system

Abstract

The system according to this embodiment aims to activate the brain function of elderly people and stimulate their motivation for rehabilitation. [Solution] The system according to the embodiment comprises a recording unit, a recognition unit, a generation unit, and a usage unit. The recording unit records daily events and the content of conversations. The recognition unit recognizes old photographs. The generation unit generates a brain training plan based on the data accumulated by the recording unit and the recognition unit. The usage unit uses the brain training plan generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to stimulate the motivation for rehabilitation and activation of brain functions in the elderly, and there is room for improvement.

[0005] The system according to the embodiment aims to activate the brain functions of the elderly and stimulate the motivation for rehabilitation.

Means for Solving the Problems

[0006] The system according to the embodiment includes a recording unit, a recognition unit, a generation unit, and a usage unit. The recording unit records daily events and the content of conversations. The recognition unit recognizes old photos. The generation unit generates a brain training plan based on the data accumulated by the recording unit and the recognition unit. The usage unit uses the brain training plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can activate the brain function of elderly people and stimulate their motivation for rehabilitation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The brain training plan generation system according to an embodiment of the present invention is a system for elderly people who are experiencing a decline in motor function or who are not motivated to engage in rehabilitation. This system generates a personalized brain training plan for the individual by recording daily events and conversations and recognizing old photographs. Specifically, a third party accumulates daily events and conversations with the individual in a diary format to create records of the recent past. In addition, records of the distant past are created by having the system recognize many old photographs, and this data is accumulated as training data. Next, the generating AI generates a personalized brain training plan for the individual based on this training data. This brain training plan includes content that the individual will want to talk about when using day services or in conversations with family, and has the effect of making their life more motivating. For example, a third party accumulates daily events and conversations with the individual in a diary format. At this time, the content of daily events and conversations is recorded in detail and saved as records of the recent past. For example, by recording the content of today's events and conversations in a diary, records of the recent past are created. Next, records of the distant past are created by having the system recognize many old photographs. For example, by having the system recognize old family photos or travel photos, records of the distant past are created. This makes it possible to recall the individual's past memories. These records from the recent and distant past are accumulated as training data. The generating AI then creates a personalized brain training plan based on this training data. For example, the generating AI analyzes the contents of diaries and photographs to create a brain training plan that includes topics and questions that the individual is likely to be interested in. The generated brain training plan is used during day care services and in conversations with family members. For example, by using the brain training plan as a basis for conversation, caregivers at day care services can provide content that the individual will want to talk about. Similarly, by using the brain training plan as a basis for conversations with family members, the individual can speak more enthusiastically. This system allows elderly individuals with declining motor function or those who are reluctant to participate in rehabilitation to activate their brains with nostalgic and enjoyable memories, stimulating brain function. This is expected to prevent delays in rehabilitation and reduce the likelihood of developing dementia.This means that the brain training plan generation system can stimulate brain function and improve motivation by generating and using brain training plans for elderly people who have declining motor function or are unwilling to participate in rehabilitation.

[0029] The brain training plan generation system according to this embodiment comprises a recording unit, a recognition unit, a generation unit, and a usage unit. The recording unit records the contents of daily events and conversations. For example, the recording unit can record daily life events or conversations on specific topics. The recording unit can record in diary format. For example, the recording unit can record a diary in text format. The recording unit can also record a diary in audio format. Furthermore, the recording unit can record a diary with photos. For example, the recording unit can record daily events along with photos. The recognition unit recognizes old photographs. For example, the recognition unit can recognize family photos, travel photos, photos of specific events, etc. The recognition unit creates records of the distant past. For example, the recognition unit can record events by decade or details of specific events. The generation unit generates a brain training plan based on the data accumulated by the recording unit and the recognition unit using a generation AI. For example, the generation unit can analyze the contents of a diary or photographs and generate a brain training plan original to the user. The generation unit can generate individually customized brain training plans using a generation AI. The user unit utilizes the generated brain training plan. The user unit can, for example, provide content that the individual will want to talk about during day care services or conversations with family members. The user unit can advance the conversation based on the generated brain training plan. For example, the user unit can advance the conversation using methods such as question-and-answer formats, dialogue formats, or storytelling. Thus, the brain training plan generation system according to this embodiment can stimulate brain function and improve motivation in elderly individuals with declining motor function or those who are unwilling to participate in rehabilitation by generating and using brain training plans.

