System

Abstract

An object of a system according to an embodiment is to find dementia risk of an elderly person at an early stage and propose appropriate measures.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a determination unit, and a proposal unit. The collection unit collects conversation contents. The analysis unit analyzes the conversation content collected by the collection unit. The determination unit determines the dementia risk based on the result analyzed by the analysis unit. The proposal unit proposes a necessary countermeasure based on a result determined by the determination unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies are not sufficient in detecting dementia risk in the elderly at an early stage and proposing appropriate countermeasures, so there is room for improvement.

[0005] The system according to the embodiment aims to detect dementia risk in elderly people at an early stage and propose appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and a proposal unit. The collection unit collects conversation content. The analysis unit analyzes the conversation content collected by the collection unit. The determination unit determines the dementia risk based on the analysis result by the analysis unit. The proposal unit proposes necessary measures based on the determination result by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect dementia risk in elderly people at an early stage and propose appropriate measures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI chatbot system according to an embodiment of the present invention serves as a conversation partner for elderly people and helps prevent dementia. This system allows elderly people to converse with the AI ​​chatbot, analyzes the conversation, assesses their dementia risk, and suggests necessary measures. For example, in an AI chatbot system, elderly people converse with the AI ​​chatbot. During this conversation, the AI ​​chatbot offers topics tailored to the elderly's interests and engages in natural conversation. For example, conversations can be held on a variety of topics, such as hobbies, past memories, and everyday events. The AI ​​chatbot system then analyzes the conversation content and the elderly's responses to assess their dementia risk. For example, the AI ​​chatbot can assess dementia risk by analyzing word choice during conversation, reaction speed, and memory decline. Furthermore, the AI ​​chatbot system suggests necessary measures based on the assessment results. For example, if a high dementia risk is determined, the AI ​​chatbot system recommends a visit to a medical institution. It can also provide advice on dementia prevention and daily life precautions. This allows the AI ​​chatbot system to improve the quality of life for elderly people. This allows the AI ​​chatbot system to serve as a conversation partner for elderly people and prevent dementia. Furthermore, by assessing dementia risk from conversation content and proposing necessary measures, early detection and appropriate measures will be possible, thereby improving the quality of life for the elderly.

[0029] The AI ​​chatbot system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and a suggestion unit. The collection unit collects conversational content between the elderly person and the AI ​​chatbot. The collection unit can collect the conversational content by, for example, voice input, text input, or image input. The collection unit can also use AI to provide topics tailored to the elderly person's interests. For example, the collection unit can analyze the elderly person's past conversation history and provide optimal topics. The collection unit can also estimate the elderly person's emotions and select conversation topics based on the estimated emotions. The analysis unit analyzes the conversational content collected by the collection unit. The analysis unit uses AI to analyze the content of the conversation and the elderly person's reactions to determine the dementia risk. For example, the analysis unit can analyze word choice in conversation, speed of response, memory decline, etc. The determination unit determines the dementia risk based on the analysis results by the analysis unit. The determination unit can score the dementia risk using AI and determine the level of risk. The suggestion unit proposes necessary measures based on the determination results by the determination unit. If the AI ​​determines that the elderly person has a high risk of dementia, the suggestion unit recommends that the elderly person visit a medical institution. The suggestion unit can also provide advice for preventing dementia and points to be aware of in daily life. As a result, the AI ​​chatbot system according to the embodiment can serve as a conversation partner for the elderly person and prevent dementia. For example, the collection unit collects the content of the elderly person's conversation, the analysis unit analyzes the content, the determination unit determines the risk of dementia, and the suggestion unit proposes necessary measures. This can improve the quality of life of the elderly person.

[0030] The collection unit can provide topics tailored to the elderly's interests and concerns. For example, the collection unit can analyze the elderly's past conversation history and provide optimal topics. The collection unit can use AI to identify the elderly's interests and concerns and provide topics based on them. For example, the collection unit can re-provide topics that the elderly frequently talked about in the past. The collection unit can also prioritize topics that the elderly have shown interest in in the past. Furthermore, the collection unit can exclude topics that the elderly have avoided in the past and provide new topics. This promotes natural conversation by providing topics tailored to the elderly's interests and is effective in preventing dementia. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input the elderly's past conversation history into AI and have the AI ​​select optimal topics.

[0031] The analysis unit can analyze the content of the conversation and the elderly person's reactions to determine the dementia risk. The analysis unit can analyze the content of the conversation using, for example, voice analysis technology. The analysis unit can analyze the word choice during the conversation and the speed of reactions using AI to determine the dementia risk. For example, the analysis unit can analyze the word choice during the conversation and determine the dementia risk using a language model. The analysis unit can also analyze the speed of reactions during the conversation and determine the dementia risk using a statistical analysis of reaction time. Furthermore, the analysis unit can analyze memory decline during the conversation and determine the dementia risk using a memory test. In this way, the dementia risk can be accurately determined by analyzing the content of the conversation and the elderly person's reactions. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the content of the conversation into AI and have the AI ​​perform a dementia risk determination.

[0032] The determination unit can analyze word choice in conversation, reaction speed, memory decline, etc. The determination unit can, for example, use a language model to analyze word choice in conversation. The determination unit can analyze reaction speed in conversation using AI to determine dementia risk. For example, the determination unit can analyze reaction speed using statistical analysis of reaction time to determine dementia risk. The determination unit can also analyze memory decline in conversation using a memory test to determine dementia risk. In this way, by analyzing word choice in conversation, reaction speed, and memory decline, dementia risk can be determined in more detail. Some or all of the above-mentioned processing in the determination unit may be performed using AI, or may be performed without AI. For example, the determination unit can input word choice in conversation and reaction speed into AI and have the AI ​​perform a dementia risk determination.

[0033] The suggestion unit can recommend a visit to a medical institution if the risk of dementia is determined to be high. For example, the suggestion unit can recommend a visit to a medical institution if the risk of dementia is determined to be high. The suggestion unit can set criteria for recommending a visit to a medical institution if the risk of dementia is determined to be high using AI. For example, the suggestion unit can set a risk score threshold and recommend a visit to a medical institution if specific symptoms appear. The suggestion unit can also generate a message recommending a visit to a medical institution if the risk of dementia is determined to be high. This enables early detection and appropriate measures by recommending a visit to a medical institution if the risk of dementia is high. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the dementia risk assessment result into AI and cause the AI ​​to generate a message recommending a visit to a medical institution.

[0034] The suggestion unit can provide advice for dementia prevention and points to be aware of in daily life. For example, the suggestion unit can provide advice for dementia prevention and points to be aware of in daily life. The suggestion unit can set criteria for providing advice for dementia prevention and points to be aware of in daily life using AI. For example, the suggestion unit can provide advice such as improving diet, recommending exercise, and cognitive training. The suggestion unit can also generate messages providing points to be aware of in daily life. This improves the quality of life of elderly people by providing advice for dementia prevention and points to be aware of in daily life. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input advice for dementia prevention and points to be aware of in daily life into AI and cause the AI ​​to provide the advice and points to be aware of.

[0035] The collection unit can analyze the elderly person's past conversation history and provide the optimal topic. For example, the collection unit can analyze the elderly person's past conversation history and provide the optimal topic. The collection unit can analyze the elderly person's past conversation history using AI and provide the topic based on the analysis. For example, the collection unit can provide topics that the elderly person frequently talked about in the past again. The collection unit can also preferentially provide topics that the elderly person has shown interest in in the past. Furthermore, the collection unit can exclude topics that the elderly person has avoided in the past and provide new topics. In this way, by analyzing the elderly person's past conversation history, more interesting topics can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the elderly person's past conversation history into AI and have the AI ​​select the optimal topic.

[0036] The collection unit can filter conversations based on the elderly person's current health condition and living situation when collecting the conversations. For example, the collection unit can filter conversations based on the elderly person's current health condition and living situation when collecting the conversations. The collection unit can use AI to analyze the elderly person's health condition and living situation and filter the conversations based on the analysis. For example, if the elderly person is not feeling well, the collection unit can provide light-hearted or relaxing topics. If the elderly person is active, the collection unit can provide topics related to going out and exercise. Furthermore, if the elderly person feels lonely, the collection unit can provide topics related to social connections and community. In this way, by filtering conversations based on the elderly person's health condition and living situation, more appropriate topics can be provided. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input data on the elderly person's health condition and living situation into AI and have the AI ​​perform conversation filtering.

