system

Abstract

The system according to the embodiment aims to enable patients with memory loss or dementia to search for information based on fragmented memories and analyze their responses. [Solution] A system according to an embodiment includes an input unit, a search unit, a display unit, a recording unit, and an analysis unit. The input unit inputs keywords. The search unit searches for information based on the keywords input by the input unit. The display unit displays the information searched for by the search unit. The recording unit records the user's reaction to the information displayed by the display unit. The analysis unit analyzes the reaction recorded by the recording unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for patients with memory loss or dementia to retrieve information based on fragmented memories and analyze their responses.

[0005] The system according to the embodiment aims to enable patients with memory loss or dementia to search for information based on fragmented memories and analyze their responses. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a search unit, a display unit, a recording unit, and an analysis unit. The input unit inputs keywords. The search unit searches for information based on the keywords input by the input unit. The display unit displays the information searched for by the search unit. The recording unit records the response of the person being operated to the information displayed by the display unit. The analysis unit analyzes the response recorded by the recording unit. [Effects of the Invention]

[0007] The system according to the embodiment allows patients with memory loss or dementia to search for information based on fragmented memories and analyze their responses. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A memory assistance system according to an embodiment of the present invention is a system that, in the event of temporary memory loss or dementia, sequentially displays local images and audio, such as information from the Internet or street views of map services, on a tablet or other device based on fragmented memory information. When the user or caregiver inputs keywords into the system, the system displays corresponding information and measures the user's memory level based on the user's reaction to the keywords. Input can be made directly on the screen or via voice. For example, when the user or caregiver inputs keywords such as "park" or "family photos," the system analyzes the keywords and searches for local images and audio, such as information from the Internet or street views of map services. The retrieved information is sequentially displayed on a tablet or other device. For example, images and audio of a park are displayed. The user's reaction is recorded. For example, if the user smiles when viewing an image, the system records the user's reaction. Furthermore, the system analyzes the user's reaction to measure the user's memory level. For example, if the user responds strongly to a particular image or audio, the system determines that the information is likely to remain in their memory. This system allows the display of information based on memory fragments and the measurement of memory level, even in the case of temporary memory loss or dementia. Furthermore, by allowing input either directly on the screen or by voice, operation is simplified, making the system easy to use for both the user and the caregiver. This allows the memory assistance system to measure the user's memory level.

[0029] The memory assistance system according to the embodiment includes an input unit, a search unit, a display unit, a recording unit, and an analysis unit. The input unit is a unit through which the user or a caregiver inputs keywords. For example, the input unit can input keywords using a touch screen of a tablet. The input unit can also accept voice input. For example, the input unit can convert keywords spoken by the user into text using voice recognition technology. The search unit is a unit that searches for information based on the keywords input by the input unit. For example, the search unit can search for information on the Internet. The search unit can also search Street View on a map service. For example, the search unit can search for images and audio related to a specific location. The display unit is a unit that displays information searched by the search unit. For example, the display unit can display images and audio on the tablet screen. The recording unit is a unit that records the user's response to the information displayed by the display unit. For example, the recording unit can record the user's facial expressions using a camera. The recording unit can also record the user's biometric data using a sensor. For example, the recording unit can record the user's heart rate and electrodermal activity. The analysis unit is a component that analyzes the responses recorded by the recording unit. For example, the analysis unit can analyze the response data of the person being operated and measure the memory level. This allows the memory assistance system according to the embodiment to measure the memory level of the person being operated.

[0030] The input unit can accept direct input on the screen and voice input. The input unit can input keywords using, for example, a touchscreen of a tablet. For example, the person being operated can input keywords using a keyboard on the tablet screen. The input unit can also accept voice input. For example, using voice recognition technology, keywords spoken by the person being operated can be converted into text. For example, if the person being operated speaks "park," the voice recognition technology can analyze the speech and convert it into the text "park." This simplifies operation by accepting direct input on the screen and voice input. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can accept voice input using an AI model that uses voice recognition technology to convert keywords spoken by the person being operated into text.

[0031] The search unit can search information on the Internet and street views of map services. The search unit can, for example, search information on the Internet. For example, the search unit can search websites and databases related to a specific keyword. The search unit can also search street views of map services. For example, the search unit can search images and audio related to a specific location. For example, when the keyword "park" is input, the search unit can search images and audio related to parks. This makes it possible to provide a wide range of information by searching information on the Internet and street views of map services. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can search for information using an AI model that searches information on the Internet.

[0032] The display unit can display information on the tablet screen. The display unit can, for example, display images and audio on the tablet screen. For example, the display unit can display images and audio of a park on the tablet screen. This allows information to be provided visually by displaying information on the screen of a tablet or the like. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can display information using an AI model that displays information searched by the search unit.

[0033] The recording unit can record the reactions of the person being operated using a camera and a temperature sensor. The recording unit can, for example, record the facial expressions of the person being operated using a camera. For example, if the person being operated smiles when looking at an image, the recording unit can record that facial expression using a camera. The recording unit can also record the biometric data of the person being operated using a temperature sensor. For example, the recording unit can record the body temperature and electrodermal activity of the person being operated. This allows the reaction of the person being operated to be accurately recorded using a camera and a sensor. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input image data of the person being operated captured by a camera to the generation AI and cause the generation AI to record the facial expressions of the person being operated.

[0034] The analysis unit can measure the level of memory based on the recorded reactions. The analysis unit can, for example, analyze the reaction data of the controlled person recorded by the recording unit and measure the level of memory. For example, if the controlled person shows a strong reaction to a particular image or sound, the analysis unit can determine that there is a high possibility that the information remains in their memory. This allows the controlled person's memory state to be evaluated by measuring the level of memory based on the recorded reactions. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the reaction data recorded by the recording unit into the generation AI and cause the generation AI to measure the level of memory.

[0035] The input unit can analyze the past input history of the person being operated and select an appropriate input method. The input unit can, for example, analyze the past input history of the person being operated and select the optimal input method. For example, the input unit can automatically display keywords that the person being operated has frequently used in the past as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the person being operated has used in the past. It can also predict and suggest keywords that will be used in a specific time period from the person being operated's past input history. In this way, by analyzing the past input history, it is possible to provide the person being operated with the optimal input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can select the optimal input method using an AI model that analyzes the person being operated's past input history.

