system

Abstract

The system according to the embodiment aims to detect signs of dementia early and provide objective data to the person and their family. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects conversation data and walking data of a user via a smartphone. The analysis unit analyzes the data collected by the collection unit and detects signs of dementia. The provision unit provides the symptoms detected by the analysis unit as specific data to the user and their family.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies are not sufficient in detecting early signs of dementia and providing objective data, so there is room for improvement.

[0005] The system according to the embodiment aims to detect signs of dementia early and provide objective data to the person and their family. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects conversation data and walking data of a user via a smartphone. The analysis unit analyzes the data collected by the collection unit and detects signs of dementia. The provision unit provides the person and their family with specific data about the signs detected by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect signs of dementia at an early stage and provide the patient and their family with objective data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An early dementia detection system according to an embodiment of the present invention collects a user's conversation data and walking data via a smartphone and analyzes them using AI to detect signs of dementia at an early stage. The early dementia detection system collects the user's conversation data and walking data, analyzes them using AI, and detects signs of dementia. The system then provides the user and their family with objective data. For example, the early dementia detection system collects the user's conversation data and walking data via a smartphone. For example, it collects voice data when the user is talking on the smartphone and walking data when the user is walking while holding the smartphone. The early dementia detection system then analyzes the collected data using AI. The AI ​​analyzes the collected conversation data and walking data to detect signs of dementia. For example, it detects abnormalities in word choice and speech flow from the conversation data, and abnormalities in walking rhythm and balance from the walking data. The early dementia detection system then provides the detected signs to the user and their family as objective data. For example, it displays the results of the AI ​​analysis in graphs or reports and provides them to the user and their family. This allows the user and their family to recognize signs of dementia at an early stage. The early dementia detection system also enables highly accurate diagnoses based on the collected data. For example, if a doctor makes a diagnosis while referring to the results of AI analysis, a more accurate diagnosis will be possible. This allows the dementia early detection system to support the early detection and diagnosis of dementia. This allows the dementia early detection system to detect signs of dementia early and provide this information to the patient and their family. For example, the patient and their family will be able to recognize the signs of dementia early, allowing for early treatment and countermeasures. Furthermore, if a doctor makes a diagnosis while referring to the results of AI analysis, a more accurate diagnosis will be possible. This is expected to slow the progression of dementia.

[0029] The dementia early detection system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects conversation data and walking data of a user via a smartphone. The conversation data includes, but is not limited to, voice data and text data. For example, the collection unit collects voice data when the user is talking on the smartphone. The collection unit can also collect walking data when the user is walking while holding the smartphone. For example, the collection unit collects walking data using an acceleration sensor or gyro sensor of the smartphone. The analysis unit analyzes the data collected by the collection unit to detect signs of dementia. For example, the analysis unit detects abnormalities in word choice and conversation flow from the conversation data. The analysis unit can also detect abnormalities in walking rhythm and balance from the walking data. For example, the analysis unit detects abnormalities in the conversation data using AI. The AI ​​analyzes the conversation data using, for example, voice recognition technology or natural language processing technology to detect abnormalities. The analysis unit can also detect abnormalities in the walking data using AI. For example, the AI ​​analyzes acceleration data and position data to detect abnormalities in walking rhythm and balance. The providing unit provides the individual and their family with objective data on the signs detected by the analysis unit. For example, the providing unit displays the results of the AI ​​analysis in graph or report format and provides the results to the individual and their family. This allows the individual and their family to recognize signs of dementia early. For example, the providing unit can provide the analysis results visually in an easy-to-understand manner to the individual and their family. This allows the early dementia detection system according to the embodiment to detect signs of dementia early and provide the information to the individual and their family. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide data using an AI model that receives the signs detected by the analysis unit as input and outputs graphs or reports.

[0030] The collection unit can collect voice data when a user is making a call on the smartphone. The collection unit, for example, collects voice data when a user is making a call on the smartphone. The voice data includes, for example, voice during the call and background sounds, but is not limited to these examples. The collection unit, for example, collects voice data using a microphone on the smartphone. The collection unit can also analyze the voice data using voice recognition technology. For example, the collection unit removes background sounds using noise reduction technology and collects voice data during the call. The collection unit can also convert the voice data into text data. For example, the collection unit converts the voice data into text data using voice recognition technology and provides it to the analysis unit. This makes it possible to collect conversation data by collecting voice data during the user's call. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data acquired by the smartphone microphone to a generation AI and have the generation AI convert the voice data into text data.

[0031] The collection unit can collect walking data while the user is walking while holding a smartphone. The collection unit, for example, collects walking data while the user is walking while holding a smartphone. The walking data includes, for example, acceleration data and position data, but is not limited to these examples. The collection unit, for example, collects walking data using an acceleration sensor or gyro sensor of the smartphone. The collection unit can also analyze the walking data. For example, the collection unit analyzes the walking data to detect abnormalities in walking rhythm and balance. This makes it possible to collect walking data by collecting data while the user is walking. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input walking data acquired by a sensor of the smartphone to a generation AI and have the generation AI analyze the walking data.

[0032] The analysis unit can detect abnormal word choice and abnormal conversation flow from the conversation data. The analysis unit, for example, detects abnormal word choice and abnormal conversation flow from the conversation data. Abnormal word choice includes, for example, word frequency and grammatical errors, but is not limited to these examples. The analysis unit, for example, analyzes word frequency to detect abnormalities. The analysis unit can also analyze grammatical errors to detect abnormalities. Abnormal conversation flow includes, for example, conversation coherence and logical connections, but is not limited to these examples. The analysis unit, for example, analyzes conversation coherence to detect abnormalities. The analysis unit can also analyze logical connections to detect abnormalities. In this way, abnormal word choice and conversation flow can be detected by analyzing the conversation data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the conversation data to a generation AI and cause the generation AI to detect abnormal word choice and conversation flow.

