system

Abstract

The system according to this embodiment aims to efficiently and accurately evaluate the user's cognitive function. [Solution] The system according to the embodiment comprises an evaluation unit, a provision unit, and an analysis unit. The evaluation unit evaluates the user's cognitive functions, including vocabulary, memory, attention, and logical thinking. The provision unit provides the evaluation results obtained by the evaluation unit. The analysis unit analyzes the evaluation unit's response to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult to efficiently and accurately evaluate the cognitive functions of users, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently and accurately evaluate the cognitive functions of users.

Means for Solving the Problems

[0006] The system according to the embodiment includes an evaluation unit, a providing unit, and an analysis unit. The evaluation unit evaluates the cognitive functions of a user's vocabulary, memory, attention, and logical thinking. The providing unit provides the evaluation result obtained by the evaluation unit. The analysis unit analyzes the user's reaction by the evaluation unit.

Effects of the Invention

[0007] The system according to this embodiment can efficiently and accurately evaluate the user's cognitive function. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The cognitive function evaluation system according to an embodiment of the present invention is a system that uses devices such as smart speakers, earphones with microphones, and audio glasses to have a target user periodically converse with a generative AI (generative AI) and evaluates cognitive functions such as vocabulary, memory, attention, and logical thinking during these conversations. In this system, the user converses with the generative AI using devices such as smart speakers, earphones with microphones, and audio glasses. This conversation takes place periodically, and the generative AI evaluates the user's cognitive functions such as vocabulary, memory, attention, and logical thinking. Next, during the conversation, topics that may lead to suspicion of dementia, such as forgetfulness or a distorted sense of time, are brought up. For example, the generative AI asks the user questions such as, "What did you eat for dinner yesterday?" or "When is your next birthday?" The generative AI observes the user's response and evaluates it. For example, if the user cannot answer a question accurately, the generative AI records that information and suggests the possibility of a decline in cognitive function. Furthermore, the generative AI provides the results of the cognitive function evaluation based on the user's response. For example, the generative AI evaluates the user's vocabulary, memory, attention, and logical thinking ability, and provides feedback on the results to the user. This allows users to understand the state of their own cognitive function. Through this mechanism, users can regularly evaluate their cognitive function by engaging in daily conversations with the generated AI. For example, by using a smart speaker to converse with the generated AI every morning, changes in the user's cognitive function can be continuously monitored. Furthermore, by using earphones with microphones or audio glasses, users can converse with the generated AI and receive cognitive function evaluations even when away from home. In this way, by regularly conversing with the generated AI using devices such as smart speakers, earphones with microphones, and audio glasses, the user's cognitive function can be evaluated, which can be used to aid in the early detection and prevention of dementia. Thus, the cognitive function evaluation system can regularly assess the user's cognitive function and contribute to the early detection and prevention of dementia.

[0029] The cognitive function evaluation system according to the embodiment comprises an evaluation unit, a provision unit, and an analysis unit. The evaluation unit evaluates the user's cognitive functions, including vocabulary, memory, attention, and logical thinking. The evaluation unit analyzes the user's responses using, for example, a generative AI and provides evaluation results. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze the user's utterances and tone of voice. For example, the generative AI analyzes the user's utterances using keyword extraction technology and evaluates the richness of the vocabulary. The generative AI can also evaluate memory based on the user's utterances. For example, the generative AI remembers what the user has said in the past and evaluates memory by asking the same questions again. Furthermore, the generative AI can analyze the user's utterances and tone of voice and evaluate attention and logical thinking ability. For example, the generative AI analyzes the consistency and logical structure of the user's utterances and evaluates logical thinking ability. The provision unit provides feedback to the user on the evaluation results obtained by the evaluation unit. The providing unit can provide feedback in various formats, such as text, audio, and visuals. For example, the providing unit can send the evaluation results to the user as a text message. The providing unit can also provide the evaluation results to the user as an audio message. Furthermore, the providing unit can display the evaluation results in a visual format, making them easily understandable to the user. The analysis unit analyzes the user's responses using generative AI and provides evaluation results. The analysis unit can, for example, analyze the user's utterances and tone of voice to evaluate cognitive function. For example, the analysis unit can analyze the user's utterances using keyword extraction technology to evaluate vocabulary richness. The analysis unit can also evaluate memory based on the user's utterances. Furthermore, the analysis unit can analyze the user's utterances and tone of voice to evaluate attention and logical thinking ability. Thus, the cognitive function evaluation system according to this embodiment can evaluate the user's cognitive function and provide the results.

[0030] The evaluation unit can analyze user responses using generative AI and provide evaluation results. Generative AI can be, for example, text generation AI (e.g., LLM) or multimodal generation AI, and can analyze the content of user statements and tone of voice. For example, the generative AI can analyze the content of user statements using keyword extraction technology and evaluate vocabulary richness. The generative AI can also evaluate memory based on the content of user statements. For example, the generative AI can remember what the user has said in the past and evaluate memory by asking the same question again. Furthermore, the generative AI can analyze the content of user statements and tone of voice and evaluate attention and logical thinking ability. For example, the generative AI can analyze the consistency and logical structure of user statements and evaluate logical thinking ability. As a result, using generative AI improves the accuracy of analyzing user responses and providing evaluation results.

[0031] The service provider can provide feedback to the user based on the evaluation results obtained by the evaluation unit. The service provider can provide feedback in various formats, such as text, audio, and visuals. For example, the service provider can send the evaluation results to the user as a text message. It can also provide the evaluation results to the user as an audio message. Furthermore, the service provider can display the evaluation results in a visual format, making them easily understandable to the user. This allows the user to understand the state of their own cognitive function by providing feedback on the evaluation results.

[0032] The analysis unit can use generative AI to analyze the user's utterances or tone of voice and evaluate cognitive function. Generative AI can be, for example, text generation AI (e.g., LLM) or multimodal generation AI, and can analyze the user's utterances and tone of voice. For example, the generative AI can analyze the user's utterances using keyword extraction technology and evaluate the richness of their vocabulary. The generative AI can also evaluate memory based on the user's utterances. For example, the generative AI can remember what the user has said in the past and evaluate memory by asking the same questions again. Furthermore, the generative AI can analyze the user's utterances and tone of voice and evaluate attention and logical thinking ability. For example, the generative AI can analyze the consistency and logical structure of the user's utterances and evaluate logical thinking ability. As a result, using generative AI improves the accuracy of analyzing the user's utterances and tone of voice and evaluating cognitive function.

[0033] The service provider can provide evaluation results to medical institutions. For example, the service provider can provide evaluation results to medical institutions via email or a dedicated web portal. For instance, the service provider can send evaluation results to medical institutions via email. Alternatively, the service provider can upload evaluation results to a dedicated web portal, making them accessible to medical institutions. This allows medical institutions to understand the user's cognitive function status by providing them with evaluation results.

