System

Abstract

An object of a system according to an embodiment is to effectively support the memory of a dementia patient.SOLUTION: A system includes an information collection part, an information analysis part, a memory generation part, and a memory presentation part. The information collection unit collects information from a smartphone, an AI speaker, or the like. The information analysis unit analyzes the information collected by the information collection unit. The memory generation unit generates a memory of the individual based on the information analyzed by the information analysis unit. The memory presentation unit presents the memory generated by the memory generation unit to the dementia patient.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to effectively collect, analyze, and present information to support the memory of dementia patients.

[0005] The system according to the embodiment aims to effectively support the memory of dementia patients. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an information analysis unit, a memory generation unit, and a memory presentation unit. The information collection unit collects information from a smartphone, an AI speaker, or the like. The information analysis unit analyzes the information collected by the information collection unit. The memory generation unit generates a personal memory based on the information analyzed by the information analysis unit. The memory presentation unit presents the memory generated by the memory generation unit to the dementia patient. [Effects of the Invention]

[0007] The system according to the embodiment can effectively support the memory of dementia patients. [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 support system according to an embodiment of the present invention is a system that generates and supports personal memories using information collected from smartphones, AI speakers, etc. This enables the memory support system to support the memories of dementia patients and improve the quality of their daily lives.

[0029] A memory support system according to an embodiment includes an information collection unit, an information analysis unit, a memory generation unit, and a memory presentation unit. The information collection unit collects information from a smartphone, an AI speaker, or the like. For example, the information includes photos stored on the smartphone, schedules registered in a calendar app, exchanges via messaging apps, and records of daily conversations with the AI ​​speaker. The information analysis unit analyzes the information collected by the information collection unit. For example, it analyzes photos to identify scenes and characters, and analyzes schedules to understand past events and plans. It analyzes the content of messages and daily conversations to understand important events and relationships. The memory generation unit generates personal memories based on the information analyzed by the information analysis unit. For example, the generation AI reconstructs memories based on this information. The memory presentation unit presents the memories generated by the memory generation unit to a dementia patient. For example, it reminds the patient of events and plans that they tend to forget in their daily lives. This allows the memory support system to support the memory of dementia patients and improve their quality of daily life.

[0030] The information collection unit can collect biometric information such as heart rate, step count, and sleep patterns from a wearable device in addition to a smartphone and an AI speaker. For example, the information collection unit collects biometric information such as heart rate, step count, and sleep patterns from a smartwatch, and reflects daily activities and health status in memories based on this data. For example, it may create a memory of how much walking you did on a particular day. The information collection unit also collects data from a fitness tracker and incorporates exercise habits and changes in physical condition into memories. For example, it may create a memory of regular jogging to remind you of the importance of exercise. The information collection unit also collects data such as stress level and body temperature from a wearable device, and reflects changes in emotions and physical condition in memories based on this information. For example, it may create a memory of events that occurred on a stressful day. This allows memories to be created based on biometric information.

[0031] The information collection unit can collect data from IoT devices in the home, such as smart refrigerators, smart lighting, and smart thermostats. The information collection unit, for example, collects data from a smart refrigerator and reflects food consumption status and purchase history in memories. For example, it may generate a memory of what was eaten on a specific day. The information collection unit also collects smart lighting usage history and incorporates daily lifestyle rhythms and activity patterns into memories. For example, it may generate a memory of the habit of turning off the lights at a specific time every night. The information collection unit also collects data from a smart thermostat and reflects changes in room temperature and energy consumption in memories. For example, it may generate a memory of heavy heating use on a specific day. In this way, memories can be generated based on data from IoT devices in the home.

[0032] The information collection unit can collect the user's social media posts and comments and analyze this data to generate memories. The information collection unit, for example, collects the user's social media posts and reflects daily events and emotional changes in the memories. For example, it generates memories based on photos and comments posted on a specific day. The information collection unit also analyzes social media comments and replies to incorporate the user's interactions and relationships into the memories. For example, it generates memories based on interactions with friends. The information collection unit also collects social media "like" and share history to reflect the user's interests and preferences in the memories. For example, it generates memories based on the days when a specific post received many "likes." This makes it possible to generate memories based on social media data.