[0037] The collection unit can select the optimal collection means according to the elderly person's input method when collecting conversations. For example, when collecting conversations, the collection unit can select the optimal collection means according to the elderly person's input method. The collection unit can analyze the elderly person's input method using AI and select the optimal collection means based on the analysis. For example, if the elderly person prefers voice input, the collection unit can prioritize voice conversation. Also, if the elderly person prefers text input, the collection unit can prioritize chat-style conversation. Furthermore, if the elderly person prefers images, the collection unit can provide conversation using images. This enables smoother conversations by selecting the optimal collection means according to the elderly person's input method. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input data on the elderly person's input method into AI and have the AI ​​select the optimal collection means.

[0038] The collection unit can prioritize collecting highly relevant topics by taking into account the geographical location information of the elderly person when collecting conversations. For example, the collection unit can prioritize collecting highly relevant topics by taking into account the geographical location information of the elderly person when collecting conversations. The collection unit can analyze the geographical location information of the elderly person using AI and provide highly relevant topics based on the analysis. For example, the collection unit can provide topics related to news and events in the area where the elderly person lives. The collection unit can also provide topics related to places the elderly person often visits. Furthermore, the collection unit can provide topics related to the local history and culture of the elderly person. In this way, more relevant topics can be provided by taking into account the geographical location information of the elderly person. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input data on the geographical location information of the elderly person into AI and cause the AI ​​to select highly relevant topics.

[0039] The collection unit can analyze the elderly's social media activities and collect related topics when collecting conversations. For example, the collection unit can analyze the elderly's social media activities and collect related topics when collecting conversations. The collection unit can use AI to analyze the elderly's social media activities and provide topics based on the analysis. For example, the collection unit can provide topics that the elderly is interested in on social media. The collection unit can also provide topics related to accounts the elderly follows on social media. Furthermore, the collection unit can analyze the content of the elderly's social media posts and provide related topics. In this way, by analyzing the elderly's social media activities, more interesting topics can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input data on the elderly's social media activities into AI and have the AI ​​select related topics.

[0040] The collection unit can customize the collection method by reflecting the elderly person's past feedback when collecting conversations. For example, the collection unit can customize the collection method by reflecting the elderly person's past feedback when collecting conversations. The collection unit can use AI to analyze the elderly person's past feedback and customize the collection method based on the analysis. For example, the collection unit can prioritize topics that the elderly person has previously preferred. The collection unit can also exclude topics that the elderly person has previously avoided. Furthermore, the collection unit can adjust the tone and style of the conversation based on the elderly person's past feedback. This makes it possible to provide more appropriate conversations by reflecting the elderly person's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input data on the elderly person's past feedback into AI and have the AI ​​customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the conversation during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the conversation during analysis. The analysis unit can evaluate the importance of the conversation using AI and adjust the level of detail of the analysis based on the evaluation. For example, the analysis unit can perform a detailed analysis of important topics. The analysis unit can also perform a concise analysis of everyday topics. Furthermore, the analysis unit can perform a detailed analysis of topics that elderly people are particularly interested in. By adjusting the level of detail of the analysis based on the importance of the conversation, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input data on the importance of the conversation into AI and have the AI ​​adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the conversation during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of the conversation during analysis. The analysis unit can classify the category of the conversation using AI and apply different analysis algorithms based on the classification. For example, the analysis unit can apply a health-related analysis algorithm to topics related to health. The analysis unit can also apply a hobby-related analysis algorithm to topics related to hobbies. Furthermore, the analysis unit can apply a daily life-related analysis algorithm to topics related to daily life. In this way, applying different analysis algorithms depending on the category of the conversation enables more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input data on the category of the conversation into AI and have the AI ​​select an appropriate analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the elderly person's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the elderly person's past analysis results during analysis. The analysis unit can analyze the elderly person's past analysis results using AI and correct the current analysis result based on the results. For example, the analysis unit can correct the current analysis result based on the elderly person's past analysis results. The analysis unit can also adjust the analysis algorithm based on the elderly person's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis based on the elderly person's past analysis results. In this way, the accuracy of the analysis is improved by referring to the elderly person's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input data on the elderly person's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the time when the conversation was submitted during analysis. For example, the analysis unit can determine the analysis priority based on the time when the conversation was submitted during analysis. The analysis unit can analyze the time when the conversation was submitted using AI and determine the analysis priority based on the time when the conversation was submitted. For example, the analysis unit can prioritize analyzing recent conversations. The analysis unit can also prioritize analyzing conversations in which the elderly person showed particular interest. Furthermore, the analysis unit can prioritize analyzing conversations related to the elderly person's health condition. In this way, by determining the analysis priority based on the time when the conversation was submitted, more important conversations can be prioritized for analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the conversation was submitted into AI and have the AI ​​determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the conversations during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the conversations during analysis. The analysis unit can evaluate the relevance of the conversations using AI and adjust the order of analysis based on the evaluation. For example, the analysis unit can prioritize analysis of highly relevant conversations. The analysis unit can also prioritize analysis of conversations related to the interests of the elderly. Furthermore, the analysis unit can prioritize analysis of conversations related to the health status of the elderly. In this way, by adjusting the order of analysis based on the relevance of the conversations, more relevant conversations can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the conversations into AI and have the AI ​​adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the elderly person's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the elderly person's level of expertise during analysis. The analysis unit can use AI to evaluate the elderly person's level of expertise and adjust the use of technical terms in the analysis based on the evaluation. For example, the analysis unit can use technical terms if the elderly person has technical knowledge. The analysis unit can also provide analysis results in simple language if the elderly person does not have technical knowledge. Furthermore, the analysis unit can adjust the use of technical terms according to the elderly person's level of understanding. In this way, by adjusting the use of technical terms in the analysis according to the elderly person's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input data on the elderly person's level of expertise into AI and have the AI ​​adjust the use of technical terms.

[0047] The determination unit can improve the accuracy of the determination by taking into account the interrelationships of the conversation during the determination. For example, the determination unit can improve the accuracy of the determination by taking into account the interrelationships of the conversation during the determination. The determination unit can analyze the interrelationships of the conversation using AI and improve the accuracy of the determination based on the analysis. For example, the determination unit can make a determination by taking into account the choice of words and the speed of reactions during the conversation. The determination unit can also make a determination by taking into account a decline in memory during the conversation. Furthermore, the determination unit can make a determination by taking into account changes in emotions during the conversation. In this way, by taking into account the interrelationships of the conversation, more accurate determination is possible. Some or all of the above-mentioned processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input data on the interrelationships of the conversation into AI and cause the AI ​​to improve the accuracy of the determination.

[0048] The determination unit can make a determination taking into account the attribute information of the elderly person when making a determination. For example, the determination unit can make a determination taking into account the attribute information of the elderly person when making a determination. The determination unit can analyze the attribute information of the elderly person using AI and make a determination based on the analyzed information. For example, the determination unit can make a determination taking into account the age of the elderly person. The determination unit can also make a determination taking into account the gender of the elderly person. Furthermore, the determination unit can make a determination taking into account the living environment of the elderly person. This enables a more individualized determination by taking into account the attribute information of the elderly person. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input data on the attribute information of the elderly person into AI and have the AI ​​perform the determination.

[0049] The determination unit can weight the determination based on the frequency of the conversation when making a determination. For example, the determination unit can weight the determination based on the frequency of the conversation when making a determination. The determination unit can analyze the frequency of the conversation using AI and weight the determination based on the frequency of the conversation. For example, the determination unit can make a determination by placing emphasis on frequently occurring conversations. The determination unit can also make a determination by placing emphasis on conversations in which the elderly person is particularly interested. Furthermore, the determination unit can make a determination by placing emphasis on conversations related to the elderly person's health condition. In this way, by weighting the determination based on the frequency of the conversation, it is possible to make a determination by placing emphasis on more important conversations. Some or all of the above-mentioned processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input data on the frequency of the conversation into AI and have the AI ​​perform the weighting of the determination.