[0034] The evaluation unit can assess the user's vocabulary, memory, attention, and logical thinking skills. For example, the evaluation unit uses generative AI to analyze the user's responses and evaluate vocabulary, memory, attention, and logical thinking skills. Generative AI can be, for example, text generation AI (e.g., LLM) or multimodal generation AI, and can analyze the user's utterances and tone of voice. For example, the generative AI can analyze the user's utterances using keyword extraction technology to evaluate vocabulary. The generative AI can also evaluate memory based on the user's utterances. For example, the generative AI can remember what the user has said in the past and evaluate memory by asking the same questions again. Furthermore, the generative AI can analyze the user's utterances and tone of voice to evaluate attention and logical thinking skills. For example, the generative AI can analyze the consistency and logical structure of the user's utterances to evaluate logical thinking skills. In this way, by evaluating the user's vocabulary, memory, attention, and logical thinking skills, the state of their cognitive function can be understood in detail.

[0035] The analysis unit can analyze user responses using generative AI and provide evaluation results. Generative AI can be, for example, text generation AI (e.g., LLM) or multimodal generation AI, and can analyze the content of user statements and tone of voice. For example, the generative AI can analyze the content of user statements using keyword extraction technology and evaluate vocabulary richness. The generative AI can also evaluate memory based on the content of user statements. For example, the generative AI can remember what the user has said in the past and evaluate memory by asking the same questions again. Furthermore, the generative AI can analyze the content of user statements and tone of voice and evaluate attention and logical thinking ability. For example, the generative AI can analyze the consistency and logical structure of user statements and evaluate logical thinking ability. As a result, using generative AI improves the accuracy of analyzing user responses and providing evaluation results.

[0036] The service provider may include a protection component to ensure user privacy. The service provider may protect user privacy through methods such as data anonymization and access control. For example, the service provider may anonymize evaluation results and provide them in a format that does not identify individuals. The service provider may also restrict access to evaluation results, allowing only users with specific permissions to access them. Furthermore, the service provider may encrypt data to prevent evaluation results from being leaked to third parties. This ensures that users can use the system with peace of mind by considering their privacy.

[0037] The evaluation unit can analyze the user's past conversation history to improve the accuracy of its evaluations. For example, the evaluation unit can use generative AI to analyze the user's past conversation history and improve the accuracy of its evaluations. Generative AI can be, for example, text generation AI (e.g., LLM) or multimodal generation AI, and can analyze the user's past conversation history. For example, the generative AI evaluates the current conversation content based on the vocabulary and phrases the user has used in the past. The generative AI can also track changes in the user's cognitive function from the user's past conversation history and reflect them in the evaluation. Furthermore, the generative AI can analyze the user's past conversation patterns and adjust the evaluation criteria. For example, the generative AI analyzes the user's past conversation patterns to improve the accuracy of its evaluations. As a result, the accuracy of the evaluation is improved by analyzing past conversation history.

[0038] The evaluation unit can perform evaluations based on the user's lifestyle and health status. For example, the evaluation unit can use generative AI to consider the user's lifestyle and health status when performing evaluations. The generative AI can collect data such as the user's sleep patterns, eating habits, and exercise habits, and reflect this in the evaluation. For example, the generative AI can adjust the evaluation criteria considering the user's sleep patterns. The generative AI can also improve the accuracy of the evaluation based on the user's eating habits. Furthermore, the generative AI can consider the user's exercise habits and reflect this in the evaluation results. For example, the generative AI can analyze the user's exercise habits to improve the accuracy of the evaluation. This makes it possible to perform more accurate evaluations by considering lifestyle and health status.

[0039] The evaluation unit can perform evaluations based on the user's geographical location information. For example, the evaluation unit can use a generative AI to consider the user's geographical location information when performing evaluations. The generative AI can collect the user's geographical location information using, for example, GPS data or location information services and reflect it in the evaluation. For example, if the user is at home, the generative AI will perform evaluations in a relaxed environment. The generative AI can also perform evaluations considering changes in the environment if the user is out and about. Furthermore, if the user is in a specific location, the generative AI can also perform evaluations considering the influence of that location. For example, if the user is in a specific location, the generative AI will adjust the evaluation criteria considering the influence of that location. This makes it possible to perform evaluations that reflect changes in the environment by considering geographical location information.

[0040] The evaluation unit can perform evaluations based on the user's social media activity. For example, the evaluation unit can use generative AI to analyze the user's social media activity and improve the accuracy of the evaluation. The generative AI can, for example, analyze the content and activity patterns of the user's social media posts. For example, the generative AI can improve the accuracy of the evaluation based on the content of the user's social media posts. Furthermore, the generative AI can analyze the user's social media activity patterns and reflect them in the evaluation. In addition, the generative AI can consider the user's social media friendships and adjust the evaluation criteria accordingly. For example, the generative AI can analyze the user's social media friendships and improve the accuracy of the evaluation. Thus, by analyzing social media activity, the accuracy of the evaluation is improved.

[0041] The service provider can select the optimal service delivery method by referring to the user's past feedback history at the time of delivery. For example, the service provider can use a generative AI to refer to the user's past feedback history and select the optimal service delivery method. The generative AI can, for example, analyze the user's past feedback history and select the optimal service delivery method. For example, the generative AI prioritizes providing feedback methods that the user has preferred in the past. The generative AI can also provide feedback at the optimal timing based on the user's past feedback history. Furthermore, the generative AI can analyze the user's past feedback history and provide feedback with optimal content. For example, the generative AI analyzes the user's past feedback history and provides feedback with optimal content. In this way, the optimal service delivery method can be selected by referring to past feedback history.

[0042] The service provider can customize the content of the feedback based on the user's living situation at the time of delivery. For example, the service provider can use a generative AI to consider the user's living situation and customize the content of the feedback. The generative AI can collect data such as the user's home environment, work situation, and health status, and reflect this in the feedback. For example, the generative AI considers the user's current living situation and provides appropriate feedback. The generative AI can also adjust the content of the feedback based on the user's lifestyle habits. Furthermore, the generative AI can also consider the user's health status and customize the content of the feedback. For example, the generative AI analyzes the user's health status and customizes the content of the feedback. By customizing the content of the feedback based on the living situation, more appropriate feedback becomes possible.

[0043] The delivery unit can select the optimal delivery method by considering the user's device information at the time of delivery. For example, the delivery unit can use a generative AI to consider the user's device information and select the optimal delivery method. The generative AI can collect information such as the user's device type and OS version and select the optimal delivery method. For example, if the user is using a smartphone, the generative AI will provide feedback tailored to the screen size. Also, if the user is using a tablet, the generative AI can provide feedback optimized for a larger screen. Furthermore, if the user is using a smartwatch, the generative AI can provide concise and highly visible feedback. For example, the generative AI will select the optimal delivery method based on the user's device information. In this way, the optimal delivery method can be selected by considering the device information.

