system

Abstract

The system according to the embodiment aims to provide reminder hints to people with mild cognitive impairment (MCI) when they forget things. [Solution] A system according to an embodiment includes a hearing unit, a recording unit, an analysis unit, and a presentation unit. The hearing unit periodically hears from a user. The recording unit saves information recorded by the hearing unit. The analysis unit acquires and analyzes information from the recording unit. The presentation unit presents a reminder hint based on the analysis results obtained by the analysis 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] With conventional technology, it was difficult for people with mild cognitive impairment (MCI) to receive appropriate support when they became forgetful.

[0005] The system according to the embodiment aims to provide reminder hints to people with mild cognitive impairment (MCI) when they forget things. [Means for solving the problem]

[0006] The system according to the embodiment includes a hearing unit, a recording unit, an analysis unit, and a presentation unit. The hearing unit periodically hears from a user. The recording unit saves information recorded by the hearing unit. The analysis unit acquires and analyzes information from the recording unit. The presentation unit presents a reminder hint based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] A system according to an embodiment can provide reminder tips to people with mild cognitive impairment (MCI) when they forget things. [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 recall support system according to an embodiment of the present invention is a system for people with mild cognitive impairment (MCI). In this system, a generation AI periodically interviews a user and records their responses and actions. When a user forgets something and makes an inquiry, the system provides memory recall hints based on the user's activity records and related information (e.g., timeline, location information, purchase history, and recorded photos). For example, the generation AI asks the user about daily events, plans, shopping lists, etc., and records the user's actions and responses. Next, when a user forgets something and makes an inquiry, the system provides memory recall hints based on the recorded information. For example, if a user asks, "What did you have for dinner last night?", the system refers to location information, purchase history, recorded photos, and other information to provide a hint such as, "I had pizza at a nearby restaurant for dinner last night." This system enables people with mild cognitive impairment to reduce memory recall in their daily lives and lead more independent lives. It also reduces the burden on family members and caregivers. This memory recall support system enables people with mild cognitive impairment to reduce memory recall in their daily lives and lead more independent lives. It also reduces the burden on family and caregivers.

[0029] A memory recall support system according to an embodiment includes a hearing unit, a recording unit, an analysis unit, and a presentation unit. The hearing unit periodically hears from a user. For example, the hearing unit can ask about daily events, plans, shopping lists, etc. The hearing unit can also record the user's actions and responses using a generation AI. For example, the generation AI asks the user, "What are your plans for today?" and records the response. The recording unit saves the information recorded by the hearing unit. For example, the recording unit can save information such as a timeline, location information, purchase history, and recorded photos. For example, the recording unit saves the user's location information as GPS data. The recording unit can also save photos taken by the user. The analysis unit acquires and analyzes information from the recording unit. For example, the analysis unit can use data mining technology to identify the user's behavioral patterns. The analysis unit can also analyze information using the generation AI. For example, the generation AI analyzes past behavioral data to identify the user's behavioral patterns. The presentation unit presents a hint for recalling based on the analysis result obtained by the analysis unit. For example, when a user asks, "What did you have for dinner last night?", the presentation unit refers to location information, purchase history, recorded photos, etc., and provides a hint such as, "I had pizza at a nearby restaurant for dinner last night." In this way, the memory recall support system according to the embodiment can record the user's actions and answers and provide support for memory recall.

[0030] The hearing unit can periodically record the user's actions and responses. For example, the hearing unit can ask about daily events, plans, shopping lists, etc. For example, the hearing unit can also use a generation AI to record the user's actions and responses. For example, the generation AI can ask the user, "What are your plans for today?" and record the response. The hearing unit can also use an interview format or question format to periodically record the user's actions and responses. For example, the hearing unit can interview the user and record the content of the interview. The hearing unit can also ask the user questions and record the responses. In this way, the hearing unit can provide support for memory loss by periodically recording the user's actions and responses.

[0031] The recording unit can store information such as a timeline, location information, purchase history, and recorded photos. For example, the recording unit can store the user's location information as GPS data. For example, the recording unit records location information of places visited by the user. The recording unit can also store a history of products purchased by the user. For example, the recording unit stores online shopping history and receipt information. The recording unit can also store photos taken by the user. For example, the recording unit stores photos taken by the user as digital data. The recording unit can also store timeline information. For example, the recording unit records the user's actions and the order of events. In this way, the recording unit can store a variety of information, thereby improving the accuracy of providing memory hints.

[0032] The analysis unit can analyze the information obtained from the recording unit and identify the user's behavioral patterns. The analysis unit can identify the user's behavioral patterns, for example, using data mining technology. For example, the analysis unit can analyze the user's behavioral data and perform frequency analysis or time series analysis. The analysis unit can also analyze the information using a generation AI. For example, the generation AI analyzes past behavioral data to identify the user's behavioral patterns. Furthermore, the analysis unit can use statistical analysis technology to identify the user's behavioral patterns. For example, the analysis unit statistically analyzes the user's behavioral data to identify the behavioral patterns. In this way, the analysis unit can provide more appropriate recall hints by identifying the user's behavioral patterns.

[0033] The presentation unit can present the user with hints for recalling memories based on the analysis results obtained by the analysis unit. For example, when the user asks, "What did you have for dinner last night?", the presentation unit can provide a hint such as, "I had pizza at a nearby restaurant for dinner last night" by referring to location information, purchase history, recorded photos, etc. For example, the presentation unit can identify places the user visited based on the user's location information and provide information related to those places. The presentation unit can also provide information about products purchased by the user based on the user's purchase history. Furthermore, the presentation unit can provide hints to remind the user of their actions based on photos taken by the user. In this way, the presentation unit can support the user in dealing with forgetfulness by presenting hints for recalling memories based on the analysis results.