[0030] The recording function records daily events and conversations. For example, it can record everyday life events or conversations on specific topics. The recording function can be used in a diary format. For example, it can record a diary in text format. It can also record a diary in audio format. Furthermore, it can record a diary with photos. For example, it can record daily events along with photos. The recording function is equipped with a function that converts voice input to text using speech recognition technology, making it easy for users to record daily events. This makes it easy for users to keep a diary. In addition, the recording function can be linked with mobile devices such as smartphones and tablets, making it easy to record even when on the go. Furthermore, the recording function uses cloud storage to securely store data and make it accessible from multiple devices. This allows users to check and edit their records anytime, anywhere. To protect user privacy, the recording function encrypts data and controls access to prevent unauthorized access by third parties. In addition, the recording function allows users to customize the format and content of their records according to their preferences. For example, features such as tagging specific topics and creating photo albums make it easier for users to organize their records. This allows the recording system to efficiently and securely record the user's daily events and conversations, making them easily accessible later.

[0031] The recognition unit recognizes old photographs. For example, it can recognize family photos, travel photos, and photos of specific events. The recognition unit creates records of the distant past. For example, it can record events by year or details of specific events. Using image recognition technology, the recognition unit identifies people, places, and objects in photographs and automatically tags them with relevant information. This allows users to easily organize and search their photos. Furthermore, the recognition unit can use AI to analyze the content of photos and automatically generate episodes and memories related to the photos. For example, it can analyze family photos and automatically extract information such as the names and relationships of the people in the photo, as well as the location and date of the photo, and provide this information to the user. The recognition unit can also collect information about past events from the internet and provide detailed information related to the photos. This allows users to look back on past events and share memories while viewing photos. To protect user privacy, the recognition unit encrypts the content of photos and tagging information to prevent unauthorized access by third parties. The recognition unit can also adjust the accuracy of photo recognition and the level of detail in tagging according to the user's preferences. This allows the recognition unit to efficiently and accurately recognize the user's past photos and provide related information, thereby enriching memories.

[0032] The generation unit uses generation AI to generate brain training plans based on data accumulated by the recording and recognition units. For example, the generation unit can analyze the contents of a user's diary or photos to generate a personalized brain training plan. The generation unit can use generation AI to generate individually customized brain training plans. The generation AI uses natural language processing technology to analyze the contents of a user's diary and create a brain training plan based on the user's interests. For example, if a user keeps many diary entries about travel, the generation unit will generate a brain training plan that includes travel-related quizzes and puzzles. The generation AI also uses image recognition technology to analyze the contents of photos and create a brain training plan based on episodes and memories related to the photos. For example, it can analyze family photos and generate a brain training plan that includes quizzes and stories about the family. The generation unit can adjust the difficulty level and content of the brain training plan according to the user's age and cognitive function. This allows the user to continue brain training without difficulty. Furthermore, the generation unit can continuously improve the brain training plan based on user feedback to provide more effective plans. For example, if a user gives a high rating to a particular brain training plan, the generation unit will prioritize generating plans with similar content. Furthermore, the generation unit monitors the user's progress and provides a new brain training plan at the appropriate time. This allows the generation unit to consistently provide the user with the optimal brain training plan, supporting the maintenance and improvement of brain function.