[0033] The analysis unit can detect abnormalities in walking rhythm and balance from the walking data. The analysis unit, for example, detects abnormalities in walking rhythm and balance from the walking data. Abnormalities in walking rhythm include, but are not limited to, variations in walking cycle and stride length. The analysis unit, for example, analyzes walking cycle to detect abnormalities. The analysis unit can also analyze variations in stride length to detect abnormalities. Abnormalities in balance include, but are not limited to, variations in center of gravity and risk of falling. The analysis unit, for example, analyzes variations in center of gravity to detect abnormalities. The analysis unit can also analyze the risk of falling to detect abnormalities. In this way, abnormalities in walking rhythm and balance can be detected by analyzing the walking data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the walking data to a generation AI and cause the generation AI to detect abnormalities in walking rhythm and balance.

[0034] The providing unit can display the results of the AI ​​analysis in graph format or report format and provide them to the individual and their family. For example, the providing unit can display the results of the AI ​​analysis in graph format or report format and provide them to the individual and their family. Graph formats include, but are not limited to, line graphs and bar graphs. For example, the providing unit can display the analysis results using a line graph. The providing unit can also display the analysis results using a bar graph. For example, report formats include, but are not limited to, PDF reports and web reports. For example, the providing unit can display the analysis results using a PDF report. The providing unit can also display the analysis results using a web report. This visually displays the analysis results, making them easy to understand for the individual and their family. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide data using an AI model that inputs the symptoms detected by the analysis unit and outputs a graph or report.

[0035] The collection unit can analyze the user's past conversation data and select an appropriate collection method. The collection unit, for example, analyzes the user's past conversation data and selects an appropriate collection method. Appropriate collection methods include, but are not limited to, audio collection and text collection. For example, the collection unit selects the content of conversation data to collect based on topics the user has frequently discussed in the past. The collection unit can also customize the collection method by referring to language and expressions used by the user in the past. The collection unit can also concentrate collection of the user's past conversation data on a specific time period. This allows the optimal collection method to be selected by analyzing the past conversation data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past conversation data into a generation AI and have the generation AI select the optimal collection method.

[0036] The collection unit may filter the conversation data based on the user's current activity status and environment when collecting the conversation data. For example, the collection unit may filter the conversation data based on the user's current activity status and environment when collecting the conversation data. Examples of activity status include, but are not limited to, exercising and resting. Examples of environments include, but are not limited to, indoors and outdoors. For example, the collection unit may collect detailed conversation data when the user is in a quiet environment. Furthermore, the collection unit may also collect conversation data after removing noise when the user is in a noisy environment. Furthermore, the collection unit may collect conversation data in conjunction with walking data when the user is moving. This allows for more accurate conversation data to be collected by filtering according to the user's activity status and environment. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's activity status and environmental data into a generation AI and have the generation AI perform filtering.

[0037] The collection unit can select an appropriate collection means depending on the user's input method when collecting conversation data. For example, when collecting conversation data, the collection unit selects an appropriate collection means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the collection unit preferentially collects voice data. Furthermore, when the user uses text input, the collection unit can also collect text data. Furthermore, when the user sends an image, the collection unit can analyze the image data and collect related conversation data. This allows for efficient collection of conversation data by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and cause the generation AI to select the optimal collection means.

[0038] When collecting conversation data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when collecting conversation data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location sensor data. For example, when the user is in a specific location, the collection unit prioritizes collecting conversation data related to that location. Furthermore, when the user is traveling, the collection unit can also collect conversation data related to the user's destination. Furthermore, when the user is at home, the collection unit can prioritize collecting conversation data within the home. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location data to the generation AI and cause the generation AI to collect highly relevant data.

[0039] The collection unit may analyze the user's social media activities and collect related data when collecting conversation data. For example, the collection unit may analyze the user's social media activities and collect related data when collecting conversation data. Social media activities include, but are not limited to, posts and comments. For example, the collection unit may collect related conversation data based on the content posted by the user on social media. The collection unit may also collect conversation data based on the user's interactions with friends on social media. The collection unit may also adjust the collection timing based on the time period during which the user is active on social media. This allows for efficient collection of related data by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting conversation data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting conversation data. Feedback includes, but is not limited to, user ratings and comments. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also prioritize collection methods that the user has previously preferred. The collection unit can also optimize the collection timing based on the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the conversation data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the conversation data during analysis. Importance includes, but is not limited to, the urgency and impact of the data. For example, the analysis unit performs a detailed analysis of important conversation data. The analysis unit can also perform a concise analysis of everyday conversation data. The analysis unit can also focus on analyzing conversation data related to a specific topic. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the conversation data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the conversation data to a generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the conversation data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the conversation data during analysis. Categories include, but are not limited to, by topic or by application. For example, the analysis unit applies a specialized analysis algorithm to medical-related conversation data. The analysis unit can also apply a general analysis algorithm to everyday conversations. The analysis unit can also apply an emotion analysis algorithm to conversation data containing many emotional expressions. This improves analysis accuracy by applying an appropriate analysis algorithm depending on the category of the conversation data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the conversation data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, past data sets and analysis reports. For example, the analysis unit improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the analysis. The analysis unit can also compare the user's past analysis results to detect anomalies. By referring to the past analysis results, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the submission time of the conversation data during analysis. For example, the analysis unit determines the analysis priority based on the submission time of the conversation data during analysis. The submission time includes, but is not limited to, for example, the submission date and the submission time. For example, the analysis unit prioritizes analysis of the most recent conversation data. The analysis unit can also prioritize analysis of conversation data collected within a specific period. The analysis unit can also prioritize analysis of conversation data within a period specified by the user. This enables efficient analysis by determining the analysis priority based on the submission time of the conversation data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the conversation data to a generation AI and have the generation AI determine the analysis priority based on the submission time.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the conversation data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the conversation data during analysis. Relevance includes, for example, topic relevance and data relevance, but is not limited to these examples. For example, the analysis unit prioritizes analyzing highly relevant conversation data. The analysis unit can also prioritize analyzing conversation data related to a specific topic. The analysis unit can also prioritize analyzing conversation data related to a topic specified by the user. This enables efficient analysis by adjusting the order of analysis based on the relevance of the conversation data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input conversation data to a generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, the analysis unit can provide analysis results that use a lot of technical terms if the user has specialized knowledge. Furthermore, the analysis unit can provide concise and easy-to-understand analysis results if the user only has general knowledge. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. This allows for appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise into a generation AI and have the generation AI use technical terms.