[0050] The determination unit can make a determination taking into account the geographical distribution of the conversation when making the determination. For example, the determination unit can make a determination taking into account the geographical distribution of the conversation when making the determination. The determination unit can analyze the geographical distribution of the conversation using AI and make a determination based on the analysis. For example, the determination unit can make a determination taking into account the characteristics of the area where the elderly person lives. The determination unit can also make a determination taking into account the characteristics of places frequently visited by the elderly person. Furthermore, the determination unit can make a determination taking into account the local culture and customs of the elderly person. In this way, taking the geographical distribution of the conversation into account enables a determination that is more locally rooted. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input data on the geographical distribution of the conversation into AI and have the AI ​​perform the determination.

[0051] The determination unit can improve the accuracy of the determination by referring to literature related to the conversation during the determination. For example, the determination unit can improve the accuracy of the determination by referring to literature related to the conversation during the determination. The determination unit can analyze literature related to the conversation using AI and improve the accuracy of the determination based on the analysis. For example, the determination unit can make a determination by referring to academic papers related to the content of the conversation. The determination unit can also make a determination by referring to specialized books related to the content of the conversation. Furthermore, the determination unit can make a determination by referring to the latest research results related to the content of the conversation. In this way, by referring to literature related to the conversation, more accurate determination is possible. Some or all of the above-mentioned processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input data on literature related to the conversation into AI and cause the AI ​​to improve the accuracy of the determination.

[0052] The determination unit can make a determination taking into account the market value of the conversation when making the determination. For example, the determination unit can make a determination taking into account the market value of the conversation when making the determination. The determination unit can analyze the market value of the conversation using AI and make a determination based on the analysis. For example, the determination unit can make a determination taking into account the market value of products or services discussed by the elderly. The determination unit can also make a determination taking into account the market value of trends in which the elderly are interested. Furthermore, the determination unit can make a determination taking into account the market value of events or activities discussed by the elderly. In this way, by taking into account the market value of the conversation, it is possible to make a determination that emphasizes more valuable conversations. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input data on the market value of the conversation into AI and have the AI ​​perform the determination.

[0053] The suggestion unit can adjust the level of detail of the proposal based on the importance of the dementia risk when making a proposal. For example, the suggestion unit can adjust the level of detail of the proposal based on the importance of the dementia risk when making a proposal. The suggestion unit can use AI to evaluate the importance of the dementia risk and adjust the level of detail of the proposal based on the evaluation. For example, the suggestion unit can make a detailed proposal when the dementia risk is high. The suggestion unit can also make a concise proposal when the dementia risk is medium. Furthermore, the suggestion unit can provide general advice when the dementia risk is low. In this way, adjusting the level of detail of the proposal based on the importance of the dementia risk enables more appropriate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input dementia risk data into AI and cause the AI ​​to adjust the level of detail of the proposal.

[0054] The suggestion unit can apply different suggestion algorithms depending on the dementia risk category when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the dementia risk category when making a suggestion. The suggestion unit can classify dementia risk categories using AI and apply different suggestion algorithms based on the classification. For example, the suggestion unit can apply a health-related suggestion algorithm to health risks. The suggestion unit can also apply a suggestion algorithm that promotes social connections to risks related to social isolation. Furthermore, the suggestion unit can apply a suggestion algorithm that improves cognitive function to risks related to cognitive decline. This enables more appropriate suggestions by applying different suggestion algorithms depending on the dementia risk category. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input data on dementia risk categories into AI and cause the AI ​​to select an appropriate suggestion algorithm.

[0055] The suggestion unit can improve the accuracy of the suggestion by referring to the elderly person's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the elderly person's past suggestion results when making a suggestion. The suggestion unit can analyze the elderly person's past suggestion results using AI and correct the current suggestion based on the results. For example, the suggestion unit can correct the current suggestion based on the elderly person's past suggestion results. The suggestion unit can also adjust the proposal algorithm based on the elderly person's past suggestion results. Furthermore, the suggestion unit can improve the accuracy of the suggestion based on the elderly person's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the elderly person's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI or without AI. For example, the suggestion unit can input data of the elderly person's past suggestion results into AI and cause the AI ​​to improve the accuracy of the suggestion.

[0056] The suggestion unit can determine the priority of the proposals based on the time of submission of the dementia risk information at the time of proposal. For example, the suggestion unit can determine the priority of the proposals based on the time of submission of the dementia risk information at the time of proposal. The suggestion unit can analyze the time of submission of the dementia risk information using AI and determine the priority of the proposals based on the analysis. For example, the suggestion unit can prioritize proposals based on the time of submission of the dementia risk information. The suggestion unit can also prioritize proposals based on the time of submission of the dementia risk information. Furthermore, the suggestion unit can prioritize proposals based on the time of submission of the dementia risk information. In addition, the suggestion unit can prioritize proposals based on the time of submission of the dementia risk information. In this way, by prioritizing proposals based on the time of submission of the dementia risk information, more important proposals can be prioritized. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input data on the time of submission of the dementia risk information into AI and have the AI ​​determine the priority of the proposals.

[0057] The suggestion unit can adjust the order of suggestions based on the relevance of dementia risk when making a suggestion. For example, the suggestion unit can adjust the order of suggestions based on the relevance of dementia risk when making a suggestion. The suggestion unit can use AI to evaluate the relevance of dementia risk and adjust the order of suggestions based on the evaluation. For example, the suggestion unit can prioritize proposing highly relevant risks. The suggestion unit can also prioritize proposing risks related to the interests of the elderly person. Furthermore, the suggestion unit can prioritize proposing risks related to the health status of the elderly person. This enables more appropriate suggestions by adjusting the order of suggestions based on the relevance of dementia risk. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input data on the relevance of dementia risk to AI and cause the AI ​​to adjust the order of suggestions.

[0058] The suggestion unit can adjust the use of technical terminology in the proposal according to the elderly person's level of expertise when making a proposal. For example, the suggestion unit can adjust the use of technical terminology in the proposal according to the elderly person's level of expertise when making a proposal. The suggestion unit can use AI to evaluate the elderly person's level of expertise and adjust the use of technical terminology in the proposal based on the evaluation. For example, the suggestion unit can use technical terminology if the elderly person has specialized knowledge. The suggestion unit can also make suggestions in simple language if the elderly person does not have specialized knowledge. Furthermore, the suggestion unit can adjust the use of technical terminology according to the elderly person's level of understanding. This enables suggestions that are easier to understand by adjusting the use of technical terminology in the proposal according to the elderly person's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI or without AI. For example, the suggestion unit can input data on the elderly person's level of expertise into AI and have the AI ​​adjust the use of technical terminology.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When collecting conversation content of the elderly person, the collection unit can analyze background sounds of the conversation and select a conversation topic based on the environmental sounds. For example, if the collection unit hears birds chirping or the sound of wind, it can provide topics related to nature and the outdoors. Also, if the collection unit hears the sound of a television or music, it can provide topics related to entertainment. Furthermore, if the collection unit hears traffic sounds or people talking, it can provide topics related to city life or travel. In this way, the collection unit can provide appropriate topics according to the elderly person's environment.

[0061] When analyzing the content of the conversation, the determination unit can refer to the elderly person's health data and determine the dementia risk based on the health condition. For example, the determination unit can refer to the elderly person's blood pressure and heart rate data and determine that the dementia risk is high if abnormalities are found. The determination unit can also refer to the elderly person's exercise amount and sleep data and determine that the dementia risk is high if there is a lack of exercise or sleep. Furthermore, the determination unit can refer to the elderly person's dietary data and determine that the dementia risk is high if the nutritional balance is poor. This allows the determination unit to make a highly accurate determination based on the elderly person's health condition.

[0062] If the risk of dementia is determined to be high, the suggestion unit can provide a specific action plan to reduce the risk. For example, the suggestion unit can recommend daily walking or light exercise. The suggestion unit can also suggest a balanced meal menu and provide specific ingredients and recipes to improve nutritional balance. Furthermore, the suggestion unit can recommend brain training such as cognitive training or puzzle games and suggest ways to maintain cognitive function by engaging in these activities on a daily basis. In this way, the suggestion unit can help elderly people reduce their risk of dementia through specific actions.

