System

Abstract

An object of the system according to the embodiment is to collect life situations, preferences, and health conditions of seniors and propose an individually optimal life plan.SOLUTION: A system includes a data collection part and a life plan suggestion part. The AI collecting part collects the life situation, taste and health condition of the senior in an interactive diary format by using the generated data. The life plan proposing unit proposes an optimal life plan to each individual based on the data collected by the data collecting 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 technology has had the problem of not being able to efficiently collect information on the diverse living situations, preferences, and health conditions of seniors and to propose individually optimal life plans.

[0005] The system according to the embodiment aims to collect information on the living conditions, preferences, and health conditions of seniors and to propose an optimal life plan for each individual. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit and a life plan proposal unit. The data collection unit uses a generation AI to collect information on the living conditions, preferences, and health status of seniors in an interactive diary format. The life plan proposal unit proposes an optimal life plan for each individual based on the data collected by the data collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect information on the living conditions, preferences, and health status of seniors and propose an optimal life plan for each individual. [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) A life planning system according to an embodiment of the present invention is a system that collects information on the living conditions, preferences, and health status of seniors in an interactive diary format and proposes an optimal life plan for each individual. As a result, the life planning system can respond to the diversification of seniors and improve the quality of life of seniors by proposing individual life plans.

[0029] A life planning system according to an embodiment includes a data collection unit and a life plan proposal unit. The data collection unit uses a generation AI to collect information about a senior's lifestyle, preferences, and health status in an interactive diary format. For example, the generation AI asks questions such as "How was your day today?" and "How's your health been lately?" to which the senior responds. The data collection unit analyzes the senior's responses and stores them as individual data. For example, the generation AI analyzes the senior's responses using natural language processing technology to extract important information. The data collection unit also periodically collects and updates data about the senior's lifestyle, preferences, and health status. For example, the generation AI periodically collects information about the senior's daily life and health and stores it in a database. The life plan proposal unit proposes an optimal life plan for each individual based on the data collected by the data collection unit. For example, the generation AI proposes appropriate exercise plans, meal plans, and hobby activities based on the senior's health status, hobbies, and lifestyle. The life plan proposal unit also generates a specific plan tailored to the senior's needs. For example, the generation AI makes suggestions such as, "It would be a good idea to incorporate a walk every morning" or "Why not try attending a hobby class once a week?" Furthermore, the life plan proposal unit periodically reviews and updates the plan according to the senior's living situation, preferences, and health condition. For example, the generation AI adjusts the plan according to changes in the senior's health condition and lifestyle rhythm, and makes optimal proposals. As a result, the life planning system according to the embodiment can improve the quality of life of seniors by responding to the diversification of seniors and proposing individual life plans.

[0030] The data collection unit can monitor the senior's living environment with sensors and customize questions based on that data. For example, the data collection unit monitors the senior's living environment with sensors, and the generation AI customizes questions based on that data. For example, it asks questions about a comfortable living environment based on room temperature and humidity data. The data collection unit also generates questions based on the senior's lifestyle rhythm and activity level based on the data collected by the sensors. For example, if the senior is not very active, it asks questions about their exercise habits. The data collection unit also analyzes living environment data and customizes questions related to the senior's health condition and lifestyle habits. For example, it asks questions about sleep quality and ways to improve it based on sleep data. This allows more appropriate data to be collected by generating questions based on the senior's living environment.

[0031] The data collection unit can analyze the senior's past response history and predict the next question based on that trend. For example, the data collection unit analyzes the senior's past response history, and the generation AI predicts the next question based on that trend. For example, if there have been many health-related questions in the past, the generation AI will continue to ask questions related to maintaining health. The data collection unit also generates questions based on the senior's interests and concerns based on the past response data. For example, if there have been many responses about hobbies, the data collection unit will ask questions about hobby activities. The data collection unit also analyzes the response history and predicts the next question based on the senior's response pattern. For example, if there have been many responses to a particular topic, the data collection unit will continue to ask questions related to that topic. In this way, by predicting the next question based on the senior's past response history, more relevant data can be collected.

[0032] The data collection unit can collect conversations between the senior and their family or friends, and collect data that takes social connections into account. For example, the data collection unit uses a generation AI to collect conversations between the senior and their family and friends, and collect data that takes social connections into account. For example, the content of conversations with family members is analyzed to understand the senior's social support situation. The data collection unit also collects conversation data between the senior and their friends, and analyzes their social connections and frequency of interactions. For example, the social activities of the senior are evaluated based on the content of conversations with friends. The data collection unit also generates questions to strengthen the senior's social connections based on the conversation data with family and friends. For example, the data collection unit asks questions about activities that deepen relationships with family members. In this way, by collecting data that takes social connections into account, a more comprehensive life plan can be proposed.

[0033] The data collection unit can integrate data from other devices used by the senior to collect comprehensive lifestyle data. The data collection unit integrates data from, for example, smart watches and fitness trackers used by the senior to collect comprehensive lifestyle data. For example, the health condition is evaluated based on heart rate and step count data. The data collection unit also analyzes data collected from other devices to comprehensively understand the senior's lifestyle rhythm and activity level. For example, sleep data and exercise data are integrated and analyzed. The data collection unit also generates questions regarding the senior's lifestyle habits and health condition based on data from the devices. For example, questions about exercise habits are asked based on exercise data. In this way, by integrating data from devices used by the senior, more comprehensive lifestyle data can be collected.

