system

Abstract

The system according to the embodiment aims to monitor the progress of dementia on a daily basis. [Solution] A system according to an embodiment includes a speech recognition unit, a response generation unit, and a monitoring unit. The speech recognition unit recognizes the speech of a person with dementia. The response generation unit generates a response based on the speech recognized by the speech recognition unit. The monitoring unit records the content and frequency of conversations based on the responses generated by the response generation unit, and monitors the progression of dementia.

Description

[Technical Field]

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

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

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

[0004] Conventional technology makes it difficult to monitor the progression of dementia on a daily basis, and there is room for improvement.

[0005] The system according to the embodiment aims to monitor the progress of dementia on a daily basis. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech recognition unit, a response generation unit, and a monitoring unit. The speech recognition unit recognizes the speech of the person with dementia. The response generation unit generates a response based on the speech recognized by the speech recognition unit. The monitoring unit records the content and frequency of conversations based on the responses generated by the response generation unit, and monitors the progression of dementia. [Effects of the Invention]

[0007] The system according to the embodiment can monitor the progress of dementia on a daily basis. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A dementia conversation support system according to an embodiment of the present invention is a system that engages in conversations with people with dementia to slow the progression of dementia. This system utilizes voice recognition technology with a smart speaker in the home to recognize what the person with dementia says, and a generation AI generates appropriate responses, stimulating cognitive function through conversation. It also has a function to record the content and frequency of conversations and monitor the progression of dementia. For example, if a smart speaker recognizes the voice of a person with dementia and asks, "What did you do today?", the person with dementia may respond, "I went for a walk." This utterance is analyzed using voice recognition technology, and the generation AI generates follow-up questions such as, "Where did you go?" and "Who did you go with?" This is designed to keep the conversation going. The content and frequency of conversations are also recorded to monitor the progression of dementia. For example, if the content of conversations gradually becomes shorter or the same topics are repeated, this may indicate the progression of dementia. This information is provided to medical professionals so that appropriate measures can be taken. This allows people with dementia to enjoy daily conversations and stimulate their cognitive function. It also makes it easier for family members and caregivers to understand the progression of dementia. For example, if a smart speaker asks, "What did you do today?" and a person with dementia replies, "I went for a walk," the details are recorded and can be checked later by family members or caregivers. In this way, by using a smart speaker and generative AI, it is possible to stimulate cognitive functions through conversations with people with dementia and slow the progression of dementia. This means that the dementia conversation support system can stimulate cognitive functions through conversations with people with dementia and slow the progression of dementia.

[0029] A dementia conversation support system according to an embodiment includes a voice recognition unit, a response generation unit, and a monitoring unit. The voice recognition unit recognizes the voice of a person with dementia. The voice of a person with dementia includes, but is not limited to, everyday conversations, questions, and responses. The voice recognition unit accurately captures the speech of the person with dementia using, for example, a voice recognition algorithm. The voice recognition unit can also improve recognition accuracy by removing background and environmental sounds using noise filtering technology. For example, when a person with dementia is watching television, the voice recognition unit can recognize speech by filtering out the sound of the television. When a person with dementia is out and about, the voice recognition unit can also recognize speech by filtering out the sound of wind and cars. Furthermore, when a person with dementia is with family, the voice recognition unit can recognize speech by filtering out the voices of other people. The response generation unit uses a generation AI to generate an appropriate response based on the speech recognized by the voice recognition unit. The response generation unit can analyze the speech of the person with dementia using, for example, natural language generation technology and generate an appropriate response. The response generation unit can also customize the content of the response based on the interests and concerns of the person with dementia. For example, if a person with dementia is interested in music, the response generation unit generates a response on a music-related topic. The response generation unit can also estimate the emotions of the person with dementia and adjust the way the response is expressed based on the emotion. For example, if a person with dementia is excited, the response generation unit generates a response in a calm tone. The monitoring unit records the content and frequency of conversations based on the responses generated by the response generation unit and monitors the progression of dementia. For example, if the content of conversations gradually becomes shorter or the same topics are repeated, the monitoring unit determines that the progression of dementia is progressing. The monitoring unit can also provide the recorded data to medical professionals so that appropriate measures can be taken. For example, the monitoring unit analyzes the content and frequency of conversations to predict the progression of dementia. As a result, the dementia conversation support system according to the embodiment can stimulate cognitive function through conversations with people with dementia and slow the progression of dementia.

[0030] The voice recognition unit can capture the speech of a person with dementia. The voice recognition unit, for example, uses a voice recognition algorithm to accurately capture the speech of a person with dementia. For example, when a person with dementia is asked, "What did you do today?", the voice recognition unit accurately recognizes the response, "I went for a walk." The voice recognition unit can also use noise filtering technology to remove background and environmental sounds to improve recognition accuracy. For example, when a person with dementia is watching television, the voice recognition unit can recognize the speech by filtering out the sound of the television. When a person with dementia is out and about, the voice recognition unit can also recognize the speech by filtering out the sound of the wind and cars. Furthermore, when a person with dementia is with family, the voice recognition unit can recognize the speech by filtering out the voices of other people. This makes it possible to accurately capture the speech of a person with dementia and generate an appropriate response. Some or all of the above-described processing in the voice recognition unit may be performed using, for example, AI, or without AI. For example, the voice recognition unit can input the speech of a person with dementia into AI and have the AI ​​recognize the speech.