[0034] The hearing unit can analyze the user's past hearing history and select appropriate questions. For example, the hearing unit can prioritize the selection of questions that the user found easy to answer in the past. For example, the hearing unit can analyze the user's past hearing history using a generation AI and select optimal questions. The hearing unit can also avoid questions that the user found difficult to answer in the past and select alternative questions. For example, the hearing unit can analyze the user's past answer patterns and select highly relevant questions. This enables the hearing unit to ask more effective questions by analyzing the user's past hearing history.

[0035] The hearing unit can customize questions asked during the hearing based on the user's current health condition and lifestyle. For example, if the user is feeling unwell, the hearing unit can prioritize questions about the user's health. For example, the hearing unit uses a generative AI to estimate the user's health condition and select appropriate questions. Furthermore, if the user is busy, the hearing unit can select questions that can be answered in a short time. For example, the hearing unit customizes appropriate questions based on the user's lifestyle. Furthermore, if the user is relaxed, the hearing unit can ask questions about detailed lifestyle information. For example, by customizing questions based on the user's health condition and lifestyle, the hearing unit can collect more appropriate information.

[0036] During the hearing, the hearing unit can prioritize relevant questions based on the user's geographical location information. For example, if the user is in a specific location, the hearing unit can ask questions related to that location. For example, the hearing unit obtains the user's geographical location information using a generation AI and selects appropriate questions. Furthermore, if the user is traveling, the hearing unit can ask questions related to the user's travel destination. For example, the hearing unit can provide information related to the user's travel destination based on the user's current location. Furthermore, if the user is at home, the hearing unit can ask questions related to the user's daily life. For example, the hearing unit can prioritize relevant questions taking the user's geographical location information into consideration, thereby enabling more appropriate information collection.

[0037] During the interview, the hearing unit can analyze the user's social media activity and ask relevant questions. For example, the hearing unit can ask questions based on the content the user has shared on social media. For example, the hearing unit can analyze the user's social media activity using generative AI and select appropriate questions. The hearing unit can also ask questions related to accounts the user follows on social media. For example, the hearing unit can provide relevant information based on the accounts the user follows. Furthermore, the hearing unit can ask questions related to events the user is participating in on social media. For example, the hearing unit can analyze the user's social media activity and ask relevant questions, thereby enabling more appropriate information collection.

[0038] The recording unit can determine the storage priority based on the importance of the information when recording. For example, the recording unit can prioritize storing information with high importance. For example, the recording unit can evaluate the importance of information using a generation AI and determine the storage priority. The recording unit can also postpone storing information with low importance. For example, the recording unit selects an appropriate storage order based on the urgency of the information and the user's level of interest. Furthermore, the recording unit can adjust the storage order of information according to its importance. For example, the recording unit can prioritize managing important information by evaluating the importance of information and determining the storage priority.

[0039] The recording unit can apply different storage algorithms depending on the category of information when recording. For example, the recording unit can store location information using a dedicated algorithm. For example, the recording unit can classify the category of information using a generative AI and apply an appropriate storage algorithm. The recording unit can also store purchase history using a different algorithm. For example, the recording unit selects a different storage method depending on the type of information. Furthermore, the recording unit can store recorded photos using an algorithm dedicated to images. For example, the recording unit can apply different storage algorithms depending on the category of information, enabling more efficient information management.

[0040] When recording, the recording unit can determine the priority of storage based on the time when the information was submitted. For example, the recording unit can prioritize storing the most recent information. For example, the recording unit can use a generation AI to evaluate the time when the information was submitted and determine the priority of storage. The recording unit can also prioritize storing older information. For example, the recording unit selects an appropriate storage order based on the date and time when the information was submitted. Furthermore, the recording unit can adjust the storage order of information depending on the time of submission. For example, the recording unit can prioritize managing the most recent information by evaluating the time when the information was submitted and determining the priority of storage.

[0041] The recording unit can adjust the order of storage based on the relevance of the information when recording. For example, the recording unit can prioritize storing highly relevant information. For example, the recording unit can evaluate the relevance of the information using a generative AI and adjust the order of storage. The recording unit can also postpone storing less relevant information. For example, the recording unit selects an appropriate order of storage based on a common theme of the information or related events. Furthermore, the recording unit can adjust the order of storage of the information according to relevance. For example, the recording unit can prioritize managing highly relevant information by evaluating the relevance of the information and adjusting the order of storage.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationships between information. The analysis unit can, for example, analyze the interrelationships between location information and purchase history. For example, the analysis unit uses generative AI to evaluate the interrelationships between information and improve the accuracy of the analysis. The analysis unit can also analyze the interrelationships between timelines and recorded photos. For example, the analysis unit selects an appropriate analysis method based on the co-occurrence and causal relationships of information. Furthermore, the analysis unit can analyze the interrelationships between purchase history and recorded photos. For example, the analysis unit can evaluate the interrelationships between information and improve the accuracy of the analysis, enabling more accurate analysis.