[0034] The life plan proposal unit can analyze the senior's lifestyle data and dynamically generate a life plan based on the season and weather. The life plan proposal unit, for example, analyzes the senior's lifestyle data and dynamically generates a life plan according to the season and weather. For example, it suggests indoor exercises in winter. The life plan proposal unit also proposes a life plan based on the senior's lifestyle rhythm and activity level based on the season and weather data. For example, it suggests taking a walk during cooler times in summer. The life plan proposal unit also integrates the lifestyle data with the season and weather data to generate a life plan based on the senior's health condition and lifestyle habits. For example, it suggests indoor relaxation activities on rainy days. In this way, the quality of life of the senior is improved by dynamically generating a life plan according to the season and weather.

[0035] The life plan proposal unit proposes a life plan that incorporates the opinions of the senior's family or friends, thereby strengthening social support. For example, the life plan proposal unit uses a generation AI to propose a life plan that incorporates the opinions of the senior's family and friends. For example, it proposes activities to do with family based on the content of conversations with family. The life plan proposal unit also collects the opinions of family and friends and reflects them in the senior's life plan. For example, it proposes activities to promote interaction with friends. The life plan proposal unit also proposes a life plan that strengthens social support for the senior based on the opinions of family and friends. For example, it suggests regular communication with family. In this way, social support can be strengthened by proposing a life plan that incorporates the opinions of the senior's family and friends.

[0036] The life plan proposal unit can incorporate activity information from the local community in which the senior participates and propose a community-based life plan. The life plan proposal unit, for example, collects activity information from the local community in which the senior participates and proposes a community-based life plan. For example, it may propose participation in local events and activities. The life plan proposal unit also customizes the senior's life plan based on the activity information from the local community. For example, it may propose participation in local hobby circles or volunteer activities. The life plan proposal unit also incorporates activity information from the local community and proposes a life plan that strengthens the senior's social connections. For example, it may propose participation in local social events. In this way, community-based support can be provided by proposing a life plan that incorporates activity information from the local community in which the senior participates.

[0037] Based on the data collected by generative AI, a business model can be built that proposes customized products and services for seniors. Based on the data collected by generative AI, a business model can be built that proposes customized products and services for seniors. For example, proposing supplements based on individual health conditions. In addition, the lifestyle data of seniors can be analyzed to propose customized products that meet specific needs. For example, proposing tools and teaching materials that suit hobbies. In addition, based on the collected data, a business model can be built that provides services tailored to the lifestyles of seniors. For example, proposing individual fitness programs and meal plans. This makes it possible to build a business model that meets the needs of seniors by proposing customized products and services for seniors.

[0038] The system can analyze lifestyle data of seniors and develop a business model that provides subscription services based on their specific needs. For example, the system analyzes lifestyle data of seniors and develops a business model that provides subscription services based on their specific needs. For example, it provides subscription services for health management or hobby activities. In addition, based on the collected data, it suggests subscription services that match the lifestyle rhythms and preferences of seniors. For example, it regularly provides monthly health checks or hobby materials. In addition, it develops subscription services that meet the needs of seniors and builds a business model that provides them on an ongoing basis. For example, it regularly provides individual fitness programs or meal plans. In this way, it is possible to build a continuous business model by analyzing lifestyle data of seniors and providing subscription services that meet their specific needs.

[0039] The system can partner with companies that provide health management services or hobby activity support services for seniors and jointly build new business models. The system, for example, partners with companies that provide health management services for seniors and jointly builds new business models. For example, health data can be shared and individual health plans can be provided. The system can also partner with companies that provide hobby activity support services and build a business model that supports seniors' hobby activities. For example, hobby classes and events can be jointly held. The system can also partner with companies that provide life support services and build a business model that comprehensively supports the lives of seniors. For example, support for daily life and nursing care services can be provided. In this way, the system can partner with companies that provide health management services or hobby activity support services for seniors and jointly build new business models, thereby improving the quality of life of seniors.

[0040] The system can develop new products and services through collaboration with different industries based on lifestyle data of seniors. For example, the system can develop new products and services through collaboration with different industries based on lifestyle data of seniors. For example, it can develop fitness equipment based on health data. It can also collaborate with companies in different industries to provide new products and services that meet the needs of seniors. For example, it can collaborate with food manufacturers to develop health foods. It can also share lifestyle data and build new business models in collaboration with companies in different industries. For example, it can collaborate with technology companies to develop smart home devices. In this way, it can build a business model that meets the needs of seniors by developing new products and services through collaboration with different industries based on lifestyle data of seniors.

[0041] The system can analyze past consumption behavior data of seniors and predict future market trends. For example, the system analyzes past consumption behavior data of seniors and predicts future market trends. For example, it predicts future consumption trends based on past purchase history. The system also predicts demand in the senior market based on past consumption data. For example, it predicts demand for specific products or services. The system also analyzes consumption behavior data and predicts future market trends. For example, it predicts future market size based on past consumption patterns. In this way, by analyzing past consumption behavior data of seniors and predicting future market trends, it is possible to grasp trends in the senior market.