[0031] The response generation unit can analyze the utterances of the person with dementia and generate a response. The response generation unit can analyze the utterances of the person with dementia using, for example, natural language generation technology and generate an appropriate response. For example, if the person with dementia answers, "I went for a walk," the response generation unit generates additional questions such as, "Where did you go?" or "Who did you go with?" The response generation unit can also customize the content of the response based on the interests and concerns of the person with dementia. For example, if the person with dementia is interested in music, the response generation unit can generate a response on a music-related topic. The response generation unit can also estimate the emotions of the person with dementia and adjust the way the response is expressed based on the emotions. For example, if the person with dementia is excited, the response generation unit can generate a response in a calm tone. In this way, by analyzing the utterances of the person with dementia, an appropriate response can be generated to keep the conversation going. Some or all of the above-mentioned processing in the response generation unit can be performed using, for example, a generation AI. For example, the response generation unit can input the utterances of the person with dementia into a generation AI and have the generation AI generate a response.

[0032] The monitoring unit can record the content and frequency of conversations and monitor the progression of dementia. For example, the monitoring unit can determine that dementia is progressing if the content of conversations gradually becomes shorter or the same topics are repeated. The monitoring unit can also provide the recorded data to medical professionals so that appropriate measures can be taken. For example, the monitoring unit can analyze the content and frequency of conversations to predict the progression of dementia. In this way, the progression of dementia can be monitored by recording the content and frequency of conversations. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the content and frequency of conversations into AI and have the AI ​​monitor the progression.

[0033] The monitoring unit can provide the recorded data to a medical professional. For example, the monitoring unit provides the recorded data to a medical professional so that an appropriate response can be taken. For example, the monitoring unit analyzes the content and frequency of conversations, predicts the progression of dementia, and provides the information to a medical professional. In this way, by providing the recorded data to a medical professional, an appropriate response can be taken. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the recorded data to AI and have the AI ​​provide the data.

[0034] The voice recognition unit can analyze the past speech history of the person with dementia and select a voice recognition algorithm. For example, the voice recognition unit selects an algorithm that prioritizes recognition of words and phrases frequently used by the person with dementia in the past. The voice recognition unit can also analyze the speech patterns of the person with dementia and select a voice recognition algorithm suitable for a specific time period. Furthermore, the voice recognition unit can select the optimal voice recognition algorithm based on the speech speed and volume of the person with dementia. In this way, by analyzing the past speech history, the optimal voice recognition algorithm can be selected and recognition accuracy can be improved. Some or all of the above-mentioned processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input the past speech history into AI and have the AI ​​select the algorithm.

[0035] The voice recognition unit can improve recognition accuracy by filtering background sounds and environmental sounds during voice recognition. For example, when a person with dementia is watching television, the voice recognition unit recognizes speech by filtering out television sounds. Furthermore, when a person with dementia is out and about, the voice recognition unit can also recognize speech by filtering out wind sounds and car sounds. Furthermore, when a person with dementia is with family, the voice recognition unit can also recognize speech by filtering out other people's voices. In this way, by filtering out background sounds and environmental sounds, recognition accuracy can be improved. Some or all of the above-described processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input data on background sounds and environmental sounds into AI and have the AI ​​perform the filtering.

[0036] During speech recognition, the speech recognition unit can prioritize recognition of highly relevant utterances based on the geographical location information of the person with dementia. For example, if the person with dementia is at home, the speech recognition unit can prioritize recognition of utterances related to the home. Furthermore, if the person with dementia is in a park, the speech recognition unit can also prioritize recognition of utterances related to the park. Furthermore, if the person with dementia is in a hospital, the speech recognition unit can also prioritize recognition of utterances related to the hospital. In this way, by taking geographical location information into consideration, it is possible to prioritize recognition of highly relevant utterances and generate an appropriate response. Some or all of the above-described processing in the speech recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the speech recognition unit can input geographical location information to AI and have the AI ​​perform priority recognition of utterances.

[0037] During voice recognition, the voice recognition unit can analyze the social media activities of the person with dementia and recognize relevant comments. For example, the voice recognition unit prioritizes recognition of words and phrases frequently used by the person with dementia on social media. The voice recognition unit can also analyze the content of the person with dementia's social media posts and recognize relevant comments. Furthermore, the voice recognition unit can prioritize recognition of the names and topics of people with whom the person with dementia interacts on social media. This makes it possible to recognize relevant comments and generate appropriate responses by analyzing social media activities. Some or all of the above-mentioned processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input data on social media activities into AI and have the AI ​​recognize the comments.

[0038] When generating a response, the response generation unit can customize the content of the response based on the interests and concerns of the person with dementia. For example, if the person with dementia is interested in music, the response generation unit can generate a response on a music-related topic. Furthermore, if the person with dementia is interested in cooking, the response generation unit can also generate a response on a cooking-related topic. Furthermore, if the person with dementia is interested in sports, the response generation unit can also generate a response on a sports-related topic. This allows the content of the response to be customized based on the interests and concerns, thereby keeping the conversation going. Some or all of the above-mentioned processing in the response generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the response generation unit can input data on the interests and concerns of the person with dementia into the generation AI and have the generation AI customize the response.

[0039] When generating a response, the response generation unit can generate a consistent response based on the past conversation history of the person with dementia. The response generation unit generates a relevant response based on, for example, content that the person with dementia has spoken in the past. The response generation unit can also generate a consistent response on a specific topic from the past conversation history of the person with dementia. Furthermore, the response generation unit can generate a response based on topics that the person with dementia has shown interest in in the past. In this way, by referring to the past conversation history, a consistent response can be generated and the conversation can be continued without interruption. Some or all of the above-mentioned processing in the response generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the response generation unit can input the past conversation history into the generation AI and cause the generation AI to generate a consistent response.