[0044] The service provider can analyze the user's social media activity and adjust the content of the feedback at the time of delivery. For example, the service provider can use generative AI to analyze the user's social media activity and adjust the content of the feedback. The generative AI can, for example, analyze the content and activity patterns of the user's social media posts. For example, the generative AI adjusts the content of the feedback based on the content of the user's social media posts. The generative AI can also analyze the user's social media activity patterns and reflect them in the feedback. Furthermore, the generative AI can consider the user's social media friendships and adjust the content of the feedback. For example, the generative AI analyzes the user's social media friendships and adjusts the content of the feedback. In this way, the content of the feedback can be adjusted by analyzing social media activity.

[0045] The analysis unit can improve the accuracy of its analysis by referring to the user's past statements during the analysis process. For example, the analysis unit can use a generative AI to refer to the user's past statements and improve the accuracy of the analysis. The generative AI can, for example, analyze the user's past statements and evaluate the current statements. For example, the generative AI analyzes the current statements based on the vocabulary and phrases the user has used in the past. The generative AI can also track changes in cognitive function from the user's past statements and reflect them in the analysis. Furthermore, the generative AI can analyze the user's past statements patterns and adjust the analysis criteria. For example, the generative AI analyzes the user's past statements patterns and improves the accuracy of the analysis. As a result, the accuracy of the analysis is improved by referring to past statements.

[0046] The analysis unit can perform analysis while considering the user's lifestyle and health condition. For example, the analysis unit uses generative AI to perform analysis while considering the user's lifestyle and health condition. The generative AI can collect data such as the user's sleep patterns, eating habits, and exercise habits, and reflect this in the analysis. For example, the generative AI adjusts the analysis criteria considering the user's sleep patterns. The generative AI can also improve the accuracy of the analysis based on the user's eating habits. Furthermore, the generative AI can consider the user's exercise habits and reflect this in the analysis results. For example, the generative AI analyzes the user's exercise habits to improve the accuracy of the analysis. As a result, more accurate analysis becomes possible by considering lifestyle and health conditions.

[0047] The analysis unit can perform analysis while considering the user's geographical location information. For example, the analysis unit uses a generative AI to perform analysis while considering the user's geographical location information. The generative AI can collect the user's geographical location information using, for example, GPS data or location information services and reflect it in the analysis. For example, if the user is at home, the generative AI will perform analysis in a relaxed environment. The generative AI can also perform analysis while considering changes in the environment if the user is out and about. Furthermore, if the user is in a specific location, the generative AI can perform analysis while considering the influence of that location. For example, if the user is in a specific location, the generative AI will adjust the analysis criteria to take into account the influence of that location. This makes it possible to perform analysis that reflects changes in the environment by considering geographical location information.

[0048] The analysis unit can improve the accuracy of the analysis by analyzing the user's social media activity during the analysis process. For example, the analysis unit can improve the accuracy of the analysis by using generative AI to analyze the user's social media activity. For example, the generative AI can analyze the content and activity patterns of the user's social media posts. For example, the generative AI can improve the accuracy of the analysis based on the content of the user's social media posts. The generative AI can also analyze the user's social media activity patterns and reflect them in the analysis. Furthermore, the generative AI can adjust the analysis criteria by considering the user's social media friendships. For example, the generative AI can improve the accuracy of the analysis by analyzing the user's social media friendships. In this way, the accuracy of the analysis is improved by analyzing social media activity.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The evaluation unit can consider the user's lifestyle data when assessing their cognitive function. For example, it can collect data such as the user's sleep patterns, eating habits, and exercise habits, and perform evaluations based on this data. The evaluation unit can analyze the user's sleep patterns and reflect the impact of sleep deprivation on cognitive function in the evaluation. It can also consider the user's eating habits and reflect the impact of nutritional balance on cognitive function in the evaluation. Furthermore, it can analyze the user's exercise habits and reflect the impact of lack of exercise on cognitive function in the evaluation. In this way, by considering the user's lifestyle data, a more accurate assessment of cognitive function becomes possible.

[0051] The service provider can select the most suitable feedback method by referring to the user's past feedback history. For example, it can prioritize providing feedback using methods the user has preferred in the past. It can also provide feedback at the optimal time based on the user's past feedback history. Furthermore, it can analyze the user's past feedback history and provide feedback with the most appropriate content. For example, if the user previously preferred text messages, feedback can be provided via text message. Similarly, if the user previously preferred voice messages, feedback can be provided via voice message. In this way, the service provider can select the most suitable feedback method by referring to past feedback history.

[0052] The analysis unit can analyze users' social media activity and improve the accuracy of the analysis. For example, it can analyze the content and activity patterns of users' social media posts and reflect this in the assessment of cognitive function. Based on the content of users' social media posts, the analysis unit can evaluate vocabulary richness and logical thinking ability. It can also analyze users' social media activity patterns and evaluate attention and memory. Furthermore, it can consider users' social media friendships and reflect the impact of social connections on cognitive function in the assessment. In this way, analyzing social media activity improves the accuracy of the analysis.

[0053] The evaluation unit can perform evaluations while taking into account the user's geographical location. For example, if the user is at home, the evaluation can be performed in a relaxed environment. If the user is out and about, the evaluation can also be performed while taking into account changes in the environment. Furthermore, if the user is in a specific location, the evaluation criteria can be adjusted to take into account the influence of that location. For example, if the user is in a park, the impact of the natural environment on cognitive function can be reflected in the evaluation. Similarly, if the user is in an office, the impact of work stress on cognitive function can be reflected in the evaluation. In this way, by considering geographical location information, it becomes possible to perform evaluations that reflect changes in the environment.

[0054] The service provider can select the optimal delivery method by considering the user's device information. For example, if the user is using a smartphone, feedback can be provided that is tailored to the screen size. If the user is using a tablet, feedback optimized for the larger screen can be provided. Furthermore, if the user is using a smartwatch, concise and highly visible feedback can be provided. For example, the optimal delivery method can be selected based on the user's device information. This allows for the selection of the optimal delivery method by considering the device information.