[0042] The system can compare senior markets in different regions and cultural spheres and predict global market size. For example, the system compares senior markets in different regions and cultural spheres and predicts global market size. For example, the system predicts market size based on the lifestyle habits and preferences of each region. The system also analyzes data for each region to grasp trends in the global senior market. For example, the system compares demand for health management services in each region. The system also compares senior markets in different cultural spheres and predicts global market trends. For example, the system predicts market size based on consumption behavior data for each region. In this way, by comparing senior markets in different regions and cultural spheres and predicting global market size, it is possible to grasp international trends in the senior market.

[0043] The system can predict the market size of new products and services for seniors and use this information to formulate corporate strategies. The system, for example, can predict the market size of new products and services for seniors and use this information to formulate corporate strategies. For example, it can predict demand for new products and formulate marketing strategies. The system can also predict the market size of new products and services and build corporate business models. For example, it can formulate plans to provide new services. The system can also formulate corporate strategies based on market size forecast data. For example, it can determine the timing of entering a new market. In this way, predicting the market size of new products and services for seniors and using this information to formulate corporate strategies can formulate business strategies that meet the needs of the senior market.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The data collection unit can monitor the senior's living environment with sensors and customize questions based on that data. For example, it asks about a comfortable living environment based on room temperature and humidity data. The data collection unit also generates questions based on the data collected by the sensors, according to the senior's lifestyle rhythm and activity level. For example, if the senior is not very active, it asks about their exercise habits. The data collection unit also analyzes the living environment data and customizes questions related to the senior's health condition and lifestyle habits. For example, it asks about the quality of sleep and how to improve it based on sleep data. This allows more appropriate data to be collected by generating questions based on the senior's living environment.

[0046] The data collection unit can analyze the senior's past response history and predict the next question based on that trend. For example, if there have been many health-related questions in the past, the data collection unit will continue to ask questions related to maintaining health. The data collection unit also generates questions based on the senior's interests and concerns based on the past response data. For example, if there have been many responses related to hobbies, the data collection unit will ask questions about hobby activities. The data collection unit also analyzes the response history and predicts the next question based on the senior's response patterns. For example, if there have been many responses to a particular topic, the data collection unit will continue to ask questions related to that topic. In this way, by predicting the next question based on the senior's past response history, more relevant data can be collected.

[0047] The data collection unit can collect conversations between the senior and their family or friends, and collect data that takes social connections into account. For example, the generation AI collects conversations between the senior and their family and friends, and collects data that takes social connections into account. For example, the content of conversations with family members is analyzed to understand the senior's social support situation. The data collection unit also collects conversation data between the senior and their friends, and analyzes their social connections and frequency of interactions. For example, the social activities of the senior are evaluated based on the content of conversations with friends. The data collection unit also generates questions to strengthen the senior's social connections based on the conversation data with family and friends. For example, it asks questions about activities that deepen relationships with family members. In this way, by collecting data that takes social connections into account, a more comprehensive life plan can be proposed.

[0048] The data collection unit can integrate data from other devices used by the senior to collect comprehensive lifestyle data. For example, data from smart watches and fitness trackers used by the senior can be integrated to collect comprehensive lifestyle data. For example, health status can be evaluated based on heart rate and step count data. The data collection unit also analyzes data collected from other devices to comprehensively understand the senior's lifestyle rhythm and activity level. For example, sleep data and exercise data can be integrated and analyzed. The data collection unit also generates questions regarding the senior's lifestyle habits and health status based on data from the devices. For example, questions about exercise habits can be asked based on exercise data. In this way, more comprehensive lifestyle data can be collected by integrating data from devices used by the senior.

[0049] The life plan proposal unit can analyze the senior's lifestyle data and dynamically generate a life plan based on the season and weather. For example, it analyzes the senior's lifestyle data and dynamically generates a life plan according to the season and weather. For example, it suggests indoor exercises that can be done in winter. The life plan proposal unit also proposes a life plan based on the senior's lifestyle rhythm and activity level based on the season and weather data. For example, it suggests taking a walk during cooler times in summer. The life plan proposal unit also integrates the lifestyle data with the season and weather data to generate a life plan based on the senior's health condition and lifestyle habits. For example, it suggests indoor relaxation activities when it rains. In this way, the quality of life of the senior is improved by dynamically generating a life plan according to the season and weather.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The data collection unit uses the generation AI to collect information about the senior's living situation, preferences, and health status in an interactive diary format. For example, the generation AI asks questions such as "How was your day today?" and "How is your health lately?" to which the senior responds. The data collection unit then analyzes the senior's responses and stores them as individual data. For example, the generation AI analyzes the senior's responses using natural language processing technology to extract important information. The data collection unit then regularly collects and updates data about the senior's living situation, preferences, and health status. For example, the generation AI regularly collects information about the senior's daily life and health and stores it in a database. Step 2: The life plan proposal unit proposes an optimal life plan for each individual based on the data collected by the data collection unit. For example, the generation AI proposes appropriate exercise plans, meal plans, and hobby activities based on the senior's health condition, hobbies, and lifestyle. The life plan proposal unit also generates specific plans tailored to the senior's needs. For example, the generation AI makes suggestions such as, "It would be a good idea to incorporate a walk every morning" or "Why not try attending a hobby class once a week?" Furthermore, the life plan proposal unit regularly reviews and updates the plan based on the senior's living situation, preferences, and health condition. For example, the generation AI adjusts the plan and makes optimal suggestions based on changes in the senior's health condition and lifestyle.