[0036] The input unit can filter the input content based on the current situation and areas of interest of the user when inputting the information. For example, the input unit can prioritize displaying keywords related to the user's current location. For example, if the user is in a park, keywords related to the park can be automatically displayed. The input unit can also suggest related keywords based on the user's areas of interest. For example, if the user is interested in gardening as a hobby, keywords related to gardening can be prioritized. The input unit can also suggest appropriate keywords based on the user's current situation (e.g., weather or time of day). For example, keywords related to indoor activities that can be enjoyed on a rainy day can be displayed. This allows the user to provide more relevant information by filtering the input content based on the user's current situation and areas of interest. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can filter the input content using an AI model that analyzes the user's current situation and areas of interest.

[0037] When inputting, the input unit can prioritize inputting highly relevant keywords based on the geographical location information of the operated person. For example, the input unit can automatically display keywords related to the location where the operated person is currently located. For example, if the operated person is in a park, keywords related to the park can be automatically displayed. The input unit can also suggest related keywords based on the operated person's past location information. For example, the input unit can display keywords related to places the operated person has visited in the past. Furthermore, if the operated person is in a specific location, the input unit can prioritize displaying keywords related to that location. For example, if the operated person is in a museum, keywords related to the museum can be displayed. This makes it possible to provide more relevant keywords by taking the geographical location information into consideration. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input highly relevant keywords using an AI model that analyzes the operated person's geographical location information.

[0038] The input unit can analyze the user's social media activity and automatically input related keywords when inputting information. The input unit can automatically display, for example, keywords frequently used by the user on social media. For example, if the user frequently uses the keyword "travel" on social media, the input unit can display keywords related to "travel." The input unit can also analyze the user's social media posts and suggest related keywords. For example, if the user posts about "cooking" on social media, the input unit can display keywords related to "cooking." The input unit can also display related keywords based on keywords used by the user's social media friends. For example, if the user's friends frequently post about "sports," the input unit can display keywords related to "sports." This allows for analysis of social media activity to provide more relevant keywords. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input related keywords using an AI model that analyzes the user's social media activity.

[0039] The search unit can automatically adjust the level of detail of search results based on the importance of keywords during a search. The search unit can adjust the level of detail of search results based on, for example, the importance of keywords. For example, detailed search results can be displayed for keywords with high importance. Also, concise search results can be displayed for keywords with low importance. The search unit can also adjust the display order of search results according to the importance of keywords. For example, search results related to keywords with high importance can be preferentially displayed. By adjusting the level of detail of search results based on the importance of keywords, more appropriate search results can be provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can adjust the level of detail of search results using an AI model that evaluates the importance of keywords.

[0040] The search unit can automatically apply different search algorithms depending on the keyword category during a search. For example, the search unit can apply different search algorithms depending on the keyword category. For example, an image search algorithm can be applied to keywords related to images. A voice search algorithm can be applied to keywords related to voice. A text search algorithm can be applied to keywords related to text. In this way, by applying different search algorithms depending on the keyword category, more appropriate search results can be provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can apply different search algorithms using an AI model that classifies keyword categories.

[0041] During a search, the search unit can automatically determine the priority of search results based on the time of keyword submission. The search unit can, for example, determine the priority of search results based on the time of keyword submission. For example, for a recently submitted keyword, the search unit can prioritize displaying the most recent search results. Also, for a previously submitted keyword, the search unit can prioritize displaying the most recent search results. The search unit can also adjust the display order of search results based on the time of keyword submission. For example, search results related to a recently submitted keyword can be prioritized. This allows for more appropriate search results to be provided by determining the priority of search results based on the time of keyword submission. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can determine the priority of search results using an AI model that evaluates the time of keyword submission.

[0042] The search unit can automatically adjust the order of search results based on the relevance of keywords during a search. The search unit can adjust the order of search results based on, for example, the relevance of keywords. For example, for a keyword with high relevance, highly relevant search results can be preferentially displayed. Also, for a keyword with low relevance, less relevant search results can be displayed. The search unit can also adjust the display order of search results according to the relevance of keywords. For example, search results related to highly relevant keywords can be preferentially displayed. In this way, by adjusting the order of search results based on the relevance of keywords, more appropriate search results can be provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can adjust the order of search results using an AI model that evaluates the relevance of keywords.

[0043] The display unit can automatically adjust the level of detail of the display based on the importance of the information when displaying the information. The display unit can adjust the level of detail of the display based on, for example, the importance of the information. For example, it can provide a detailed display for information with high importance. It can also provide a concise display for information with low importance. The display unit can also adjust the display order according to the importance of the information. For example, it can display information with high importance preferentially. By adjusting the level of detail of the display based on the importance of the information, it is possible to provide a more appropriate display. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can adjust the level of detail of the display using an AI model that evaluates the importance of the information.

[0044] The display unit can automatically apply different display algorithms depending on the category of information when displaying the information. The display unit can apply different display algorithms depending on the category of information, for example. For example, an image display algorithm can be applied to image information. Also, an audio display algorithm can be applied to audio information. Also, a text display algorithm can be applied to text information. By applying different display algorithms depending on the category of information, more appropriate display can be provided. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can apply different display algorithms using an AI model that classifies the category of information.

[0045] The display unit can automatically determine the display priority based on the time of submission of the information when displaying the information. The display unit can determine the display priority based on, for example, the time of submission of the information. For example, for recently submitted information, the latest display content can be given priority. Furthermore, for information submitted in the past, the older display content can be given priority. The display unit can also adjust the display order according to the time of submission of the information. For example, the most recently submitted information can be given priority. In this way, by determining the display priority based on the time of submission of the information, a more appropriate display can be provided. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can determine the display priority using an AI model that evaluates the time of submission of the information.

[0046] The display unit can automatically adjust the display order based on the relevance of the information when displaying the information. The display unit can adjust the display order based on, for example, the relevance of the information. For example, it can prioritize display content that is highly relevant to information with high relevance. Also, it can display display content that is less relevant to information with low relevance. The display unit can also adjust the display order according to the relevance of the information. For example, it can prioritize displaying information that is highly relevant. As a result, by adjusting the display order based on the relevance of the information, a more appropriate display can be provided. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can adjust the display order using an AI model that evaluates the relevance of the information.