[0040] When generating responses, the response generation unit can determine the order of responses based on the frequency of utterances made by the person with dementia. For example, if the person with dementia speaks frequently, the response generation unit sets a high priority for the response. The response generation unit can also set a low priority for the response if the person with dementia does not speak much. Furthermore, the response generation unit can dynamically adjust the priority of responses according to the frequency of utterances made by the person with dementia. This makes it possible to generate appropriate responses by determining the priority of responses based on the frequency of utterances. Some or all of the above-mentioned processing in the response generation unit may be performed using, or without, a generation AI. For example, the response generation unit can input utterance frequency data into the generation AI and have the generation AI determine the priority of responses.

[0041] When generating a response, the response generation unit can adjust the content of the response based on the medical data of the person with dementia. For example, the response generation unit generates a response according to the health condition from the medical data of the person with dementia. The response generation unit can also generate an appropriate response by referring to the medication information of the person with dementia. Furthermore, the response generation unit can adjust the content of the response based on the diagnosis data of the person with dementia. This makes it possible to generate an appropriate response by referring to related medical data. Some or all of the above-mentioned processing in the response generation unit may be performed using, or without, a generation AI. For example, the response generation unit can input medical data into the generation AI and have the generation AI adjust the content of the response.

[0042] During monitoring, the monitoring unit can predict the progression of dementia by analyzing the content and frequency of conversations. For example, if the content of conversations gradually becomes shorter, the monitoring unit can predict that the progression of dementia is progressing. The monitoring unit can also predict that the progression of dementia is progressing if the same topic is repeated. Furthermore, the monitoring unit can predict that the progression of dementia is progressing if the frequency of conversations decreases. In this way, the progression of dementia can be predicted by analyzing the content and frequency of conversations. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input conversation data into AI and have the AI ​​execute a progress prediction.

[0043] During monitoring, the monitoring unit can evaluate the progress of the person with dementia based on the lifestyle habit data. The monitoring unit can evaluate the progress by referring to, for example, the sleep pattern of the person with dementia. The monitoring unit can also evaluate the progress by referring to the eating pattern of the person with dementia. The monitoring unit can also evaluate the progress by referring to the exercise pattern of the person with dementia. This makes it possible to more accurately evaluate the progress of dementia by referring to the lifestyle habit data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the lifestyle habit data into AI and have the AI ​​evaluate the progress.

[0044] During monitoring, the monitoring unit can evaluate the progress of the person with dementia based on the geographical location information of the person with dementia. For example, if the person with dementia is at home, the monitoring unit can evaluate the progress by referring to their behavioral patterns at home. Furthermore, if the person with dementia is out and about, the monitoring unit can evaluate the progress by referring to their behavioral patterns while out and about. Furthermore, if the person with dementia is in a hospital, the monitoring unit can evaluate the progress by referring to their behavioral patterns at the hospital. In this way, by taking the geographical location information into consideration, the progress of dementia can be more accurately evaluated. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the geographical location information into AI and have the AI ​​evaluate the progress.

[0045] During monitoring, the monitoring unit can analyze the social media activity of the person with dementia to evaluate their progress. For example, the monitoring unit can evaluate their progress by referring to the frequency of posts on social media by the person with dementia. The monitoring unit can also evaluate their progress by referring to the interaction patterns of the person with dementia on social media. The monitoring unit can also evaluate their progress by referring to the language used by the person with dementia on social media. This allows for a more accurate evaluation of the progression of dementia by analyzing social media activity. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input social media activity data into AI and have the AI ​​evaluate their progress.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The dementia conversation support system may further include a lifestyle acquisition unit that acquires lifestyle data of the user. The lifestyle acquisition unit collects data such as the user's sleep patterns, dietary habits, and amount of exercise. This allows the system to customize the conversation content based on the user's lifestyle habits and provide more personalized support. For example, if the user has not been exercising recently, the system can generate a question such as "Have you been exercising recently?" to encourage exercise. The system can also provide topics related to healthy eating based on the user's dietary habits. Furthermore, the system can check whether the user is getting enough rest based on their sleep patterns and suggest relaxation methods if necessary.

[0048] The dementia conversation support system can further include a location information acquisition unit that acquires the user's geographical location information. For example, if the user is at home, the location information acquisition unit can provide topics related to the user's home. If the user is in a park, the location information acquisition unit can also provide topics related to the park. If the user is in a hospital, the location information acquisition unit can also provide topics related to the hospital. This makes it possible to provide topics that are highly relevant based on the user's current location and improve the naturalness of the conversation. For example, if the user is at home, a question such as "What have you been doing at home recently?" can be generated.

[0049] The dementia conversation support system can further include a social media analysis unit that analyzes the user's social media activity. The social media analysis unit, for example, prioritizes recognizing words and phrases frequently used by the user on social media. It can also analyze the content of the user's social media posts and provide related topics. It can also prioritize recognizing the names and topics of people the user interacts with on social media. By analyzing social media activity, it can provide topics that are highly relevant to the user and improve the quality of conversation. For example, it can ask about hobbies that the user has recently been talking about on social media.

[0050] The dementia conversation support system can further include a medical data acquisition unit that acquires the user's medical data. The medical data acquisition unit collects, for example, the user's health condition and medication information. This allows the system to adjust the content of the conversation based on the user's health condition and provide more appropriate support. For example, if the user is taking a specific medication, the system can provide topics related to that medication. It can also provide advice on appropriate lifestyle habits depending on the user's health condition. Furthermore, the content of the conversation can be adjusted based on the user's diagnostic data. This allows for more personalized support to be achieved by referring to the medical data.