[0033] The user unit utilizes the generated brain training plan. For example, it can provide content that the user will want to talk about during day care sessions or conversations with family. The user unit can conduct conversations based on the generated brain training plan. For example, it can conduct conversations using question-and-answer formats, dialogue formats, or storytelling. The user unit has a function to provide interactive feedback when the user is working through the brain training plan. For example, when a user answers a quiz, it provides immediate feedback on whether the answer is correct or incorrect and encourages them to move on to the next question. The user unit also monitors the user's progress in real time and provides encouraging messages and advice at appropriate times. This allows the user to maintain motivation and continue brain training. Furthermore, the user unit can collaborate with the user's family and caregivers to share the progress and effectiveness of the brain training plan. This allows family members and caregivers to understand the user's progress and provide appropriate support. The user unit can also customize the content and format of the brain training plan according to the user's preferences. For example, if a user is interested in a particular topic, it will prioritize providing brain training plans related to that topic. This allows the user to consistently provide interesting and effective brain training plans, supporting the maintenance and improvement of brain function.

[0034] The recording unit can record daily events and conversations in diary format. For example, the recording unit can record daily life events or conversations on specific topics in detail. The recording unit can record diaries in text format. For example, the recording unit can record today's events and conversations in text format. The recording unit can also record diaries in audio format. For example, the recording unit can record conversations in audio format. Furthermore, the recording unit can record diaries with photos. For example, the recording unit can record daily events along with photos. This improves the accuracy of the brain training plan by recording daily events and conversations in detail. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input daily events and conversations into AI, and the AI ​​can automatically record them in diary format.

[0035] The recognition unit can recognize old photographs and create records of the distant past. The recognition unit can recognize, for example, family photographs, travel photographs, and photographs of specific events. The recognition unit can record events by era and details of specific events. For example, the recognition unit can recognize old family photographs and record family history. It can also recognize travel photographs and record travel memories. Furthermore, the recognition unit can recognize photographs of specific events and record details of those events. This allows for the creation of records of the distant past by recognizing old photographs, which can then be incorporated into brain training plans. Some or all of the above processing in the recognition unit may be performed using AI, or it may be performed without AI. For example, the recognition unit can input old photographs into an AI, which can automatically recognize the photographs and create records of the distant past.

[0036] The generation unit can use a generation AI to analyze the contents of a diary and photographs and generate a personalized brain training plan for the individual. For example, the generation unit can analyze the contents of a diary and generate a brain training plan that includes topics and questions that the individual might be interested in. The generation unit can also analyze the contents of photographs and generate a brain training plan that will evoke the individual's memories. The generation unit can use the generation AI to generate individually customized brain training plans. For example, the generation unit can input the contents of a diary into the generation AI, which will automatically analyze and generate a brain training plan. Alternatively, the generation unit can input the contents of a photograph into the generation AI, which will automatically analyze and generate a brain training plan. This allows for the generation of individually customized brain training plans using the generation AI. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the generation unit can input the contents of a diary and photographs into the generation AI, which will automatically analyze and generate a brain training plan.

[0037] The user unit can conduct conversations based on the generated brain training plan. The user unit can provide content that the user will want to talk about, for example, during day care services or conversations with family members. The user unit can conduct conversations based on the generated brain training plan using methods such as questioning, dialogue, and storytelling. For example, the user unit can ask questions to the user based on the brain training plan. The user unit can also engage in dialogue with the user based on the brain training plan. Furthermore, the user unit can engage in storytelling based on the brain training plan. In this way, by conducting conversations based on the generated brain training plan, it is possible to provide content that the user will want to talk about. Some or all of the above processing in the user unit may be performed using AI or not. For example, the user unit can input the generated brain training plan into AI, and the AI ​​can automatically conduct the conversation.

[0038] The user unit can provide content that encourages the individual to talk during day service sessions or conversations with family members. For example, the user unit can provide content that encourages the individual to talk by having a caregiver at the day service center guide the conversation based on the brain training plan. The user unit can also encourage the individual to speak more actively during conversations with family members by guiding the conversation based on the brain training plan. The user unit can ask the individual questions based on the brain training plan. For example, the user unit can provide topics that are likely to interest the individual based on the brain training plan. The user unit can also engage in dialogue with the individual based on the brain training plan. For example, the user unit can engage in storytelling with the individual based on the brain training plan. This allows the individual to speak more actively during day service sessions or conversations with family members. Some or all of the above processing in the user unit may be performed using AI or not. For example, the user unit can input the generated brain training plan into AI, which can then automatically guide the conversation.