[0055] The evaluation unit can analyze the user's past conversation history to improve the accuracy of evaluations. For example, it can evaluate the current conversation content based on the vocabulary and phrases the user has used in the past. It can also track changes in cognitive function from the user's past conversation history and reflect them in the evaluation. Furthermore, it can analyze the user's past conversation patterns and adjust the evaluation criteria. For example, it can evaluate the current conversation content based on the vocabulary and phrases the user has used frequently in the past. It can also analyze the user's past conversation patterns to improve the accuracy of evaluations. In this way, the accuracy of evaluations is improved by analyzing past conversation history.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The evaluation unit assesses the user's cognitive functions, including vocabulary, memory, attention, and logical thinking. The evaluation unit analyzes the user's responses using a generative AI and provides evaluation results. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze the user's utterances and tone of voice. For example, the generative AI can analyze the user's utterances using keyword extraction technology to evaluate vocabulary richness. The generative AI can also evaluate memory based on the user's utterances. Furthermore, the generative AI can analyze the user's utterances and tone of voice to evaluate attention and logical thinking ability. For example, the generative AI can analyze the consistency and logical structure of the user's utterances to evaluate logical thinking ability. Step 2: The service provider provides feedback to the user based on the evaluation results obtained by the evaluation unit. The service provider can provide feedback in various formats, such as text, audio, and visuals. For example, the service provider can send the evaluation results to the user as a text message. The service provider can also provide the evaluation results to the user as an audio message. Furthermore, the service provider can display the evaluation results in a visual format, providing them in a way that is easy for the user to understand visually. Step 3: The analysis unit uses generative AI to analyze the user's responses and provides evaluation results. The analysis unit analyzes the user's utterances and tone of voice to evaluate cognitive function. For example, the analysis unit analyzes the user's utterances using keyword extraction technology to evaluate vocabulary richness. The analysis unit can also evaluate memory based on the user's utterances. Furthermore, the analysis unit can analyze the user's utterances and tone of voice to evaluate attention and logical thinking ability.

[0058] (Example of form 2) The cognitive function evaluation system according to an embodiment of the present invention is a system that uses devices such as smart speakers, earphones with microphones, and audio glasses to have a target user periodically converse with a generative AI (generative AI) and evaluates cognitive functions such as vocabulary, memory, attention, and logical thinking during these conversations. In this system, the user converses with the generative AI using devices such as smart speakers, earphones with microphones, and audio glasses. This conversation takes place periodically, and the generative AI evaluates the user's cognitive functions such as vocabulary, memory, attention, and logical thinking. Next, during the conversation, topics that may lead to suspicion of dementia, such as forgetfulness or a distorted sense of time, are brought up. For example, the generative AI asks the user questions such as, "What did you eat for dinner yesterday?" or "When is your next birthday?" The generative AI observes the user's response and evaluates it. For example, if the user cannot answer a question accurately, the generative AI records that information and suggests the possibility of a decline in cognitive function. Furthermore, the generative AI provides the results of the cognitive function evaluation based on the user's response. For example, the generative AI evaluates the user's vocabulary, memory, attention, and logical thinking ability, and provides feedback on the results to the user. This allows users to understand the state of their own cognitive function. Through this mechanism, users can regularly evaluate their cognitive function by engaging in daily conversations with the generated AI. For example, by using a smart speaker to converse with the generated AI every morning, changes in the user's cognitive function can be continuously monitored. Furthermore, by using earphones with microphones or audio glasses, users can converse with the generated AI and receive cognitive function evaluations even when away from home. In this way, by regularly conversing with the generated AI using devices such as smart speakers, earphones with microphones, and audio glasses, the user's cognitive function can be evaluated, which can be used to aid in the early detection and prevention of dementia. Thus, the cognitive function evaluation system can regularly assess the user's cognitive function and contribute to the early detection and prevention of dementia.

[0059] The cognitive function evaluation system according to the embodiment comprises an evaluation unit, a provision unit, and an analysis unit. The evaluation unit evaluates the user's cognitive functions, including vocabulary, memory, attention, and logical thinking. The evaluation unit analyzes the user's responses using, for example, a generative AI and provides evaluation results. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze the user's utterances and tone of voice. For example, the generative AI analyzes the user's utterances using keyword extraction technology and evaluates the richness of the vocabulary. The generative AI can also evaluate memory based on the user's utterances. For example, the generative AI remembers what the user has said in the past and evaluates memory by asking the same questions again. Furthermore, the generative AI can analyze the user's utterances and tone of voice and evaluate attention and logical thinking ability. For example, the generative AI analyzes the consistency and logical structure of the user's utterances and evaluates logical thinking ability. The provision unit provides feedback to the user on the evaluation results obtained by the evaluation unit. The providing unit can provide feedback in various formats, such as text, audio, and visuals. For example, the providing unit can send the evaluation results to the user as a text message. The providing unit can also provide the evaluation results to the user as an audio message. Furthermore, the providing unit can display the evaluation results in a visual format, making them easily understandable to the user. The analysis unit analyzes the user's responses using generative AI and provides evaluation results. The analysis unit can, for example, analyze the user's utterances and tone of voice to evaluate cognitive function. For example, the analysis unit can analyze the user's utterances using keyword extraction technology to evaluate vocabulary richness. The analysis unit can also evaluate memory based on the user's utterances. Furthermore, the analysis unit can analyze the user's utterances and tone of voice to evaluate attention and logical thinking ability. Thus, the cognitive function evaluation system according to this embodiment can evaluate the user's cognitive function and provide the results.

[0060] The evaluation unit can analyze user responses using generative AI and provide evaluation results. Generative AI can be, for example, text generation AI (e.g., LLM) or multimodal generation AI, and can analyze the content of user statements and tone of voice. For example, the generative AI can analyze the content of user statements using keyword extraction technology and evaluate vocabulary richness. The generative AI can also evaluate memory based on the content of user statements. For example, the generative AI can remember what the user has said in the past and evaluate memory by asking the same question again. Furthermore, the generative AI can analyze the content of user statements and tone of voice and evaluate attention and logical thinking ability. For example, the generative AI can analyze the consistency and logical structure of user statements and evaluate logical thinking ability. As a result, using generative AI improves the accuracy of analyzing user responses and providing evaluation results.

[0061] The service provider can provide feedback to the user based on the evaluation results obtained by the evaluation unit. The service provider can provide feedback in various formats, such as text, audio, and visuals. For example, the service provider can send the evaluation results to the user as a text message. It can also provide the evaluation results to the user as an audio message. Furthermore, the service provider can display the evaluation results in a visual format, making them easily understandable to the user. This allows the user to understand the state of their own cognitive function by providing feedback on the evaluation results.