[0047] During recording, the recording unit can analyze the user's past responses and select an appropriate recording method. The recording unit can, for example, analyze the user's past responses and select an optimal recording method. For example, the recording unit can prioritize recording information to which the user has previously shown a strong response. The recording unit can also analyze the user's past response patterns and suggest an optimal recording method. The level of detail of the recording can also be adjusted based on the user's past response data. This makes it possible to provide a more appropriate recording method by analyzing past responses. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can select an optimal recording method using an AI model that analyzes the user's past response data.

[0048] During recording, the recording unit can automatically customize the recording means based on the current situation of the person being operated. The recording unit can customize the recording means based on, for example, the current situation of the person being operated. For example, if the person being operated is relaxed, detailed recording can be performed. If the person being operated is nervous, brief recording can be performed. If the person being operated is excited, visually stimulating recording can be performed. In this way, by customizing the recording means based on the current situation, more appropriate recording can be provided. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can customize the recording means using an AI model that analyzes the current situation of the person being operated.

[0049] During recording, the recording unit can select an appropriate recording method based on the geographical location information of the operated person. For example, the recording unit can prioritize recording information related to the location where the operated person is currently located. Furthermore, the recording unit can record related information based on the operated person's past location information. Furthermore, if the operated person is in a specific location, information related to that location can be prioritized. This makes it possible to provide a more appropriate recording method by taking the geographical location information into consideration. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can select an appropriate recording method using an AI model that analyzes the operated person's geographical location information.

[0050] During recording, the recording unit can analyze the social media activity of the controlled person and automatically suggest a means of recording. The recording unit can, for example, perform recording based on keywords frequently used by the controlled person on social media. It can also analyze the content of the controlled person's social media posts and record related information. It can also record related information by referring to keywords used by the controlled person's social media friends. This allows for analyzing social media activity to provide a more appropriate means of recording. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can suggest a means of recording using an AI model that analyzes the controlled person's social media activity.

[0051] During analysis, the analysis unit can appropriately optimize the analysis algorithm by referring to the user's past reaction data. The analysis unit can, for example, optimize the analysis algorithm by referring to the user's past reaction data. For example, the analysis can be performed based on information on which the user has shown a strong reaction in the past. The analysis unit can also analyze the user's past reaction patterns and propose an optimal analysis algorithm. The level of detail of the analysis can also be adjusted based on the user's past reaction data. This makes it possible to provide a more appropriate analysis algorithm by referring to the past reaction data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can optimize the analysis algorithm using an AI model that analyzes the user's past reaction data.

[0052] During analysis, the analysis unit can automatically customize the analysis means based on the current situation of the controlled person. The analysis unit can customize the analysis means based on, for example, the current situation of the controlled person. For example, if the controlled person is relaxed, a detailed analysis can be performed. If the controlled person is tense, a brief analysis can be performed. If the controlled person is excited, a visually stimulating analysis can be performed. In this way, by customizing the analysis means based on the current situation, a more appropriate analysis can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can customize the analysis means using an AI model that analyzes the current situation of the controlled person.

[0053] During analysis, the analysis unit can select an appropriate analysis method based on the geographical location information of the controlled person. For example, the analysis unit can prioritize analysis of information related to the controlled person's current location. Furthermore, the analysis unit can analyze related information based on the controlled person's past location information. Furthermore, if the controlled person is in a specific location, the analysis unit can prioritize analysis of information related to that location. This makes it possible to provide a more appropriate analysis method by taking geographical location information into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can select an appropriate analysis method using an AI model that analyzes the controlled person's geographical location information.

[0054] During analysis, the analysis unit can analyze the social media activity of the controlled person and automatically suggest a means of analysis. The analysis unit can perform analysis based on, for example, keywords frequently used by the controlled person on social media. The analysis unit can also analyze the content of the controlled person's social media posts and analyze related information. The analysis unit can also analyze related information by referring to keywords used by the controlled person's social media friends. This allows for more appropriate analysis means to be provided by analyzing social media activity. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can suggest a means of analysis using an AI model that analyzes the controlled person's social media activity.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The memory assistance system may further include a biometric monitoring unit that monitors the user's biometric data in real time. The biometric monitoring unit acquires biometric data such as heart rate, blood pressure, and body temperature, and transmits this data to the analysis unit. The analysis unit estimates the user's stress level and relaxation level based on this biometric data, allowing for more accurate measurement of the memory level. For example, if the user's heart rate increases when viewing a particular image, it can be determined that the image evokes strong emotions. Furthermore, if the user hears a particular sound while relaxed, it can be determined that the sound is likely to be memorable. In this way, by utilizing the user's biometric data, the memory level can be measured with greater accuracy.

[0057] The memory assistance system can further include a behavior analysis unit that analyzes the user's past behavior history. The behavior analysis unit records the places the user has visited and the activities he or she has performed in the past, and estimates the user's interests and concerns based on this data. For example, it can prioritize the display of information related to places the user has frequently visited in the past. It can also automatically suggest keywords related to the user's past activities. This makes it possible to provide more relevant information by utilizing the user's past behavior history.

[0058] The memory assistance system may further include a gaze analysis unit that analyzes the user's gaze data. The gaze analysis unit can track in real time which part of the screen the user is looking at and estimate the user's interest. For example, if the user is looking at a particular image for a long time, information related to that image can be displayed preferentially. Also, if the user is frequently moving their gaze, it can be determined that the user is not interested and different information can be displayed. In this way, by utilizing the user's gaze data, more relevant information can be provided.

[0059] The memory assistance system can further include an analysis optimization unit that refers to the user's past reaction data and appropriately optimizes the analysis algorithm. The analysis optimization unit can optimize the analysis algorithm based on the user's past reaction data and perform analysis based on information to which the user showed strong reactions in the past. It can also analyze the user's past reaction patterns and propose an optimal analysis algorithm. This makes it possible to provide a more appropriate analysis algorithm by referring to past reaction data.

[0060] The memory assistance system may further include a social media analysis unit that analyzes the user's social media activity and automatically suggests analysis methods. The social media analysis unit performs analysis based on keywords frequently used by the user on social media, and analyzes the content of the user's social media posts to analyze related information. It may also analyze related information based on keywords used by the user's social media friends. This allows for the analysis of social media activity to provide more appropriate analysis methods.