[0039] The recording unit can select the optimal recording method by referring to the user's past conversation history during recording. For example, the recording unit may prioritize recording topics that the user has enjoyed discussing in the past. It can also exclude topics that the user has avoided in the past from the recording. Furthermore, the recording unit can analyze the user's past conversation patterns and select the optimal recording format. This allows the optimal recording method to be selected by referring to the user's past conversation history. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's past conversation history into the AI, which can then automatically select the optimal recording method.

[0040] The recording unit can filter recordings based on the user's current lifestyle and areas of interest. For example, the recording unit can prioritize recording topics that the user is currently interested in. The recording unit can also record content that is highly relevant to the user's lifestyle. Furthermore, the recording unit can select recording content based on the user's areas of interest. This allows for recordings that are highly relevant by selecting recording content based on the user's current lifestyle and areas of interest. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's current lifestyle and areas of interest into the AI, which can then automatically perform the filtering.

[0041] The recording unit can prioritize recording highly relevant content by considering the user's geographical location information during recording. For example, if the user is in a specific location, the recording unit can record events and conversations related to that location. The recording unit can also prioritize recording highly relevant content based on the user's geographical location information. Furthermore, if the user is on the move, the recording unit can record content related to their destination. This allows the recording unit to prioritize recording highly relevant content based on the user's geographical location information. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's geographical location information into the AI, which can then automatically prioritize recording highly relevant content.

[0042] The recording unit can analyze the user's social media activity and record relevant content during recording. For example, the recording unit can record content that the user has shared on social media. The recording unit can also record highly relevant topics from the user's social media activity. Furthermore, the recording unit can analyze the user's interests on social media and select content to record. This allows for the recording of highly relevant content by analyzing the user's social media activity. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's social media activity into AI, and the AI ​​can automatically record relevant content.

[0043] The recognition unit can improve the accuracy of recognition based on the time and location where the photograph was taken. For example, the recognition unit can recognize related events based on the time the photograph was taken. The recognition unit can also recognize related information based on the location where the photograph was taken. Furthermore, the recognition unit can improve the accuracy of recognition by combining the time and location where the photograph was taken. This allows for more accurate recognition by improving the accuracy of recognition based on the time and location where the photograph was taken. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input the time and location where the photograph was taken into the AI, and the AI ​​can automatically improve the accuracy of recognition.

[0044] The recognition unit can apply different recognition algorithms depending on the content of the photograph during recognition. For example, the recognition unit can apply a face recognition algorithm to photographs of people. It can also apply a landscape recognition algorithm to photographs of landscapes. Furthermore, it can apply an event recognition algorithm to photographs of events. By applying different recognition algorithms depending on the content of the photograph, the accuracy of recognition is improved. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input the content of the photograph into the AI, and the AI ​​can automatically apply an appropriate recognition algorithm.

[0045] The recognition unit can perform recognition while considering the attribute information of the photographer of the photograph. For example, if the photographer is a family member, the recognition unit will prioritize recognizing family photos. Similarly, if the photographer is a friend, the recognition unit can prioritize recognizing photos of friends. Furthermore, the recognition unit can improve the accuracy of recognition based on the attribute information of the photographer. Thus, considering the attribute information of the photographer improves the accuracy of recognition. Some or all of the above processing in the recognition unit may be performed using AI, or without AI. For example, the recognition unit can input the attribute information of the photographer into the AI, which can then perform recognition automatically.

[0046] The recognition unit can improve the accuracy of recognition by referring to relevant literature for the photograph during recognition. For example, the recognition unit can improve the accuracy of recognition by referring to literature related to the photograph. The recognition unit can also obtain information related to the content of the photograph from the literature and reflect it in the recognition. Furthermore, the recognition unit can improve the accuracy of recognition by referring to literature related to the time and place where the photograph was taken. In this way, the accuracy of recognition is improved by referring to relevant literature for the photograph. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input relevant literature for the photograph into the AI, and the AI ​​can automatically improve the accuracy of recognition.