[0033] The information collection unit synchronizes information in real time between devices such as smartphones, tablets, and AI speakers, allowing the collection of always-updated data. The information collection unit synchronizes information in real time between devices such as smartphones, tablets, and AI speakers, allowing the collection of always-updated data. For example, photos taken on a smartphone are instantly updated on other devices. The information collection unit also uses cloud services to synchronize data between different devices, maintaining consistency of information. For example, calendar app appointments are synchronized across all devices. The information collection unit also automates the synchronization of information between devices, eliminating the need for users to manually transfer data. For example, messaging app interactions are updated across all devices in real time. This allows for real-time synchronization of information between different devices.

[0034] The information analysis unit uses natural language processing technology to understand the context of everyday conversations in detail and generate memories based on the context. For example, when the generation AI creates a summary, the information analysis unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The information analysis unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The information analysis unit also references related background information and topic models when the generation AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0035] The information analysis unit has advanced image analysis technology, and is able to analyze the background and small items in a photograph in detail, and generate memories based on the analysis. For example, the information analysis unit uses image analysis technology to analyze the background and small items in a photograph in detail and reflect them in a memory. For example, it generates memories of scenery from a travel destination and belongings. The information analysis unit also identifies people and objects in a photograph and generates memories based on that information. For example, it generates memories of the names of everyone in a family photo. The information analysis unit also has advanced image analysis technology, and analyzes emotional elements in a photograph. For example, it generates emotional memories based on smiling and crying expressions. This allows the image analysis technology to be advanced, and it is able to analyze the background and small items in a photograph in detail.

[0036] The information analysis unit can recreate the generated memories as 3D models and VR content, allowing the user to visually experience the memories. For example, the information analysis unit recreates the generated memories as 3D models, allowing the user to visually experience them. For example, memories of a family trip are recreated as a 3D model. The information analysis unit also recreates the generated memories in a virtual space using VR technology, allowing the user to experience them immersively. For example, past events are recreated as VR content. The information analysis unit also builds a system that automatically generates 3D models and VR content, allowing the user to easily visually experience memories. For example, specific places or events are recreated as 3D models. This allows the generated memories to be recreated as 3D models and VR content.

[0037] The memory presentation unit can analyze the user's lifestyle rhythm and behavioral patterns and develop an algorithm that presents memories at the optimal timing. The memory presentation unit, for example, analyzes the user's lifestyle rhythm and develops an algorithm that presents memories at the optimal timing. For example, it may remind the user of memories in line with their morning routine. The memory presentation unit may also analyze behavioral patterns and present memories during the time of day when the user is most relaxed. For example, it may present memories during relaxation time after dinner. The memory presentation unit may also analyze the user's schedule and develop an algorithm that reminds the user of memories before an important appointment. For example, it may remind the user of past medical examination details before a doctor's appointment. This makes it possible to analyze the user's lifestyle rhythm and behavioral patterns and develop an algorithm that presents memories at the optimal timing.

[0038] The memory presentation unit can combine voice and music with the presented memory to evoke the memory in a sensory way. For example, the memory presentation unit adds an audio message related to the presented memory, allowing the user to auditorily evoke the memory. For example, a past event is explained using the voice of a family member. The memory presentation unit can also combine music related to the memory to evoke the memory in a sensory way. For example, a song related to a specific memory is played. The memory presentation unit can also use audio guidance to explain the details of the memory, allowing the user to re-experience the memory more deeply. For example, an episode from that trip can be explained aloud while looking at photos. In this way, combining voice and music with the presented memory can evoke the memory in a more sensory way.

[0039] The memory presentation unit can visually present memories using a smart mirror and a projector. The memory presentation unit visually presents memories using, for example, a smart mirror. For example, memories can be presented as reminders displayed on the mirror while getting ready in the morning. The memory presentation unit can also use a projector to project memories onto the walls or ceiling of a room. For example, old family photos can be displayed large in the living room. The memory presentation unit can also link the smart mirror and projector to build a system that allows users to visually check their memories. For example, memories can be automatically displayed at specific times. This makes it possible to visually present memories using a smart mirror or projector.