[0047] The providing unit can adjust the level of detail of the display based on the importance of the analysis result when providing the analysis results. For example, the providing unit adjusts the level of detail of the display based on the importance of the analysis result when providing the analysis results. The importance includes, but is not limited to, for example, the urgency and impact of the data. For example, the providing unit displays detailed data for important analysis results. The providing unit can also display concise data for routine analysis results. The providing unit can also display focused data for analysis results related to a specific topic. This enables efficient information provision by adjusting the level of detail of the display based on the importance of the analysis results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results to a generation AI and cause the generation AI to adjust the level of detail of the display based on the importance.

[0048] The providing unit can apply different display algorithms depending on the category of the analysis results when providing the analysis results. For example, the providing unit applies different display algorithms depending on the category of the analysis results when providing the analysis results. Categories include, but are not limited to, by topic or by application. For example, the providing unit applies a specialized display algorithm to medical-related analysis results. The providing unit can also apply a general display algorithm to everyday analysis results. The providing unit can also apply an emotion analysis algorithm to analysis results containing many emotional expressions. This improves the accuracy of information provision by applying an appropriate display algorithm depending on the category of the analysis results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results to a generation AI and cause the generation AI to apply a display algorithm depending on the category.

[0049] The providing unit can improve the accuracy of the display by referring to the user's past feedback when providing the data. For example, the providing unit improves the accuracy of the display by referring to the user's past feedback when providing the data. Feedback includes, for example, user ratings and comments, but is not limited to these examples. For example, the providing unit improves the display method based on the user's past feedback. The providing unit can also preferentially use a display method that the user has previously preferred. The providing unit can also optimize the display timing based on the user's past feedback. This improves the accuracy of the display by referring to the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's feedback data into the generation AI and cause the generation AI to improve the accuracy of the display.

[0050] The providing unit can determine the display priority based on the submission time of the analysis results at the time of provision. For example, the providing unit determines the display priority based on the submission time of the analysis results at the time of provision. The submission time includes, but is not limited to, for example, the submission date and the submission time. For example, the providing unit prioritizes displaying the most recent analysis results. The providing unit can also prioritize displaying analysis results collected within a specific period. The providing unit can also prioritize displaying analysis results collected within a period specified by the user. This enables efficient information provision by determining the display priority based on the submission time of the analysis results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results to a generation AI and cause the generation AI to determine the display priority based on the submission time.

[0051] The providing unit can adjust the display order based on the relevance of the analysis results when providing the results. For example, the providing unit adjusts the display order based on the relevance of the analysis results when providing the results. Relevance includes, but is not limited to, topic relevance and data relevance. For example, the providing unit prioritizes displaying highly relevant analysis results. The providing unit can also prioritize displaying analysis results related to a specific topic. The providing unit can also prioritize displaying analysis results related to a topic specified by the user. This enables efficient information provision by adjusting the display order based on the relevance of the analysis results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results to a generation AI and cause the generation AI to adjust the display order based on the relevance.

[0052] The providing unit may adjust the use of technical terms in the display according to the user's level of expertise at the time of providing. For example, the providing unit may adjust the use of technical terms in the display according to the user's level of expertise at the time of providing. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, the providing unit may provide a display that uses a lot of technical terms if the user has specialized knowledge. Furthermore, the providing unit may provide a concise and easy-to-understand display if the user only has general knowledge. Furthermore, the providing unit may adjust the display expression method according to the user's level of expertise. This enables appropriate information provision by adjusting the use of technical terms in the display according to the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's level of expertise into the generating AI and cause the generating AI to use technical terms.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The collection unit can analyze the user's past conversation data and select an appropriate collection method. For example, the content of the conversation data to be collected can be selected based on topics that the user has frequently discussed in the past. The collection method can also be customized by referring to the language and expressions the user has used in the past. Furthermore, the collection can be concentrated on a specific time period based on the user's past conversation data. This makes it possible to select the optimal collection method by analyzing the past conversation data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input past conversation data into a generation AI and have the generation AI select the optimal collection method.