[0043] During analysis, the analysis unit can perform analysis based on the attribute information of the information submitter. The analysis unit can perform analysis based on the user's age, for example. For example, the analysis unit evaluates the attribute information of the information submitter using a generation AI and selects an appropriate analysis method. The analysis unit can also perform analysis based on the user's gender. For example, the analysis unit selects appropriate analysis criteria based on the attribute information of the information submitter. Furthermore, the analysis unit can perform analysis based on the user's occupation. For example, the analysis unit can evaluate the attribute information of the information submitter and improve the accuracy of the analysis, thereby enabling more accurate analysis.

[0044] During analysis, the analysis unit can perform analysis based on the geographic distribution of information. The analysis unit can perform analysis based on location information, for example. For example, the analysis unit evaluates the geographic distribution of information using generative AI and selects an appropriate analysis method. The analysis unit can also perform analysis taking geographic clusters into consideration. For example, the analysis unit selects appropriate analysis criteria based on the geographic distribution of information. Furthermore, the analysis unit can perform analysis taking geographic trends into consideration. For example, the analysis unit can evaluate the geographic distribution of information and improve the accuracy of the analysis, enabling more accurate analysis.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis based on literature related to the information. The analysis unit can, for example, perform the analysis by referring to related academic papers. For example, the analysis unit evaluates literature related to the information using generative AI and selects an appropriate analysis method. The analysis unit can also perform the analysis by referring to related patent documents. For example, the analysis unit selects appropriate analysis criteria based on literature related to the information. Furthermore, the analysis unit can also perform the analysis by referring to related technical literature. For example, the analysis unit can evaluate literature related to the information and improve the accuracy of the analysis, thereby enabling more accurate analysis.

[0046] The presentation unit can adjust the level of detail of the presentation based on the importance of the hint when presenting the hint. For example, the presentation unit can present highly important hints in detail. For example, the presentation unit can evaluate the importance of the hint using a generation AI and adjust the level of detail of the presentation. The presentation unit can also present less important hints in a concise manner. For example, the presentation unit selects an appropriate presentation method based on the urgency of the hint and the user's level of interest. Furthermore, the presentation unit can adjust the level of detail of the hint depending on the importance. For example, the presentation unit can evaluate the importance of the hint and adjust the level of detail of the presentation, thereby providing more appropriate information.

[0047] The presentation unit can apply different presentation algorithms depending on the category of the hint when presenting the hint. For example, the presentation unit can present hints related to location information using a map. For example, the presentation unit can classify the category of hints using a generation AI and apply an appropriate presentation algorithm. The presentation unit can also present hints related to purchase history in list format. For example, the presentation unit selects different presentation methods depending on the type of information. Furthermore, the presentation unit can present hints related to record photos using images. For example, the presentation unit can provide more appropriate information by applying different presentation algorithms depending on the category of hint.

[0048] The presentation unit can determine the presentation priority based on the time of submission of the hint when presenting the hint. For example, the presentation unit can prioritize the presentation of the most recent hint. For example, the presentation unit can evaluate the time of submission of the hint using a generation AI and determine the presentation priority. The presentation unit can also present older hints later. For example, the presentation unit selects an appropriate presentation order based on the submission date or submission time of the hint. Furthermore, the presentation unit can adjust the presentation order of the hints depending on the submission time. For example, the presentation unit can evaluate the time of submission of the hint and determine the presentation priority, thereby providing more appropriate information.

[0049] The presentation unit can adjust the presentation order based on the relevance of the hints when presenting them. For example, the presentation unit can prioritize the presentation of highly relevant hints. For example, the presentation unit can evaluate the relevance of the hints using a generation AI and adjust the presentation order. The presentation unit can also postpone the presentation of less relevant hints. For example, the presentation unit selects an appropriate presentation order based on a common theme or related events of the hints. Furthermore, the presentation unit can adjust the presentation order of the hints according to the relevance. For example, the presentation unit can evaluate the relevance of the hints and adjust the presentation order, thereby providing more appropriate information.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The hearing unit can analyze the user's past hearing history and select appropriate questions. For example, it can prioritize the selection of questions that the user found easy to answer in the past. The hearing unit can also avoid questions that the user found difficult to answer in the past and select alternative questions. Furthermore, the hearing unit can analyze the user's past answer patterns and select highly relevant questions. This allows the hearing unit to ask more effective questions by analyzing the user's past hearing history.

[0052] The recording unit can determine the priority of storage based on the importance of the information when recording. For example, it can prioritize storage of information with high importance. The recording unit can also postpone storage of information with low importance. Furthermore, the recording unit can adjust the order in which information is stored according to its importance. In this way, the recording unit can prioritize management of important information by evaluating the importance of information and determining the priority of storage.

[0053] During the analysis, the analysis unit can improve the accuracy of the analysis based on the correlation of information. For example, the analysis unit can analyze the correlation between location information and purchase history. The analysis unit can also analyze the correlation between a timeline and recorded photos. Furthermore, the analysis unit can analyze the correlation between purchase history and recorded photos. This allows the analysis unit to evaluate the correlation of information and improve the accuracy of the analysis, thereby enabling more accurate analysis.

[0054] The presentation unit can adjust the level of detail of the presentation based on the importance of the hint when presenting the hint. For example, a highly important hint can be presented in detail. The presentation unit can also present a less important hint in a concise manner. Furthermore, the presentation unit can adjust the level of detail of the hint according to the importance. In this way, the presentation unit can provide more appropriate information by evaluating the importance of the hint and adjusting the level of detail of the presentation.