[0061] The memory assistance system may further include a geographic information analysis unit that selects an appropriate analysis method based on the user's geographic location information. The geographic information analysis unit may prioritize analysis of information related to the user's current location and analyze related information based on the user's past location information. Furthermore, if the user is in a specific location, it may prioritize analysis of information related to that location. This allows for a more appropriate analysis method to be provided by taking geographic location information into consideration.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The input unit is a section where the person being operated or the caregiver inputs keywords. For example, the input unit can input keywords using a touch screen of a tablet. The input unit can also accept voice input. For example, keywords spoken by the person being operated can be converted into text using voice recognition technology. Step 2: The search unit is a component that searches for information based on the keywords entered by the input unit. For example, the search unit can search for information on the Internet. The search unit can also search for street views on map services. For example, the search unit can search for images or sounds related to a specific location. Step 3: The display unit is a component that displays the information searched by the search unit. For example, the display unit can display images and sounds on the screen of a tablet. Step 4: The recording unit is a part that records the reaction of the person being operated to the information displayed by the display unit. For example, the recording unit can record the facial expressions of the person being operated using a camera. The recording unit can also record the biometric data of the person being operated using a sensor. For example, the recording unit can record the heart rate and electrodermal activity. Step 5: The analysis unit analyzes the responses recorded by the recording unit. For example, the analysis unit can analyze the response data of the person being operated and measure the memory level. This allows the memory assistance system according to the embodiment to measure the memory level of the person being operated.

[0064] (Example 2) A memory assistance system according to an embodiment of the present invention is a system that, in the event of temporary memory loss or dementia, sequentially displays local images and audio, such as information from the Internet or street views of map services, on a tablet or other device based on fragmented memory information. When the user or caregiver inputs keywords into the system, the system displays corresponding information and measures the user's memory level based on the user's reaction to the keywords. Input can be made directly on the screen or via voice. For example, when the user or caregiver inputs keywords such as "park" or "family photos," the system analyzes the keywords and searches for local images and audio, such as information from the Internet or street views of map services. The retrieved information is sequentially displayed on a tablet or other device. For example, images and audio of a park are displayed. The user's reaction is recorded. For example, if the user smiles when viewing an image, the system records the user's reaction. Furthermore, the system analyzes the user's reaction to measure the user's memory level. For example, if the user responds strongly to a particular image or audio, the system determines that the information is likely to remain in their memory. This system allows the display of information based on memory fragments and the measurement of memory level, even in the case of temporary memory loss or dementia. Furthermore, by allowing input either directly on the screen or by voice, operation is simplified, making the system easy to use for both the user and the caregiver. This allows the memory assistance system to measure the user's memory level.

[0065] The memory assistance system according to the embodiment includes an input unit, a search unit, a display unit, a recording unit, and an analysis unit. The input unit is a unit through which the user or a caregiver inputs keywords. For example, the input unit can input keywords using a touch screen of a tablet. The input unit can also accept voice input. For example, the input unit can convert keywords spoken by the user into text using voice recognition technology. The search unit is a unit that searches for information based on the keywords input by the input unit. For example, the search unit can search for information on the Internet. The search unit can also search Street View on a map service. For example, the search unit can search for images and audio related to a specific location. The display unit is a unit that displays information searched by the search unit. For example, the display unit can display images and audio on the tablet screen. The recording unit is a unit that records the user's response to the information displayed by the display unit. For example, the recording unit can record the user's facial expressions using a camera. The recording unit can also record the user's biometric data using a sensor. For example, the recording unit can record the user's heart rate and electrodermal activity. The analysis unit is a component that analyzes the responses recorded by the recording unit. For example, the analysis unit can analyze the response data of the person being operated and measure the memory level. This allows the memory assistance system according to the embodiment to measure the memory level of the person being operated.

[0066] The input unit can accept direct input on the screen and voice input. The input unit can input keywords using, for example, a touchscreen of a tablet. For example, the person being operated can input keywords using a keyboard on the tablet screen. The input unit can also accept voice input. For example, using voice recognition technology, keywords spoken by the person being operated can be converted into text. For example, if the person being operated speaks "park," the voice recognition technology can analyze the speech and convert it into the text "park." This simplifies operation by accepting direct input on the screen and voice input. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can accept voice input using an AI model that uses voice recognition technology to convert keywords spoken by the person being operated into text.

[0067] The search unit can search information on the Internet and street views of map services. The search unit can, for example, search information on the Internet. For example, the search unit can search websites and databases related to a specific keyword. The search unit can also search street views of map services. For example, the search unit can search images and audio related to a specific location. For example, when the keyword "park" is input, the search unit can search images and audio related to parks. This makes it possible to provide a wide range of information by searching information on the Internet and street views of map services. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can search for information using an AI model that searches information on the Internet.

[0068] The display unit can display information on the tablet screen. The display unit can, for example, display images and audio on the tablet screen. For example, the display unit can display images and audio of a park on the tablet screen. This allows information to be provided visually by displaying information on the screen of a tablet or the like. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can display information using an AI model that displays information searched by the search unit.

[0069] The recording unit can record the reactions of the person being operated using a camera and a temperature sensor. The recording unit can, for example, record the facial expressions of the person being operated using a camera. For example, if the person being operated smiles when looking at an image, the recording unit can record that facial expression using a camera. The recording unit can also record the biometric data of the person being operated using a temperature sensor. For example, the recording unit can record the body temperature and electrodermal activity of the person being operated. This allows the reaction of the person being operated to be accurately recorded using a camera and a sensor. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input image data of the person being operated captured by a camera to the generation AI and cause the generation AI to record the facial expressions of the person being operated.

[0070] The analysis unit can measure the level of memory based on the recorded reactions. The analysis unit can, for example, analyze the reaction data of the controlled person recorded by the recording unit and measure the level of memory. For example, if the controlled person shows a strong reaction to a particular image or sound, the analysis unit can determine that there is a high possibility that the information remains in their memory. This allows the controlled person's memory state to be evaluated by measuring the level of memory based on the recorded reactions. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the reaction data recorded by the recording unit into the generation AI and cause the generation AI to measure the level of memory.