[0051] The dementia conversation support system can further include a conversation history analysis unit that analyzes the user's past conversation history. The conversation history analysis unit, for example, provides related topics based on what the user has said in the past. It can also generate consistent responses on specific topics from the user's past conversation history. It can also generate responses based on topics in which the user has shown interest in the past. This makes it possible to generate consistent responses by referring to the past conversation history and prevent the conversation from coming to a halt. For example, it can continue the conversation by revisiting a topic about travel that the user has talked about in the past.

[0052] The dementia conversation support system may further include a lifestyle analysis unit that analyzes the user's lifestyle data and adjusts the content of the conversation based on the lifestyle. The lifestyle analysis unit analyzes data such as the user's sleep patterns, diet, and amount of exercise. This allows the system to customize the content of the conversation based on the user's lifestyle and provide more personalized support. For example, if the user has not been exercising recently, the system can generate a question such as "Have you been exercising recently?" to encourage the user to exercise. The system can also provide topics related to healthy eating based on the user's diet. Furthermore, the system can check whether the user is getting enough rest based on their sleep patterns and suggest relaxation methods if necessary.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The speech recognition unit recognizes the speech of the person with dementia. Speech from the person with dementia includes everyday conversations, questions, and responses. The speech recognition unit uses a speech recognition algorithm to accurately capture what is being said and noise filtering technology to remove background and environmental sounds. For example, it filters out the sound of the television, wind, cars, and other people's voices to recognize what is being said. Step 2: The response generation unit generates an appropriate response based on the speech recognized by the speech recognition unit. It analyzes the speech using generative AI and natural language generation technology, and customizes the response based on the interests and concerns of the person with dementia. It also estimates the emotions of the person with dementia and adjusts the way the response is expressed based on those emotions. Step 3: The monitoring unit records the content and frequency of conversations based on the responses generated by the response generation unit, and monitors the progression of dementia. If the content of conversations becomes shorter or the same topics are repeated, it is determined that the dementia is progressing. The recorded data is also provided to medical professionals so that appropriate measures can be taken.

[0055] (Example 2) A dementia conversation support system according to an embodiment of the present invention is a system that engages in conversations with people with dementia to slow the progression of dementia. This system utilizes voice recognition technology with a smart speaker in the home to recognize what the person with dementia says, and a generation AI generates appropriate responses, stimulating cognitive function through conversation. It also has a function to record the content and frequency of conversations and monitor the progression of dementia. For example, if a smart speaker recognizes the voice of a person with dementia and asks, "What did you do today?", the person with dementia may respond, "I went for a walk." This utterance is analyzed using voice recognition technology, and the generation AI generates follow-up questions such as, "Where did you go?" and "Who did you go with?" This is designed to keep the conversation going. The content and frequency of conversations are also recorded to monitor the progression of dementia. For example, if the content of conversations gradually becomes shorter or the same topics are repeated, this may indicate the progression of dementia. This information is provided to medical professionals so that appropriate measures can be taken. This allows people with dementia to enjoy daily conversations and stimulate their cognitive function. It also makes it easier for family members and caregivers to understand the progression of dementia. For example, if a smart speaker asks, "What did you do today?" and a person with dementia replies, "I went for a walk," the details are recorded and can be checked later by family members or caregivers. In this way, by using a smart speaker and generative AI, it is possible to stimulate cognitive functions through conversations with people with dementia and slow the progression of dementia. This means that the dementia conversation support system can stimulate cognitive functions through conversations with people with dementia and slow the progression of dementia.

[0056] A dementia conversation support system according to an embodiment includes a voice recognition unit, a response generation unit, and a monitoring unit. The voice recognition unit recognizes the voice of a person with dementia. The voice of a person with dementia includes, but is not limited to, everyday conversations, questions, and responses. The voice recognition unit accurately captures the speech of the person with dementia using, for example, a voice recognition algorithm. The voice recognition unit can also improve recognition accuracy by removing background and environmental sounds using noise filtering technology. For example, when a person with dementia is watching television, the voice recognition unit can recognize speech by filtering out the sound of the television. When a person with dementia is out and about, the voice recognition unit can also recognize speech by filtering out the sound of wind and cars. Furthermore, when a person with dementia is with family, the voice recognition unit can recognize speech by filtering out the voices of other people. The response generation unit uses a generation AI to generate an appropriate response based on the speech recognized by the voice recognition unit. The response generation unit can analyze the speech of the person with dementia using, for example, natural language generation technology and generate an appropriate response. The response generation unit can also customize the content of the response based on the interests and concerns of the person with dementia. For example, if a person with dementia is interested in music, the response generation unit generates a response on a music-related topic. The response generation unit can also estimate the emotions of the person with dementia and adjust the way the response is expressed based on the emotion. For example, if a person with dementia is excited, the response generation unit generates a response in a calm tone. The monitoring unit records the content and frequency of conversations based on the responses generated by the response generation unit and monitors the progression of dementia. For example, if the content of conversations gradually becomes shorter or the same topics are repeated, the monitoring unit determines that the progression of dementia is progressing. The monitoring unit can also provide the recorded data to medical professionals so that appropriate measures can be taken. For example, the monitoring unit analyzes the content and frequency of conversations to predict the progression of dementia. As a result, the dementia conversation support system according to the embodiment can stimulate cognitive function through conversations with people with dementia and slow the progression of dementia.