[0052] (Example 2) A life planning system according to an embodiment of the present invention is a system that collects information on the living conditions, preferences, and health status of seniors in an interactive diary format and proposes an optimal life plan for each individual. As a result, the life planning system can respond to the diversification of seniors and improve the quality of life of seniors by proposing individual life plans.

[0053] A life planning system according to an embodiment includes a data collection unit and a life plan proposal unit. The data collection unit uses a generation AI to collect information about a senior's lifestyle, preferences, and health status in an interactive diary format. For example, the generation AI asks questions such as "How was your day today?" and "How's your health been lately?" to which the senior responds. The data collection unit analyzes the senior's responses and stores them as individual data. For example, the generation AI analyzes the senior's responses using natural language processing technology to extract important information. The data collection unit also periodically collects and updates data about the senior's lifestyle, preferences, and health status. For example, the generation AI periodically collects information about the senior's daily life and health and stores it in a database. The life plan proposal unit proposes an optimal life plan for each individual based on the data collected by the data collection unit. For example, the generation AI proposes appropriate exercise plans, meal plans, and hobby activities based on the senior's health status, hobbies, and lifestyle. The life plan proposal unit also generates a specific plan tailored to the senior's needs. For example, the generation AI makes suggestions such as, "It would be a good idea to incorporate a walk every morning" or "Why not try attending a hobby class once a week?" Furthermore, the life plan proposal unit periodically reviews and updates the plan according to the senior's living situation, preferences, and health condition. For example, the generation AI adjusts the plan according to changes in the senior's health condition and lifestyle rhythm, and makes optimal proposals. As a result, the life planning system according to the embodiment can improve the quality of life of seniors by responding to the diversification of seniors and proposing individual life plans.

[0054] The data collection unit can estimate the emotions of seniors and dynamically generate questions based on those emotions. For example, the data collection unit uses a generation AI to analyze the emotions of seniors in real time and dynamically generate questions based on those emotions. For example, if a senior is feeling stressed, it asks questions about ways to relax. The data collection unit also analyzes the senior's emotional state and generates questions that elicit positive emotions. For example, if a senior is feeling happy, it asks detailed questions about the cause of that joy. The data collection unit also customizes questions based on the senior's emotions based on the emotion estimation data and collects more detailed data. For example, if a senior is feeling anxious, it asks questions about the cause of that anxiety and how to deal with it. In this way, more detailed data can be collected by generating questions based on the senior's emotions.

[0055] The data collection unit can monitor the senior's living environment with sensors and customize questions based on that data. For example, the data collection unit monitors the senior's living environment with sensors, and the generation AI customizes questions based on that data. For example, it asks questions about a comfortable living environment based on room temperature and humidity data. The data collection unit also generates questions based on the senior's lifestyle rhythm and activity level based on the data collected by the sensors. For example, if the senior is not very active, it asks questions about their exercise habits. The data collection unit also analyzes living environment data and customizes questions related to the senior's health condition and lifestyle habits. For example, it asks questions about sleep quality and ways to improve it based on sleep data. This allows more appropriate data to be collected by generating questions based on the senior's living environment.

[0056] The data collection unit can analyze the senior's past response history and predict the next question based on that trend. For example, the data collection unit analyzes the senior's past response history, and the generation AI predicts the next question based on that trend. For example, if there have been many health-related questions in the past, the generation AI will continue to ask questions related to maintaining health. The data collection unit also generates questions based on the senior's interests and concerns based on the past response data. For example, if there have been many responses about hobbies, the data collection unit will ask questions about hobby activities. The data collection unit also analyzes the response history and predicts the next question based on the senior's response pattern. For example, if there have been many responses to a particular topic, the data collection unit will continue to ask questions related to that topic. In this way, by predicting the next question based on the senior's past response history, more relevant data can be collected.

[0057] The data collection unit can collect conversations between the senior and their family or friends, and collect data that takes social connections into account. For example, the data collection unit uses a generation AI to collect conversations between the senior and their family and friends, and collect data that takes social connections into account. For example, the content of conversations with family members is analyzed to understand the senior's social support situation. The data collection unit also collects conversation data between the senior and their friends, and analyzes their social connections and frequency of interactions. For example, the social activities of the senior are evaluated based on the content of conversations with friends. The data collection unit also generates questions to strengthen the senior's social connections based on the conversation data with family and friends. For example, the data collection unit asks questions about activities that deepen relationships with family members. In this way, by collecting data that takes social connections into account, a more comprehensive life plan can be proposed.