[0071] The input unit can estimate the emotion of the person being operated and automatically adjust the timing of the input based on the estimated emotion. The input unit can, for example, estimate the emotion of the person being operated and adjust the timing of the input based on the estimated emotion. For example, if the person being operated is relaxed, the timing of the input can be slowed down to avoid rushing. Also, if the person being operated is nervous, the timing of the input can be accelerated to provide information quickly. Also, if the person being operated is tired, the timing of the input can be adjusted to include a break. In this way, by adjusting the timing of the input based on the emotion of the person being operated, input can be performed at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can adjust the timing of the input using an AI model that estimates the emotion of the person being operated.

[0072] The input unit can analyze the past input history of the person being operated and select an appropriate input method. The input unit can, for example, analyze the past input history of the person being operated and select the optimal input method. For example, the input unit can automatically display keywords that the person being operated has frequently used in the past as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the person being operated has used in the past. It can also predict and suggest keywords that will be used in a specific time period from the person being operated's past input history. In this way, by analyzing the past input history, it is possible to provide the person being operated with the optimal input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can select the optimal input method using an AI model that analyzes the person being operated's past input history.

[0073] The input unit can filter the input content based on the current situation and areas of interest of the user when inputting the information. For example, the input unit can prioritize displaying keywords related to the user's current location. For example, if the user is in a park, keywords related to the park can be automatically displayed. The input unit can also suggest related keywords based on the user's areas of interest. For example, if the user is interested in gardening as a hobby, keywords related to gardening can be prioritized. The input unit can also suggest appropriate keywords based on the user's current situation (e.g., weather or time of day). For example, keywords related to indoor activities that can be enjoyed on a rainy day can be displayed. This allows the user to provide more relevant information by filtering the input content based on the user's current situation and areas of interest. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can filter the input content using an AI model that analyzes the user's current situation and areas of interest.

[0074] The input unit can estimate the emotions of the user and automatically determine the priority of keywords to be input based on the estimated emotions. The input unit can, for example, estimate the emotions of the user and determine the priority of keywords to be input based on the estimated emotions. For example, if the user is excited, keywords that attract the user's attention can be displayed preferentially. If the user is calm, keywords that help the user relax can be displayed preferentially. If the user is anxious, keywords that give the user a sense of security can be displayed preferentially. This allows for more appropriate keywords to be provided by determining the priority of keywords based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the input unit may be performed using AI, or may be performed without AI. For example, the input unit can determine the priority of keywords using an AI model that estimates the user's emotions.

[0075] When inputting, the input unit can prioritize inputting highly relevant keywords based on the geographical location information of the operated person. For example, the input unit can automatically display keywords related to the location where the operated person is currently located. For example, if the operated person is in a park, keywords related to the park can be automatically displayed. The input unit can also suggest related keywords based on the operated person's past location information. For example, the input unit can display keywords related to places the operated person has visited in the past. Furthermore, if the operated person is in a specific location, the input unit can prioritize displaying keywords related to that location. For example, if the operated person is in a museum, keywords related to the museum can be displayed. This makes it possible to provide more relevant keywords by taking the geographical location information into consideration. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input highly relevant keywords using an AI model that analyzes the operated person's geographical location information.

[0076] The input unit can analyze the user's social media activity and automatically input related keywords when inputting information. The input unit can automatically display, for example, keywords frequently used by the user on social media. For example, if the user frequently uses the keyword "travel" on social media, the input unit can display keywords related to "travel." The input unit can also analyze the user's social media posts and suggest related keywords. For example, if the user posts about "cooking" on social media, the input unit can display keywords related to "cooking." The input unit can also display related keywords based on keywords used by the user's social media friends. For example, if the user's friends frequently post about "sports," the input unit can display keywords related to "sports." This allows for analysis of social media activity to provide more relevant keywords. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input related keywords using an AI model that analyzes the user's social media activity.

[0077] The search unit can estimate the emotion of the person being operated and automatically adjust the display method of the search results based on the estimated emotion. The search unit can, for example, estimate the emotion of the person being operated and adjust the display method of the search results based on the estimated emotion. For example, if the person being operated is relaxed, the search results can be displayed at a leisurely pace. If the person being operated is in a hurry, the search results can be displayed quickly. If the person being operated is excited, the search results can be displayed in a visually stimulating manner. This allows for adjusting the display method of the search results based on the emotion of the person being operated, thereby providing more appropriate search results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can adjust the display method of the search results using an AI model that estimates the emotion of the person being operated.

[0078] The search unit can automatically adjust the level of detail of search results based on the importance of keywords during a search. The search unit can adjust the level of detail of search results based on, for example, the importance of keywords. For example, detailed search results can be displayed for keywords with high importance. Also, concise search results can be displayed for keywords with low importance. The search unit can also adjust the display order of search results according to the importance of keywords. For example, search results related to keywords with high importance can be preferentially displayed. By adjusting the level of detail of search results based on the importance of keywords, more appropriate search results can be provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can adjust the level of detail of search results using an AI model that evaluates the importance of keywords.

[0079] The search unit can automatically apply different search algorithms depending on the keyword category during a search. For example, the search unit can apply different search algorithms depending on the keyword category. For example, an image search algorithm can be applied to keywords related to images. A voice search algorithm can be applied to keywords related to voice. A text search algorithm can be applied to keywords related to text. In this way, by applying different search algorithms depending on the keyword category, more appropriate search results can be provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can apply different search algorithms using an AI model that classifies keyword categories.

[0080] The search unit can estimate the emotion of the person being operated and automatically adjust the display order of search results based on the estimated emotion. The search unit can, for example, estimate the emotion of the person being operated and adjust the display order of search results based on the estimated emotion. For example, if the person being operated is relaxed, search results that are relaxing can be preferentially displayed. Also, if the person being operated is excited, search results that are interesting can be preferentially displayed. Also, if the person being operated is anxious, search results that give a sense of security can be preferentially displayed. By adjusting the display order of search results based on the emotion of the person being operated, more appropriate search results can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the search unit can be performed using, for example, AI, or without AI. For example, the search unit can adjust the display order of search results using an AI model that estimates the emotion of the person being operated.

[0081] During a search, the search unit can automatically determine the priority of search results based on the time of keyword submission. The search unit can, for example, determine the priority of search results based on the time of keyword submission. For example, for a recently submitted keyword, the search unit can prioritize displaying the most recent search results. Also, for a previously submitted keyword, the search unit can prioritize displaying the most recent search results. The search unit can also adjust the display order of search results based on the time of keyword submission. For example, search results related to a recently submitted keyword can be prioritized. This allows for more appropriate search results to be provided by determining the priority of search results based on the time of keyword submission. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can determine the priority of search results using an AI model that evaluates the time of keyword submission.

