plan to do with family. This makes it possible to make suggestions that will make it easier to receive social support.

[0061] The lifestyle suggestion unit can make a variety of suggestions by taking into account lifestyles from different cultural spheres and regions. For example, the generation AI collects lifestyle data from different cultural spheres and regions and makes a variety of suggestions. For example, it makes suggestions based on the Mediterranean diet or Asian health methods. This makes it possible to make a variety of suggestions by taking into account lifestyles from different cultural spheres and regions.

[0062] The lifestyle suggestion unit can take into account the user's hobbies and interests and make lifestyle suggestions linked to hobby activities. For example, the generation AI analyzes the user's hobbies and interests and makes lifestyle suggestions linked to them. For example, for a user whose hobby is gardening, an exercise plan incorporating gardening is proposed. This makes it possible to make lifestyle suggestions linked to hobby activities.

[0063] The lifestyle suggestion unit uses the emotion estimation function to identify lifestyle suggestions that evoke the most positive emotions in the user and prioritize those suggestions. The lifestyle suggestion unit, for example, uses the emotion estimation function to identify lifestyle suggestions that evoke the most positive emotions in the user. For example, the lifestyle suggestion unit analyzes emotional responses to past suggestions and prioritizes suggestions that evoke the most positive responses. This allows the lifestyle suggestions that evoke the most positive emotions in the user to be prioritized.

[0064] The health management unit can create an individually customized health management plan by referencing the user's genetic information and past medical history. For example, the health management unit uses a generative AI to analyze the user's genetic information and create a health management plan that takes genetic risk factors into account. For example, if there is a genetic risk of high blood pressure, the unit customizes diet and exercise suggestions. This makes it possible to create an individually customized health management plan based on the user's genetic information and past medical history.

[0065] The health management unit takes into account the user's living environment and can make health management suggestions suited to the environment. For example, the generation AI monitors the temperature and humidity of the user's home in real time and makes health management suggestions suited to the environment. For example, if the humidity is high, it will suggest appropriate hydration. This makes it possible to make health management suggestions that take the living environment into account.

[0066] The health management unit can compare the health data of other users to identify common health risks. For example, the generative AI collects health data from multiple users and identifies common health risks. For example, it compares data from users of the same age group to understand general health risks. This makes it possible to identify common health risks by comparing the health data of other users.

[0067] The health management unit collects the user's dietary data and exercise data in real time and can provide immediate health management suggestions. For example, the generation AI collects the user's dietary data in real time and provides immediate health management suggestions. For example, it analyzes the contents of meals and makes suggestions that take nutritional balance into consideration. This makes it possible to collect dietary and exercise data in real time and provide immediate health management suggestions.

[0068] The health management unit uses the emotion estimation function to identify health management suggestions that evoke the most positive emotions in the user and prioritize those suggestions. The health management unit, for example, uses the emotion estimation function to identify health management suggestions that evoke the most positive emotions in the user. For example, the health management unit analyzes emotional responses to past suggestions and prioritizes suggestions that have received the most positive responses. This allows the health management suggestions that evoke the most positive emotions in the user to be prioritized.

[0069] The hobby suggestion unit can use the emotion estimation function to analyze the user's emotional state in real time and suggest hobbies that elicit positive emotions. For example, the hobby suggestion unit uses a generative AI to analyze the user's emotional state in real time and suggest hobbies that elicit positive emotions. For example, hobbies that elicit positive emotions are identified based on emotional responses to past hobby activities. This allows the user's emotional state to be analyzed in real time and hobbies that elicit positive emotions to be suggested.

[0070] The hobby suggestion unit can consider the user's physical limitations and suggest hobbies that can be enjoyed without strain. For example, the hobby suggestion unit uses a generation AI to analyze the user's physical limitations and suggest hobbies that can be enjoyed without strain. For example, for a user with low athletic ability, it can suggest hobbies that include light exercise. This makes it possible to suggest hobbies that can be enjoyed without strain, taking physical limitations into consideration.

[0071] The hobby suggestion unit can suggest a variety of hobbies by referring to hobby activities in different cultural spheres and regions. For example, the generation AI collects hobby activity data from different cultural spheres and regions and suggests a variety of hobbies. For example, it makes suggestions by referring to traditional Asian hobbies and European hobby activities. This makes it possible to suggest a variety of hobbies by referring to hobby activities in different cultural spheres and regions.