[0055] When collecting conversation data, the collection unit can filter the conversation data based on the user's current activity status and environment. For example, if the user is in a quiet environment, detailed conversation data can be collected. Also, if the user is in a noisy environment, noise can be removed and conversation data can be collected. Furthermore, if the user is moving, conversation data can be collected in conjunction with walking data. This allows for more accurate conversation data to be collected by filtering according to the user's activity status and environment. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's activity status and environmental data into the generation AI and have the generation AI perform filtering.

[0056] When collecting conversation data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user is using voice input, it can prioritize collection of voice data. Also, if the user is using text input, it can collect text data. Furthermore, if the user is sending images, it can analyze the image data and collect related conversation data. This allows conversation data to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0057] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the conversation data. For example, a detailed analysis can be performed on important conversation data. A concise analysis can also be performed on everyday conversation data. Furthermore, a focused analysis can be performed on conversation data related to a specific topic. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the conversation data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the conversation data to a generation AI and have the generation AI adjust the level of detail of the analysis based on the importance.

[0058] During analysis, the analysis unit can apply different analysis algorithms depending on the category of conversation data. For example, a specialized analysis algorithm can be applied to medical-related conversation data. A general analysis algorithm can also be applied to everyday conversations. Furthermore, an emotion analysis algorithm can be applied to conversation data containing many emotional expressions. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the category of conversation data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input conversation data into a generation AI and have the generation AI apply an analysis algorithm depending on the category.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects conversation data and walking data of the user via the smartphone. Conversation data includes voice data and text data. For example, voice data is collected when the user is talking on the smartphone. The collection unit can also collect walking data when the user is walking while holding the smartphone. For example, walking data is collected using the smartphone's acceleration sensor and gyro sensor. Step 2: The analysis unit analyzes the data collected by the collection unit and detects signs of dementia. For example, it can detect abnormalities in word choice and conversation flow from conversation data. It can also detect abnormalities in walking rhythm and balance from walking data. The analysis unit uses AI to detect abnormalities in conversation data and analyzes it using voice recognition technology and natural language processing technology. It also uses AI to detect abnormalities in walking data and analyzes acceleration data and position data. Step 3: The information provider provides the individual and their family with specific data on the signs detected by the analysis component. For example, the results of the AI ​​analysis can be displayed in graph or report format and provided to the individual and their family. This allows the individual and their family to recognize the signs of dementia at an early stage. By visually displaying the analysis results, the information provider can provide the individual and their family with information that is easy to understand.

[0061] (Example 2) An early dementia detection system according to an embodiment of the present invention collects a user's conversation data and walking data via a smartphone and analyzes them using AI to detect signs of dementia at an early stage. The early dementia detection system collects the user's conversation data and walking data, analyzes them using AI, and detects signs of dementia. The system then provides the user and their family with objective data. For example, the early dementia detection system collects the user's conversation data and walking data via a smartphone. For example, it collects voice data when the user is talking on the smartphone and walking data when the user is walking while holding the smartphone. The early dementia detection system then analyzes the collected data using AI. The AI ​​analyzes the collected conversation data and walking data to detect signs of dementia. For example, it detects abnormalities in word choice and speech flow from the conversation data, and abnormalities in walking rhythm and balance from the walking data. The early dementia detection system then provides the detected signs to the user and their family as objective data. For example, it displays the results of the AI ​​analysis in graphs or reports and provides them to the user and their family. This allows the user and their family to recognize signs of dementia at an early stage. The early dementia detection system also enables highly accurate diagnoses based on the collected data. For example, if a doctor makes a diagnosis while referring to the results of AI analysis, a more accurate diagnosis will be possible. This allows the dementia early detection system to support the early detection and diagnosis of dementia. This allows the dementia early detection system to detect signs of dementia early and provide this information to the patient and their family. For example, the patient and their family will be able to recognize the signs of dementia early, allowing for early treatment and countermeasures. Furthermore, if a doctor makes a diagnosis while referring to the results of AI analysis, a more accurate diagnosis will be possible. This is expected to slow the progression of dementia.

[0062] The dementia early detection system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects conversation data and walking data of a user via a smartphone. The conversation data includes, but is not limited to, voice data and text data. For example, the collection unit collects voice data when the user is talking on the smartphone. The collection unit can also collect walking data when the user is walking while holding the smartphone. For example, the collection unit collects walking data using an acceleration sensor or gyro sensor of the smartphone. The analysis unit analyzes the data collected by the collection unit to detect signs of dementia. For example, the analysis unit detects abnormalities in word choice and conversation flow from the conversation data. The analysis unit can also detect abnormalities in walking rhythm and balance from the walking data. For example, the analysis unit detects abnormalities in the conversation data using AI. The AI ​​analyzes the conversation data using, for example, voice recognition technology or natural language processing technology to detect abnormalities. The analysis unit can also detect abnormalities in the walking data using AI. For example, the AI ​​analyzes acceleration data and position data to detect abnormalities in walking rhythm and balance. The providing unit provides the individual and their family with objective data on the signs detected by the analysis unit. For example, the providing unit displays the results of the AI ​​analysis in graph or report format and provides the results to the individual and their family. This allows the individual and their family to recognize signs of dementia early. For example, the providing unit can provide the analysis results visually in an easy-to-understand manner to the individual and their family. This allows the early dementia detection system according to the embodiment to detect signs of dementia early and provide the information to the individual and their family. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide data using an AI model that receives the signs detected by the analysis unit as input and outputs graphs or reports.