[0082] The search unit can automatically adjust the order of search results based on the relevance of keywords during a search. The search unit can adjust the order of search results based on, for example, the relevance of keywords. For example, for a keyword with high relevance, highly relevant search results can be preferentially displayed. Also, for a keyword with low relevance, less relevant search results can be displayed. The search unit can also adjust the display order of search results according to the relevance of keywords. For example, search results related to highly relevant keywords can be preferentially displayed. In this way, by adjusting the order of search results based on the relevance of keywords, more appropriate search results can be provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can adjust the order of search results using an AI model that evaluates the relevance of keywords.

[0083] The display unit can estimate the emotion of the person being operated and automatically adjust the way in which the display content is presented based on the estimated emotion. The display unit can, for example, estimate the emotion of the person being operated and adjust the way in which the display content is presented based on the estimated emotion. For example, if the person being operated is relaxed, the display content can be presented in calm colors. If the person being operated is excited, the display content can be presented in vivid colors. If the person being operated is anxious, the display content can be presented in colors that give a sense of security. This allows for adjusting the way in which the display content is presented based on the emotion of the person being operated, thereby providing a more appropriate display. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can adjust the way in which the display content is presented using an AI model that estimates the emotion of the person being operated.

[0084] The display unit can automatically adjust the level of detail of the display based on the importance of the information when displaying the information. The display unit can adjust the level of detail of the display based on, for example, the importance of the information. For example, it can provide a detailed display for information with high importance. It can also provide a concise display for information with low importance. The display unit can also adjust the display order according to the importance of the information. For example, it can display information with high importance preferentially. By adjusting the level of detail of the display based on the importance of the information, it is possible to provide a more appropriate display. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can adjust the level of detail of the display using an AI model that evaluates the importance of the information.

[0085] The display unit can automatically apply different display algorithms depending on the category of information when displaying the information. The display unit can apply different display algorithms depending on the category of information, for example. For example, an image display algorithm can be applied to image information. Also, an audio display algorithm can be applied to audio information. Also, a text display algorithm can be applied to text information. By applying different display algorithms depending on the category of information, more appropriate display can be provided. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can apply different display algorithms using an AI model that classifies the category of information.

[0086] The display unit can estimate the emotion of the person being operated and automatically adjust the length of the display content based on the estimated emotion. For example, the display unit can estimate the emotion of the person being operated and adjust the length of the display content based on the estimated emotion. For example, if the person being operated is relaxed, it can provide longer display content. If the person being operated is in a hurry, it can provide short, to-the-point display content. If the person being operated is excited, it can provide visually stimulating display content. By adjusting the length of the display content based on the emotion of the person being operated, a more appropriate display can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can adjust the length of the display content using an AI model that estimates the emotion of the person being operated.

[0087] The display unit can automatically determine the display priority based on the time of submission of the information when displaying the information. The display unit can determine the display priority based on, for example, the time of submission of the information. For example, for recently submitted information, the latest display content can be given priority. Furthermore, for information submitted in the past, the older display content can be given priority. The display unit can also adjust the display order according to the time of submission of the information. For example, the most recently submitted information can be given priority. In this way, by determining the display priority based on the time of submission of the information, a more appropriate display can be provided. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can determine the display priority using an AI model that evaluates the time of submission of the information.

[0088] The display unit can automatically adjust the display order based on the relevance of the information when displaying the information. The display unit can adjust the display order based on, for example, the relevance of the information. For example, it can prioritize display content that is highly relevant to information with high relevance. Also, it can display display content that is less relevant to information with low relevance. The display unit can also adjust the display order according to the relevance of the information. For example, it can prioritize displaying information that is highly relevant. As a result, by adjusting the display order based on the relevance of the information, a more appropriate display can be provided. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can adjust the display order using an AI model that evaluates the relevance of the information.

[0089] The recording unit can estimate the emotion of the person being operated and automatically adjust the recording method based on the estimated emotion. For example, the recording unit can estimate the emotion of the person being operated and adjust the recording method based on the estimated emotion. For example, if the person being operated is relaxed, detailed recording can be performed. If the person being operated is nervous, concise recording can be performed. If the person being operated is excited, visually stimulating recording can be performed. By adjusting the recording method based on the emotion of the person being operated, more appropriate recording can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the recording unit can be performed using AI, for example, or without AI. For example, the recording unit can adjust the recording method using an AI model that estimates the emotion of the person being operated.

[0090] During recording, the recording unit can analyze the user's past responses and select an appropriate recording method. The recording unit can, for example, analyze the user's past responses and select an optimal recording method. For example, the recording unit can prioritize recording information to which the user has previously shown a strong response. The recording unit can also analyze the user's past response patterns and suggest an optimal recording method. The level of detail of the recording can also be adjusted based on the user's past response data. This makes it possible to provide a more appropriate recording method by analyzing past responses. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can select an optimal recording method using an AI model that analyzes the user's past response data.

[0091] During recording, the recording unit can automatically customize the recording means based on the current situation of the person being operated. The recording unit can customize the recording means based on, for example, the current situation of the person being operated. For example, if the person being operated is relaxed, detailed recording can be performed. If the person being operated is nervous, brief recording can be performed. If the person being operated is excited, visually stimulating recording can be performed. In this way, by customizing the recording means based on the current situation, more appropriate recording can be provided. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can customize the recording means using an AI model that analyzes the current situation of the person being operated.

[0092] The recording unit can estimate the emotions of the person being operated and automatically determine the priority of recording based on the estimated emotions. The recording unit can, for example, estimate the emotions of the person being operated and determine the priority of recording based on the estimated emotions. For example, if the person being operated is relaxed, it can prioritize recording information that helps the person being operated. Also, if the person being operated is excited, it can prioritize recording information that attracts the person being operated. Also, if the person being operated is anxious, it can prioritize recording information that gives the person being operated a sense of security. This allows for more appropriate recording by determining the priority of recording based on the person being operated's emotions. The estimation of emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or without AI. For example, the recording unit can determine the priority of recording using an AI model that estimates the person being operated's emotions.