[0057] The voice recognition unit can capture the speech of a person with dementia. The voice recognition unit, for example, uses a voice recognition algorithm to accurately capture the speech of a person with dementia. For example, when a person with dementia is asked, "What did you do today?", the voice recognition unit accurately recognizes the response, "I went for a walk." The voice recognition unit can also use noise filtering technology to remove background and environmental sounds to improve recognition accuracy. For example, when a person with dementia is watching television, the voice recognition unit can recognize the speech by filtering out the sound of the television. When a person with dementia is out and about, the voice recognition unit can also recognize the speech by filtering out the sound of the wind and cars. Furthermore, when a person with dementia is with family, the voice recognition unit can recognize the speech by filtering out the voices of other people. This makes it possible to accurately capture the speech of a person with dementia and generate an appropriate response. Some or all of the above-described processing in the voice recognition unit may be performed using, for example, AI, or without AI. For example, the voice recognition unit can input the speech of a person with dementia into AI and have the AI ​​recognize the speech.

[0058] The response generation unit can analyze the utterances of the person with dementia and generate a response. The response generation unit can analyze the utterances of the person with dementia using, for example, natural language generation technology and generate an appropriate response. For example, if the person with dementia answers, "I went for a walk," the response generation unit generates additional questions such as, "Where did you go?" or "Who did you go with?" The response generation unit can also customize the content of the response based on the interests and concerns of the person with dementia. For example, if the person with dementia is interested in music, the response generation unit can generate a response on a music-related topic. The response generation unit can also estimate the emotions of the person with dementia and adjust the way the response is expressed based on the emotions. For example, if the person with dementia is excited, the response generation unit can generate a response in a calm tone. In this way, by analyzing the utterances of the person with dementia, an appropriate response can be generated to keep the conversation going. Some or all of the above-mentioned processing in the response generation unit can be performed using, for example, a generation AI. For example, the response generation unit can input the utterances of the person with dementia into a generation AI and have the generation AI generate a response.

[0059] The monitoring unit can record the content and frequency of conversations and monitor the progression of dementia. For example, the monitoring unit can determine that dementia is progressing if the content of conversations gradually becomes shorter or the same topics are repeated. The monitoring unit can also provide the recorded data to medical professionals so that appropriate measures can be taken. For example, the monitoring unit can analyze the content and frequency of conversations to predict the progression of dementia. In this way, the progression of dementia can be monitored by recording the content and frequency of conversations. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the content and frequency of conversations into AI and have the AI ​​monitor the progression.

[0060] The monitoring unit can provide the recorded data to a medical professional. For example, the monitoring unit provides the recorded data to a medical professional so that an appropriate response can be taken. For example, the monitoring unit analyzes the content and frequency of conversations, predicts the progression of dementia, and provides the information to a medical professional. In this way, by providing the recorded data to a medical professional, an appropriate response can be taken. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the recorded data to AI and have the AI ​​provide the data.

[0061] The speech recognition unit can estimate the emotions of the person with dementia and adjust the accuracy of speech recognition based on the emotions. For example, if the person with dementia is excited, the speech recognition unit can reduce false recognitions by lowering the sensitivity of speech recognition. Furthermore, if the person with dementia is calm, the speech recognition unit can increase the sensitivity of speech recognition to capture even small details. Furthermore, if the person with dementia is tired, the speech recognition unit can set the sensitivity of speech recognition to a medium level to allow the person with dementia to continue the conversation without strain. This adjusts the accuracy of speech recognition according to the emotions of the person with dementia, reducing false recognitions and allowing the conversation to continue appropriately. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the speech recognition unit may be performed using AI, or without AI. For example, the speech recognition unit can input emotion data of the person with dementia into AI and have the AI ​​adjust the accuracy of speech recognition.

[0062] The voice recognition unit can analyze the past speech history of the person with dementia and select a voice recognition algorithm. For example, the voice recognition unit selects an algorithm that prioritizes recognition of words and phrases frequently used by the person with dementia in the past. The voice recognition unit can also analyze the speech patterns of the person with dementia and select a voice recognition algorithm suitable for a specific time period. Furthermore, the voice recognition unit can select the optimal voice recognition algorithm based on the speech speed and volume of the person with dementia. In this way, by analyzing the past speech history, the optimal voice recognition algorithm can be selected and recognition accuracy can be improved. Some or all of the above-mentioned processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input the past speech history into AI and have the AI ​​select the algorithm.

[0063] The voice recognition unit can improve recognition accuracy by filtering background sounds and environmental sounds during voice recognition. For example, when a person with dementia is watching television, the voice recognition unit recognizes speech by filtering out television sounds. Furthermore, when a person with dementia is out and about, the voice recognition unit can also recognize speech by filtering out wind sounds and car sounds. Furthermore, when a person with dementia is with family, the voice recognition unit can also recognize speech by filtering out other people's voices. In this way, by filtering out background sounds and environmental sounds, recognition accuracy can be improved. Some or all of the above-described processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input data on background sounds and environmental sounds into AI and have the AI ​​perform the filtering.

[0064] The voice recognition unit can estimate the emotion of the person with dementia and adjust the timing of voice recognition based on the emotion. For example, if the person with dementia is excited, the voice recognition unit delays the timing of voice recognition until they calm down. Furthermore, if the person with dementia is relaxed, the voice recognition unit can advance the timing of voice recognition to smoothly continue the conversation. Furthermore, if the person with dementia is tired, the voice recognition unit can set the timing of voice recognition to a medium level to allow the conversation to continue without strain. This allows the conversation to continue smoothly by adjusting the timing of voice recognition according to the emotion of the person with dementia. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the voice recognition unit may be performed using, for example, AI, or without AI. For example, the voice recognition unit can input emotion data of the person with dementia into AI and have the AI ​​adjust the timing of voice recognition.