[0047] The generation unit can generate an optimal brain training plan by thoroughly analyzing the contents of the diary and photos during the generation process. For example, the generation unit can analyze the contents of the diary and generate a brain training plan that includes topics likely to interest the user. The generation unit can also analyze the contents of photos and generate a brain training plan that will evoke the user's memories. Furthermore, the generation unit can combine the contents of the diary and photos to generate an optimal brain training plan. In this way, a more effective brain training plan can be generated by thoroughly analyzing the contents of the diary and photos. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the contents of the diary and photos into a generation AI, which can then automatically analyze them in detail and generate an optimal brain training plan.

[0048] The generation unit can optimize its generation algorithm by referring to the user's past brain training plan history during generation. For example, the generation unit can refer to the user's past brain training plan history and generate a plan that includes effective content. The generation unit can also analyze the user's past brain training plan history and apply the optimal generation algorithm. Furthermore, the generation unit can generate a customized plan based on the user's past brain training plan history. This allows the generation algorithm to be optimized by referring to the past brain training plan history, providing a more effective plan. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's past brain training plan history into a generation AI, which can then automatically optimize the generation algorithm.

[0049] The generation unit can customize brain training plans during generation, taking into account the user's lifestyle and schedule. For example, the generation unit can generate brain training plans that can be performed at the optimal time, according to the user's lifestyle. The generation unit can also generate plans that can be easily implemented, taking into account the user's schedule. Furthermore, the generation unit can combine the user's lifestyle and schedule to generate customized plans. This allows the generation unit to provide plans that can be easily implemented, taking into account the user's lifestyle and schedule. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's lifestyle and schedule into a generation AI, which can then automatically generate a customized plan.

[0050] The generation unit can adjust the content of the brain training plan based on the user's hobbies and interests during the generation process. For example, the generation unit can generate a brain training plan that includes content related to the user's hobbies. The generation unit can also generate a plan with interesting content based on the user's interests. Furthermore, the generation unit can combine the user's hobbies and interests to generate an optimal plan. This allows for the provision of more engaging plans by adjusting the plan content based on the user's hobbies and interests. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's hobbies and interests into the generation AI, which can then automatically adjust the plan content.

[0051] The user unit can select the optimal usage method by referring to the user's past conversation history during use. For example, the user unit can implement a brain training plan based on topics the user has enjoyed talking about in the past. The user unit can also implement a brain training plan while avoiding topics the user has avoided in the past. Furthermore, the user unit can analyze the user's past conversation patterns and select the optimal usage method. In this way, the optimal usage method can be selected by referring to the user's past conversation history. Some or all of the above processing in the user unit may be performed using AI or not. For example, the user unit can input the user's past conversation history into AI, and the AI ​​can automatically select the optimal usage method.

[0052] The user unit can customize the method of using the brain training plan based on the user's current lifestyle when in use. For example, the user unit can implement the brain training plan in a way that is manageable for the user, according to their lifestyle. The user unit can also implement the brain training plan at the optimal time based on the user's lifestyle. Furthermore, the user unit can implement the brain training plan in a customized way, taking into account the user's lifestyle. This allows the user unit to provide a plan that is manageable by customizing the method of use based on the user's current lifestyle. Some or all of the above processing in the user unit may be performed using AI or not. For example, the user unit can input the user's lifestyle into the AI, which can then automatically customize the method of use.

[0053] The user unit can select the optimal usage method when in use, taking into account the user's geographical location information. For example, if the user is in a specific location, the user unit will implement the brain training plan in a way that is appropriate for that location. The user unit can also select the optimal usage method based on the user's geographical location information. Furthermore, if the user is on the move, the user unit can implement the brain training plan in a way that is appropriate for the destination. This allows for the implementation of a more effective plan by selecting the optimal usage method based on the user's geographical location information. Some or all of the above processing in the user unit may be performed using AI or not. For example, the user unit can input the user's geographical location information into the AI, which can then automatically select the optimal usage method.