[0063] When collecting the content of the elderly person's conversation, the collection unit analyzes the tempo and rhythm of the conversation and can provide appropriate responses according to the flow of the conversation. For example, if the elderly person speaks slowly, the collection unit can respond at a slower tempo. Also, if the elderly person speaks quickly, the collection unit can respond by matching the tempo. Furthermore, if the elderly person speaks with pauses, the collection unit can respond by taking appropriate pauses. This allows the collection unit to provide natural conversation that matches the elderly person's speaking style.

[0064] When analyzing the content of the conversation, the determination unit can refer to the social activity data of the elderly person and determine the risk of social isolation. For example, the determination unit can refer to data on community activities and events in which the elderly person participates and determine that the risk of social isolation is high if the frequency of participation is low. The determination unit can also refer to data on the elderly person's interactions with family and friends and determine that the risk of social isolation is high if the elderly person has few interactions. Furthermore, the determination unit can refer to data on the elderly person's online interactions and determine that the risk of social isolation is high if the elderly person has few online activities. This allows the determination unit to make highly accurate determinations based on the elderly person's social activities.

[0065] If the dementia risk is determined to be high, the suggestion unit can recommend participation in community activities to reduce the risk. For example, the suggestion unit can recommend participation in local circles or club activities. The suggestion unit can also recommend volunteer activities or participation in local events. Furthermore, the suggestion unit can recommend interactions through online communities or social media and suggest ways to maintain social connections. In this way, the suggestion unit can help the elderly prevent social isolation and reduce the dementia risk.

[0066] When collecting the conversation content of the elderly person, the collection unit can analyze the context of the conversation and provide an appropriate response based on the context. For example, if the elderly person is talking about a past event, the collection unit can provide questions or comments related to that event. Also, if the elderly person is talking about a current situation, the collection unit can provide information or advice related to that situation. Furthermore, if the elderly person is talking about future plans, the collection unit can provide suggestions or support related to those plans. In this way, the collection unit can provide a natural conversation that matches the context of the elderly person's conversation.

[0067] When analyzing the content of the conversation, the analysis unit can refer to the elderly person's lifestyle habit data and determine the dementia risk based on the lifestyle habit. For example, the analysis unit can refer to the elderly person's eating habit data and determine that the elderly person has a high dementia risk if their nutritional balance is poor. The analysis unit can also refer to the elderly person's exercise habit data and determine that the elderly person has a high dementia risk if they are not getting enough exercise. Furthermore, the analysis unit can refer to the elderly person's sleeping habit data and determine that the elderly person has a high dementia risk if they are not getting enough sleep. This allows the analysis unit to make highly accurate determinations based on the elderly person's lifestyle habits.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The collection unit collects the conversation content that the elderly person has with the AI ​​chatbot. The collection unit can collect the conversation content using methods such as voice input, text input, and image input. The collection unit can also use AI to provide topics that match the elderly person's interests and provide the most appropriate topics by analyzing past conversation history. Furthermore, the collection unit can estimate the elderly person's emotions and select conversation topics based on the estimated emotions. Step 2: The analysis unit analyzes the conversation content collected by the collection unit. The analysis unit uses AI to analyze the content of the conversation and the elderly person's reactions to determine the risk of dementia. For example, the analysis unit can analyze word choice during conversation, speed of reaction, and memory decline. Step 3: The assessment unit assesses the dementia risk based on the results of the analysis by the analysis unit. The assessment unit can use AI to score the dementia risk and determine whether the risk is high or low. Step 4: The proposal unit proposes necessary measures based on the results of the assessment by the assessment unit. If the proposal unit determines using AI that the person is at high risk of dementia, it recommends that the person visit a medical institution. The proposal unit can also provide advice on dementia prevention and points to be careful about in daily life.

[0070] (Example 2) An AI chatbot system according to an embodiment of the present invention serves as a conversation partner for elderly people and helps prevent dementia. This system allows elderly people to converse with the AI ​​chatbot, analyzes the conversation, assesses their dementia risk, and suggests necessary measures. For example, in an AI chatbot system, elderly people converse with the AI ​​chatbot. During this conversation, the AI ​​chatbot offers topics tailored to the elderly's interests and engages in natural conversation. For example, conversations can be held on a variety of topics, such as hobbies, past memories, and everyday events. The AI ​​chatbot system then analyzes the conversation content and the elderly's responses to assess their dementia risk. For example, the AI ​​chatbot can assess dementia risk by analyzing word choice during conversation, reaction speed, and memory decline. Furthermore, the AI ​​chatbot system suggests necessary measures based on the assessment results. For example, if a high dementia risk is determined, the AI ​​chatbot system recommends a visit to a medical institution. It can also provide advice on dementia prevention and daily life precautions. This allows the AI ​​chatbot system to improve the quality of life for elderly people. This allows the AI ​​chatbot system to serve as a conversation partner for elderly people and prevent dementia. Furthermore, by assessing dementia risk from conversation content and proposing necessary measures, early detection and appropriate measures will be possible, thereby improving the quality of life for the elderly.

[0071] The AI ​​chatbot system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and a suggestion unit. The collection unit collects conversational content between the elderly person and the AI ​​chatbot. The collection unit can collect the conversational content by, for example, voice input, text input, or image input. The collection unit can also use AI to provide topics tailored to the elderly person's interests. For example, the collection unit can analyze the elderly person's past conversation history and provide optimal topics. The collection unit can also estimate the elderly person's emotions and select conversation topics based on the estimated emotions. The analysis unit analyzes the conversational content collected by the collection unit. The analysis unit uses AI to analyze the content of the conversation and the elderly person's reactions to determine the dementia risk. For example, the analysis unit can analyze word choice in conversation, speed of response, memory decline, etc. The determination unit determines the dementia risk based on the analysis results by the analysis unit. The determination unit can score the dementia risk using AI and determine the level of risk. The suggestion unit proposes necessary measures based on the determination results by the determination unit. If the AI ​​determines that the elderly person has a high risk of dementia, the suggestion unit recommends that the elderly person visit a medical institution. The suggestion unit can also provide advice for preventing dementia and points to be aware of in daily life. As a result, the AI ​​chatbot system according to the embodiment can serve as a conversation partner for the elderly person and prevent dementia. For example, the collection unit collects the content of the elderly person's conversation, the analysis unit analyzes the content, the determination unit determines the risk of dementia, and the suggestion unit proposes necessary measures. This can improve the quality of life of the elderly person.

[0072] The collection unit can provide topics tailored to the elderly's interests and concerns. For example, the collection unit can analyze the elderly's past conversation history and provide optimal topics. The collection unit can use AI to identify the elderly's interests and concerns and provide topics based on them. For example, the collection unit can re-provide topics that the elderly frequently talked about in the past. The collection unit can also prioritize topics that the elderly have shown interest in in the past. Furthermore, the collection unit can exclude topics that the elderly have avoided in the past and provide new topics. This promotes natural conversation by providing topics tailored to the elderly's interests and is effective in preventing dementia. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input the elderly's past conversation history into AI and have the AI ​​select optimal topics.

[0073] The analysis unit can analyze the content of the conversation and the elderly person's reactions to determine the dementia risk. The analysis unit can analyze the content of the conversation using, for example, voice analysis technology. The analysis unit can analyze the word choice during the conversation and the speed of reactions using AI to determine the dementia risk. For example, the analysis unit can analyze the word choice during the conversation and determine the dementia risk using a language model. The analysis unit can also analyze the speed of reactions during the conversation and determine the dementia risk using a statistical analysis of reaction time. Furthermore, the analysis unit can analyze memory decline during the conversation and determine the dementia risk using a memory test. In this way, the dementia risk can be accurately determined by analyzing the content of the conversation and the elderly person's reactions. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the content of the conversation into AI and have the AI ​​perform a dementia risk determination.