[0040] The memory presentation unit can collect the user's reaction to the presented memory using a camera and microphone, analyze it in real time, and reflect it in the next presentation. The memory presentation unit, for example, collects the user's reaction to the presented memory using a camera and microphone and analyzes it in real time. For example, it evaluates the reaction by analyzing the user's facial expression and tone of voice. The memory presentation unit also adjusts the next memory presentation method based on the user's reaction data. For example, it prioritizes presenting memories that have received many positive reactions. The memory presentation unit also analyzes the reaction data collected in real time and presents memories that suit the user's preferences. For example, if the reaction to a particular memory is good, it will present that memory frequently. This allows the user's reaction to the presented memory to be collected in real time and reflected in the next presentation.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The memory support system can also generate memories based on the user's hobbies and interests. For example, if the user's hobby is reading, the system generates memories of the titles of books they have read and their impressions of them. If the user's hobby is cooking, the system generates memories of recipes and photos of dishes they have made. If the user's hobby is traveling, the system generates memories of places they have visited and photos they have taken. This makes it possible to generate memories based on the user's hobbies and interests.

[0043] The memory support system can also monitor the user's health condition and generate health-related memories. For example, it can generate memories of the results of regular health checkups and doctor's examinations. It can also generate memories of the medications the user is taking and their medication schedule. It can also generate memories of health advice based on the user's diet and exercise records. This allows it to generate memories based on the user's health condition.

[0044] The memory support system can also generate memories to strengthen the user's social connections. For example, it can generate memories of specific events or happenings based on records of interactions with friends and family. It can also generate memories of community activities and volunteer activities in which the user participated. It can also generate memories of relationships with new people the user meets, strengthening social connections. This allows it to generate memories based on the user's social connections.

[0045] The memory support system can also generate memories based on the user's learning history. For example, it can generate memories of new knowledge and skills the user has learned. It can also generate memories of the contents of online courses and seminars the user has taken. It can also generate memories of summaries of academic papers and articles the user has read. This allows it to generate memories based on the user's learning history.

[0046] The memory support system can also analyze the user's daily rhythm and present memories at the optimal timing. For example, it can remind users to remember things based on their morning routine. It can also present memories at the time of day when the user is most relaxed. It can also remind users of past related memories before important appointments. This allows it to present memories at the optimal timing based on the user's daily rhythm.

[0047] The memory support system can also generate memories based on the user's past travel history. For example, it can generate memories of places visited and photos taken. It can also generate memories of events experienced and impressions felt during the trip. It can also generate memories of interactions with people encountered at travel destinations. In this way, it can generate memories based on the user's past travel history.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The information collection unit collects information from the smartphone, the AI ​​speaker, etc. This includes, for example, photos stored on the smartphone, schedules registered in the calendar app, exchanges via messaging apps, and records of daily conversations with the AI ​​speaker. Step 2: The information analysis unit analyzes the information collected by the information collection unit. For example, it analyzes photos to identify scenes and people, analyzes schedules to understand past events and plans, and analyzes the content of messages and everyday conversations to understand important events and relationships. Step 3: The memory generation unit generates personal memories based on the information analyzed by the information analysis unit. For example, the generation AI reconstructs memories based on this information. Step 4: The memory presentation unit presents the memories generated by the memory generation unit to the dementia patient, for example, reminding them of events or schedules that they tend to forget in their daily lives.

[0050] (Example 2) A memory support system according to an embodiment of the present invention is a system that generates and supports personal memories using information collected from smartphones, AI speakers, etc. This enables the memory support system to support the memories of dementia patients and improve the quality of their daily lives.