[0055] During analysis, the analysis unit can perform the analysis based on the geographic distribution of the information. For example, the analysis can be performed based on location information. The analysis unit can also perform the analysis taking into account geographic clusters. Furthermore, the analysis unit can perform the analysis taking into account geographic trends. This allows the analysis unit to evaluate the geographic distribution of the information and improve the accuracy of the analysis, thereby enabling more accurate analysis.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The hearing section periodically interviews the user. For example, it can ask about daily events, plans, shopping lists, etc. It can also record the user's actions and responses using generation AI. Step 2: The recording unit saves the information recorded by the hearing unit. For example, it can save information such as a timeline, location information, purchase history, and recorded photos. It can also save the user's location information as GPS data and photos taken by the user. Step 3: The analysis unit retrieves and analyzes the information from the recording unit. For example, data mining techniques can be used to identify user behavior patterns. Generative AI can also be used to analyze the information and analyze past behavioral data. Step 4: The presentation unit presents hints for recall based on the analysis results obtained by the analysis unit. For example, if the user asks, "What did you have for dinner last night?", the presentation unit will refer to location information, purchase history, recorded photos, etc. to provide hints such as, "You had pizza at a nearby restaurant for dinner last night."

[0058] (Example 2) A memory recall support system according to an embodiment of the present invention is a system for people with mild cognitive impairment (MCI). In this system, a generation AI periodically interviews a user and records their responses and actions. When a user forgets something and makes an inquiry, the system provides memory recall hints based on the user's activity records and related information (e.g., timeline, location information, purchase history, and recorded photos). For example, the generation AI asks the user about daily events, plans, shopping lists, etc., and records the user's actions and responses. Next, when a user forgets something and makes an inquiry, the system provides memory recall hints based on the recorded information. For example, if a user asks, "What did you have for dinner last night?", the system refers to location information, purchase history, recorded photos, and other information to provide a hint such as, "I had pizza at a nearby restaurant for dinner last night." This system enables people with mild cognitive impairment to reduce memory recall in their daily lives and lead more independent lives. It also reduces the burden on family members and caregivers. This memory recall support system enables people with mild cognitive impairment to reduce memory recall in their daily lives and lead more independent lives. It also reduces the burden on family and caregivers.

[0059] A memory recall support system according to an embodiment includes a hearing unit, a recording unit, an analysis unit, and a presentation unit. The hearing unit periodically hears from a user. For example, the hearing unit can ask about daily events, plans, shopping lists, etc. The hearing unit can also record the user's actions and responses using a generation AI. For example, the generation AI asks the user, "What are your plans for today?" and records the response. The recording unit saves the information recorded by the hearing unit. For example, the recording unit can save information such as a timeline, location information, purchase history, and recorded photos. For example, the recording unit saves the user's location information as GPS data. The recording unit can also save photos taken by the user. The analysis unit acquires and analyzes information from the recording unit. For example, the analysis unit can use data mining technology to identify the user's behavioral patterns. The analysis unit can also analyze information using the generation AI. For example, the generation AI analyzes past behavioral data to identify the user's behavioral patterns. The presentation unit presents a hint for recalling based on the analysis result obtained by the analysis unit. For example, when a user asks, "What did you have for dinner last night?", the presentation unit refers to location information, purchase history, recorded photos, etc., and provides a hint such as, "I had pizza at a nearby restaurant for dinner last night." In this way, the memory recall support system according to the embodiment can record the user's actions and answers and provide support for memory recall.

[0060] The hearing unit can periodically record the user's actions and responses. For example, the hearing unit can ask about daily events, plans, shopping lists, etc. For example, the hearing unit can also use a generation AI to record the user's actions and responses. For example, the generation AI can ask the user, "What are your plans for today?" and record the response. The hearing unit can also use an interview format or question format to periodically record the user's actions and responses. For example, the hearing unit can interview the user and record the content of the interview. The hearing unit can also ask the user questions and record the responses. In this way, the hearing unit can provide support for memory loss by periodically recording the user's actions and responses.

[0061] The recording unit can store information such as a timeline, location information, purchase history, and recorded photos. For example, the recording unit can store the user's location information as GPS data. For example, the recording unit records location information of places visited by the user. The recording unit can also store a history of products purchased by the user. For example, the recording unit stores online shopping history and receipt information. The recording unit can also store photos taken by the user. For example, the recording unit stores photos taken by the user as digital data. The recording unit can also store timeline information. For example, the recording unit records the user's actions and the order of events. In this way, the recording unit can store a variety of information, thereby improving the accuracy of providing memory hints.

[0062] The analysis unit can analyze the information obtained from the recording unit and identify the user's behavioral patterns. The analysis unit can identify the user's behavioral patterns, for example, using data mining technology. For example, the analysis unit can analyze the user's behavioral data and perform frequency analysis or time series analysis. The analysis unit can also analyze the information using a generation AI. For example, the generation AI analyzes past behavioral data to identify the user's behavioral patterns. Furthermore, the analysis unit can use statistical analysis technology to identify the user's behavioral patterns. For example, the analysis unit statistically analyzes the user's behavioral data to identify the behavioral patterns. In this way, the analysis unit can provide more appropriate recall hints by identifying the user's behavioral patterns.

[0063] The presentation unit can present the user with hints for recalling memories based on the analysis results obtained by the analysis unit. For example, when the user asks, "What did you have for dinner last night?", the presentation unit can provide a hint such as, "I had pizza at a nearby restaurant for dinner last night" by referring to location information, purchase history, recorded photos, etc. For example, the presentation unit can identify places the user visited based on the user's location information and provide information related to those places. The presentation unit can also provide information about products purchased by the user based on the user's purchase history. Furthermore, the presentation unit can provide hints to remind the user of their actions based on photos taken by the user. In this way, the presentation unit can support the user in dealing with forgetfulness by presenting hints for recalling memories based on the analysis results.