[0062] The analysis unit can use generative AI to analyze the user's utterances or tone of voice and evaluate cognitive function. Generative AI can be, for example, text generation AI (e.g., LLM) or multimodal generation AI, and can analyze the user's utterances and tone of voice. For example, the generative AI can analyze the user's utterances using keyword extraction technology and evaluate the richness of their vocabulary. The generative AI can also evaluate memory based on the user's utterances. For example, the generative AI can remember what the user has said in the past and evaluate memory by asking the same questions again. Furthermore, the generative AI can analyze the user's utterances and tone of voice and evaluate attention and logical thinking ability. For example, the generative AI can analyze the consistency and logical structure of the user's utterances and evaluate logical thinking ability. As a result, using generative AI improves the accuracy of analyzing the user's utterances and tone of voice and evaluating cognitive function.

[0063] The service provider can provide evaluation results to medical institutions. For example, the service provider can provide evaluation results to medical institutions via email or a dedicated web portal. For instance, the service provider can send evaluation results to medical institutions via email. Alternatively, the service provider can upload evaluation results to a dedicated web portal, making them accessible to medical institutions. This allows medical institutions to understand the user's cognitive function status by providing them with evaluation results.

[0064] The evaluation unit can assess the user's vocabulary, memory, attention, and logical thinking skills. For example, the evaluation unit uses generative AI to analyze the user's responses and evaluate vocabulary, memory, attention, and logical thinking skills. Generative AI can be, for example, text generation AI (e.g., LLM) or multimodal generation AI, and can analyze the user's utterances and tone of voice. For example, the generative AI can analyze the user's utterances using keyword extraction technology to evaluate vocabulary. The generative AI can also evaluate memory based on the user's utterances. For example, the generative AI can remember what the user has said in the past and evaluate memory by asking the same questions again. Furthermore, the generative AI can analyze the user's utterances and tone of voice to evaluate attention and logical thinking skills. For example, the generative AI can analyze the consistency and logical structure of the user's utterances to evaluate logical thinking skills. In this way, by evaluating the user's vocabulary, memory, attention, and logical thinking skills, the state of their cognitive function can be understood in detail.

[0065] The analysis unit can analyze user responses using generative AI and provide evaluation results. Generative AI can be, for example, text generation AI (e.g., LLM) or multimodal generation AI, and can analyze the content of user statements and tone of voice. For example, the generative AI can analyze the content of user statements using keyword extraction technology and evaluate vocabulary richness. The generative AI can also evaluate memory based on the content of user statements. For example, the generative AI can remember what the user has said in the past and evaluate memory by asking the same questions again. Furthermore, the generative AI can analyze the content of user statements and tone of voice and evaluate attention and logical thinking ability. For example, the generative AI can analyze the consistency and logical structure of user statements and evaluate logical thinking ability. As a result, using generative AI improves the accuracy of analyzing user responses and providing evaluation results.

[0066] The service provider may include a protection component to ensure user privacy. The service provider may protect user privacy through methods such as data anonymization and access control. For example, the service provider may anonymize evaluation results and provide them in a format that does not identify individuals. The service provider may also restrict access to evaluation results, allowing only users with specific permissions to access them. Furthermore, the service provider may encrypt data to prevent evaluation results from being leaked to third parties. This ensures that users can use the system with peace of mind by considering their privacy.

[0067] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on those estimated emotions. For example, the evaluation unit can use generative AI to estimate the user's emotions and adjust the evaluation criteria. Generative AI can estimate the user's emotions using technologies such as facial expression analysis and voice analysis. For example, the generative AI can capture the user's facial expression with a camera and estimate the emotion using a facial expression analysis algorithm. The generative AI can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generative AI can analyze the tone and speed of the user's voice and estimate the emotion. Furthermore, the generative AI can adjust the evaluation criteria based on the user's emotions. For example, if the user is stressed, the evaluation criteria can be relaxed to reduce the user's burden. Conversely, if the user is relaxed, more detailed evaluation criteria can be applied to perform a more precise evaluation. This allows for a more appropriate evaluation by adjusting the evaluation criteria based on the user's emotions.

[0068] The evaluation unit can analyze the user's past conversation history to improve the accuracy of its evaluations. For example, the evaluation unit can use generative AI to analyze the user's past conversation history and improve the accuracy of its evaluations. Generative AI can be, for example, text generation AI (e.g., LLM) or multimodal generation AI, and can analyze the user's past conversation history. For example, the generative AI evaluates the current conversation content based on the vocabulary and phrases the user has used in the past. The generative AI can also track changes in the user's cognitive function from the user's past conversation history and reflect them in the evaluation. Furthermore, the generative AI can analyze the user's past conversation patterns and adjust the evaluation criteria. For example, the generative AI analyzes the user's past conversation patterns to improve the accuracy of its evaluations. As a result, the accuracy of the evaluation is improved by analyzing past conversation history.

[0069] The evaluation unit can perform evaluations based on the user's lifestyle and health status. For example, the evaluation unit can use generative AI to consider the user's lifestyle and health status when performing evaluations. The generative AI can collect data such as the user's sleep patterns, eating habits, and exercise habits, and reflect this in the evaluation. For example, the generative AI can adjust the evaluation criteria considering the user's sleep patterns. The generative AI can also improve the accuracy of the evaluation based on the user's eating habits. Furthermore, the generative AI can consider the user's exercise habits and reflect this in the evaluation results. For example, the generative AI can analyze the user's exercise habits to improve the accuracy of the evaluation. This makes it possible to perform more accurate evaluations by considering lifestyle and health status.

[0070] The evaluation unit can estimate the user's emotions and adjust the frequency of evaluations based on the estimated emotions. For example, the evaluation unit can use generative AI to estimate the user's emotions and adjust the frequency of evaluations. Generative AI can estimate the user's emotions using technologies such as facial expression analysis and voice analysis. For example, the generative AI can capture the user's facial expression with a camera and estimate the emotion using a facial expression analysis algorithm. The generative AI can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generative AI can analyze the tone and speed of the user's voice and estimate the emotion. Furthermore, the generative AI can adjust the frequency of evaluations based on the user's emotions. For example, if the user is stressed, the frequency of evaluations can be reduced to alleviate the burden. Conversely, if the user is relaxed, the frequency of evaluations can be increased to collect more detailed data. This allows for the collection of detailed data while reducing the burden by adjusting the frequency of evaluations based on the user's emotions.

[0071] The evaluation unit can perform evaluations based on the user's geographical location information. For example, the evaluation unit can use a generative AI to consider the user's geographical location information when performing evaluations. The generative AI can collect the user's geographical location information using, for example, GPS data or location information services and reflect it in the evaluation. For example, if the user is at home, the generative AI will perform evaluations in a relaxed environment. The generative AI can also perform evaluations considering changes in the environment if the user is out and about. Furthermore, if the user is in a specific location, the generative AI can also perform evaluations considering the influence of that location. For example, if the user is in a specific location, the generative AI will adjust the evaluation criteria considering the influence of that location. This makes it possible to perform evaluations that reflect changes in the environment by considering geographical location information.