[0074] The determination unit can analyze word choice in conversation, reaction speed, memory decline, etc. The determination unit can, for example, use a language model to analyze word choice in conversation. The determination unit can analyze reaction speed in conversation using AI to determine dementia risk. For example, the determination unit can analyze reaction speed using statistical analysis of reaction time to determine dementia risk. The determination unit can also analyze memory decline in conversation using a memory test to determine dementia risk. In this way, by analyzing word choice in conversation, reaction speed, and memory decline, dementia risk can be determined in more detail. Some or all of the above-mentioned processing in the determination unit may be performed using AI, or may be performed without AI. For example, the determination unit can input word choice in conversation and reaction speed into AI and have the AI ​​perform a dementia risk determination.

[0075] The suggestion unit can recommend a visit to a medical institution if the risk of dementia is determined to be high. For example, the suggestion unit can recommend a visit to a medical institution if the risk of dementia is determined to be high. The suggestion unit can set criteria for recommending a visit to a medical institution if the risk of dementia is determined to be high using AI. For example, the suggestion unit can set a risk score threshold and recommend a visit to a medical institution if specific symptoms appear. The suggestion unit can also generate a message recommending a visit to a medical institution if the risk of dementia is determined to be high. This enables early detection and appropriate measures by recommending a visit to a medical institution if the risk of dementia is high. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the dementia risk assessment result into AI and cause the AI ​​to generate a message recommending a visit to a medical institution.

[0076] The suggestion unit can provide advice for dementia prevention and points to be aware of in daily life. For example, the suggestion unit can provide advice for dementia prevention and points to be aware of in daily life. The suggestion unit can set criteria for providing advice for dementia prevention and points to be aware of in daily life using AI. For example, the suggestion unit can provide advice such as improving diet, recommending exercise, and cognitive training. The suggestion unit can also generate messages providing points to be aware of in daily life. This improves the quality of life of elderly people by providing advice for dementia prevention and points to be aware of in daily life. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input advice for dementia prevention and points to be aware of in daily life into AI and cause the AI ​​to provide the advice and points to be aware of.

[0077] The collection unit can estimate the elderly person's emotions and select a conversation topic based on the estimated elderly person's emotions. For example, the collection unit can estimate the elderly person's emotions and select a conversation topic based on the estimated emotions. The collection unit can estimate the elderly person's emotions using AI and select a conversation topic based on the emotions. For example, if the elderly person is sad, the collection unit can provide topics related to happy memories or hobbies. If the elderly person is excited, the collection unit can provide topics related to interesting new information or news. Furthermore, if the elderly person is relaxed, the collection unit can provide topics related to everyday events or casual conversation. This allows for more appropriate conversation by selecting a conversation topic based on the elderly person's emotions. Some or all of the above-described processing by the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the elderly person's emotional data into AI and have the AI ​​select a conversation topic.

[0078] The collection unit can analyze the elderly person's past conversation history and provide the optimal topic. For example, the collection unit can analyze the elderly person's past conversation history and provide the optimal topic. The collection unit can analyze the elderly person's past conversation history using AI and provide the topic based on the analysis. For example, the collection unit can provide topics that the elderly person frequently talked about in the past again. The collection unit can also preferentially provide topics that the elderly person has shown interest in in the past. Furthermore, the collection unit can exclude topics that the elderly person has avoided in the past and provide new topics. In this way, by analyzing the elderly person's past conversation history, more interesting topics can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the elderly person's past conversation history into AI and have the AI ​​select the optimal topic.

[0079] The collection unit can filter conversations based on the elderly person's current health condition and living situation when collecting the conversations. For example, the collection unit can filter conversations based on the elderly person's current health condition and living situation when collecting the conversations. The collection unit can use AI to analyze the elderly person's health condition and living situation and filter the conversations based on the analysis. For example, if the elderly person is not feeling well, the collection unit can provide light-hearted or relaxing topics. If the elderly person is active, the collection unit can provide topics related to going out and exercise. Furthermore, if the elderly person feels lonely, the collection unit can provide topics related to social connections and community. In this way, by filtering conversations based on the elderly person's health condition and living situation, more appropriate topics can be provided. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input data on the elderly person's health condition and living situation into AI and have the AI ​​perform conversation filtering.

[0080] The collection unit can select the optimal collection means according to the elderly person's input method when collecting conversations. For example, when collecting conversations, the collection unit can select the optimal collection means according to the elderly person's input method. The collection unit can analyze the elderly person's input method using AI and select the optimal collection means based on the analysis. For example, if the elderly person prefers voice input, the collection unit can prioritize voice conversation. Also, if the elderly person prefers text input, the collection unit can prioritize chat-style conversation. Furthermore, if the elderly person prefers images, the collection unit can provide conversation using images. This enables smoother conversations by selecting the optimal collection means according to the elderly person's input method. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input data on the elderly person's input method into AI and have the AI ​​select the optimal collection means.

[0081] The collection unit can estimate the emotions of the elderly person and determine the priority of conversations to be collected based on the estimated emotions of the elderly person. For example, the collection unit can estimate the emotions of the elderly person and determine the priority of conversations to be collected based on the estimated emotions. The collection unit can estimate the emotions of the elderly person using AI and determine the priority of conversations based on the emotions. For example, if the elderly person is feeling anxious, the collection unit can prioritize topics that give a sense of security. Also, if the elderly person is happy, the collection unit can prioritize fun topics. Furthermore, if the elderly person is tired, the collection unit can prioritize topics that are relaxing. In this way, by determining the priority of conversations based on the emotions of the elderly person, more appropriate conversations can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input emotional data of the elderly person into AI and have the AI ​​determine the priority of conversations.

[0082] The collection unit can prioritize collecting highly relevant topics by taking into account the geographical location information of the elderly person when collecting conversations. For example, the collection unit can prioritize collecting highly relevant topics by taking into account the geographical location information of the elderly person when collecting conversations. The collection unit can analyze the geographical location information of the elderly person using AI and provide highly relevant topics based on the analysis. For example, the collection unit can provide topics related to news and events in the area where the elderly person lives. The collection unit can also provide topics related to places the elderly person often visits. Furthermore, the collection unit can provide topics related to the local history and culture of the elderly person. In this way, more relevant topics can be provided by taking into account the geographical location information of the elderly person. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input data on the geographical location information of the elderly person into AI and cause the AI ​​to select highly relevant topics.

[0083] The collection unit can analyze the elderly's social media activities and collect related topics when collecting conversations. For example, the collection unit can analyze the elderly's social media activities and collect related topics when collecting conversations. The collection unit can use AI to analyze the elderly's social media activities and provide topics based on the analysis. For example, the collection unit can provide topics that the elderly is interested in on social media. The collection unit can also provide topics related to accounts the elderly follows on social media. Furthermore, the collection unit can analyze the content of the elderly's social media posts and provide related topics. In this way, by analyzing the elderly's social media activities, more interesting topics can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input data on the elderly's social media activities into AI and have the AI ​​select related topics.

[0084] The collection unit can customize the collection method by reflecting the elderly person's past feedback when collecting conversations. For example, the collection unit can customize the collection method by reflecting the elderly person's past feedback when collecting conversations. The collection unit can use AI to analyze the elderly person's past feedback and customize the collection method based on the analysis. For example, the collection unit can prioritize topics that the elderly person has previously preferred. The collection unit can also exclude topics that the elderly person has previously avoided. Furthermore, the collection unit can adjust the tone and style of the conversation based on the elderly person's past feedback. This makes it possible to provide more appropriate conversations by reflecting the elderly person's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input data on the elderly person's past feedback into AI and have the AI ​​customize the collection method.