[0064] The hearing unit can estimate the user's emotions and adjust the timing of hearings based on the user's emotions. For example, if the user is feeling stressed, the hearing unit can reduce the frequency of hearings and conduct hearings at a time when the user is relaxed. For example, the hearing unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the hearing unit can increase the frequency of hearings and collect more detailed information. For example, the hearing unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the hearing unit can prioritize questions that can be answered in a short time. For example, the hearing unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the hearing unit to adjust the timing of hearings according to the user's emotions, enabling more appropriate information collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0065] The hearing unit can analyze the user's past hearing history and select appropriate questions. For example, the hearing unit can prioritize the selection of questions that the user found easy to answer in the past. For example, the hearing unit can analyze the user's past hearing history using a generation AI and select optimal questions. The hearing unit can also avoid questions that the user found difficult to answer in the past and select alternative questions. For example, the hearing unit can analyze the user's past answer patterns and select highly relevant questions. This enables the hearing unit to ask more effective questions by analyzing the user's past hearing history.

[0066] The hearing unit can customize questions asked during the hearing based on the user's current health condition and lifestyle. For example, if the user is feeling unwell, the hearing unit can prioritize questions about the user's health. For example, the hearing unit uses a generative AI to estimate the user's health condition and select appropriate questions. Furthermore, if the user is busy, the hearing unit can select questions that can be answered in a short time. For example, the hearing unit customizes appropriate questions based on the user's lifestyle. Furthermore, if the user is relaxed, the hearing unit can ask questions about detailed lifestyle information. For example, by customizing questions based on the user's health condition and lifestyle, the hearing unit can collect more appropriate information.

[0067] The hearing unit can estimate the user's emotions and determine the hearing priority based on the user's emotions. For example, if the user is feeling stressed, the hearing unit can postpone less important questions. For example, the hearing unit can estimate the user's emotions using a generation AI and determine the hearing priority. Furthermore, if the user is relaxed, the hearing unit can prioritize more important questions. For example, the hearing unit selects appropriate questions based on the user's emotional state. Furthermore, if the user is in a hurry, the hearing unit can prioritize more urgent questions. For example, the hearing unit can estimate the user's emotions and determine the hearing priority, enabling more effective information collection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0068] During the hearing, the hearing unit can prioritize relevant questions based on the user's geographical location information. For example, if the user is in a specific location, the hearing unit can ask questions related to that location. For example, the hearing unit obtains the user's geographical location information using a generation AI and selects appropriate questions. Furthermore, if the user is traveling, the hearing unit can ask questions related to the user's travel destination. For example, the hearing unit can provide information related to the user's travel destination based on the user's current location. Furthermore, if the user is at home, the hearing unit can ask questions related to the user's daily life. For example, the hearing unit can prioritize relevant questions taking the user's geographical location information into consideration, thereby enabling more appropriate information collection.

[0069] During the interview, the hearing unit can analyze the user's social media activity and ask relevant questions. For example, the hearing unit can ask questions based on the content the user has shared on social media. For example, the hearing unit can analyze the user's social media activity using generative AI and select appropriate questions. The hearing unit can also ask questions related to accounts the user follows on social media. For example, the hearing unit can provide relevant information based on the accounts the user follows. Furthermore, the hearing unit can ask questions related to events the user is participating in on social media. For example, the hearing unit can analyze the user's social media activity and ask relevant questions, thereby enabling more appropriate information collection.

[0070] The recording unit can estimate the user's emotions and adjust the level of detail of the information to be recorded based on the user's emotions. For example, if the user is relaxed, the recording unit can record detailed information. For example, the recording unit can estimate the user's emotions using a generation AI and adjust the level of detail of the information to be recorded. The recording unit can also record concise information if the user is feeling stressed. For example, the recording unit can select an appropriate level of detail of information based on the user's emotional state. Furthermore, if the user is in a hurry, the recording unit can record only the main points. For example, the recording unit can estimate the user's emotions and adjust the level of detail of the information to be recorded, enabling more appropriate information management. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The recording unit can determine the storage priority based on the importance of the information when recording. For example, the recording unit can prioritize storing information with high importance. For example, the recording unit can evaluate the importance of information using a generation AI and determine the storage priority. The recording unit can also postpone storing information with low importance. For example, the recording unit selects an appropriate storage order based on the urgency of the information and the user's level of interest. Furthermore, the recording unit can adjust the storage order of information according to its importance. For example, the recording unit can prioritize managing important information by evaluating the importance of information and determining the storage priority.

[0072] The recording unit can apply different storage algorithms depending on the category of information when recording. For example, the recording unit can store location information using a dedicated algorithm. For example, the recording unit can classify the category of information using a generative AI and apply an appropriate storage algorithm. The recording unit can also store purchase history using a different algorithm. For example, the recording unit selects a different storage method depending on the type of information. Furthermore, the recording unit can store recorded photos using an algorithm dedicated to images. For example, the recording unit can apply different storage algorithms depending on the category of information, enabling more efficient information management.