[0063] The collection unit can collect voice data when a user is making a call on the smartphone. The collection unit, for example, collects voice data when a user is making a call on the smartphone. The voice data includes, for example, voice during the call and background sounds, but is not limited to these examples. The collection unit, for example, collects voice data using a microphone on the smartphone. The collection unit can also analyze the voice data using voice recognition technology. For example, the collection unit removes background sounds using noise reduction technology and collects voice data during the call. The collection unit can also convert the voice data into text data. For example, the collection unit converts the voice data into text data using voice recognition technology and provides it to the analysis unit. This makes it possible to collect conversation data by collecting voice data during the user's call. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data acquired by the smartphone microphone to a generation AI and have the generation AI convert the voice data into text data.

[0064] The collection unit can collect walking data while the user is walking while holding a smartphone. The collection unit, for example, collects walking data while the user is walking while holding a smartphone. The walking data includes, for example, acceleration data and position data, but is not limited to these examples. The collection unit, for example, collects walking data using an acceleration sensor or gyro sensor of the smartphone. The collection unit can also analyze the walking data. For example, the collection unit analyzes the walking data to detect abnormalities in walking rhythm and balance. This makes it possible to collect walking data by collecting data while the user is walking. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input walking data acquired by a sensor of the smartphone to a generation AI and have the generation AI analyze the walking data.

[0065] The analysis unit can detect abnormal word choice and abnormal conversation flow from the conversation data. The analysis unit, for example, detects abnormal word choice and abnormal conversation flow from the conversation data. Abnormal word choice includes, for example, word frequency and grammatical errors, but is not limited to these examples. The analysis unit, for example, analyzes word frequency to detect abnormalities. The analysis unit can also analyze grammatical errors to detect abnormalities. Abnormal conversation flow includes, for example, conversation coherence and logical connections, but is not limited to these examples. The analysis unit, for example, analyzes conversation coherence to detect abnormalities. The analysis unit can also analyze logical connections to detect abnormalities. In this way, abnormal word choice and conversation flow can be detected by analyzing the conversation data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the conversation data to a generation AI and cause the generation AI to detect abnormal word choice and conversation flow.

[0066] The analysis unit can detect abnormalities in walking rhythm and balance from the walking data. The analysis unit, for example, detects abnormalities in walking rhythm and balance from the walking data. Abnormalities in walking rhythm include, but are not limited to, variations in walking cycle and stride length. The analysis unit, for example, analyzes walking cycle to detect abnormalities. The analysis unit can also analyze variations in stride length to detect abnormalities. Abnormalities in balance include, but are not limited to, variations in center of gravity and risk of falling. The analysis unit, for example, analyzes variations in center of gravity to detect abnormalities. The analysis unit can also analyze the risk of falling to detect abnormalities. In this way, abnormalities in walking rhythm and balance can be detected by analyzing the walking data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the walking data to a generation AI and cause the generation AI to detect abnormalities in walking rhythm and balance.

[0067] The providing unit can display the results of the AI ​​analysis in graph format or report format and provide them to the individual and their family. For example, the providing unit can display the results of the AI ​​analysis in graph format or report format and provide them to the individual and their family. Graph formats include, but are not limited to, line graphs and bar graphs. For example, the providing unit can display the analysis results using a line graph. The providing unit can also display the analysis results using a bar graph. For example, report formats include, but are not limited to, PDF reports and web reports. For example, the providing unit can display the analysis results using a PDF report. The providing unit can also display the analysis results using a web report. This visually displays the analysis results, making them easy to understand for the individual and their family. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide data using an AI model that inputs the symptoms detected by the analysis unit and outputs a graph or report.

[0068] The collection unit can estimate the user's emotions and adjust the timing of conversation data collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of conversation data collection based on the estimated user emotions. To estimate emotions, technologies such as voice analysis and facial expression analysis can be used. For example, when the user is relaxed, the collection unit collects data during natural conversation. When the user is stressed, the collection unit can temporarily refrain from collecting conversation data. When the user is excited, the collection unit can collect data at a time when emotional changes are significant. This allows for more natural conversation data to be collected by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's voice data into the generation AI and cause the generation AI to estimate emotions.

[0069] The collection unit can analyze the user's past conversation data and select an appropriate collection method. The collection unit, for example, analyzes the user's past conversation data and selects an appropriate collection method. Appropriate collection methods include, but are not limited to, audio collection and text collection. For example, the collection unit selects the content of conversation data to collect based on topics the user has frequently discussed in the past. The collection unit can also customize the collection method by referring to language and expressions used by the user in the past. The collection unit can also concentrate collection of the user's past conversation data on a specific time period. This allows the optimal collection method to be selected by analyzing the past conversation data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past conversation data into a generation AI and have the generation AI select the optimal collection method.

[0070] The collection unit may filter the conversation data based on the user's current activity status and environment when collecting the conversation data. For example, the collection unit may filter the conversation data based on the user's current activity status and environment when collecting the conversation data. Examples of activity status include, but are not limited to, exercising and resting. Examples of environments include, but are not limited to, indoors and outdoors. For example, the collection unit may collect detailed conversation data when the user is in a quiet environment. Furthermore, the collection unit may also collect conversation data after removing noise when the user is in a noisy environment. Furthermore, the collection unit may collect conversation data in conjunction with walking data when the user is moving. This allows for more accurate conversation data to be collected by filtering according to the user's activity status and environment. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's activity status and environmental data into a generation AI and have the generation AI perform filtering.