[0051] A memory support system according to an embodiment includes an information collection unit, an information analysis unit, a memory generation unit, and a memory presentation unit. The information collection unit collects information from a smartphone, an AI speaker, or the like. For example, the information includes photos stored on the smartphone, schedules registered in a calendar app, exchanges via messaging apps, and records of daily conversations with the AI ​​speaker. The information analysis unit analyzes the information collected by the information collection unit. For example, it analyzes photos to identify scenes and characters, and analyzes schedules to understand past events and plans. It analyzes the content of messages and daily conversations to understand important events and relationships. The memory generation unit generates personal memories based on the information analyzed by the information analysis unit. For example, the generation AI reconstructs memories based on this information. The memory presentation unit presents the memories generated by the memory generation unit to a dementia patient. For example, it reminds the patient of events and plans that they tend to forget in their daily lives. This allows the memory support system to support the memory of dementia patients and improve their quality of daily life.

[0052] The information collection unit can collect biometric information such as heart rate, step count, and sleep patterns from a wearable device in addition to a smartphone and an AI speaker. For example, the information collection unit collects biometric information such as heart rate, step count, and sleep patterns from a smartwatch, and reflects daily activities and health status in memories based on this data. For example, it may create a memory of how much walking you did on a particular day. The information collection unit also collects data from a fitness tracker and incorporates exercise habits and changes in physical condition into memories. For example, it may create a memory of regular jogging to remind you of the importance of exercise. The information collection unit also collects data such as stress level and body temperature from a wearable device, and reflects changes in emotions and physical condition in memories based on this information. For example, it may create a memory of events that occurred on a stressful day. This allows memories to be created based on biometric information.

[0053] The information collection unit can collect data from IoT devices in the home, such as smart refrigerators, smart lighting, and smart thermostats. The information collection unit, for example, collects data from a smart refrigerator and reflects food consumption status and purchase history in memories. For example, it may generate a memory of what was eaten on a specific day. The information collection unit also collects smart lighting usage history and incorporates daily lifestyle rhythms and activity patterns into memories. For example, it may generate a memory of the habit of turning off the lights at a specific time every night. The information collection unit also collects data from a smart thermostat and reflects changes in room temperature and energy consumption in memories. For example, it may generate a memory of heavy heating use on a specific day. In this way, memories can be generated based on data from IoT devices in the home.

[0054] The information collection unit can analyze the user's facial expression and tone of voice using the smartphone's camera and microphone and estimate the emotional state based on the emotion estimation function. The information collection unit, for example, uses the smartphone's camera and microphone to analyze the user's facial expression and tone of voice to estimate the emotional state. For example, a day with many smiling photos is generated as a positive memory. The information collection unit also analyzes the content of everyday conversations and performs emotion analysis. For example, conversations with many positive words are generated as memories to reflect the user's emotional state. The information collection unit also analyzes exchanges on a messaging app and estimates the intensity and type of emotion. For example, a day with many thank you messages is generated as a positive memory. This makes it possible to estimate the emotional state using the emotion estimation function.

[0055] The information collection unit can collect the user's social media posts and comments and analyze this data to generate memories. The information collection unit, for example, collects the user's social media posts and reflects daily events and emotional changes in the memories. For example, it generates memories based on photos and comments posted on a specific day. The information collection unit also analyzes social media comments and replies to incorporate the user's interactions and relationships into the memories. For example, it generates memories based on interactions with friends. The information collection unit also collects social media "like" and share history to reflect the user's interests and preferences in the memories. For example, it generates memories based on the days when a specific post received many "likes." This makes it possible to generate memories based on social media data.

[0056] The information collection unit synchronizes information in real time between devices such as smartphones, tablets, and AI speakers, allowing the collection of always-updated data. The information collection unit synchronizes information in real time between devices such as smartphones, tablets, and AI speakers, allowing the collection of always-updated data. For example, photos taken on a smartphone are instantly updated on other devices. The information collection unit also uses cloud services to synchronize data between different devices, maintaining consistency of information. For example, calendar app appointments are synchronized across all devices. The information collection unit also automates the synchronization of information between devices, eliminating the need for users to manually transfer data. For example, messaging app interactions are updated across all devices in real time. This allows for real-time synchronization of information between different devices.