[0085] The analysis unit can estimate the elderly person's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, the analysis unit can estimate the elderly person's emotions and adjust the way the analysis is presented based on the estimated emotions. The analysis unit can estimate the elderly person's emotions using AI and adjust the way the analysis is presented based on the emotions. For example, if the elderly person is feeling anxious, the analysis unit can present the analysis results in a gentle tone. If the elderly person is excited, the analysis unit can also provide detailed analysis results. Furthermore, if the elderly person is relaxed, the analysis unit can also provide concise and easy-to-understand analysis results. By adjusting the way the analysis is presented based on the elderly person's emotions, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the elderly person's emotion data into AI and have the AI ​​adjust the way the analysis is presented.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the conversation during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the conversation during analysis. The analysis unit can evaluate the importance of the conversation using AI and adjust the level of detail of the analysis based on the evaluation. For example, the analysis unit can perform a detailed analysis of important topics. The analysis unit can also perform a concise analysis of everyday topics. Furthermore, the analysis unit can perform a detailed analysis of topics that elderly people are particularly interested in. By adjusting the level of detail of the analysis based on the importance of the conversation, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input data on the importance of the conversation into AI and have the AI ​​adjust the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the category of the conversation during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of the conversation during analysis. The analysis unit can classify the category of the conversation using AI and apply different analysis algorithms based on the classification. For example, the analysis unit can apply a health-related analysis algorithm to topics related to health. The analysis unit can also apply a hobby-related analysis algorithm to topics related to hobbies. Furthermore, the analysis unit can apply a daily life-related analysis algorithm to topics related to daily life. In this way, applying different analysis algorithms depending on the category of the conversation enables more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input data on the category of the conversation into AI and have the AI ​​select an appropriate analysis algorithm.

[0088] The analysis unit can improve the accuracy of the analysis by referring to the elderly person's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the elderly person's past analysis results during analysis. The analysis unit can analyze the elderly person's past analysis results using AI and correct the current analysis result based on the results. For example, the analysis unit can correct the current analysis result based on the elderly person's past analysis results. The analysis unit can also adjust the analysis algorithm based on the elderly person's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis based on the elderly person's past analysis results. In this way, the accuracy of the analysis is improved by referring to the elderly person's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input data on the elderly person's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0089] The analysis unit can estimate the elderly person's emotions and adjust the length of the analysis based on the estimated elderly person's emotions. For example, the analysis unit can estimate the elderly person's emotions and adjust the length of the analysis based on the estimated emotions. The analysis unit can estimate the elderly person's emotions using AI and adjust the length of the analysis based on the emotions. For example, the analysis unit can provide a short and to-the-point analysis result if the elderly person is in a hurry. The analysis unit can also provide a detailed analysis result if the elderly person is relaxed. Furthermore, the analysis unit can provide a visually stimulating analysis result if the elderly person is excited. By adjusting the length of the analysis based on the elderly person's emotions, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the elderly person's emotion data into AI and have the AI ​​adjust the length of the analysis.

[0090] The analysis unit can determine the analysis priority based on the time when the conversation was submitted during analysis. For example, the analysis unit can determine the analysis priority based on the time when the conversation was submitted during analysis. The analysis unit can analyze the time when the conversation was submitted using AI and determine the analysis priority based on the time when the conversation was submitted. For example, the analysis unit can prioritize analyzing recent conversations. The analysis unit can also prioritize analyzing conversations in which the elderly person showed particular interest. Furthermore, the analysis unit can prioritize analyzing conversations related to the elderly person's health condition. In this way, by determining the analysis priority based on the time when the conversation was submitted, more important conversations can be prioritized for analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the conversation was submitted into AI and have the AI ​​determine the analysis priority.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the conversations during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the conversations during analysis. The analysis unit can evaluate the relevance of the conversations using AI and adjust the order of analysis based on the evaluation. For example, the analysis unit can prioritize analysis of highly relevant conversations. The analysis unit can also prioritize analysis of conversations related to the interests of the elderly. Furthermore, the analysis unit can prioritize analysis of conversations related to the health status of the elderly. In this way, by adjusting the order of analysis based on the relevance of the conversations, more relevant conversations can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the conversations into AI and have the AI ​​adjust the order of analysis.

[0092] The analysis unit can adjust the use of technical terms in the analysis according to the elderly person's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the elderly person's level of expertise during analysis. The analysis unit can use AI to evaluate the elderly person's level of expertise and adjust the use of technical terms in the analysis based on the evaluation. For example, the analysis unit can use technical terms if the elderly person has technical knowledge. The analysis unit can also provide analysis results in simple language if the elderly person does not have technical knowledge. Furthermore, the analysis unit can adjust the use of technical terms according to the elderly person's level of understanding. In this way, by adjusting the use of technical terms in the analysis according to the elderly person's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input data on the elderly person's level of expertise into AI and have the AI ​​adjust the use of technical terms.

[0093] The determination unit can estimate the elderly person's emotions and adjust the determination criteria based on the estimated elderly person's emotions. The determination unit can, for example, estimate the elderly person's emotions and adjust the determination criteria based on the estimated emotions. The determination unit can estimate the elderly person's emotions using AI and adjust the determination criteria based on the emotions. For example, the determination unit can relax strict determination criteria when the elderly person is feeling anxious. The determination unit can also apply normal determination criteria when the elderly person is relaxed. Furthermore, the determination unit can apply detailed determination criteria when the elderly person is excited. This enables more appropriate determination by adjusting the determination criteria based on the elderly person's emotions. Some or all of the above-mentioned processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input the elderly person's emotion data into AI and have the AI ​​adjust the determination criteria.

[0094] The determination unit can improve the accuracy of the determination by taking into account the interrelationships of the conversation during the determination. For example, the determination unit can improve the accuracy of the determination by taking into account the interrelationships of the conversation during the determination. The determination unit can analyze the interrelationships of the conversation using AI and improve the accuracy of the determination based on the analysis. For example, the determination unit can make a determination by taking into account the choice of words and the speed of reactions during the conversation. The determination unit can also make a determination by taking into account a decline in memory during the conversation. Furthermore, the determination unit can make a determination by taking into account changes in emotions during the conversation. In this way, by taking into account the interrelationships of the conversation, more accurate determination is possible. Some or all of the above-mentioned processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input data on the interrelationships of the conversation into AI and cause the AI ​​to improve the accuracy of the determination.

[0095] The determination unit can make a determination taking into account the attribute information of the elderly person when making a determination. For example, the determination unit can make a determination taking into account the attribute information of the elderly person when making a determination. The determination unit can analyze the attribute information of the elderly person using AI and make a determination based on the analyzed information. For example, the determination unit can make a determination taking into account the age of the elderly person. The determination unit can also make a determination taking into account the gender of the elderly person. Furthermore, the determination unit can make a determination taking into account the living environment of the elderly person. This enables a more individualized determination by taking into account the attribute information of the elderly person. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input data on the attribute information of the elderly person into AI and have the AI ​​perform the determination.

[0096] The determination unit can weight the determination based on the frequency of the conversation when making a determination. For example, the determination unit can weight the determination based on the frequency of the conversation when making a determination. The determination unit can analyze the frequency of the conversation using AI and weight the determination based on the frequency of the conversation. For example, the determination unit can make a determination by placing emphasis on frequently occurring conversations. The determination unit can also make a determination by placing emphasis on conversations in which the elderly person is particularly interested. Furthermore, the determination unit can make a determination by placing emphasis on conversations related to the elderly person's health condition. In this way, by weighting the determination based on the frequency of the conversation, it is possible to make a determination by placing emphasis on more important conversations. Some or all of the above-mentioned processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input data on the frequency of the conversation into AI and have the AI ​​perform the weighting of the determination.

[0097] The determination unit can estimate the elderly person's emotions and adjust the order in which the determination results are displayed based on the estimated elderly person's emotions. The determination unit can, for example, estimate the elderly person's emotions and adjust the order in which the determination results are displayed based on the estimated emotions. The determination unit can estimate the elderly person's emotions using AI and adjust the order in which the determination results are displayed based on the emotions. For example, if the elderly person is feeling anxious, the determination unit can first display results that provide a sense of security. Furthermore, if the elderly person is relaxed, the determination unit can first display detailed results. Furthermore, if the elderly person is excited, the determination unit can first display important results. This allows for more appropriate result display by adjusting the order in which the determination results are displayed based on the elderly person's emotions. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without AI. For example, the determination unit can input the elderly person's emotion data into AI and have the AI ​​adjust the order in which the results are displayed.

[0098] The determination unit can make a determination taking into account the geographical distribution of the conversation when making the determination. For example, the determination unit can make a determination taking into account the geographical distribution of the conversation when making the determination. The determination unit can analyze the geographical distribution of the conversation using AI and make a determination based on the analysis. For example, the determination unit can make a determination taking into account the characteristics of the area where the elderly person lives. The determination unit can also make a determination taking into account the characteristics of places frequently visited by the elderly person. Furthermore, the determination unit can make a determination taking into account the local culture and customs of the elderly person. In this way, taking the geographical distribution of the conversation into account enables a determination that is more locally rooted. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input data on the geographical distribution of the conversation into AI and have the AI ​​perform the determination.