[0072] The hobby suggestion unit can take into account the hobbies of the user's friends and family and suggest common hobbies. For example, the generation AI analyzes the hobbies of the user's friends and family and suggests common hobbies. For example, it suggests hobby activities that the whole family can enjoy. This makes it possible to suggest common hobbies that take into account the hobbies of friends and family.

[0073] The hobby suggestion unit can use the emotion estimation function to identify the hobby for which the user feels the most positive emotion and preferentially suggest that hobby. The hobby suggestion unit, for example, uses the emotion estimation function to identify the hobby for which the user feels the most positive emotion. For example, it analyzes emotional responses to past hobby activities and prioritizes hobbies that have received many positive responses. This allows the hobby for which the user feels the most positive emotion to be preferentially suggested.

[0074] The conversation partner function unit can use the emotion estimation function to analyze the user's emotional state in real time and select an appropriate topic. For example, the conversation partner function unit uses a generation AI to analyze the user's emotional state in real time and select an appropriate topic. For example, when the user is relaxed, it selects a light topic, and when the user is feeling stressed, it selects an encouraging topic. This allows the user's emotional state to be analyzed in real time and an appropriate topic to be selected.

[0075] The conversation partner function unit can refer to the user's past conversation history and learn topics of interest and topics that the user wants to avoid. For example, the conversation partner function unit uses a generation AI to analyze the user's past conversation history and learn topics of interest and topics that the user wants to avoid. For example, it can suggest new conversations based on topics that were popular in the past. This makes it possible to learn topics of interest and topics that the user wants to avoid based on past conversation history.

[0076] The conversation partner function unit can provide highly relevant topics by taking into account the user's living environment and daily events. For example, the conversation partner function unit uses a generation AI to analyze the user's living environment and provide highly relevant topics. For example, it can provide topics about news and events in the area where the user lives. This makes it possible to provide highly relevant topics that take into account the living environment and daily events.

[0077] The conversation partner function unit can provide common topics by taking into account the topics of the user's friends and family. For example, the conversation partner function unit uses a generation AI to analyze the topics of the user's friends and family and provide common topics. For example, it can suggest topics that interest all family members. This makes it possible to provide common topics that take into account the topics of friends and family.

[0078] The conversation partner function unit can use the emotion estimation function to identify topics that evoke the most positive emotions in the user and provide those topics preferentially. The conversation partner function unit, for example, uses the emotion estimation function to identify topics that evoke the most positive emotions in the user. For example, it analyzes emotional responses to past conversations and prioritizes topics that have received many positive responses. This allows the conversation partner function unit to preferentially provide topics that evoke the most positive emotions in the user.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The lifestyle suggestion system further includes a music therapy suggestion unit. The music therapy suggestion unit can analyze the user's past musical preferences and music with a relaxing effect to suggest appropriate music. For example, the music therapy suggestion unit can suggest music for stress reduction based on the user's history of listening to relaxing music. The music therapy suggestion unit can also provide relaxing music in real time according to the user's emotional state. This allows the user's quality of life to be improved through music.

[0081] The lifestyle suggestion system further includes a pet care suggestion unit. The pet care suggestion unit can analyze the health condition and lifestyle habits of the user's pet and suggest appropriate care methods. For example, it can record the pet's feeding and exercise frequency and provide health management advice. The pet care suggestion unit can also estimate the pet's emotional state and make suggestions for stress reduction. This can improve the quality of life of both the pet and the user.

[0082] The lifestyle suggestion system further includes a travel suggestion unit. The travel suggestion unit can analyze the user's past travel history and interests to suggest appropriate travel plans. For example, it can suggest new travel destinations based on places the user has visited in the past or tourist spots that interest the user. The travel suggestion unit can also estimate the user's emotional state and suggest travel plans that will have a refreshing effect. This can further enrich the user's travel experience.

[0083] The lifestyle suggestion system further includes a nutrition management unit. The nutrition management unit can perform detailed analysis of the user's dietary data and propose a nutritionally balanced meal plan. For example, it can record the user's dietary content and evaluate whether there are any nutrient deficiencies or excesses. The nutrition management unit can also estimate the user's emotional state and propose ingredients that are effective in reducing stress. This allows for comprehensive support of the user's health.