[0057] The information collection unit can use a camera equipped with an emotion estimation function to analyze the user's facial expressions and movements and estimate emotions. The information collection unit, for example, uses a camera equipped with an emotion estimation function to analyze the user's facial expressions and estimate an emotional state. For example, a day with many smiling photos is generated as a positive memory. The information collection unit also analyzes the user's movements and estimates changes in emotions. For example, a day when the user is active is generated as a positive memory. The information collection unit also analyzes video captured by the camera and generates memories based on emotion estimation data. For example, a video of the user having a good time with family is generated as a positive memory. This makes it possible to estimate emotions using the emotion estimation function.

[0058] The information analysis unit uses natural language processing technology to understand the context of everyday conversations in detail and generate memories based on the context. For example, when the generation AI creates a summary, the information analysis unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The information analysis unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The information analysis unit also references related background information and topic models when the generation AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0059] The information analysis unit has advanced image analysis technology, and is able to analyze the background and small items in a photograph in detail, and generate memories based on the analysis. For example, the information analysis unit uses image analysis technology to analyze the background and small items in a photograph in detail and reflect them in a memory. For example, it generates memories of scenery from a travel destination and belongings. The information analysis unit also identifies people and objects in a photograph and generates memories based on that information. For example, it generates memories of the names of everyone in a family photo. The information analysis unit also has advanced image analysis technology, and analyzes emotional elements in a photograph. For example, it generates emotional memories based on smiling and crying expressions. This allows the image analysis technology to be advanced, and it is able to analyze the background and small items in a photograph in detail.

[0060] The information analysis unit can use the emotion estimation function to estimate the user's emotions based on the analyzed information and generate memories based on the emotions. The information analysis unit, for example, analyzes collected information and identifies the user's emotional state using the emotion estimation function. For example, it generates a memory of a day when the user had strong positive emotions. The information analysis unit also generates a memory based on the user's emotions based on the emotion estimation data. For example, it generates a memory of the joy or sadness felt on a particular day. The information analysis unit also generates a memory using the emotion estimation function based on the analyzed information. For example, it generates a memory of a day when the user experienced a large change in emotions. This makes it possible to estimate the user's emotions based on the information analyzed using the emotion estimation function.

[0061] The information analysis unit can recreate the generated memories as 3D models and VR content, allowing the user to visually experience the memories. For example, the information analysis unit recreates the generated memories as 3D models, allowing the user to visually experience them. For example, memories of a family trip are recreated as a 3D model. The information analysis unit also recreates the generated memories in a virtual space using VR technology, allowing the user to experience them immersively. For example, past events are recreated as VR content. The information analysis unit also builds a system that automatically generates 3D models and VR content, allowing the user to easily visually experience memories. For example, specific places or events are recreated as 3D models. This allows the generated memories to be recreated as 3D models and VR content.

[0062] The memory presentation unit can analyze the user's lifestyle rhythm and behavioral patterns and develop an algorithm that presents memories at the optimal timing. The memory presentation unit, for example, analyzes the user's lifestyle rhythm and develops an algorithm that presents memories at the optimal timing. For example, it may remind the user of memories in line with their morning routine. The memory presentation unit may also analyze behavioral patterns and present memories during the time of day when the user is most relaxed. For example, it may present memories during relaxation time after dinner. The memory presentation unit may also analyze the user's schedule and develop an algorithm that reminds the user of memories before an important appointment. For example, it may remind the user of past medical examination details before a doctor's appointment. This makes it possible to analyze the user's lifestyle rhythm and behavioral patterns and develop an algorithm that presents memories at the optimal timing.

[0063] The memory presentation unit can combine voice and music with the presented memory to evoke the memory in a sensory way. For example, the memory presentation unit adds an audio message related to the presented memory, allowing the user to auditorily evoke the memory. For example, a past event is explained using the voice of a family member. The memory presentation unit can also combine music related to the memory to evoke the memory in a sensory way. For example, a song related to a specific memory is played. The memory presentation unit can also use audio guidance to explain the details of the memory, allowing the user to re-experience the memory more deeply. For example, an episode from that trip can be explained aloud while looking at photos. In this way, combining voice and music with the presented memory can evoke the memory in a more sensory way.