[0058] The data collection unit can integrate data from other devices used by the senior to collect comprehensive lifestyle data. The data collection unit integrates data from, for example, smart watches and fitness trackers used by the senior to collect comprehensive lifestyle data. For example, the health condition is evaluated based on heart rate and step count data. The data collection unit also analyzes data collected from other devices to comprehensively understand the senior's lifestyle rhythm and activity level. For example, sleep data and exercise data are integrated and analyzed. The data collection unit also generates questions regarding the senior's lifestyle habits and health condition based on data from the devices. For example, questions about exercise habits are asked based on exercise data. In this way, by integrating data from devices used by the senior, more comprehensive lifestyle data can be collected.

[0059] The data collection unit can use the emotion estimation function to analyze the emotions of seniors when they write their diaries in real time and generate questions that elicit positive emotions. The data collection unit, for example, uses the emotion estimation function to analyze the emotions of seniors when they write their diaries in real time. For example, if a senior is feeling positive emotions, it generates questions that elicit those emotions. The data collection unit also analyzes the senior's emotional state in real time and generates questions to elicit positive emotions. For example, if a senior is feeling happy, it asks detailed questions about the cause of that joy. The data collection unit also customizes questions according to the senior's emotions based on the emotion estimation data to elicit positive emotions. For example, if a senior is feeling anxious, it asks questions to alleviate that anxiety. In this way, by analyzing the senior's emotions in real time and generating questions that elicit positive emotions, it is possible to support the psychological health of seniors.

[0060] The life plan proposal unit can estimate the emotions of a senior and propose a life plan based on the emotions. For example, the generation AI in the life plan proposal unit estimates the emotions of a senior and proposes a life plan based on the emotions. For example, if a senior is feeling stressed, it proposes relaxation activities. The life plan proposal unit also analyzes the emotional state of the senior and proposes a life plan according to the emotions. For example, if a senior is feeling anxious, it proposes activities to reduce the anxiety. The life plan proposal unit also customizes a life plan according to the senior's emotions based on the emotion estimation data. For example, if a senior is feeling joy, it proposes activities to maintain that joy. In this way, by proposing a life plan based on the senior's emotions, the quality of life of the senior is improved.

[0061] The life plan proposal unit can analyze the senior's lifestyle data and dynamically generate a life plan based on the season and weather. The life plan proposal unit, for example, analyzes the senior's lifestyle data and dynamically generates a life plan according to the season and weather. For example, it suggests indoor exercises in winter. The life plan proposal unit also proposes a life plan based on the senior's lifestyle rhythm and activity level based on the season and weather data. For example, it suggests taking a walk during cooler times in summer. The life plan proposal unit also integrates the lifestyle data with the season and weather data to generate a life plan based on the senior's health condition and lifestyle habits. For example, it suggests indoor relaxation activities on rainy days. In this way, the quality of life of the senior is improved by dynamically generating a life plan according to the season and weather.

[0062] The life plan proposal unit proposes a life plan that incorporates the opinions of the senior's family or friends, thereby strengthening social support. For example, the life plan proposal unit uses a generation AI to propose a life plan that incorporates the opinions of the senior's family and friends. For example, it proposes activities to do with family based on the content of conversations with family. The life plan proposal unit also collects the opinions of family and friends and reflects them in the senior's life plan. For example, it proposes activities to promote interaction with friends. The life plan proposal unit also proposes a life plan that strengthens social support for the senior based on the opinions of family and friends. For example, it suggests regular communication with family. In this way, social support can be strengthened by proposing a life plan that incorporates the opinions of the senior's family and friends.

[0063] The life plan proposal unit can incorporate activity information from the local community in which the senior participates and propose a community-based life plan. The life plan proposal unit, for example, collects activity information from the local community in which the senior participates and proposes a community-based life plan. For example, it may propose participation in local events and activities. The life plan proposal unit also customizes the senior's life plan based on the activity information from the local community. For example, it may propose participation in local hobby circles or volunteer activities. The life plan proposal unit also incorporates activity information from the local community and proposes a life plan that strengthens the senior's social connections. For example, it may propose participation in local social events. In this way, community-based support can be provided by proposing a life plan that incorporates activity information from the local community in which the senior participates.

[0064] The life plan proposal unit can use the emotion estimation function to analyze in real time how the senior feels about the proposed plan and adjust the plan. The life plan proposal unit, for example, uses the emotion estimation function to analyze in real time how the senior feels about the proposed plan. For example, if the senior has positive emotions, the plan is maintained. The life plan proposal unit also analyzes the senior's emotional response in real time and adjusts the proposed plan. For example, if the senior has negative emotions, the plan is changed. The life plan proposal unit also customizes the plan according to the senior's emotions based on the emotion estimation data. For example, if the senior is feeling anxious, the life plan proposal unit proposes a plan to alleviate that anxiety. In this way, the emotion the senior feels about the proposed plan is analyzed in real time and the plan is adjusted, thereby improving the senior's satisfaction.