[0093] During recording, the recording unit can select an appropriate recording method based on the geographical location information of the operated person. For example, the recording unit can prioritize recording information related to the location where the operated person is currently located. Furthermore, the recording unit can record related information based on the operated person's past location information. Furthermore, if the operated person is in a specific location, information related to that location can be prioritized. This makes it possible to provide a more appropriate recording method by taking the geographical location information into consideration. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can select an appropriate recording method using an AI model that analyzes the operated person's geographical location information.

[0094] During recording, the recording unit can analyze the social media activity of the controlled person and automatically suggest a means of recording. The recording unit can, for example, perform recording based on keywords frequently used by the controlled person on social media. It can also analyze the content of the controlled person's social media posts and record related information. It can also record related information by referring to keywords used by the controlled person's social media friends. This allows for analyzing social media activity to provide a more appropriate means of recording. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can suggest a means of recording using an AI model that analyzes the controlled person's social media activity.

[0095] The analysis unit can estimate the emotion of the person being operated and automatically adjust the analysis method based on the estimated emotion. The analysis unit can, for example, estimate the emotion of the person being operated and adjust the analysis method based on the estimated emotion. For example, if the person being operated is relaxed, a detailed analysis can be performed. If the person being operated is nervous, a concise analysis can be performed. If the person being operated is excited, a visually stimulating analysis can be performed. By adjusting the analysis method based on the emotion of the person being operated, a more appropriate analysis can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can adjust the analysis method using an AI model that estimates the emotion of the person being operated.

[0096] During analysis, the analysis unit can appropriately optimize the analysis algorithm by referring to the user's past reaction data. The analysis unit can, for example, optimize the analysis algorithm by referring to the user's past reaction data. For example, the analysis can be performed based on information on which the user has shown a strong reaction in the past. The analysis unit can also analyze the user's past reaction patterns and propose an optimal analysis algorithm. The level of detail of the analysis can also be adjusted based on the user's past reaction data. This makes it possible to provide a more appropriate analysis algorithm by referring to the past reaction data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can optimize the analysis algorithm using an AI model that analyzes the user's past reaction data.

[0097] During analysis, the analysis unit can automatically customize the analysis means based on the current situation of the controlled person. The analysis unit can customize the analysis means based on, for example, the current situation of the controlled person. For example, if the controlled person is relaxed, a detailed analysis can be performed. If the controlled person is tense, a brief analysis can be performed. If the controlled person is excited, a visually stimulating analysis can be performed. In this way, by customizing the analysis means based on the current situation, a more appropriate analysis can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can customize the analysis means using an AI model that analyzes the current situation of the controlled person.

[0098] The analysis unit can estimate the emotions of the controlled person and automatically determine the analysis priority based on the estimated emotions. The analysis unit can, for example, estimate the emotions of the controlled person and determine the analysis priority based on the estimated emotions. For example, if the controlled person is relaxed, it can prioritize analyzing information that helps the controlled person relax. Furthermore, if the controlled person is excited, it can prioritize analyzing information that interests the controlled person. Furthermore, if the controlled person is anxious, it can prioritize analyzing information that gives the controlled person a sense of security. Thus, by determining the analysis priority based on the controlled person's emotions, more appropriate analysis can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can determine the analysis priority using an AI model that estimates the controlled person's emotions.

[0099] During analysis, the analysis unit can select an appropriate analysis method based on the geographical location information of the controlled person. For example, the analysis unit can prioritize analysis of information related to the controlled person's current location. Furthermore, the analysis unit can analyze related information based on the controlled person's past location information. Furthermore, if the controlled person is in a specific location, the analysis unit can prioritize analysis of information related to that location. This makes it possible to provide a more appropriate analysis method by taking geographical location information into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can select an appropriate analysis method using an AI model that analyzes the controlled person's geographical location information.

[0100] During analysis, the analysis unit can analyze the social media activity of the controlled person and automatically suggest a means of analysis. The analysis unit can perform analysis based on, for example, keywords frequently used by the controlled person on social media. The analysis unit can also analyze the content of the controlled person's social media posts and analyze related information. The analysis unit can also analyze related information by referring to keywords used by the controlled person's social media friends. This allows for more appropriate analysis means to be provided by analyzing social media activity. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can suggest a means of analysis using an AI model that analyzes the controlled person's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, search unit, display unit, recording unit, and analysis unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized using a touch screen or voice recognition technology of the smart device 14. The search unit is realized by searching for information on the Internet or street views of map services using the specific processing unit 290 of the data processing device 12. The display unit is realized by displaying the searched information on the display 40A of the smart device 14. The recording unit is realized by recording the responses of the person being operated using the camera 42 or sensors of the smart device 14. The analysis unit is realized by analyzing the response data recorded by the specific processing unit 290 of the data processing device 12 and measuring the level of memory. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, search unit, display unit, recording unit, and analysis unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized using the voice recognition technology of the smart glasses 214. The search unit is realized by searching for information on the Internet or street views of map services using the specific processing unit 290 of the data processing device 12. The display unit is realized by displaying the searched information on the display of the smart glasses 214. The recording unit is realized by recording the responses of the person being operated using the camera 42 or sensors of the smart glasses 214. The analysis unit is realized by analyzing the response data recorded by the specific processing unit 290 of the data processing device 12 and measuring the level of memory. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, search unit, display unit, recording unit, and analysis unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the input unit is realized using the voice recognition technology of the headset type terminal 314. The search unit is realized by searching for information on the Internet or street views of map services using the specific processing unit 290 of the data processing device 12. The display unit is realized by displaying the searched information on the display 343 of the headset type terminal 314. The recording unit is realized by recording the responses of the person being operated using the camera 42 or sensors of the headset type terminal 314. The analysis unit is realized by analyzing the response data recorded by the specific processing unit 290 of the data processing device 12 and measuring the level of memory. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, search unit, display unit, recording unit, and analysis unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized using the voice recognition technology of the robot 414. The search unit is realized by searching information on the Internet or street views of map services using the specific processing unit 290 of the data processing device 12. The display unit is realized by displaying the searched information on the display of the robot 414. The recording unit is realized by recording the reactions of the person being operated using the camera 42 and sensors of the robot 414. The analysis unit is realized by analyzing the reaction data recorded by the specific processing unit 290 of the data processing device 12 and measuring the level of memory.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The memory assistance system may further include a biometric monitoring unit that monitors the user's biometric data in real time. The biometric monitoring unit acquires biometric data such as heart rate, blood pressure, and body temperature, and transmits this data to the analysis unit. The analysis unit estimates the user's stress level and relaxation level based on this biometric data, allowing for more accurate measurement of the memory level. For example, if the user's heart rate increases when viewing a particular image, it can be determined that the image evokes strong emotions. Furthermore, if the user hears a particular sound while relaxed, it can be determined that the sound is likely to be memorable. In this way, by utilizing the user's biometric data, the memory level can be measured with greater accuracy.