[0064] The memory presentation unit can use the emotion estimation function to analyze the user's emotional state in real time and customize the memory presentation method according to the emotional state. For example, the memory presentation unit can use the emotion estimation function to analyze the user's emotional state in real time and present memories when the user has strong positive emotions. For example, happy memories are presented when the user is relaxed. The memory presentation unit also customizes the memory presentation method according to the user's emotional state. For example, memories that give a sense of security are presented when the user is feeling anxious. The memory presentation unit also selects a memory presentation method that matches the user's emotion based on the emotion estimation data. For example, memories that include encouraging messages are presented when the user is sad. In this way, the emotion estimation function can be used to customize the memory presentation method according to the user's emotional state.

[0065] The memory presentation unit can visually present memories using a smart mirror and a projector. The memory presentation unit visually presents memories using, for example, a smart mirror. For example, memories can be presented as reminders displayed on the mirror while getting ready in the morning. The memory presentation unit can also use a projector to project memories onto the walls or ceiling of a room. For example, old family photos can be displayed large in the living room. The memory presentation unit can also link the smart mirror and projector to build a system that allows users to visually check their memories. For example, memories can be automatically displayed at specific times. This makes it possible to visually present memories using a smart mirror or projector.

[0066] The memory presentation unit can collect the user's reaction to the presented memory using a camera and microphone, analyze it in real time, and reflect it in the next presentation. The memory presentation unit, for example, collects the user's reaction to the presented memory using a camera and microphone and analyzes it in real time. For example, it evaluates the reaction by analyzing the user's facial expression and tone of voice. The memory presentation unit also adjusts the next memory presentation method based on the user's reaction data. For example, it prioritizes presenting memories that have received many positive reactions. The memory presentation unit also analyzes the reaction data collected in real time and presents memories that suit the user's preferences. For example, if the reaction to a particular memory is good, it will present that memory frequently. This allows the user's reaction to the presented memory to be collected in real time and reflected in the next presentation.

[0067] The memory presentation unit can use the emotion estimation function to analyze the user's emotional state in real time and customize the memory presentation method according to the emotional state. For example, the memory presentation unit can use the emotion estimation function to analyze the user's emotional state in real time and present memories when the user has strong positive emotions. For example, happy memories are presented when the user is relaxed. The memory presentation unit also customizes the memory presentation method according to the user's emotional state. For example, memories that give a sense of security are presented when the user is feeling anxious. The memory presentation unit also selects a memory presentation method that matches the user's emotion based on the emotion estimation data. For example, memories that include encouraging messages are presented when the user is sad. In this way, the emotion estimation function can be used to customize the memory presentation method according to the user's emotional state.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The memory support system can also generate memories based on the user's hobbies and interests. For example, if the user's hobby is reading, the system generates memories of the titles of books they have read and their impressions of them. If the user's hobby is cooking, the system generates memories of recipes and photos of dishes they have made. If the user's hobby is traveling, the system generates memories of places they have visited and photos they have taken. This makes it possible to generate memories based on the user's hobbies and interests.

[0070] The memory support system can also monitor the user's health condition and generate health-related memories. For example, it can generate memories of the results of regular health checkups and doctor's examinations. It can also generate memories of the medications the user is taking and their medication schedule. It can also generate memories of health advice based on the user's diet and exercise records. This allows it to generate memories based on the user's health condition.

[0071] The memory support system can also generate memories to strengthen the user's social connections. For example, it can generate memories of specific events or happenings based on records of interactions with friends and family. It can also generate memories of community activities and volunteer activities in which the user participated. It can also generate memories of relationships with new people the user meets, strengthening social connections. This allows it to generate memories based on the user's social connections.

[0072] The memory support system can also estimate the user's emotional state and generate memories based on the emotions. For example, it can generate memories of the joy or sadness the user felt on a particular day. It can also generate memories of events that occurred on a day when the user felt stressed and identify the cause of the stress. It can also generate memories of events that occurred when the user was relaxed and identify the factors that caused the relaxation. In this way, it can generate memories based on the user's emotional state.