[0071] The collection unit can select an appropriate collection means depending on the user's input method when collecting conversation data. For example, when collecting conversation data, the collection unit selects an appropriate collection means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the collection unit preferentially collects voice data. Furthermore, when the user uses text input, the collection unit can also collect text data. Furthermore, when the user sends an image, the collection unit can analyze the image data and collect related conversation data. This allows for efficient collection of conversation data by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and cause the generation AI to select the optimal collection means.

[0072] The collection unit can estimate the user's emotions and determine the priority of the conversation data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of the conversation data to be collected based on the estimated user emotions. To estimate emotions, for example, technologies such as voice analysis and facial expression analysis can be used. For example, when the user is feeling stressed, the collection unit can prioritize collecting stress-related conversation data. Furthermore, when the user is relaxed, the collection unit can prioritize collecting everyday conversation data. Furthermore, when the user is excited, the collection unit can prioritize collecting conversation data showing significant changes in emotions. In this way, by prioritizing the conversation data according to the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a 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-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0073] When collecting conversation data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when collecting conversation data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location sensor data. For example, when the user is in a specific location, the collection unit prioritizes collecting conversation data related to that location. Furthermore, when the user is traveling, the collection unit can also collect conversation data related to the user's destination. Furthermore, when the user is at home, the collection unit can prioritize collecting conversation data within the home. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location data to the generation AI and cause the generation AI to collect highly relevant data.

[0074] The collection unit may analyze the user's social media activities and collect related data when collecting conversation data. For example, the collection unit may analyze the user's social media activities and collect related data when collecting conversation data. Social media activities include, but are not limited to, posts and comments. For example, the collection unit may collect related conversation data based on the content posted by the user on social media. The collection unit may also collect conversation data based on the user's interactions with friends on social media. The collection unit may also adjust the collection timing based on the time period during which the user is active on social media. This allows for efficient collection of related data by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0075] The collection unit can customize the collection method by reflecting the user's past feedback when collecting conversation data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting conversation data. Feedback includes, but is not limited to, user ratings and comments. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also prioritize collection methods that the user has previously preferred. The collection unit can also optimize the collection timing based on the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0076] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the presentation method of the analysis based on the estimated user's emotion. To estimate the emotion, technologies such as voice analysis and facial expression analysis can be used. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise and concise analysis results when the user is stressed. The analysis unit can also provide visually easy-to-understand analysis results when the user is excited. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's voice data into the generation AI and have the generation AI estimate emotions.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the conversation data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the conversation data during analysis. Importance includes, but is not limited to, the urgency and impact of the data. For example, the analysis unit performs a detailed analysis of important conversation data. The analysis unit can also perform a concise analysis of everyday conversation data. The analysis unit can also focus on analyzing conversation data related to a specific topic. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the conversation data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the conversation data to a generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0078] The analysis unit can apply different analysis algorithms depending on the category of the conversation data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the conversation data during analysis. Categories include, but are not limited to, by topic or by application. For example, the analysis unit applies a specialized analysis algorithm to medical-related conversation data. The analysis unit can also apply a general analysis algorithm to everyday conversations. The analysis unit can also apply an emotion analysis algorithm to conversation data containing many emotional expressions. This improves analysis accuracy by applying an appropriate analysis algorithm depending on the category of the conversation data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the conversation data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0079] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, past data sets and analysis reports. For example, the analysis unit improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the analysis. The analysis unit can also compare the user's past analysis results to detect anomalies. By referring to the past analysis results, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0080] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. To estimate the emotion, technologies such as voice analysis and facial expression analysis can be used. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a concise analysis when the user is stressed. The analysis unit can also perform a visually easy-to-understand analysis when the user is excited. This allows appropriate analysis results to be provided by adjusting the length of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a 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 analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0081] The analysis unit can determine the analysis priority based on the submission time of the conversation data during analysis. For example, the analysis unit determines the analysis priority based on the submission time of the conversation data during analysis. The submission time includes, but is not limited to, for example, the submission date and the submission time. For example, the analysis unit prioritizes analysis of the most recent conversation data. The analysis unit can also prioritize analysis of conversation data collected within a specific period. The analysis unit can also prioritize analysis of conversation data within a period specified by the user. This enables efficient analysis by determining the analysis priority based on the submission time of the conversation data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the conversation data to a generation AI and have the generation AI determine the analysis priority based on the submission time.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the conversation data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the conversation data during analysis. Relevance includes, for example, topic relevance and data relevance, but is not limited to these examples. For example, the analysis unit prioritizes analyzing highly relevant conversation data. The analysis unit can also prioritize analyzing conversation data related to a specific topic. The analysis unit can also prioritize analyzing conversation data related to a topic specified by the user. This enables efficient analysis by adjusting the order of analysis based on the relevance of the conversation data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input conversation data to a generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0083] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, the analysis unit can provide analysis results that use a lot of technical terms if the user has specialized knowledge. Furthermore, the analysis unit can provide concise and easy-to-understand analysis results if the user only has general knowledge. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. This allows for appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise into a generation AI and have the generation AI use technical terms.