[0065] During speech recognition, the speech recognition unit can prioritize recognition of highly relevant utterances based on the geographical location information of the person with dementia. For example, if the person with dementia is at home, the speech recognition unit can prioritize recognition of utterances related to the home. Furthermore, if the person with dementia is in a park, the speech recognition unit can also prioritize recognition of utterances related to the park. Furthermore, if the person with dementia is in a hospital, the speech recognition unit can also prioritize recognition of utterances related to the hospital. In this way, by taking geographical location information into consideration, it is possible to prioritize recognition of highly relevant utterances and generate an appropriate response. Some or all of the above-described processing in the speech recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the speech recognition unit can input geographical location information to AI and have the AI ​​perform priority recognition of utterances.

[0066] During voice recognition, the voice recognition unit can analyze the social media activities of the person with dementia and recognize relevant comments. For example, the voice recognition unit prioritizes recognition of words and phrases frequently used by the person with dementia on social media. The voice recognition unit can also analyze the content of the person with dementia's social media posts and recognize relevant comments. Furthermore, the voice recognition unit can prioritize recognition of the names and topics of people with whom the person with dementia interacts on social media. This makes it possible to recognize relevant comments and generate appropriate responses by analyzing social media activities. Some or all of the above-mentioned processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input data on social media activities into AI and have the AI ​​recognize the comments.

[0067] The response generation unit can estimate the emotions of the person with dementia and adjust the way a response is expressed based on the emotions. For example, if the person with dementia is excited, the response generation unit can generate a response in a calm tone. Furthermore, if the person with dementia is relaxed, the response generation unit can generate a response in a bright tone. Furthermore, if the person with dementia is tired, the response generation unit can generate a response in a gentle tone. This allows for adjusting the way a response is expressed based on the emotions of the person with dementia, thereby generating a more appropriate response. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the response generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the response generation unit can input emotion data of the person with dementia into the generation AI and have the generation AI adjust the way a response is expressed.

[0068] When generating a response, the response generation unit can customize the content of the response based on the interests and concerns of the person with dementia. For example, if the person with dementia is interested in music, the response generation unit can generate a response on a music-related topic. Furthermore, if the person with dementia is interested in cooking, the response generation unit can also generate a response on a cooking-related topic. Furthermore, if the person with dementia is interested in sports, the response generation unit can also generate a response on a sports-related topic. This allows the content of the response to be customized based on the interests and concerns, thereby keeping the conversation going. Some or all of the above-mentioned processing in the response generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the response generation unit can input data on the interests and concerns of the person with dementia into the generation AI and have the generation AI customize the response.

[0069] When generating a response, the response generation unit can generate a consistent response based on the past conversation history of the person with dementia. The response generation unit generates a relevant response based on, for example, content that the person with dementia has spoken in the past. The response generation unit can also generate a consistent response on a specific topic from the past conversation history of the person with dementia. Furthermore, the response generation unit can generate a response based on topics that the person with dementia has shown interest in in the past. In this way, by referring to the past conversation history, a consistent response can be generated and the conversation can be continued without interruption. Some or all of the above-mentioned processing in the response generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the response generation unit can input the past conversation history into the generation AI and cause the generation AI to generate a consistent response.

[0070] The response generation unit can estimate the emotion of the person with dementia and adjust the length of the response based on the emotion. For example, if the person with dementia is excited, the response generation unit can generate a short response. Furthermore, if the person with dementia is relaxed, the response generation unit can generate a longer response. Furthermore, if the person with dementia is tired, the response generation unit can generate a medium-length response. This allows the conversation to continue by adjusting the length of the response according to the emotion of the person with dementia. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the response generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the response generation unit can input emotion data of the person with dementia into the generation AI and have the generation AI adjust the length of the response.

[0071] When generating responses, the response generation unit can determine the order of responses based on the frequency of utterances made by the person with dementia. For example, if the person with dementia speaks frequently, the response generation unit sets a high priority for the response. The response generation unit can also set a low priority for the response if the person with dementia does not speak much. Furthermore, the response generation unit can dynamically adjust the priority of responses according to the frequency of utterances made by the person with dementia. This makes it possible to generate appropriate responses by determining the priority of responses based on the frequency of utterances. Some or all of the above-mentioned processing in the response generation unit may be performed using, or without, a generation AI. For example, the response generation unit can input utterance frequency data into the generation AI and have the generation AI determine the priority of responses.

[0072] When generating a response, the response generation unit can adjust the content of the response based on the medical data of the person with dementia. For example, the response generation unit generates a response according to the health condition from the medical data of the person with dementia. The response generation unit can also generate an appropriate response by referring to the medication information of the person with dementia. Furthermore, the response generation unit can adjust the content of the response based on the diagnosis data of the person with dementia. This makes it possible to generate an appropriate response by referring to related medical data. Some or all of the above-mentioned processing in the response generation unit may be performed using, or without, a generation AI. For example, the response generation unit can input medical data into the generation AI and have the generation AI adjust the content of the response.

[0073] The monitoring unit can estimate the emotions of the person with dementia and adjust the monitoring method based on the emotions. For example, the monitoring unit can increase the frequency of monitoring when the person with dementia is excited. The monitoring unit can also decrease the frequency of monitoring when the person with dementia is relaxed. The monitoring unit can also simplify the monitoring method when the person with dementia is tired. This allows appropriate monitoring by adjusting the monitoring method according to the emotions of the person with dementia. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input emotion data into AI and have the AI ​​adjust the monitoring method.