[0073] The recording unit can estimate the user's emotions and adjust the display method of the recorded information based on the user's emotions. For example, if the user is relaxed, the recording unit can display detailed information. For example, the recording unit can estimate the user's emotions using a generation AI and adjust the display method of the recorded information. The recording unit can also display concise information if the user is stressed. For example, the recording unit selects an appropriate display method based on the user's emotional state. Furthermore, if the user is in a hurry, the recording unit can display only the main points. For example, the recording unit can estimate the user's emotions and adjust the display method of the recorded information to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] When recording, the recording unit can determine the priority of storage based on the time when the information was submitted. For example, the recording unit can prioritize storing the most recent information. For example, the recording unit can use a generation AI to evaluate the time when the information was submitted and determine the priority of storage. The recording unit can also prioritize storing older information. For example, the recording unit selects an appropriate storage order based on the date and time when the information was submitted. Furthermore, the recording unit can adjust the storage order of information depending on the time of submission. For example, the recording unit can prioritize managing the most recent information by evaluating the time when the information was submitted and determining the priority of storage.

[0075] The recording unit can adjust the order of storage based on the relevance of the information when recording. For example, the recording unit can prioritize storing highly relevant information. For example, the recording unit can evaluate the relevance of the information using a generative AI and adjust the order of storage. The recording unit can also postpone storing less relevant information. For example, the recording unit selects an appropriate order of storage based on a common theme of the information or related events. Furthermore, the recording unit can adjust the order of storage of the information according to relevance. For example, the recording unit can prioritize managing highly relevant information by evaluating the relevance of the information and adjusting the order of storage.

[0076] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the user's emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. For example, the analysis unit can estimate the user's emotions using a generation AI and adjust the analysis criteria. The analysis unit can also perform a concise analysis if the user is stressed. For example, the analysis unit selects appropriate analysis criteria based on the user's emotional state. Furthermore, if the user is in a hurry, the analysis unit can analyze only the main points. For example, the analysis unit can estimate the user's emotions and adjust the analysis criteria to perform a more appropriate analysis. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] During analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationships between information. The analysis unit can, for example, analyze the interrelationships between location information and purchase history. For example, the analysis unit uses generative AI to evaluate the interrelationships between information and improve the accuracy of the analysis. The analysis unit can also analyze the interrelationships between timelines and recorded photos. For example, the analysis unit selects an appropriate analysis method based on the co-occurrence and causal relationships of information. Furthermore, the analysis unit can analyze the interrelationships between purchase history and recorded photos. For example, the analysis unit can evaluate the interrelationships between information and improve the accuracy of the analysis, enabling more accurate analysis.

[0078] During analysis, the analysis unit can perform analysis based on the attribute information of the information submitter. The analysis unit can perform analysis based on the user's age, for example. For example, the analysis unit evaluates the attribute information of the information submitter using a generation AI and selects an appropriate analysis method. The analysis unit can also perform analysis based on the user's gender. For example, the analysis unit selects appropriate analysis criteria based on the attribute information of the information submitter. Furthermore, the analysis unit can perform analysis based on the user's occupation. For example, the analysis unit can evaluate the attribute information of the information submitter and improve the accuracy of the analysis, thereby enabling more accurate analysis.

[0079] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the user's emotions. For example, if the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. For example, the analysis unit can estimate the user's emotions using a generation AI and adjust the display order of the analysis results. Furthermore, if the user is feeling stressed, the analysis unit can prioritize displaying concise analysis results. For example, the analysis unit selects an appropriate display order based on the user's emotional state. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying only the main points. For example, the analysis unit can estimate the user's emotions and adjust the display order of the analysis results, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] During analysis, the analysis unit can perform analysis based on the geographic distribution of information. The analysis unit can perform analysis based on location information, for example. For example, the analysis unit evaluates the geographic distribution of information using generative AI and selects an appropriate analysis method. The analysis unit can also perform analysis taking geographic clusters into consideration. For example, the analysis unit selects appropriate analysis criteria based on the geographic distribution of information. Furthermore, the analysis unit can perform analysis taking geographic trends into consideration. For example, the analysis unit can evaluate the geographic distribution of information and improve the accuracy of the analysis, enabling more accurate analysis.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis based on literature related to the information. The analysis unit can, for example, perform the analysis by referring to related academic papers. For example, the analysis unit evaluates literature related to the information using generative AI and selects an appropriate analysis method. The analysis unit can also perform the analysis by referring to related patent documents. For example, the analysis unit selects appropriate analysis criteria based on literature related to the information. Furthermore, the analysis unit can also perform the analysis by referring to related technical literature. For example, the analysis unit can evaluate literature related to the information and improve the accuracy of the analysis, thereby enabling more accurate analysis.

[0082] The presentation unit can estimate the user's emotions and adjust the way in which hints are presented based on the user's emotions. For example, if the user is relaxed, the presentation unit can provide detailed hints. For example, the presentation unit can estimate the user's emotions using a generation AI and adjust the way in which hints are presented. The presentation unit can also provide concise hints if the user is stressed. For example, the presentation unit selects an appropriate way of presentation based on the user's emotional state. Furthermore, if the user is in a hurry, the presentation unit can provide only the main points. For example, the presentation unit can estimate the user's emotions and adjust the way in which hints are presented, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The presentation unit can adjust the level of detail of the presentation based on the importance of the hint when presenting the hint. For example, the presentation unit can present highly important hints in detail. For example, the presentation unit can evaluate the importance of the hint using a generation AI and adjust the level of detail of the presentation. The presentation unit can also present less important hints in a concise manner. For example, the presentation unit selects an appropriate presentation method based on the urgency of the hint and the user's level of interest. Furthermore, the presentation unit can adjust the level of detail of the hint depending on the importance. For example, the presentation unit can evaluate the importance of the hint and adjust the level of detail of the presentation, thereby providing more appropriate information.