[0054] The user unit can analyze the user's social media activity during use and suggest ways to use the brain training plan. For example, the user unit can suggest a brain training plan containing highly relevant content based on the user's social media activity. The user unit can also analyze the user's social media activity and suggest the most suitable way to use it. Furthermore, the user unit can suggest ways to use the brain training plan based on the user's interests on social media. In this way, by analyzing the user's social media activity, it is possible to suggest ways to use a plan containing highly relevant content. Some or all of the above processing in the user unit may be performed using AI or not. For example, the user unit can input the user's social media activity into AI, and the AI ​​can automatically suggest ways to use it.

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

[0056] The recording unit can select the optimal recording method by referring to the user's past conversation history during recording. For example, it can prioritize recording topics that the user has enjoyed talking about in the past. It can also exclude topics that the user has avoided in the past from the recording. Furthermore, it can analyze the user's past conversation patterns and select the optimal recording format. In this way, the optimal recording method can be selected by referring to the user's past conversation history. Some or all of the above processing in the recording unit may be performed using AI or not.

[0057] The recording unit can filter recordings based on the user's current lifestyle and areas of interest. For example, it can prioritize recording topics the user is currently interested in. It can also record content that is highly relevant to the user's lifestyle. Furthermore, it can select recording content based on the user's areas of interest. This allows for recordings that are highly relevant by selecting recording content based on the user's current lifestyle and areas of interest. Some or all of the above processing in the recording unit may be performed using AI, or it may be performed without using AI.

[0058] The recording unit can prioritize recording highly relevant content by considering the user's geographical location information during recording. For example, if the user is in a specific location, it can record events and conversations related to that location. It can also prioritize recording highly relevant content based on the user's geographical location information. Furthermore, if the user is on the move, it can record content related to their destination. This allows for the prioritization of recording highly relevant content based on the user's geographical location information. Some or all of the above processing in the recording unit may be performed using AI, or it may be performed without using AI.

[0059] The recording unit can analyze the user's social media activity and record relevant content during recording. For example, it can record content shared by the user on social media. It can also record highly relevant topics from the user's social media activity. Furthermore, it can analyze the user's interests on social media and select content to record. This allows for the recording of highly relevant content by analyzing the user's social media activity. Some or all of the above processing in the recording unit may be performed using AI or not.

[0060] The recognition unit can improve the accuracy of recognition based on the time and location in which the photograph was taken. For example, it can recognize related events based on the time the photograph was taken. It can also recognize related information based on the location in which the photograph was taken. Furthermore, it can improve the accuracy of recognition by combining the time and location in which the photograph was taken. This allows for more accurate recognition by improving the accuracy of recognition based on the time and location in which the photograph was taken. Some or all of the above processing in the recognition unit may be performed using AI, or it may be performed without using AI.

[0061] The recognition unit can apply different recognition algorithms depending on the content of the photograph during recognition. For example, a face recognition algorithm can be applied to photographs of people. A landscape recognition algorithm can also be applied to photographs of landscapes. Furthermore, an event recognition algorithm can be applied to photographs of events. By applying different recognition algorithms depending on the content of the photograph, the accuracy of recognition is improved. Some or all of the above-described processing in the recognition unit may be performed using AI, or it may be performed without using AI.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The recording section records daily events and conversations. The recording section can record daily life events and conversations on specific topics, and can be done in text, audio, or photo diary format. Step 2: The recognition unit recognizes old photographs. The recognition unit can recognize family photos, travel photos, photos of specific events, etc., and record events by year or details of specific events. Step 3: The generation unit uses generation AI to generate brain training plans based on the data accumulated by the recording and recognition units. The generation unit can analyze the contents of diaries and photos to generate individually customized brain training plans. Step 4: The user uses the generated brain training plan. The user can provide content that the individual will want to talk about during day care services or conversations with family members, and can facilitate conversations using methods such as questions, dialogues, and storytelling.