[0065] Based on the data collected by generative AI, a business model can be built that proposes customized products and services for seniors. Based on the data collected by generative AI, a business model can be built that proposes customized products and services for seniors. For example, proposing supplements based on individual health conditions. In addition, the lifestyle data of seniors can be analyzed to propose customized products that meet specific needs. For example, proposing tools and teaching materials that suit hobbies. In addition, based on the collected data, a business model can be built that provides services tailored to the lifestyles of seniors. For example, proposing individual fitness programs and meal plans. This makes it possible to build a business model that meets the needs of seniors by proposing customized products and services for seniors.

[0066] The system can analyze lifestyle data of seniors and develop a business model that provides subscription services based on their specific needs. For example, the system analyzes lifestyle data of seniors and develops a business model that provides subscription services based on their specific needs. For example, it provides subscription services for health management or hobby activities. In addition, based on the collected data, it suggests subscription services that match the lifestyle rhythms and preferences of seniors. For example, it regularly provides monthly health checks or hobby materials. In addition, it develops subscription services that meet the needs of seniors and builds a business model that provides them on an ongoing basis. For example, it regularly provides individual fitness programs or meal plans. In this way, it is possible to build a continuous business model by analyzing lifestyle data of seniors and providing subscription services that meet their specific needs.

[0067] The system can utilize emotional data of seniors to build a business model that deploys an emotion-based marketing strategy. The system, for example, utilizes emotional data of seniors to build a business model that deploys an emotion-based marketing strategy. For example, it carries out advertising and promotions that elicit positive emotions. It also deploys a marketing strategy that proposes products and services that correspond to seniors' emotions based on the emotional data. For example, it prioritizes the promotion of products with high emotional scores. The system also analyzes emotional data of seniors to build a business model that deploys an emotion-based marketing campaign. For example, it provides messages and content that correspond to emotions. In this way, an effective business model can be built by utilizing emotional data of seniors to deploy an emotion-based marketing strategy.

[0068] The system can partner with companies that provide health management services or hobby activity support services for seniors and jointly build new business models. The system, for example, partners with companies that provide health management services for seniors and jointly builds new business models. For example, health data can be shared and individual health plans can be provided. The system can also partner with companies that provide hobby activity support services and build a business model that supports seniors' hobby activities. For example, hobby classes and events can be jointly held. The system can also partner with companies that provide life support services and build a business model that comprehensively supports the lives of seniors. For example, support for daily life and nursing care services can be provided. In this way, the system can partner with companies that provide health management services or hobby activity support services for seniors and jointly build new business models, thereby improving the quality of life of seniors.

[0069] The system can develop new products and services through collaboration with different industries based on lifestyle data of seniors. For example, the system can develop new products and services through collaboration with different industries based on lifestyle data of seniors. For example, it can develop fitness equipment based on health data. It can also collaborate with companies in different industries to provide new products and services that meet the needs of seniors. For example, it can collaborate with food manufacturers to develop health foods. It can also share lifestyle data and build new business models in collaboration with companies in different industries. For example, it can collaborate with technology companies to develop smart home devices. In this way, it can build a business model that meets the needs of seniors by developing new products and services through collaboration with different industries based on lifestyle data of seniors.

[0070] The system can use the emotion estimation function to identify products and services that seniors are most interested in and build a business model based on that. For example, the system uses the emotion estimation function to identify products and services that seniors are most interested in. For example, it prioritizes promoting products with high emotion scores. It can also analyze seniors' emotion data and build a business model that provides products and services based on their emotions. For example, it can suggest customized products based on their emotions. It can also build a business model based on seniors' interests and concerns based on the emotion estimation data. For example, it can provide services with high emotion scores with priority. This makes it possible to identify products and services that seniors are most interested in and build a business model based on them, thereby building a business model that meets the needs of seniors.

[0071] Generative AI can analyze senior emotional data and predict market trends based on emotions. Generative AI, for example, analyzes senior emotional data and predicts market trends based on emotions. For example, it predicts demand for products and services with a high level of positive emotions. It also predicts trends in the senior market based on emotional data. For example, it predicts the market size for products and services with high emotional scores. Generative AI also analyzes senior emotional data and provides market insights based on emotions. For example, it predicts demand for products and services based on emotions. In this way, by analyzing senior emotional data and predicting market trends based on emotions, it is possible to grasp trends in the senior market.

[0072] The system can analyze past consumption behavior data of seniors and predict future market trends. For example, the system analyzes past consumption behavior data of seniors and predicts future market trends. For example, it predicts future consumption trends based on past purchase history. The system also predicts demand in the senior market based on past consumption data. For example, it predicts demand for specific products or services. The system also analyzes consumption behavior data and predicts future market trends. For example, it predicts future market size based on past consumption patterns. In this way, by analyzing past consumption behavior data of seniors and predicting future market trends, it is possible to grasp trends in the senior market.

[0073] The system can compare senior markets in different regions and cultural spheres and predict global market size. For example, the system compares senior markets in different regions and cultural spheres and predicts global market size. For example, the system predicts market size based on the lifestyle habits and preferences of each region. The system also analyzes data for each region to grasp trends in the global senior market. For example, the system compares demand for health management services in each region. The system also compares senior markets in different cultural spheres and predicts global market trends. For example, the system predicts market size based on consumption behavior data for each region. In this way, by comparing senior markets in different regions and cultural spheres and predicting global market size, it is possible to grasp international trends in the senior market.