[0103] The memory assistance system can further include a behavior analysis unit that analyzes the user's past behavior history. The behavior analysis unit records the places the user has visited and the activities he or she has performed in the past, and estimates the user's interests and concerns based on this data. For example, it can prioritize the display of information related to places the user has frequently visited in the past. It can also automatically suggest keywords related to the user's past activities. This makes it possible to provide more relevant information by utilizing the user's past behavior history.

[0104] The memory assistance system may further include a display customization unit that estimates the user's emotions and customizes the display content based on the estimated emotions. The display customization unit analyzes the user's emotions and can present the display content in calm colors when the user is relaxed, and in vivid colors when the user is excited. Furthermore, if the user is feeling anxious, the display content can be presented in colors that give a sense of security. In this way, by customizing the display content based on the user's emotions, a more appropriate display can be provided.

[0105] The memory assistance system may further include a voice analysis unit that analyzes the user's voice data. The voice analysis unit can analyze what the user has said and infer the user's emotions and intentions. For example, if the user emphasizes a particular keyword when speaking, information related to that keyword can be displayed preferentially. The voice analysis unit can also analyze the speed and tone of the user's speech to determine whether the user is relaxed or nervous. This makes it possible to provide more appropriate information by utilizing the user's voice data.

[0106] The memory assistance system may further include a gaze analysis unit that analyzes the user's gaze data. The gaze analysis unit can track in real time which part of the screen the user is looking at and estimate the user's interest. For example, if the user is looking at a particular image for a long time, information related to that image can be displayed preferentially. Also, if the user is frequently moving their gaze, it can be determined that the user is not interested and different information can be displayed. In this way, by utilizing the user's gaze data, more relevant information can be provided.

[0107] The memory assistance system can further include a search result adjustment unit that estimates the user's emotions and automatically adjusts the display order of search results based on the estimated emotions. The search result adjustment unit analyzes the user's emotions and can prioritize displaying search results that are relaxing when the user is relaxed, and prioritize displaying search results that are interesting when the user is excited. Furthermore, when the user is feeling anxious, it can prioritize displaying search results that give a sense of security. In this way, by adjusting the display order of search results based on the user's emotions, more appropriate search results can be provided.

[0108] The memory assistance system can further include a recording adjustment unit that estimates the user's emotions and automatically adjusts the recording method based on the estimated emotions. The recording adjustment unit analyzes the user's emotions and can perform detailed recording when the user is relaxed and brief recording when the user is nervous. Also, it can perform visually stimulating recording when the user is excited. In this way, by adjusting the recording method based on the user's emotions, it is possible to provide more appropriate records.

[0109] The memory assistance system can further include an analysis optimization unit that refers to the user's past reaction data and appropriately optimizes the analysis algorithm. The analysis optimization unit can optimize the analysis algorithm based on the user's past reaction data and perform analysis based on information to which the user showed strong reactions in the past. It can also analyze the user's past reaction patterns and propose an optimal analysis algorithm. This makes it possible to provide a more appropriate analysis algorithm by referring to past reaction data.

[0110] The memory assistance system may further include a social media analysis unit that analyzes the user's social media activity and automatically suggests analysis methods. The social media analysis unit performs analysis based on keywords frequently used by the user on social media, and analyzes the content of the user's social media posts to analyze related information. It may also analyze related information based on keywords used by the user's social media friends. This allows for the analysis of social media activity to provide more appropriate analysis methods.

[0111] The memory assistance system may further include a geographic information analysis unit that selects an appropriate analysis method based on the user's geographic location information. The geographic information analysis unit may prioritize analysis of information related to the user's current location and analyze related information based on the user's past location information. Furthermore, if the user is in a specific location, it may prioritize analysis of information related to that location. This allows for a more appropriate analysis method to be provided by taking geographic location information into consideration.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The input unit is a section where the person being operated or the caregiver inputs keywords. For example, the input unit can input keywords using a touch screen of a tablet. The input unit can also accept voice input. For example, keywords spoken by the person being operated can be converted into text using voice recognition technology. Step 2: The search unit is a component that searches for information based on the keywords entered by the input unit. For example, the search unit can search for information on the Internet. The search unit can also search for street views on map services. For example, the search unit can search for images or sounds related to a specific location. Step 3: The display unit is a component that displays the information searched by the search unit. For example, the display unit can display images and sounds on the screen of a tablet. Step 4: The recording unit is a part that records the reaction of the person being operated to the information displayed by the display unit. For example, the recording unit can record the facial expressions of the person being operated using a camera. The recording unit can also record the biometric data of the person being operated using a sensor. For example, the recording unit can record the heart rate and electrodermal activity. Step 5: The analysis unit analyzes the responses recorded by the recording unit. For example, the analysis unit can analyze the response data of the person being operated and measure the memory level. This allows the memory assistance system according to the embodiment to measure the memory level of the person being operated.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0137] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] 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.

[0177] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0178] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0179] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0183] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting a keyword; a search unit that searches for information based on the keyword input by the input unit; a display unit that displays the information searched by the search unit; a recording unit that records a response of the person being operated to the information displayed by the display unit; an analysis unit that analyzes the reaction recorded by the recording unit; Equipped with A system characterized by:

2. The input unit Accepts on-screen and voice input The system of claim 1 .

3. The search unit Search Street View for information and map services on the Internet The system of claim 1 .

4. The display unit Displaying information on a tablet screen The system of claim 1 .

5. The recording unit Recording the subject's reactions using cameras and temperature sensors The system of claim 1 .

6. The analysis unit Measure the level of memory based on recorded responses The system of claim 1 .

7. The input unit Estimate the emotions of the person being controlled and automatically adjust the timing of inputs based on the estimated emotions. The system of claim 1 .

8. The input unit Analyze the user's past input history and select the appropriate input method The system of claim 1 .

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