[0064] (Example of form 2) The brain training plan generation system according to an embodiment of the present invention is a system for elderly people who are experiencing a decline in motor function or who are not motivated to engage in rehabilitation. This system generates a personalized brain training plan for the individual by recording daily events and conversations and recognizing old photographs. Specifically, a third party accumulates daily events and conversations with the individual in a diary format to create records of the recent past. In addition, records of the distant past are created by having the system recognize many old photographs, and this data is accumulated as training data. Next, the generating AI generates a personalized brain training plan for the individual based on this training data. This brain training plan includes content that the individual will want to talk about when using day services or in conversations with family, and has the effect of making their life more motivating. For example, a third party accumulates daily events and conversations with the individual in a diary format. At this time, the content of daily events and conversations is recorded in detail and saved as records of the recent past. For example, by recording the content of today's events and conversations in a diary, records of the recent past are created. Next, records of the distant past are created by having the system recognize many old photographs. For example, by having the system recognize old family photos or travel photos, records of the distant past are created. This makes it possible to recall the individual's past memories. These records from the recent and distant past are accumulated as training data. The generating AI then creates a personalized brain training plan based on this training data. For example, the generating AI analyzes the contents of diaries and photographs to create a brain training plan that includes topics and questions that the individual is likely to be interested in. The generated brain training plan is used during day care services and in conversations with family members. For example, by using the brain training plan as a basis for conversation, caregivers at day care services can provide content that the individual will want to talk about. Similarly, by using the brain training plan as a basis for conversations with family members, the individual can speak more enthusiastically. This system allows elderly individuals with declining motor function or those who are reluctant to participate in rehabilitation to activate their brains with nostalgic and enjoyable memories, stimulating brain function. This is expected to prevent delays in rehabilitation and reduce the likelihood of developing dementia.This means that the brain training plan generation system can stimulate brain function and improve motivation by generating and using brain training plans for elderly people who have declining motor function or are unwilling to participate in rehabilitation.

[0065] The brain training plan generation system according to this embodiment comprises a recording unit, a recognition unit, a generation unit, and a usage unit. The recording unit records the contents of daily events and conversations. For example, the recording unit can record daily life events or conversations on specific topics. The recording unit can record in diary format. For example, the recording unit can record a diary in text format. The recording unit can also record a diary in audio format. Furthermore, the recording unit can record a diary with photos. For example, the recording unit can record daily events along with photos. The recognition unit recognizes old photographs. For example, the recognition unit can recognize family photos, travel photos, photos of specific events, etc. The recognition unit creates records of the distant past. For example, the recognition unit can record events by decade or details of specific events. The generation unit generates a brain training plan based on the data accumulated by the recording unit and the recognition unit using a generation AI. For example, the generation unit can analyze the contents of a diary or photographs and generate a brain training plan original to the user. The generation unit can generate individually customized brain training plans using a generation AI. The user unit utilizes the generated brain training plan. The user unit can, for example, provide content that the individual will want to talk about during day care services or conversations with family members. The user unit can advance the conversation based on the generated brain training plan. For example, the user unit can advance the conversation using methods such as question-and-answer formats, dialogue formats, or storytelling. Thus, the brain training plan generation system according to this embodiment can stimulate brain function and improve motivation in elderly individuals with declining motor function or those who are unwilling to participate in rehabilitation by generating and using brain training plans.