[0074] The system can predict the market size of new products and services for seniors and use this information to formulate corporate strategies. The system, for example, can predict the market size of new products and services for seniors and use this information to formulate corporate strategies. For example, it can predict demand for new products and formulate marketing strategies. The system can also predict the market size of new products and services and build corporate business models. For example, it can formulate plans to provide new services. The system can also formulate corporate strategies based on market size forecast data. For example, it can determine the timing of entering a new market. In this way, predicting the market size of new products and services for seniors and using this information to formulate corporate strategies can formulate business strategies that meet the needs of the senior market.

[0075] The system can use the emotion estimation function to identify market segments that seniors are most interested in and predict their market size. For example, the system uses the emotion estimation function to identify market segments that seniors are most interested in and predict their market size. For example, the system predicts the market size of products and services with high emotion scores. The system also analyzes emotion data from seniors to identify market segments that they are interested in. For example, the system predicts demand for products and services based on their emotions. The system also predicts the market size of market segments that seniors are interested in based on emotion estimation data. For example, the system predicts demand for market segments with high emotion scores. In this way, by identifying market segments that seniors are most interested in and predicting their market size, it is possible to formulate a business strategy that meets the needs of the senior market.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The data collection unit can monitor the senior's living environment with sensors and customize questions based on that data. For example, it asks about a comfortable living environment based on room temperature and humidity data. The data collection unit also generates questions based on the data collected by the sensors, according to the senior's lifestyle rhythm and activity level. For example, if the senior is not very active, it asks about their exercise habits. The data collection unit also analyzes the living environment data and customizes questions related to the senior's health condition and lifestyle habits. For example, it asks about the quality of sleep and how to improve it based on sleep data. This allows more appropriate data to be collected by generating questions based on the senior's living environment.

[0078] The data collection unit can analyze the senior's past response history and predict the next question based on that trend. For example, if there have been many health-related questions in the past, the data collection unit will continue to ask questions related to maintaining health. The data collection unit also generates questions based on the senior's interests and concerns based on the past response data. For example, if there have been many responses related to hobbies, the data collection unit will ask questions about hobby activities. The data collection unit also analyzes the response history and predicts the next question based on the senior's response patterns. For example, if there have been many responses to a particular topic, the data collection unit will continue to ask questions related to that topic. In this way, by predicting the next question based on the senior's past response history, more relevant data can be collected.

[0079] The data collection unit can collect conversations between the senior and their family or friends, and collect data that takes social connections into account. For example, the generation AI collects conversations between the senior and their family and friends, and collects data that takes social connections into account. For example, the content of conversations with family members is analyzed to understand the senior's social support situation. The data collection unit also collects conversation data between the senior and their friends, and analyzes their social connections and frequency of interactions. For example, the social activities of the senior are evaluated based on the content of conversations with friends. The data collection unit also generates questions to strengthen the senior's social connections based on the conversation data with family and friends. For example, it asks questions about activities that deepen relationships with family members. In this way, by collecting data that takes social connections into account, a more comprehensive life plan can be proposed.

[0080] The data collection unit can integrate data from other devices used by the senior to collect comprehensive lifestyle data. For example, data from smart watches and fitness trackers used by the senior can be integrated to collect comprehensive lifestyle data. For example, health status can be evaluated based on heart rate and step count data. The data collection unit also analyzes data collected from other devices to comprehensively understand the senior's lifestyle rhythm and activity level. For example, sleep data and exercise data can be integrated and analyzed. The data collection unit also generates questions regarding the senior's lifestyle habits and health status based on data from the devices. For example, questions about exercise habits can be asked based on exercise data. In this way, more comprehensive lifestyle data can be collected by integrating data from devices used by the senior.

[0081] The life plan proposal unit can analyze the senior's lifestyle data and dynamically generate a life plan based on the season and weather. For example, it analyzes the senior's lifestyle data and dynamically generates a life plan according to the season and weather. For example, it suggests indoor exercises that can be done in winter. The life plan proposal unit also proposes a life plan based on the senior's lifestyle rhythm and activity level based on the season and weather data. For example, it suggests taking a walk during cooler times in summer. The life plan proposal unit also integrates the lifestyle data with the season and weather data to generate a life plan based on the senior's health condition and lifestyle habits. For example, it suggests indoor relaxation activities when it rains. In this way, the quality of life of the senior is improved by dynamically generating a life plan according to the season and weather.

[0082] The data collection unit can estimate the emotions of seniors and dynamically generate questions based on those emotions. For example, the generation AI analyzes the emotions of seniors in real time and dynamically generates questions based on those emotions. For example, if a senior is feeling stressed, it asks questions about ways to relax. The data collection unit also analyzes the emotional state of seniors and generates questions that elicit positive emotions. For example, if a senior is feeling happy, it asks detailed questions about the cause of that joy. The data collection unit also customizes questions based on the emotions of seniors based on the emotion estimation data and collects more detailed data. For example, if a senior is feeling anxious, it asks questions about the cause of that anxiety and how to deal with it. In this way, more detailed data can be collected by generating questions based on the emotions of seniors.