[0074] During monitoring, the monitoring unit can predict the progression of dementia by analyzing the content and frequency of conversations. For example, if the content of conversations gradually becomes shorter, the monitoring unit can predict that the progression of dementia is progressing. The monitoring unit can also predict that the progression of dementia is progressing if the same topic is repeated. Furthermore, the monitoring unit can predict that the progression of dementia is progressing if the frequency of conversations decreases. In this way, the progression of dementia can be predicted by analyzing the content and frequency of conversations. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input conversation data into AI and have the AI ​​execute a progress prediction.

[0075] During monitoring, the monitoring unit can evaluate the progress of the person with dementia based on the lifestyle habit data. The monitoring unit can evaluate the progress by referring to, for example, the sleep pattern of the person with dementia. The monitoring unit can also evaluate the progress by referring to the eating pattern of the person with dementia. The monitoring unit can also evaluate the progress by referring to the exercise pattern of the person with dementia. This makes it possible to more accurately evaluate the progress of dementia by referring to the lifestyle habit data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the lifestyle habit data into AI and have the AI ​​evaluate the progress.

[0076] The monitoring unit can estimate the emotions of the person with dementia and adjust the monitoring frequency based on the emotions. For example, if the person with dementia is excited, the monitoring unit can increase the monitoring frequency. Also, if the person with dementia is relaxed, the monitoring unit can decrease the monitoring frequency. Also, if the person with dementia is tired, the monitoring unit can set the monitoring frequency to a medium level. This allows appropriate monitoring by adjusting the monitoring frequency according to the emotions of the person with dementia. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input emotion data into AI and have the AI ​​adjust the monitoring frequency.

[0077] During monitoring, the monitoring unit can evaluate the progress of the person with dementia based on the geographical location information of the person with dementia. For example, if the person with dementia is at home, the monitoring unit can evaluate the progress by referring to their behavioral patterns at home. Furthermore, if the person with dementia is out and about, the monitoring unit can evaluate the progress by referring to their behavioral patterns while out and about. Furthermore, if the person with dementia is in a hospital, the monitoring unit can evaluate the progress by referring to their behavioral patterns at the hospital. In this way, by taking the geographical location information into consideration, the progress of dementia can be more accurately evaluated. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the geographical location information into AI and have the AI ​​evaluate the progress.

[0078] During monitoring, the monitoring unit can analyze the social media activity of the person with dementia to evaluate their progress. For example, the monitoring unit can evaluate their progress by referring to the frequency of posts on social media by the person with dementia. The monitoring unit can also evaluate their progress by referring to the interaction patterns of the person with dementia on social media. The monitoring unit can also evaluate their progress by referring to the language used by the person with dementia on social media. This allows for a more accurate evaluation of the progression of dementia by analyzing social media activity. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input social media activity data into AI and have the AI ​​evaluate their progress. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned voice recognition unit, response generation unit, and monitoring unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the voice recognition unit recognizes the voice of the person with dementia using the microphone 38B of the smart device 14, and executes a voice recognition algorithm using the control unit 46A. The response generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an appropriate response using a generation AI. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and records the content and frequency of conversations and monitors the progression of dementia. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned voice recognition unit, response generation unit, and monitoring unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the voice recognition unit recognizes the voice of the person with dementia using the microphone 238 of the smart glasses 214 and executes a voice recognition algorithm using the control unit 46A. The response generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an appropriate response using a generation AI. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the content and frequency of conversations and monitors the progression of dementia. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned voice recognition unit, response generation unit, and monitoring unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the voice recognition unit recognizes the voice of the person with dementia using the microphone 238 of the headset-type terminal 314, and executes a voice recognition algorithm using the control unit 46A. The response generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an appropriate response using a generation AI. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and records the content and frequency of conversations and monitors the progression of dementia. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned voice recognition unit, response generation unit, and monitoring unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the voice recognition unit recognizes the voice of the person with dementia using the microphone 238 of the robot 414, and executes a voice recognition algorithm using the control unit 46A. The response generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an appropriate response using a generation AI. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and records the content and frequency of conversations and monitors the progression of dementia.

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

[0080] The dementia conversation support system may further include a lifestyle acquisition unit that acquires lifestyle data of the user. The lifestyle acquisition unit collects data such as the user's sleep patterns, dietary habits, and amount of exercise. This allows the system to customize the conversation content based on the user's lifestyle habits and provide more personalized support. For example, if the user has not been exercising recently, the system can generate a question such as "Have you been exercising recently?" to encourage exercise. The system can also provide topics related to healthy eating based on the user's dietary habits. Furthermore, the system can check whether the user is getting enough rest based on their sleep patterns and suggest relaxation methods if necessary.

[0081] The dementia conversation support system can further include an emotional topic selection unit that estimates the user's emotions and selects a conversation topic based on the estimated emotions. For example, if the user is sad, the emotional topic selection unit can improve the user's mood by offering fun topics or stories about memories. Also, if the user is excited, the emotional topic selection unit can relax the user by offering calming topics. Furthermore, if the user is tired, the system can reduce the user's burden by offering short conversations or simple questions. This allows for the selection of appropriate conversation topics according to the user's emotions, thereby achieving more effective conversation support.

[0082] The dementia conversation support system can further include a location information acquisition unit that acquires the user's geographical location information. For example, if the user is at home, the location information acquisition unit can provide topics related to the user's home. If the user is in a park, the location information acquisition unit can also provide topics related to the park. If the user is in a hospital, the location information acquisition unit can also provide topics related to the hospital. This makes it possible to provide topics that are highly relevant based on the user's current location and improve the naturalness of the conversation. For example, if the user is at home, a question such as "What have you been doing at home recently?" can be generated.