[0072] The evaluation unit can perform evaluations based on the user's social media activity. For example, the evaluation unit can use generative AI to analyze the user's social media activity and improve the accuracy of the evaluation. The generative AI can, for example, analyze the content and activity patterns of the user's social media posts. For example, the generative AI can improve the accuracy of the evaluation based on the content of the user's social media posts. Furthermore, the generative AI can analyze the user's social media activity patterns and reflect them in the evaluation. In addition, the generative AI can consider the user's social media friendships and adjust the evaluation criteria accordingly. For example, the generative AI can analyze the user's social media friendships and improve the accuracy of the evaluation. Thus, by analyzing social media activity, the accuracy of the evaluation is improved.

[0073] The service provider can estimate the user's emotions and adjust the way feedback is expressed based on those emotions. For example, the service provider can use generative AI to estimate the user's emotions and adjust the way feedback is expressed. Generative AI can estimate the user's emotions using technologies such as facial expression analysis and voice analysis. For example, the generative AI can capture the user's facial expression with a camera and estimate the emotion using a facial expression analysis algorithm. The generative AI can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generative AI can analyze the tone and speed of the user's voice and estimate the emotion. Furthermore, the generative AI can adjust the way feedback is expressed based on the user's emotions. For example, if the user is stressed, it can provide feedback in gentle words. If the user is relaxed, it can also provide detailed feedback. By adjusting the way feedback is expressed based on the user's emotions, more appropriate feedback becomes possible.

[0074] The service provider can select the optimal service delivery method by referring to the user's past feedback history at the time of delivery. For example, the service provider can use a generative AI to refer to the user's past feedback history and select the optimal service delivery method. The generative AI can, for example, analyze the user's past feedback history and select the optimal service delivery method. For example, the generative AI prioritizes providing feedback methods that the user has preferred in the past. The generative AI can also provide feedback at the optimal timing based on the user's past feedback history. Furthermore, the generative AI can analyze the user's past feedback history and provide feedback with optimal content. For example, the generative AI analyzes the user's past feedback history and provides feedback with optimal content. In this way, the optimal service delivery method can be selected by referring to past feedback history.

[0075] The service provider can customize the content of the feedback based on the user's living situation at the time of delivery. For example, the service provider can use a generative AI to consider the user's living situation and customize the content of the feedback. The generative AI can collect data such as the user's home environment, work situation, and health status, and reflect this in the feedback. For example, the generative AI considers the user's current living situation and provides appropriate feedback. The generative AI can also adjust the content of the feedback based on the user's lifestyle habits. Furthermore, the generative AI can also consider the user's health status and customize the content of the feedback. For example, the generative AI analyzes the user's health status and customizes the content of the feedback. By customizing the content of the feedback based on the living situation, more appropriate feedback becomes possible.

[0076] The service provider can estimate the user's emotions and adjust the timing of feedback based on those emotions. For example, the service provider can use generative AI to estimate the user's emotions and adjust the timing of feedback. Generative AI can estimate the user's emotions using technologies such as facial expression analysis and voice analysis. For example, the generative AI can capture the user's facial expression with a camera and estimate the emotion using a facial expression analysis algorithm. The generative AI can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generative AI can analyze the tone and speed of the user's voice and estimate the emotion. Furthermore, the generative AI can adjust the timing of feedback based on the user's emotions. For example, if the user is stressed, the timing of feedback can be delayed. Conversely, if the user is relaxed, the timing of feedback can be advanced. By adjusting the timing of feedback based on the user's emotions, feedback can be provided at a more appropriate time.

[0077] The delivery unit can select the optimal delivery method by considering the user's device information at the time of delivery. For example, the delivery unit can use a generative AI to consider the user's device information and select the optimal delivery method. The generative AI can collect information such as the user's device type and OS version and select the optimal delivery method. For example, if the user is using a smartphone, the generative AI will provide feedback tailored to the screen size. Also, if the user is using a tablet, the generative AI can provide feedback optimized for a larger screen. Furthermore, if the user is using a smartwatch, the generative AI can provide concise and highly visible feedback. For example, the generative AI will select the optimal delivery method based on the user's device information. In this way, the optimal delivery method can be selected by considering the device information.

[0078] The service provider can analyze the user's social media activity and adjust the content of the feedback at the time of delivery. For example, the service provider can use generative AI to analyze the user's social media activity and adjust the content of the feedback. The generative AI can, for example, analyze the content and activity patterns of the user's social media posts. For example, the generative AI adjusts the content of the feedback based on the content of the user's social media posts. The generative AI can also analyze the user's social media activity patterns and reflect them in the feedback. Furthermore, the generative AI can consider the user's social media friendships and adjust the content of the feedback. For example, the generative AI analyzes the user's social media friendships and adjusts the content of the feedback. In this way, the content of the feedback can be adjusted by analyzing social media activity.

[0079] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, the analysis unit can use generative AI to estimate the user's emotions and adjust the analysis criteria. Generative AI can estimate the user's emotions using technologies such as facial expression analysis and voice analysis. For example, the generative AI can capture the user's facial expression with a camera and estimate the emotions using a facial expression analysis algorithm. The generative AI can also record the user's voice and estimate the emotions using voice analysis technology. For example, the generative AI can analyze the tone and speed of the user's voice and estimate the emotions. Furthermore, the generative AI can adjust the analysis criteria based on the user's emotions. For example, if the user is stressed, the analysis criteria can be relaxed to reduce the user's burden. Conversely, if the user is relaxed, more detailed analysis criteria can be applied to perform a more precise analysis. In this way, adjusting the analysis criteria based on the user's emotions enables more appropriate analysis.

[0080] The analysis unit can improve the accuracy of its analysis by referring to the user's past statements during the analysis process. For example, the analysis unit can use a generative AI to refer to the user's past statements and improve the accuracy of the analysis. The generative AI can, for example, analyze the user's past statements and evaluate the current statements. For example, the generative AI analyzes the current statements based on the vocabulary and phrases the user has used in the past. The generative AI can also track changes in cognitive function from the user's past statements and reflect them in the analysis. Furthermore, the generative AI can analyze the user's past statements patterns and adjust the analysis criteria. For example, the generative AI analyzes the user's past statements patterns and improves the accuracy of the analysis. As a result, the accuracy of the analysis is improved by referring to past statements.