[0083] The data collection unit can use the emotion estimation function to analyze the emotions of seniors when they write their diaries in real time and generate questions that will elicit positive emotions. For example, the emotion estimation function is used to analyze the emotions of seniors when they write their diaries in real time. For example, if a senior is feeling positive emotions, questions that will elicit those emotions are generated. The data collection unit also analyzes the senior's emotional state in real time and generates questions to elicit positive emotions. For example, if a senior is feeling happy, the data collection unit asks detailed questions about the cause of that joy. The data collection unit also customizes questions based on the emotion estimation data according to the senior's emotions and elicits positive emotions. For example, if a senior is feeling anxious, the data collection unit asks questions to alleviate that anxiety. In this way, by analyzing the senior's emotions in real time and generating questions that will elicit positive emotions, it is possible to support the psychological health of seniors.

[0084] The life plan proposal unit can estimate the emotions of a senior and propose a life plan based on the emotions. For example, the generation AI estimates the emotions of a senior and proposes a life plan based on the emotions. For example, if a senior is feeling stressed, it proposes relaxation activities. The life plan proposal unit also analyzes the emotional state of the senior and proposes a life plan according to the emotions. For example, if a senior is feeling anxious, it proposes activities to reduce the anxiety. The life plan proposal unit also customizes a life plan according to the senior's emotions based on the emotion estimation data. For example, if a senior is feeling joy, it proposes activities to maintain that joy. In this way, by proposing a life plan based on the senior's emotions, the quality of life of the senior is improved.

[0085] The life plan proposal unit can use the emotion estimation function to analyze in real time how the senior feels about the proposed plan and adjust the plan. For example, the emotion estimation function is used to analyze in real time how the senior feels about the proposed plan. For example, if the senior has positive emotions, the plan is maintained. The life plan proposal unit also analyzes the senior's emotional response in real time and adjusts the proposed plan. For example, if the senior has negative emotions, the plan is changed. The life plan proposal unit also customizes the plan according to the senior's emotions based on the emotion estimation data. For example, if the senior is feeling anxious, the life plan proposal unit proposes a plan to alleviate that anxiety. In this way, the emotion the senior feels about the proposed plan is analyzed in real time and the plan is adjusted, thereby improving the senior's satisfaction.

[0086] The system can use the emotion estimation function to identify products and services that seniors are most interested in and build a business model based on that. For example, the emotion estimation function can be used to identify products and services that seniors are most interested in. For example, products with high emotion scores can be promoted preferentially. Furthermore, the system can analyze seniors' emotion data and build a business model that provides products and services based on their emotions. For example, customized products can be suggested based on emotions. Furthermore, a business model can be built based on the emotion estimation data in accordance with seniors' interests and concerns. For example, services with high emotion scores can be provided preferentially. In this way, the system can identify products and services that seniors are most interested in and build a business model based on them, thereby building a business model that meets the needs of seniors.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The data collection unit uses the generation AI to collect information about the senior's living situation, preferences, and health status in an interactive diary format. For example, the generation AI asks questions such as "How was your day today?" and "How is your health lately?" to which the senior responds. The data collection unit then analyzes the senior's responses and stores them as individual data. For example, the generation AI analyzes the senior's responses using natural language processing technology to extract important information. The data collection unit then regularly collects and updates data about the senior's living situation, preferences, and health status. For example, the generation AI regularly collects information about the senior's daily life and health and stores it in a database. Step 2: The life plan proposal unit proposes an optimal life plan for each individual based on the data collected by the data collection unit. For example, the generation AI proposes appropriate exercise plans, meal plans, and hobby activities based on the senior's health condition, hobbies, and lifestyle. The life plan proposal unit also generates specific plans tailored to the senior's needs. For example, the generation AI makes suggestions such as, "It would be a good idea to incorporate a walk every morning" or "Why not try attending a hobby class once a week?" Furthermore, the life plan proposal unit regularly reviews and updates the plan based on the senior's living situation, preferences, and health condition. For example, the generation AI adjusts the plan and makes optimal suggestions based on changes in the senior's health condition and lifestyle.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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]

[0156] 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 data collection unit uses generative AI to collect information on seniors' living conditions, preferences, and health status in an interactive diary format. a life plan proposal unit that proposes an optimal life plan for each individual based on the data collected by the data collection unit. A system characterized by:

2. The data collection unit Estimating the senior's emotions and dynamically generating questions based on the emotions.

2. The system of claim 1.

3. The data collection unit Collecting data on the senior's interactions with family or friends, taking into account their social connections 2. The system of claim 1.

4. The life plan proposal unit Estimating the senior's feelings and proposing a life plan based on the feelings 2. The system of claim 1.

5. The life plan proposal unit Analyze in real time how the senior feels about the proposed plan and adjust the plan 2. The system of claim 1.

6. The system comprises: Utilizing the emotional data of seniors, we will develop a business model that develops emotional marketing strategies.

2. The system of claim 1.

7. The system comprises: Identify the products and services that interest these seniors most and build a business model based on them.

2. The system of claim 1.

8. The generated AI is Analyze the sentiment data of seniors and predict market trends based on their sentiments 2. The system of claim 1.

Images