[0084] The providing unit can estimate the user's emotion and adjust the display method of the data to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the display method of the data to be provided based on the estimated user's emotion. To estimate the emotion, for example, technologies such as voice analysis and facial expression analysis can be used. For example, the providing unit can display detailed data when the user is relaxed. Furthermore, the providing unit can display concise and to-the-point data when the user is stressed. Furthermore, the providing unit can display visually easy-to-understand data when the user is excited. This enables appropriate information to be provided by adjusting the data display method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a 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-mentioned processing in the providing unit may be performed using, for example, an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0085] The providing unit can adjust the level of detail of the display based on the importance of the analysis result when providing the analysis results. For example, the providing unit adjusts the level of detail of the display based on the importance of the analysis result when providing the analysis results. The importance includes, but is not limited to, for example, the urgency and impact of the data. For example, the providing unit displays detailed data for important analysis results. The providing unit can also display concise data for routine analysis results. The providing unit can also display focused data for analysis results related to a specific topic. This enables efficient information provision by adjusting the level of detail of the display based on the importance of the analysis results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results to a generation AI and cause the generation AI to adjust the level of detail of the display based on the importance.

[0086] The providing unit can apply different display algorithms depending on the category of the analysis results when providing the analysis results. For example, the providing unit applies different display algorithms depending on the category of the analysis results when providing the analysis results. Categories include, but are not limited to, by topic or by application. For example, the providing unit applies a specialized display algorithm to medical-related analysis results. The providing unit can also apply a general display algorithm to everyday analysis results. The providing unit can also apply an emotion analysis algorithm to analysis results containing many emotional expressions. This improves the accuracy of information provision by applying an appropriate display algorithm depending on the category of the analysis results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results to a generation AI and cause the generation AI to apply a display algorithm depending on the category.

[0087] The providing unit can improve the accuracy of the display by referring to the user's past feedback when providing the data. For example, the providing unit improves the accuracy of the display by referring to the user's past feedback when providing the data. Feedback includes, for example, user ratings and comments, but is not limited to these examples. For example, the providing unit improves the display method based on the user's past feedback. The providing unit can also preferentially use a display method that the user has previously preferred. The providing unit can also optimize the display timing based on the user's past feedback. This improves the accuracy of the display by referring to the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's feedback data into the generation AI and cause the generation AI to improve the accuracy of the display.

[0088] The providing unit can estimate the user's emotions and determine the priority of data to be provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of data to be provided based on the estimated user emotions. To estimate emotions, technologies such as voice analysis and facial expression analysis can be used. For example, if the user is feeling stressed, the providing unit can prioritize providing data related to stress. Furthermore, if the user is relaxed, the providing unit can prioritize providing daily data. Furthermore, if the user is excited, the providing unit can prioritize providing data showing significant changes in emotions. This allows important information to be provided preferentially by determining the priority of data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 providing unit can be performed using, for example, an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0089] The providing unit can determine the display priority based on the submission time of the analysis results at the time of provision. For example, the providing unit determines the display priority based on the submission time of the analysis results at the time of provision. The submission time includes, but is not limited to, for example, the submission date and the submission time. For example, the providing unit prioritizes displaying the most recent analysis results. The providing unit can also prioritize displaying analysis results collected within a specific period. The providing unit can also prioritize displaying analysis results collected within a period specified by the user. This enables efficient information provision by determining the display priority based on the submission time of the analysis results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results to a generation AI and cause the generation AI to determine the display priority based on the submission time.

[0090] The providing unit can adjust the display order based on the relevance of the analysis results when providing the results. For example, the providing unit adjusts the display order based on the relevance of the analysis results when providing the results. Relevance includes, but is not limited to, topic relevance and data relevance. For example, the providing unit prioritizes displaying highly relevant analysis results. The providing unit can also prioritize displaying analysis results related to a specific topic. The providing unit can also prioritize displaying analysis results related to a topic specified by the user. This enables efficient information provision by adjusting the display order based on the relevance of the analysis results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results to a generation AI and cause the generation AI to adjust the display order based on the relevance.

[0091] The providing unit may adjust the use of technical terms in the display according to the user's level of expertise at the time of providing. For example, the providing unit may adjust the use of technical terms in the display according to the user's level of expertise at the time of providing. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, the providing unit may provide a display that uses a lot of technical terms if the user has specialized knowledge. Furthermore, the providing unit may provide a concise and easy-to-understand display if the user only has general knowledge. Furthermore, the providing unit may adjust the display expression method according to the user's level of expertise. This enables appropriate information provision by adjusting the use of technical terms in the display according to the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's level of expertise into the generating AI and cause the generating AI to use technical terms. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects conversation data and walking data of the user using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and displays the analysis results in graphs or report format and provides them to the user and their family. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's conversation data and walking data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and displays the analysis results in graphs or report format and provides them to the user and their family. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects conversation data and walking data of the user using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data. The provision unit is realized, for example, by the control unit 46A of the headset terminal 314, and displays the analysis results in graphs or report format and provides them to the user and their family. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects conversation data and walking data of the user using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data. The provision unit is realized, for example, by the control unit 46A of the robot 414, and displays the analysis results in the form of graphs or reports, and provides them to the user and their family.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, stress-related data can be analyzed preferentially. Furthermore, if the user is relaxed, daily data can be analyzed preferentially. Furthermore, if the user is excited, data showing significant changes in emotions can be analyzed preferentially. Thus, by determining the analysis priority according to the user's emotions, important data can be analyzed preferentially. For example, techniques such as voice analysis and facial expression analysis can be used to estimate emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's voice data into a generation AI and have the generation AI perform emotion estimation.

[0094] The providing unit can estimate the user's emotions and adjust the display method of the data to be provided based on the estimated user's emotions. For example, if the user is relaxed, detailed data can be displayed. If the user is stressed, concise data that focuses on the main points can be displayed. Furthermore, if the user is excited, visually easy-to-understand data can be displayed. This makes it possible to provide appropriate information by adjusting the data display method according to the user's emotions. Emotion estimation can be performed using technologies such as voice analysis and facial expression analysis. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can input the user's emotion data into the generating AI and have the generating AI adjust the display method.