[0081] The analysis unit can perform analysis while considering the user's lifestyle and health condition. For example, the analysis unit uses generative AI to perform analysis while considering the user's lifestyle and health condition. The generative AI can collect data such as the user's sleep patterns, eating habits, and exercise habits, and reflect this in the analysis. For example, the generative AI adjusts the analysis criteria considering the user's sleep patterns. The generative AI can also improve the accuracy of the analysis based on the user's eating habits. Furthermore, the generative AI can consider the user's exercise habits and reflect this in the analysis results. For example, the generative AI analyzes the user's exercise habits to improve the accuracy of the analysis. As a result, more accurate analysis becomes possible by considering lifestyle and health conditions.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis frequency based on the estimated emotions. For example, the analysis unit can use generative AI to estimate the user's emotions and adjust the analysis frequency. The generative AI can estimate the user's emotions using technologies such as facial expression analysis and voice analysis. For example, the generative AI can capture the user's facial expression with a camera and estimate the emotion using a facial expression analysis algorithm. The generative AI can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generative AI can analyze the tone and speed of the user's voice and estimate the emotion. Furthermore, the generative AI can adjust the analysis frequency based on the user's emotions. For example, if the user is stressed, the analysis frequency can be reduced to alleviate the burden. Conversely, if the user is relaxed, the analysis frequency can be increased to collect more detailed data. This allows for the collection of detailed data while reducing the burden by adjusting the analysis frequency based on the user's emotions.

[0083] The analysis unit can perform analysis while considering the user's geographical location information. For example, the analysis unit uses a generative AI to perform analysis while considering the user's geographical location information. The generative AI can collect the user's geographical location information using, for example, GPS data or location information services and reflect it in the analysis. For example, if the user is at home, the generative AI will perform analysis in a relaxed environment. The generative AI can also perform analysis while considering changes in the environment if the user is out and about. Furthermore, if the user is in a specific location, the generative AI can perform analysis while considering the influence of that location. For example, if the user is in a specific location, the generative AI will adjust the analysis criteria to take into account the influence of that location. This makes it possible to perform analysis that reflects changes in the environment by considering geographical location information.

[0084] The analysis unit can improve the accuracy of the analysis by analyzing the user's social media activity during the analysis process. For example, the analysis unit can improve the accuracy of the analysis by using generative AI to analyze the user's social media activity. For example, the generative AI can analyze the content and activity patterns of the user's social media posts. For example, the generative AI can improve the accuracy of the analysis based on the content of the user's social media posts. The generative AI can also analyze the user's social media activity patterns and reflect them in the analysis. Furthermore, the generative AI can adjust the analysis criteria by considering the user's social media friendships. For example, the generative AI can improve the accuracy of the analysis by analyzing the user's social media friendships. In this way, the accuracy of the analysis is improved by analyzing social media activity.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The evaluation unit can consider the user's lifestyle data when assessing their cognitive function. For example, it can collect data such as the user's sleep patterns, eating habits, and exercise habits, and perform evaluations based on this data. The evaluation unit can analyze the user's sleep patterns and reflect the impact of sleep deprivation on cognitive function in the evaluation. It can also consider the user's eating habits and reflect the impact of nutritional balance on cognitive function in the evaluation. Furthermore, it can analyze the user's exercise habits and reflect the impact of lack of exercise on cognitive function in the evaluation. In this way, by considering the user's lifestyle data, a more accurate assessment of cognitive function becomes possible.

[0087] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on those estimated emotions. For example, if the user is stressed, the evaluation criteria can be relaxed to reduce the user's burden. Conversely, if the user is relaxed, more detailed evaluation criteria can be applied to perform a more precise evaluation. The evaluation unit can also analyze the user's facial expressions and tone of voice to estimate emotions. For example, the user's facial expressions can be captured with a camera, and emotions can be estimated using a facial expression analysis algorithm. Alternatively, the user's voice can be recorded, and emotions can be estimated using voice analysis technology. This allows for more appropriate evaluations by adjusting the evaluation criteria based on the user's emotions.

[0088] The service provider can select the most suitable feedback method by referring to the user's past feedback history. For example, it can prioritize providing feedback using methods the user has preferred in the past. It can also provide feedback at the optimal time based on the user's past feedback history. Furthermore, it can analyze the user's past feedback history and provide feedback with the most appropriate content. For example, if the user previously preferred text messages, feedback can be provided via text message. Similarly, if the user previously preferred voice messages, feedback can be provided via voice message. In this way, the service provider can select the most suitable feedback method by referring to past feedback history.

[0089] The analysis unit can analyze users' social media activity and improve the accuracy of the analysis. For example, it can analyze the content and activity patterns of users' social media posts and reflect this in the assessment of cognitive function. Based on the content of users' social media posts, the analysis unit can evaluate vocabulary richness and logical thinking ability. It can also analyze users' social media activity patterns and evaluate attention and memory. Furthermore, it can consider users' social media friendships and reflect the impact of social connections on cognitive function in the assessment. In this way, analyzing social media activity improves the accuracy of the analysis.

[0090] The service provider can estimate the user's emotions and adjust the way feedback is expressed based on those emotions. For example, if the user is stressed, it can provide feedback in gentle language. Conversely, if the user is relaxed, it can provide detailed feedback. The service provider can also analyze the user's facial expressions and tone of voice to estimate emotions. For example, it can capture the user's facial expressions with a camera and estimate emotions using a facial expression analysis algorithm. It can also record the user's voice and estimate emotions using voice analysis technology. This allows for more appropriate feedback by adjusting the way feedback is expressed based on the user's emotions.

[0091] The evaluation unit can perform evaluations while taking into account the user's geographical location. For example, if the user is at home, the evaluation can be performed in a relaxed environment. If the user is out and about, the evaluation can also be performed while taking into account changes in the environment. Furthermore, if the user is in a specific location, the evaluation criteria can be adjusted to take into account the influence of that location. For example, if the user is in a park, the impact of the natural environment on cognitive function can be reflected in the evaluation. Similarly, if the user is in an office, the impact of work stress on cognitive function can be reflected in the evaluation. In this way, by considering geographical location information, it becomes possible to perform evaluations that reflect changes in the environment.

[0092] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on those estimated emotions. For example, if the user is stressed, the analysis criteria can be relaxed to reduce the user's burden. Conversely, if the user is relaxed, more detailed analysis criteria can be applied to perform a more precise analysis. The analysis unit can also analyze the user's facial expressions and tone of voice to estimate emotions. For example, the user's facial expressions can be captured with a camera, and emotions can be estimated using a facial expression analysis algorithm. It can also record the user's voice and estimate emotions using voice analysis technology. This allows for more appropriate analysis by adjusting the analysis criteria based on the user's emotions.

[0093] The service provider can select the optimal delivery method by considering the user's device information. For example, if the user is using a smartphone, feedback can be provided that is tailored to the screen size. If the user is using a tablet, feedback optimized for the larger screen can be provided. Furthermore, if the user is using a smartwatch, concise and highly visible feedback can be provided. For example, the optimal delivery method can be selected based on the user's device information. This allows for the selection of the optimal delivery method by considering the device information.