[0084] The lifestyle suggestion system further includes a sleep management unit. The sleep management unit collects the user's sleep data and can make suggestions to promote high-quality sleep. For example, it records the user's sleep time and sleep quality and suggests areas for improvement. The sleep management unit can also estimate the user's emotional state and suggest a sleeping environment that has a relaxing effect. This can improve the user's sleep quality.

[0085] The lifestyle suggestion system further includes an exercise suggestion unit. The exercise suggestion unit can analyze the user's physical ability and health condition and suggest an appropriate exercise plan. For example, it can suggest reasonable exercises based on the user's exercise history and physical strength. The exercise suggestion unit can also estimate the user's emotional state and suggest exercises to increase motivation. This can support the user in maintaining their health.

[0086] The lifestyle suggestion system further includes a reading suggestion unit. The reading suggestion unit can analyze the user's reading history and interests to suggest appropriate books. For example, it can suggest new books based on books the user has read in the past and genres in which the user is interested. The reading suggestion unit can also estimate the user's emotional state and suggest books that have a relaxing effect. This can further enhance the user's reading experience.

[0087] The lifestyle suggestion system further includes a gardening suggestion unit. The gardening suggestion unit can analyze the condition of the user's garden and the types of plants to suggest an appropriate gardening plan. For example, it can suggest optimal plant selection and cultivation methods based on the soil and climatic conditions of the user's garden. The gardening suggestion unit can also estimate the user's emotional state and suggest gardening activities that have a relaxing effect. This can further enrich the user's gardening experience.

[0088] The lifestyle proposal system further includes a recipe suggestion unit. The recipe suggestion unit can analyze the user's ingredient inventory and past cooking history to suggest appropriate recipes. For example, it can suggest recipes that use ingredients efficiently based on the ingredients in the user's refrigerator. The recipe suggestion unit can also estimate the user's emotional state and suggest dishes that are effective in reducing stress. This can further enhance the user's cooking experience.

[0089] The lifestyle suggestion system further includes a volunteer activity suggestion unit. The volunteer activity suggestion unit can analyze the user's interests and skills and suggest appropriate volunteer activities. For example, it can suggest new volunteer activities based on volunteer activities the user has participated in in the past and areas in which the user is interested. The volunteer activity suggestion unit can also estimate the user's emotional state and suggest volunteer activities that elicit positive emotions. This can further enhance the user's social contribution activities.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The lifestyle data collection unit collects data on the elderly person's daily life, such as dietary habits, exercise frequency, and hospital visit schedules. Step 2: The lifestyle suggestion unit proposes a lifestyle suited to the elderly based on the data collected by the lifestyle data collection unit, such as providing advice on appropriate eating habits, exercise, and suggesting a schedule for medical visits.

[0092] 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.

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

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0098] The 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.

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

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

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

[0102] Fig. 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.

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

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0107] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0109] The data processing system 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

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

[0113] The 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.

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

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0117] 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.

[0118] 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.

[0119] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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, in order to avoid confusion and to 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.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a lifestyle data collection unit that collects data on the daily lives of elderly people; a lifestyle suggestion unit that suggests a lifestyle suited to the elderly person based on the data collected by the lifestyle habit data collection unit. A system characterized by:

2. The lifestyle habit data collection unit Analyze the user's emotional state in real time and collect data according to emotional fluctuations 2. The system of claim 1.

3. The lifestyle habit data collection unit Collect audio and image data simultaneously and learn lifestyle habits using multimodal data 2. The system of claim 1.

4. The lifestyle suggestion unit Identify the lifestyle suggestions that evoke the most positive feelings in users and prioritize those suggestions 2. The system of claim 1.

5. The Health Management Department Identify health management suggestions that evoke the most positive feelings from users and prioritize those suggestions.

2. The system of claim 1.

6. The Hobby Suggestion Department Analyzes the user's emotional state in real time and suggests hobbies that elicit positive emotions 2. The system of claim 1.

7. The conversation partner function unit is Analyze the user's emotional state in real time and select appropriate topics 2. The system of claim 1.

8. The conversation partner function unit is Identify topics that users feel most positive about and prioritize serving those topics.

2. The system of claim 1.

Images