[0095] The collection unit can estimate the user's emotions and determine the priority of conversation data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, conversation data related to stress can be collected preferentially. Furthermore, if the user is relaxed, daily conversation data can be collected preferentially. Furthermore, if the user is excited, conversation data showing significant changes in emotions can be collected preferentially. By determining the priority of conversation data according to the user's emotions, important data can be collected preferentially. Emotion estimation can be performed using techniques such as voice analysis and facial expression analysis. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's voice data into a generation AI and have the generation AI perform emotion estimation.

[0096] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is stressed, concise and to the point analysis results can be provided. Furthermore, if the user is excited, visually easy-to-understand analysis results can be provided. This allows for adjusting the way the analysis is presented according to the user's emotions to provide more appropriate analysis results. Emotion estimation can use technologies such as voice analysis and facial expression analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's voice data into a generation AI and have the generation AI perform emotion estimation.

[0097] The providing unit can estimate the user's emotions and determine the priority of data to be provided based on the estimated user emotions. For example, if the user is feeling stressed, stress-related data can be provided preferentially. Also, if the user is relaxed, daily data can be provided preferentially. Furthermore, if the user is excited, data showing significant changes in emotions can be provided preferentially. This allows important information to be provided preferentially by determining the priority of data according to the user's emotions. Emotion estimation can be performed using techniques such as voice analysis and facial expression analysis. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0098] The collection unit can analyze the user's past conversation data and select an appropriate collection method. For example, the content of the conversation data to be collected can be selected based on topics that the user has frequently discussed in the past. The collection method can also be customized by referring to the language and expressions the user has used in the past. Furthermore, the collection can be concentrated on a specific time period based on the user's past conversation data. This makes it possible to select the optimal collection method by analyzing the past conversation data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input past conversation data into a generation AI and have the generation AI select the optimal collection method.

[0099] When collecting conversation data, the collection unit can filter the conversation data based on the user's current activity status and environment. For example, if the user is in a quiet environment, detailed conversation data can be collected. Also, if the user is in a noisy environment, noise can be removed and conversation data can be collected. Furthermore, if the user is moving, conversation data can be collected in conjunction with walking data. This allows for more accurate conversation data to be collected by filtering according to the user's activity status and environment. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's activity status and environmental data into the generation AI and have the generation AI perform filtering.

[0100] When collecting conversation data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user is using voice input, it can prioritize collection of voice data. Also, if the user is using text input, it can collect text data. Furthermore, if the user is sending images, it can analyze the image data and collect related conversation data. This allows conversation data to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0101] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the conversation data. For example, a detailed analysis can be performed on important conversation data. A concise analysis can also be performed on everyday conversation data. Furthermore, a focused analysis can be performed on conversation data related to a specific topic. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the conversation data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the conversation data to a generation AI and have the generation AI adjust the level of detail of the analysis based on the importance.

[0102] During analysis, the analysis unit can apply different analysis algorithms depending on the category of conversation data. For example, a specialized analysis algorithm can be applied to medical-related conversation data. A general analysis algorithm can also be applied to everyday conversations. Furthermore, an emotion analysis algorithm can be applied to conversation data containing many emotional expressions. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the category of conversation data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input conversation data into a generation AI and have the generation AI apply an analysis algorithm depending on the category.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects conversation data and walking data of the user via the smartphone. Conversation data includes voice data and text data. For example, voice data is collected when the user is talking on the smartphone. The collection unit can also collect walking data when the user is walking while holding the smartphone. For example, walking data is collected using the smartphone's acceleration sensor and gyro sensor. Step 2: The analysis unit analyzes the data collected by the collection unit and detects signs of dementia. For example, it can detect abnormalities in word choice and conversation flow from conversation data. It can also detect abnormalities in walking rhythm and balance from walking data. The analysis unit uses AI to detect abnormalities in conversation data and analyzes it using voice recognition technology and natural language processing technology. It also uses AI to detect abnormalities in walking data and analyzes acceleration data and position data. Step 3: The information provider provides the individual and their family with specific data on the signs detected by the analysis component. For example, the results of the AI ​​analysis can be displayed in graph or report format and provided to the individual and their family. This allows the individual and their family to recognize the signs of dementia at an early stage. By visually displaying the analysis results, the information provider can provide the individual and their family with information that is easy to understand.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0152] In the 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.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 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.

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

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects conversation data and walking data of a user via a smartphone; an analysis unit that analyzes the data collected by the collection unit and detects signs of dementia; a providing unit that provides the symptom detected by the analysis unit as specific data to the patient and his / her family. A system characterized by:

2. The collecting unit Collecting voice data when users are talking on their smartphones 2. The system of claim 1.

3. The collecting unit Collect walking data while walking with a smartphone 2. The system of claim 1.

4. The analysis unit Detecting anomalies in word choice and conversation flow from conversation data 2. The system of claim 1.

5. The analysis unit Detecting abnormalities in walking rhythm and balance from walking data 2. The system of claim 1.

6. The providing unit The results of the AI ​​analysis are displayed in graphs and reports and provided to the individual and their family.

2. The system of claim 1.

7. The collecting unit The system estimates the user's emotions and adjusts the timing of conversation data collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze users' past conversation data and select the appropriate collection method 2. The system of claim 1.

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