[0084] The presentation unit can apply different presentation algorithms depending on the category of the hint when presenting the hint. For example, the presentation unit can present hints related to location information using a map. For example, the presentation unit can classify the category of hints using a generation AI and apply an appropriate presentation algorithm. The presentation unit can also present hints related to purchase history in list format. For example, the presentation unit selects different presentation methods depending on the type of information. Furthermore, the presentation unit can present hints related to record photos using images. For example, the presentation unit can provide more appropriate information by applying different presentation algorithms depending on the category of hint.

[0085] The presentation unit can estimate the user's emotions and adjust the length of the hint to be presented based on the user's emotions. For example, if the user is relaxed, the presentation unit can provide a longer hint. For example, the presentation unit can estimate the user's emotions using a generation AI and adjust the length of the hint. Furthermore, the presentation unit can provide a shorter hint if the user is stressed. For example, the presentation unit selects an appropriate length of the hint based on the user's emotional state. Furthermore, if the user is in a hurry, the presentation unit can provide only the main points. For example, the presentation unit can estimate the user's emotions and adjust the length of the hint to provide more appropriate information. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The presentation unit can determine the presentation priority based on the time of submission of the hint when presenting the hint. For example, the presentation unit can prioritize the presentation of the most recent hint. For example, the presentation unit can evaluate the time of submission of the hint using a generation AI and determine the presentation priority. The presentation unit can also present older hints later. For example, the presentation unit selects an appropriate presentation order based on the submission date or submission time of the hint. Furthermore, the presentation unit can adjust the presentation order of the hints depending on the submission time. For example, the presentation unit can evaluate the time of submission of the hint and determine the presentation priority, thereby providing more appropriate information.

[0087] The presentation unit can adjust the presentation order based on the relevance of the hints when presenting them. For example, the presentation unit can prioritize the presentation of highly relevant hints. For example, the presentation unit can evaluate the relevance of the hints using a generation AI and adjust the presentation order. The presentation unit can also postpone the presentation of less relevant hints. For example, the presentation unit selects an appropriate presentation order based on a common theme or related events of the hints. Furthermore, the presentation unit can adjust the presentation order of the hints according to the relevance. For example, the presentation unit can evaluate the relevance of the hints and adjust the presentation order, thereby providing more appropriate information. === Hard Collateral 1-1 === Each of the multiple elements, including the hearing unit, recording unit, analysis unit, and presentation 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 hearing unit is realized by the microphone 38B and control unit 46A of the smart device 14 and periodically hears from the user. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and stores information recorded by the hearing unit. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires and analyzes information from the recording unit. The presentation unit is realized, for example, by the display 40A and speaker 40B of the smart device 14 and presents reminder hints based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned hearing unit, recording unit, analysis unit, and presentation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the hearing unit is realized by the microphone 238 and control unit 46A of the smart glasses 214 and periodically hears from the user. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and stores information recorded by the hearing unit. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires and analyzes information from the recording unit. The presentation unit is realized, for example, by the speaker 240 and display of the smart glasses 214 and presents a reminder hint based on the analysis result. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned hearing unit, recording unit, analysis unit, and presentation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the hearing unit is realized by the microphone 238 and control unit 46A of the headset type terminal 314 and periodically hears from the user. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and saves information recorded by the hearing unit. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires and analyzes information from the recording unit. The presentation unit is realized, for example, by the display 343 and speaker 240 of the headset type terminal 314 and presents reminder hints based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned hearing unit, recording unit, analysis unit, and presentation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the hearing unit is realized by the microphone 238 and control unit 46A of the robot 414 and periodically hears from the user. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and stores information recorded by the hearing unit. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires and analyzes information from the recording unit. The presentation unit is realized, for example, by the speaker 240 and display of the robot 414 and presents a reminder hint based on the analysis result.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can perform a brief analysis, and if the user is relaxed, the analysis unit can perform a detailed analysis. The analysis unit can also change the priority of the analysis based on the user's emotions. For example, if the user is in a hurry, the analysis unit can prioritize analysis of information with high urgency. Furthermore, the analysis unit can adjust the display method of the analysis results based on the user's emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results, and if the user is feeling stressed, the analysis unit can display brief analysis results. This allows the analysis unit to flexibly perform analysis according to the user's emotions.

[0090] The recording unit can estimate the user's emotions and adjust the level of detail of the information to be recorded based on the estimated emotions. For example, if the user is relaxed, detailed information can be recorded, and if the user is feeling stressed, brief information can be recorded. The recording unit can also change the frequency of recording according to the user's emotions. For example, if the user is relaxed, information can be recorded frequently, and if the user is feeling stressed, the recording frequency can be reduced. Furthermore, the recording unit can adjust the categories of information to be recorded based on the user's emotions. For example, if the user is relaxed, detailed categorization can be performed, and if the user is feeling stressed, brief categorization can be performed. This allows the recording unit to flexibly record information according to the user's emotions.