[0083] The dementia conversation support system can further include a social media analysis unit that analyzes the user's social media activity. The social media analysis unit, for example, prioritizes recognizing words and phrases frequently used by the user on social media. It can also analyze the content of the user's social media posts and provide related topics. It can also prioritize recognizing the names and topics of people the user interacts with on social media. By analyzing social media activity, it can provide topics that are highly relevant to the user and improve the quality of conversation. For example, it can ask about hobbies that the user has recently been talking about on social media.

[0084] The dementia conversation support system can further include a medical data acquisition unit that acquires the user's medical data. The medical data acquisition unit collects, for example, the user's health condition and medication information. This allows the system to adjust the content of the conversation based on the user's health condition and provide more appropriate support. For example, if the user is taking a specific medication, the system can provide topics related to that medication. It can also provide advice on appropriate lifestyle habits depending on the user's health condition. Furthermore, the content of the conversation can be adjusted based on the user's diagnostic data. This allows for more personalized support to be achieved by referring to the medical data.

[0085] The dementia conversation support system can further include an emotional tempo adjustment unit that estimates the user's emotions and adjusts the tempo of the conversation based on the estimated emotions. For example, if the user is excited, the emotional tempo adjustment unit slows down the tempo of the conversation to calm the user down. Also, if the user is relaxed, the emotional tempo adjustment unit can speed up the tempo of the conversation to make it progress smoothly. Furthermore, if the user is tired, the tempo of the conversation can be set to a medium level to allow the conversation to continue without strain. In this way, more effective conversation support can be achieved by adjusting the tempo of the conversation according to the user's emotions.

[0086] The dementia conversation support system can further include a conversation history analysis unit that analyzes the user's past conversation history. The conversation history analysis unit, for example, provides related topics based on what the user has said in the past. It can also generate consistent responses on specific topics from the user's past conversation history. It can also generate responses based on topics in which the user has shown interest in the past. This makes it possible to generate consistent responses by referring to the past conversation history and prevent the conversation from coming to a halt. For example, it can continue the conversation by revisiting a topic about travel that the user has talked about in the past.

[0087] The dementia conversation support system can further include an emotion length adjustment unit that estimates the user's emotion and adjusts the length of the conversation based on the estimated emotion. The emotion length adjustment unit can, for example, provide a short conversation when the user is excited. Also, it can provide a longer conversation when the user is relaxed. Furthermore, it can also provide a medium-length conversation when the user is tired. In this way, the conversation length can be adjusted according to the user's emotion to keep the conversation going. For example, if the user is excited, it can repeat short questions to relax the user.

[0088] The dementia conversation support system may further include a lifestyle analysis unit that analyzes the user's lifestyle data and adjusts the content of the conversation based on the lifestyle. The lifestyle analysis unit analyzes data such as the user's sleep patterns, diet, and amount of exercise. This allows the system to customize the content of the conversation based on the user's lifestyle and provide more personalized support. For example, if the user has not been exercising recently, the system can generate a question such as "Have you been exercising recently?" to encourage the user to exercise. The system can also provide topics related to healthy eating based on the user's diet. Furthermore, the system can check whether the user is getting enough rest based on their sleep patterns and suggest relaxation methods if necessary.

[0089] The dementia conversation support system can further include an emotion frequency adjustment unit that estimates the user's emotion and adjusts the frequency of conversation based on the estimated emotion. For example, if the user is excited, the emotion frequency adjustment unit reduces the frequency of conversation to calm the user. Also, if the user is relaxed, the emotion frequency adjustment unit can increase the frequency of conversation to actively communicate. Furthermore, if the user is tired, the frequency of conversation can be set to a medium level so that the user can continue the conversation without straining themselves. In this way, more effective conversation support can be achieved by adjusting the frequency of conversation according to the user's emotion.

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

[0091] Step 1: The speech recognition unit recognizes the speech of the person with dementia. Speech from the person with dementia includes everyday conversations, questions, and responses. The speech recognition unit uses a speech recognition algorithm to accurately capture what is being said and noise filtering technology to remove background and environmental sounds. For example, it filters out the sound of the television, wind, cars, and other people's voices to recognize what is being said. Step 2: The response generation unit generates an appropriate response based on the speech recognized by the speech recognition unit. It analyzes the speech using generative AI and natural language generation technology, and customizes the response based on the interests and concerns of the person with dementia. It also estimates the emotions of the person with dementia and adjusts the way the response is expressed based on those emotions. Step 3: The monitoring unit records the content and frequency of conversations based on the responses generated by the response generation unit, and monitors the progression of dementia. If the content of conversations becomes shorter or the same topics are repeated, it is determined that the dementia is progressing. The recorded data is also provided to medical professionals so that appropriate measures can be taken.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] [Explanation of symbols]

[0164] 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 speech recognition unit that recognizes the speech of people with dementia; a response generation unit that generates a response based on the speech recognized by the speech recognition unit; a monitoring unit that records the content and frequency of conversations based on the responses generated by the response generation unit and monitors the progress of dementia. A system characterized by:

2. The voice recognition unit Capturing what people with dementia say 2. The system of claim 1.

3. The response generation unit Analyzing what people with dementia say and generating responses 2. The system of claim 1.

4. The monitoring unit Record the content and frequency of conversations to monitor the progression of dementia 2. The system of claim 1.

5. The monitoring unit Providing data to medical professionals 2. The system of claim 1.

6. The voice recognition unit Estimate the emotions of people with dementia and adjust the accuracy of speech recognition based on their emotions 2. The system of claim 1.

7. The voice recognition unit Analyzing the past speech history of people with dementia and selecting a voice recognition algorithm 2. The system of claim 1.

8. The voice recognition unit During speech recognition, background and environmental sounds are filtered to improve recognition accuracy.

2. The system of claim 1.

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