[0094] The evaluation unit can analyze the user's past conversation history to improve the accuracy of evaluations. For example, it can evaluate the current conversation content based on the vocabulary and phrases the user has used in the past. It can also track changes in cognitive function from the user's past conversation history and reflect them in the evaluation. Furthermore, it can analyze the user's past conversation patterns and adjust the evaluation criteria. For example, it can evaluate the current conversation content based on the vocabulary and phrases the user has used frequently in the past. It can also analyze the user's past conversation patterns to improve the accuracy of evaluations. In this way, the accuracy of evaluations is improved by analyzing past conversation history.

[0095] The system can estimate the user's emotions and adjust the timing of feedback based on those emotions. For example, if the user is stressed, the timing of feedback can be delayed. Conversely, if the user is relaxed, the timing of feedback can be advanced. The system can also analyze the user's facial expressions and tone of voice to estimate emotions. For example, the system can capture the user's facial expressions with a camera and estimate emotions using a facial expression analysis algorithm. It can also record the user's voice and estimate emotions using voice analysis technology. By adjusting the timing of feedback based on the user's emotions, feedback can be provided at a more appropriate time.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The evaluation unit assesses the user's cognitive functions, including vocabulary, memory, attention, and logical thinking. The evaluation unit analyzes the user's responses using a generative AI and provides evaluation results. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze the user's utterances and tone of voice. For example, the generative AI can analyze the user's utterances using keyword extraction technology to evaluate vocabulary richness. The generative AI can also evaluate memory based on the user's utterances. Furthermore, the generative AI can analyze the user's utterances and tone of voice to evaluate attention and logical thinking ability. For example, the generative AI can analyze the consistency and logical structure of the user's utterances to evaluate logical thinking ability. Step 2: The service provider provides feedback to the user based on the evaluation results obtained by the evaluation unit. The service provider can provide feedback in various formats, such as text, audio, and visuals. For example, the service provider can send the evaluation results to the user as a text message. The service provider can also provide the evaluation results to the user as an audio message. Furthermore, the service provider can display the evaluation results in a visual format, providing them in a way that is easy for the user to understand visually. Step 3: The analysis unit uses generative AI to analyze the user's responses and provides evaluation results. The analysis unit analyzes the user's utterances and tone of voice to evaluate cognitive function. For example, the analysis unit analyzes the user's utterances using keyword extraction technology to evaluate vocabulary richness. The analysis unit can also evaluate memory based on the user's utterances. Furthermore, the analysis unit can analyze the user's utterances and tone of voice to evaluate attention and logical thinking ability.

[0098] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0099] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0100] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0101] Each of the multiple elements described above, including the evaluation unit, the provision unit, and the analysis unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the evaluation unit is implemented by the control unit 46A of the smart device 14 and evaluates the user's cognitive functions such as vocabulary, memory, attention, and logical thinking. The provision unit is implemented by the specific processing unit 290 of the data processing device 12 and provides feedback of the evaluation results to the user. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the user's response using generated AI and provides evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] As shown in Figure 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.

[0104] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0108] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0109] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0110] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0111] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0112] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0114] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0116] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the evaluation unit, the provision unit, and the analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the evaluation unit is implemented by the control unit 46A of the smart glasses 214 and evaluates the user's cognitive functions such as vocabulary, memory, attention, and logical thinking. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback of the evaluation results to the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's response using generated AI and provides evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the evaluation unit, the provision unit, and the analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the evaluation unit is implemented by the control unit 46A of the headset terminal 314 and evaluates the user's cognitive functions such as vocabulary, memory, attention, and logical thinking. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback of the evaluation results to the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's response using generated AI and provides evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0142] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the evaluation unit, the provision unit, and the analysis unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the evaluation unit is implemented by the control unit 46A of the robot 414 and evaluates the user's cognitive functions such as vocabulary, memory, attention, and logical thinking. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback of the evaluation results to the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's response using generated AI and provides evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0153] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0154] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0155] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0159] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0160] 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.

[0161] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0162] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0163] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0164] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0166] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0167] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0168] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0169] (Note 1) An evaluation unit that assesses the user's cognitive functions, including vocabulary, memory, attention, and logical thinking, A providing unit that provides the evaluation results obtained by the evaluation unit, The evaluation unit comprises an analysis unit that analyzes the user's response. A system characterized by the following features. (Note 2) The evaluation unit, We use generative AI to analyze user responses and provide evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, The evaluation results obtained by the evaluation unit are fed back to the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We use generative AI to analyze the content of the user's speech or their tone of voice and evaluate their cognitive function. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide evaluation results to medical institutions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The evaluation unit, The system evaluates the user's vocabulary, memory, attention span, and logical thinking skills. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We use generative AI to analyze user responses and provide evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, It includes a protective section to consider the protection of user privacy. The system described in Appendix 1, characterized by the features described herein. (Note 9) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The evaluation unit, Analyze the user's past conversation history to improve the accuracy of evaluations. The system described in Appendix 1, characterized by the features described herein. (Note 11) The evaluation unit, During the evaluation, the assessment is based on the user's lifestyle and health condition. The system described in Appendix 1, characterized by the features described herein. (Note 12) The evaluation unit, It estimates the user's emotions and adjusts the frequency of evaluations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit, During the evaluation process, the evaluation will be based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit, During the evaluation process, the assessment will be based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing the service, the feedback content will be customized based on the user's living situation. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts the timing of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and adjust the feedback accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by referencing the user's past statements. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During the analysis, the user's lifestyle and health status will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, It estimates the user's emotions and adjusts the frequency of analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, During analysis, we analyze users' social media activity and improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An evaluation unit that assesses the user's cognitive functions, including vocabulary, memory, attention, and logical thinking, A providing unit that provides the evaluation results obtained by the evaluation unit, The evaluation unit comprises an analysis unit that analyzes the user's response. A system characterized by the following features.

2. The evaluation unit, We use generative AI to analyze user reactions and provide evaluation results. The system according to feature 1.

3. The aforementioned supply unit is, The evaluation results obtained by the evaluation unit are fed back to the user. The system according to feature 1.

4. The aforementioned analysis unit, The system uses generative AI to analyze the user's speech content or tone of voice and evaluate their cognitive function. The system according to feature 1.

5. The aforementioned supply unit is, Provide evaluation results to medical institutions. The system according to feature 1.

6. The evaluation unit, The system evaluates the user's vocabulary, memory, attention span, and logical thinking skills. The system according to feature 1.

7. The aforementioned analysis unit, We use generative AI to analyze user reactions and provide evaluation results. The system according to feature 1.

8. The aforementioned supply unit is, It includes a protective section to consider the protection of user privacy. The system according to feature 1.

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