[0091] The presentation unit can estimate the user's emotions and adjust the presentation method of the hints to be presented based on the estimated emotions. For example, if the user is relaxed, detailed hints can be provided, and if the user is stressed, brief hints can be provided. The presentation unit can also change the priority of hints according to the user's emotions. For example, if the user is in a hurry, it can provide more urgent hints first. Furthermore, the presentation unit can adjust the presentation method of hints based on the user's emotions. For example, if the user is relaxed, detailed hints can be displayed, and if the user is stressed, brief hints can be displayed. This allows the presentation unit to flexibly provide hints according to the user's emotions.

[0092] The hearing unit can estimate the user's emotions and adjust the timing of hearings based on the estimated emotions. For example, if the user is feeling stressed, the frequency of hearings can be reduced and hearings can be conducted at a time when the user is relaxed. The hearing unit can also change the content of hearings based on the user's emotions. For example, if the user is relaxed, detailed questions can be asked, and if the user is feeling stressed, brief questions can be asked. Furthermore, the hearing unit can adjust the method of hearing based on the user's emotions. For example, if the user is relaxed, face-to-face hearings can be conducted, and if the user is feeling stressed, online hearings can be conducted. This allows the hearing unit to flexibly conduct hearings according to the user's emotions.

[0093] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is relaxed, a detailed analysis can be performed, and if the user is feeling stressed, a brief analysis can be performed. The analysis unit can also change the priority of the analysis based on the user's emotions. For example, if the user is in a hurry, it can prioritize analysis of information with high urgency. Furthermore, the analysis unit can adjust the display method of the analysis results based on the user's emotions. For example, if the user is relaxed, a detailed analysis result can be displayed, and if the user is feeling stressed, a brief analysis result can be displayed. This allows the analysis unit to flexibly perform analysis according to the user's emotions.

[0094] The hearing unit can analyze the user's past hearing history and select appropriate questions. For example, it can prioritize the selection of questions that the user found easy to answer in the past. The hearing unit can also avoid questions that the user found difficult to answer in the past and select alternative questions. Furthermore, the hearing unit can analyze the user's past answer patterns and select highly relevant questions. This allows the hearing unit to ask more effective questions by analyzing the user's past hearing history.

[0095] The recording unit can determine the priority of storage based on the importance of the information when recording. For example, it can prioritize storage of information with high importance. The recording unit can also postpone storage of information with low importance. Furthermore, the recording unit can adjust the order in which information is stored according to its importance. In this way, the recording unit can prioritize management of important information by evaluating the importance of information and determining the priority of storage.

[0096] During the analysis, the analysis unit can improve the accuracy of the analysis based on the correlation of information. For example, the analysis unit can analyze the correlation between location information and purchase history. The analysis unit can also analyze the correlation between a timeline and recorded photos. Furthermore, the analysis unit can analyze the correlation between purchase history and recorded photos. This allows the analysis unit to evaluate the correlation of information and improve the accuracy of the analysis, thereby enabling more accurate analysis.

[0097] The presentation unit can adjust the level of detail of the presentation based on the importance of the hint when presenting the hint. For example, a highly important hint can be presented in detail. The presentation unit can also present a less important hint in a concise manner. Furthermore, the presentation unit can adjust the level of detail of the hint according to the importance. In this way, the presentation unit can provide more appropriate information by evaluating the importance of the hint and adjusting the level of detail of the presentation.

[0098] During analysis, the analysis unit can perform the analysis based on the geographic distribution of the information. For example, the analysis can be performed based on location information. The analysis unit can also perform the analysis taking into account geographic clusters. Furthermore, the analysis unit can perform the analysis taking into account geographic trends. This allows the analysis unit to evaluate the geographic distribution of the information and improve the accuracy of the analysis, thereby enabling more accurate analysis.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The hearing section periodically interviews the user. For example, it can ask about daily events, plans, shopping lists, etc. It can also record the user's actions and responses using generation AI. Step 2: The recording unit saves the information recorded by the hearing unit. For example, it can save information such as a timeline, location information, purchase history, and recorded photos. It can also save the user's location information as GPS data and photos taken by the user. Step 3: The analysis unit retrieves and analyzes the information from the recording unit. For example, data mining techniques can be used to identify user behavior patterns. Generative AI can also be used to analyze the information and analyze past behavioral data. Step 4: The presentation unit presents hints for recall based on the analysis results obtained by the analysis unit. For example, if the user asks, "What did you have for dinner last night?", the presentation unit will refer to location information, purchase history, recorded photos, etc. to provide hints such as, "You had pizza at a nearby restaurant for dinner last night."

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a hearing department that periodically interviews users; a recording unit for storing information recorded by the hearing unit; an analysis unit that acquires and analyzes information from the recording unit; a presentation unit that presents a reminder hint based on the analysis result obtained by the analysis unit; Equipped with A system characterized by:

2. The hearing section Regularly record user actions and responses The system of claim 1 .

3. The recording unit Save information about your timeline, location, purchase history, and recorded photos The system of claim 1 .

4. The analysis unit Analyzing the information acquired from the recording unit and identifying the user's behavior pattern The system of claim 1 .

5. The presentation unit Based on the analysis result obtained by the analysis unit, a hint for recall is presented to the user. The system of claim 1 .

6. The hearing section Estimate the user's emotions and adjust the timing of hearings based on the user's emotions The system of claim 1 .

7. The hearing section Analyze the user's past interview history and select appropriate questions The system of claim 1 .

8. The hearing section During the interview, customize the questions based on the user's current health and lifestyle situation. The system of claim 1 .

9. The hearing section Estimate user emotions and prioritize hearings based on user emotions The system of claim 1 .

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