[0073] The memory support system can also generate memories based on the user's learning history. For example, it can generate memories of new knowledge and skills the user has learned. It can also generate memories of the contents of online courses and seminars the user has taken. It can also generate memories of summaries of academic papers and articles the user has read. This allows it to generate memories based on the user's learning history.

[0074] The memory support system can also estimate the user's emotional state and customize the way memories are presented based on the user's emotions. For example, when the user is relaxed, it can present happy memories. When the user is feeling anxious, it can present memories that give a sense of security. When the user is sad, it can present memories that include encouraging messages. This allows the way memories are presented to be customized according to the user's emotional state.

[0075] The memory support system can also analyze the user's daily rhythm and present memories at the optimal timing. For example, it can remind users to remember things based on their morning routine. It can also present memories at the time of day when the user is most relaxed. It can also remind users of past related memories before important appointments. This allows it to present memories at the optimal timing based on the user's daily rhythm.

[0076] The memory support system can also estimate the user's emotional state and generate memories based on the emotions. For example, it can generate memories of the joy or sadness the user felt on a particular day. It can also generate memories of events that occurred on a day when the user felt stressed and identify the cause of the stress. It can also generate memories of events that occurred when the user was relaxed and identify the factors that caused the relaxation. In this way, it can generate memories based on the user's emotional state.

[0077] The memory support system can also generate memories based on the user's past travel history. For example, it can generate memories of places visited and photos taken. It can also generate memories of events experienced and impressions felt during the trip. It can also generate memories of interactions with people encountered at travel destinations. In this way, it can generate memories based on the user's past travel history.

[0078] The memory support system can also estimate the user's emotional state and generate memories based on the emotions. For example, it can generate memories of the joy or sadness the user felt on a particular day. It can also generate memories of events that occurred on a day when the user felt stressed and identify the cause of the stress. It can also generate memories of events that occurred when the user was relaxed and identify the factors that caused the relaxation. In this way, it can generate memories based on the user's emotional state.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The information collection unit collects information from the smartphone, the AI ​​speaker, etc. This includes, for example, photos stored on the smartphone, schedules registered in the calendar app, exchanges via messaging apps, and records of daily conversations with the AI ​​speaker. Step 2: The information analysis unit analyzes the information collected by the information collection unit. For example, it analyzes photos to identify scenes and people, analyzes schedules to understand past events and plans, and analyzes the content of messages and everyday conversations to understand important events and relationships. Step 3: The memory generation unit generates personal memories based on the information analyzed by the information analysis unit. For example, the generation AI reconstructs memories based on this information. Step 4: The memory presentation unit presents the memories generated by the memory generation unit to the dementia patient, for example, reminding them of events or schedules that they tend to forget in their daily lives.

[0081] 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.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0110] 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.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] In the robot 414, 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 robot 414 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.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An information gathering department that collects information from smartphones, AI speakers, etc. an information analysis unit that analyzes the information collected by the information collection unit; a memory generation unit that generates personal memories based on the information analyzed by the information analysis unit; a memory presentation unit that presents the memory generated by the memory generation unit to the dementia patient. A system characterized by:

2. The information collecting unit In addition to the smartphone and AI speaker, biometric information such as heart rate, steps, and sleep patterns will be collected from a wearable device.

2. The system of claim 1.

3. The information collecting unit Collect data from IoT devices in the home, such as smart refrigerators, smart lighting, and smart thermostats 2. The system of claim 1.

4. The information collecting unit Using the smartphone's camera and microphone, the system analyzes the user's facial expressions and tone of voice, and estimates their emotional state based on the emotion estimation function.

2. The system of claim 1.

5. The information collecting unit Collect users' social media posts and comments and analyze this data to generate memories 2. The system of claim 1.

6. The information collecting unit The information is synchronized in real time between smartphones, tablets, and AI speakers, and the latest data is always collected.

2. The system of claim 1.

7. The information collecting unit Using a camera equipped with emotion estimation functionality, the system analyzes the user's facial expressions and movements to estimate their emotions.

2. The system of claim 1.

8. The information analysis unit Using natural language processing technology, the system understands the context of everyday conversations in detail and generates memories based on that context.

2. The system of claim 1.

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