[0099] The determination unit can improve the accuracy of the determination by referring to literature related to the conversation during the determination. For example, the determination unit can improve the accuracy of the determination by referring to literature related to the conversation during the determination. The determination unit can analyze literature related to the conversation using AI and improve the accuracy of the determination based on the analysis. For example, the determination unit can make a determination by referring to academic papers related to the content of the conversation. The determination unit can also make a determination by referring to specialized books related to the content of the conversation. Furthermore, the determination unit can make a determination by referring to the latest research results related to the content of the conversation. In this way, by referring to literature related to the conversation, more accurate determination is possible. Some or all of the above-mentioned processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input data on literature related to the conversation into AI and cause the AI ​​to improve the accuracy of the determination.

[0100] The determination unit can make a determination taking into account the market value of the conversation when making the determination. For example, the determination unit can make a determination taking into account the market value of the conversation when making the determination. The determination unit can analyze the market value of the conversation using AI and make a determination based on the analysis. For example, the determination unit can make a determination taking into account the market value of products or services discussed by the elderly. The determination unit can also make a determination taking into account the market value of trends in which the elderly are interested. Furthermore, the determination unit can make a determination taking into account the market value of events or activities discussed by the elderly. In this way, by taking into account the market value of the conversation, it is possible to make a determination that emphasizes more valuable conversations. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input data on the market value of the conversation into AI and have the AI ​​perform the determination.

[0101] The suggestion unit can estimate the elderly person's emotions and adjust the way the suggestion is expressed based on the estimated elderly person's emotions. The suggestion unit can, for example, estimate the elderly person's emotions and adjust the way the suggestion is expressed based on the estimated emotions. The suggestion unit can estimate the elderly person's emotions using AI and adjust the way the suggestion is expressed based on the emotions. For example, if the elderly person is feeling anxious, the suggestion unit can make a suggestion in a gentle tone. Also, if the elderly person is relaxed, the suggestion unit can make a detailed suggestion. Furthermore, if the elderly person is excited, the suggestion unit can make a visually stimulating suggestion. This enables more appropriate suggestions by adjusting the way the suggestion is expressed based on the elderly person's emotions. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the elderly person's emotion data into AI and cause the AI ​​to adjust the way the suggestion is expressed.

[0102] The suggestion unit can adjust the level of detail of the proposal based on the importance of the dementia risk when making a proposal. For example, the suggestion unit can adjust the level of detail of the proposal based on the importance of the dementia risk when making a proposal. The suggestion unit can use AI to evaluate the importance of the dementia risk and adjust the level of detail of the proposal based on the evaluation. For example, the suggestion unit can make a detailed proposal when the dementia risk is high. The suggestion unit can also make a concise proposal when the dementia risk is medium. Furthermore, the suggestion unit can provide general advice when the dementia risk is low. In this way, adjusting the level of detail of the proposal based on the importance of the dementia risk enables more appropriate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input dementia risk data into AI and cause the AI ​​to adjust the level of detail of the proposal.

[0103] The suggestion unit can apply different suggestion algorithms depending on the dementia risk category when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the dementia risk category when making a suggestion. The suggestion unit can classify dementia risk categories using AI and apply different suggestion algorithms based on the classification. For example, the suggestion unit can apply a health-related suggestion algorithm to health risks. The suggestion unit can also apply a suggestion algorithm that promotes social connections to risks related to social isolation. Furthermore, the suggestion unit can apply a suggestion algorithm that improves cognitive function to risks related to cognitive decline. This enables more appropriate suggestions by applying different suggestion algorithms depending on the dementia risk category. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input data on dementia risk categories into AI and cause the AI ​​to select an appropriate suggestion algorithm.

[0104] The suggestion unit can improve the accuracy of the suggestion by referring to the elderly person's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the elderly person's past suggestion results when making a suggestion. The suggestion unit can analyze the elderly person's past suggestion results using AI and correct the current suggestion based on the results. For example, the suggestion unit can correct the current suggestion based on the elderly person's past suggestion results. The suggestion unit can also adjust the proposal algorithm based on the elderly person's past suggestion results. Furthermore, the suggestion unit can improve the accuracy of the suggestion based on the elderly person's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the elderly person's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI or without AI. For example, the suggestion unit can input data of the elderly person's past suggestion results into AI and cause the AI ​​to improve the accuracy of the suggestion.

[0105] The suggestion unit can estimate the elderly person's emotions and adjust the length of the suggestions based on the estimated elderly person's emotions. The suggestion unit can, for example, estimate the elderly person's emotions and adjust the length of the suggestions based on the estimated emotions. The suggestion unit can estimate the elderly person's emotions using AI and adjust the length of the suggestions based on the emotions. For example, if the elderly person is in a hurry, the suggestion unit can make short and to-the-point suggestions. Also, if the elderly person is relaxed, the suggestion unit can make detailed suggestions. Furthermore, if the elderly person is excited, the suggestion unit can make visually stimulating suggestions. This enables more appropriate suggestions by adjusting the length of the suggestions based on the elderly person's emotions. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the elderly person's emotion data into AI and cause the AI ​​to adjust the length of the suggestions.

[0106] The suggestion unit can determine the priority of the proposals based on the time of submission of the dementia risk information at the time of proposal. For example, the suggestion unit can determine the priority of the proposals based on the time of submission of the dementia risk information at the time of proposal. The suggestion unit can analyze the time of submission of the dementia risk information using AI and determine the priority of the proposals based on the analysis. For example, the suggestion unit can prioritize proposals based on the time of submission of the dementia risk information. The suggestion unit can also prioritize proposals based on the time of submission of the dementia risk information. Furthermore, the suggestion unit can prioritize proposals based on the time of submission of the dementia risk information. In addition, the suggestion unit can prioritize proposals based on the time of submission of the dementia risk information. In this way, by prioritizing proposals based on the time of submission of the dementia risk information, more important proposals can be prioritized. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input data on the time of submission of the dementia risk information into AI and have the AI ​​determine the priority of the proposals.

[0107] The suggestion unit can adjust the order of suggestions based on the relevance of dementia risk when making a suggestion. For example, the suggestion unit can adjust the order of suggestions based on the relevance of dementia risk when making a suggestion. The suggestion unit can use AI to evaluate the relevance of dementia risk and adjust the order of suggestions based on the evaluation. For example, the suggestion unit can prioritize proposing highly relevant risks. The suggestion unit can also prioritize proposing risks related to the interests of the elderly person. Furthermore, the suggestion unit can prioritize proposing risks related to the health status of the elderly person. This enables more appropriate suggestions by adjusting the order of suggestions based on the relevance of dementia risk. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input data on the relevance of dementia risk to AI and cause the AI ​​to adjust the order of suggestions.

[0108] The suggestion unit can adjust the use of technical terminology in the proposal according to the elderly person's level of expertise when making a proposal. For example, the suggestion unit can adjust the use of technical terminology in the proposal according to the elderly person's level of expertise when making a proposal. The suggestion unit can use AI to evaluate the elderly person's level of expertise and adjust the use of technical terminology in the proposal based on the evaluation. For example, the suggestion unit can use technical terminology if the elderly person has specialized knowledge. The suggestion unit can also make suggestions in simple language if the elderly person does not have specialized knowledge. Furthermore, the suggestion unit can adjust the use of technical terminology according to the elderly person's level of understanding. This enables suggestions that are easier to understand by adjusting the use of technical terminology in the proposal according to the elderly person's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI or without AI. For example, the suggestion unit can input data on the elderly person's level of expertise into AI and have the AI ​​adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects conversation content of the elderly person using the microphone 38B or camera 42 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected conversation content. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the dementia risk based on the analysis results. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests necessary measures. Some or all of the collection unit, analysis unit, determination unit, and suggestion unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects conversation content of the elderly person using the microphone 238 or the camera 42 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected conversation content. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the dementia risk based on the analysis results. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests necessary measures. Some or all of the collection unit, analysis unit, determination unit, and suggestion unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, determination unit, and suggestion unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects conversation content of the elderly person using the microphone 238 or camera 42 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected conversation content. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the dementia risk based on the analysis results. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests necessary measures. Some or all of the collection unit, analysis unit, determination unit, and suggestion unit may be realized, for example, by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, determination unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects conversation content of the elderly person using the microphone 238 or camera 42 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected conversation content. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the dementia risk based on the analysis results. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests necessary measures. Some or all of the collection unit, analysis unit, determination unit, and suggestion unit may be realized, for example, by the control unit 46A of the robot 414.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] When collecting conversation content of the elderly person, the collection unit can analyze background sounds of the conversation and select a conversation topic based on the environmental sounds. For example, if the collection unit hears birds chirping or the sound of wind, it can provide topics related to nature and the outdoors. Also, if the collection unit hears the sound of a television or music, it can provide topics related to entertainment. Furthermore, if the collection unit hears traffic sounds or people talking, it can provide topics related to city life or travel. In this way, the collection unit can provide appropriate topics according to the elderly person's environment.

[0111] When analyzing the content of the conversation, the analysis unit can analyze the tone and pitch of the elderly person's voice to detect changes in emotion. For example, the analysis unit can determine that if the elderly person's voice gets higher, it may indicate excitement or joy, and perform analysis based on that emotion. The analysis unit can also determine that if the elderly person's voice gets lower, it may indicate sadness or fatigue, and perform analysis based on that emotion. Furthermore, if the elderly person's voice has a certain tone, it may determine that they are relaxed, and perform analysis based on that emotion. This allows the analysis unit to perform analysis according to the elderly person's emotions.

[0112] When analyzing the content of the conversation, the determination unit can refer to the elderly person's health data and determine the dementia risk based on the health condition. For example, the determination unit can refer to the elderly person's blood pressure and heart rate data and determine that the dementia risk is high if abnormalities are found. The determination unit can also refer to the elderly person's exercise amount and sleep data and determine that the dementia risk is high if there is a lack of exercise or sleep. Furthermore, the determination unit can refer to the elderly person's dietary data and determine that the dementia risk is high if the nutritional balance is poor. This allows the determination unit to make a highly accurate determination based on the elderly person's health condition.

[0113] If the risk of dementia is determined to be high, the suggestion unit can provide a specific action plan to reduce the risk. For example, the suggestion unit can recommend daily walking or light exercise. The suggestion unit can also suggest a balanced meal menu and provide specific ingredients and recipes to improve nutritional balance. Furthermore, the suggestion unit can recommend brain training such as cognitive training or puzzle games and suggest ways to maintain cognitive function by engaging in these activities on a daily basis. In this way, the suggestion unit can help elderly people reduce their risk of dementia through specific actions.

[0114] When collecting the content of the elderly person's conversation, the collection unit analyzes the tempo and rhythm of the conversation and can provide appropriate responses according to the flow of the conversation. For example, if the elderly person speaks slowly, the collection unit can respond at a slower tempo. Also, if the elderly person speaks quickly, the collection unit can respond by matching the tempo. Furthermore, if the elderly person speaks with pauses, the collection unit can respond by taking appropriate pauses. This allows the collection unit to provide natural conversation that matches the elderly person's speaking style.

[0115] When analyzing the content of a conversation, the analysis unit can refer to the facial expression data of the elderly person and estimate emotions based on changes in facial expressions. For example, if the elderly person smiles, the analysis unit can determine that the elderly person is expressing joy or happiness, and perform analysis based on that emotion. In addition, if the elderly person frowns, the analysis unit can determine that the elderly person is expressing confusion or anxiety, and perform analysis based on that emotion. Furthermore, if the elderly person has a neutral expression, the analysis unit can determine that the elderly person is relaxed, and perform analysis based on that emotion. This allows the analysis unit to perform analysis according to the elderly person's facial expressions.

[0116] When analyzing the content of the conversation, the determination unit can refer to the social activity data of the elderly person and determine the risk of social isolation. For example, the determination unit can refer to data on community activities and events in which the elderly person participates and determine that the risk of social isolation is high if the frequency of participation is low. The determination unit can also refer to data on the elderly person's interactions with family and friends and determine that the risk of social isolation is high if the elderly person has few interactions. Furthermore, the determination unit can refer to data on the elderly person's online interactions and determine that the risk of social isolation is high if the elderly person has few online activities. This allows the determination unit to make highly accurate determinations based on the elderly person's social activities.

[0117] If the dementia risk is determined to be high, the suggestion unit can recommend participation in community activities to reduce the risk. For example, the suggestion unit can recommend participation in local circles or club activities. The suggestion unit can also recommend volunteer activities or participation in local events. Furthermore, the suggestion unit can recommend interactions through online communities or social media and suggest ways to maintain social connections. In this way, the suggestion unit can help the elderly prevent social isolation and reduce the dementia risk.

[0118] When collecting the conversation content of the elderly person, the collection unit can analyze the context of the conversation and provide an appropriate response based on the context. For example, if the elderly person is talking about a past event, the collection unit can provide questions or comments related to that event. Also, if the elderly person is talking about a current situation, the collection unit can provide information or advice related to that situation. Furthermore, if the elderly person is talking about future plans, the collection unit can provide suggestions or support related to those plans. In this way, the collection unit can provide a natural conversation that matches the context of the elderly person's conversation.

[0119] When analyzing the content of the conversation, the analysis unit can refer to the elderly person's lifestyle habit data and determine the dementia risk based on the lifestyle habit. For example, the analysis unit can refer to the elderly person's eating habit data and determine that the elderly person has a high dementia risk if their nutritional balance is poor. The analysis unit can also refer to the elderly person's exercise habit data and determine that the elderly person has a high dementia risk if they are not getting enough exercise. Furthermore, the analysis unit can refer to the elderly person's sleeping habit data and determine that the elderly person has a high dementia risk if they are not getting enough sleep. This allows the analysis unit to make highly accurate determinations based on the elderly person's lifestyle habits.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The collection unit collects the conversation content that the elderly person has with the AI ​​chatbot. The collection unit can collect the conversation content using methods such as voice input, text input, and image input. The collection unit can also use AI to provide topics that match the elderly person's interests and provide the most appropriate topics by analyzing past conversation history. Furthermore, the collection unit can estimate the elderly person's emotions and select conversation topics based on the estimated emotions. Step 2: The analysis unit analyzes the conversation content collected by the collection unit. The analysis unit uses AI to analyze the content of the conversation and the elderly person's reactions to determine the risk of dementia. For example, the analysis unit can analyze word choice during conversation, speed of reaction, and memory decline. Step 3: The assessment unit assesses the dementia risk based on the results of the analysis by the analysis unit. The assessment unit can use AI to score the dementia risk and determine whether the risk is high or low. Step 4: The proposal unit proposes necessary measures based on the results of the assessment by the assessment unit. If the proposal unit determines using AI that the person is at high risk of dementia, it recommends that the person visit a medical institution. The proposal unit can also provide advice on dementia prevention and points to be careful about in daily life.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0140] The data processing system 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0169] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 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 collection unit that collects conversation content; an analysis unit that analyzes the conversation content collected by the collection unit; a determination unit for determining a dementia risk based on the results of the analysis by the analysis unit; a proposal unit that proposes necessary measures based on the result of the determination by the determination unit; Equipped with A system characterized by:

2. The collecting unit Providing topics that match the interests of seniors 2. The system of claim 1.

3. The analysis unit Analyzing the content of conversations and the elderly's reactions to determine dementia risk 2. The system of claim 1.

4. The determination unit Analyzing word choice in conversation, reaction speed, and memory decline 2. The system of claim 1.

5. The proposal unit If a person is judged to be at high risk of dementia, they are advised to visit a medical institution.

2. The system of claim 1.

6. The proposal unit Providing advice on dementia prevention and points to be aware of in daily life 2. The system of claim 1.

7. The collecting unit Estimating the emotions of elderly people and selecting conversation topics based on the estimated emotions of elderly people 2. The system of claim 1.

8. The collecting unit Analyzing the elderly person's past conversation history and providing the most appropriate topics 2. The system of claim 1.

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