[0066] The recording function records daily events and conversations. For example, it can record everyday life events or conversations on specific topics. The recording function can be used in a diary format. For example, it can record a diary in text format. It can also record a diary in audio format. Furthermore, it can record a diary with photos. For example, it can record daily events along with photos. The recording function is equipped with a function that converts voice input to text using speech recognition technology, making it easy for users to record daily events. This makes it easy for users to keep a diary. In addition, the recording function can be linked with mobile devices such as smartphones and tablets, making it easy to record even when on the go. Furthermore, the recording function uses cloud storage to securely store data and make it accessible from multiple devices. This allows users to check and edit their records anytime, anywhere. To protect user privacy, the recording function encrypts data and controls access to prevent unauthorized access by third parties. In addition, the recording function allows users to customize the format and content of their records according to their preferences. For example, features such as tagging specific topics and creating photo albums make it easier for users to organize their records. This allows the recording system to efficiently and securely record the user's daily events and conversations, making them easily accessible later.

[0067] The recognition unit recognizes old photographs. For example, it can recognize family photos, travel photos, and photos of specific events. The recognition unit creates records of the distant past. For example, it can record events by year or details of specific events. Using image recognition technology, the recognition unit identifies people, places, and objects in photographs and automatically tags them with relevant information. This allows users to easily organize and search their photos. Furthermore, the recognition unit can use AI to analyze the content of photos and automatically generate episodes and memories related to the photos. For example, it can analyze family photos and automatically extract information such as the names and relationships of the people in the photo, as well as the location and date of the photo, and provide this information to the user. The recognition unit can also collect information about past events from the internet and provide detailed information related to the photos. This allows users to look back on past events and share memories while viewing photos. To protect user privacy, the recognition unit encrypts the content of photos and tagging information to prevent unauthorized access by third parties. The recognition unit can also adjust the accuracy of photo recognition and the level of detail in tagging according to the user's preferences. This allows the recognition unit to efficiently and accurately recognize the user's past photos and provide related information, thereby enriching memories.

[0068] The generation unit uses generation AI to generate brain training plans based on data accumulated by the recording and recognition units. For example, the generation unit can analyze the contents of a user's diary or photos to generate a personalized brain training plan. The generation unit can use generation AI to generate individually customized brain training plans. The generation AI uses natural language processing technology to analyze the contents of a user's diary and create a brain training plan based on the user's interests. For example, if a user keeps many diary entries about travel, the generation unit will generate a brain training plan that includes travel-related quizzes and puzzles. The generation AI also uses image recognition technology to analyze the contents of photos and create a brain training plan based on episodes and memories related to the photos. For example, it can analyze family photos and generate a brain training plan that includes quizzes and stories about the family. The generation unit can adjust the difficulty level and content of the brain training plan according to the user's age and cognitive function. This allows the user to continue brain training without difficulty. Furthermore, the generation unit can continuously improve the brain training plan based on user feedback to provide more effective plans. For example, if a user gives a high rating to a particular brain training plan, the generation unit will prioritize generating plans with similar content. Furthermore, the generation unit monitors the user's progress and provides a new brain training plan at the appropriate time. This allows the generation unit to consistently provide the user with the optimal brain training plan, supporting the maintenance and improvement of brain function.

[0069] The user unit utilizes the generated brain training plan. For example, it can provide content that the user will want to talk about during day care sessions or conversations with family. The user unit can conduct conversations based on the generated brain training plan. For example, it can conduct conversations using question-and-answer formats, dialogue formats, or storytelling. The user unit has a function to provide interactive feedback when the user is working through the brain training plan. For example, when a user answers a quiz, it provides immediate feedback on whether the answer is correct or incorrect and encourages them to move on to the next question. The user unit also monitors the user's progress in real time and provides encouraging messages and advice at appropriate times. This allows the user to maintain motivation and continue brain training. Furthermore, the user unit can collaborate with the user's family and caregivers to share the progress and effectiveness of the brain training plan. This allows family members and caregivers to understand the user's progress and provide appropriate support. The user unit can also customize the content and format of the brain training plan according to the user's preferences. For example, if a user is interested in a particular topic, it will prioritize providing brain training plans related to that topic. This allows the user to consistently provide interesting and effective brain training plans, supporting the maintenance and improvement of brain function.

[0070] The recording unit can record daily events and conversations in diary format. For example, the recording unit can record daily life events or conversations on specific topics in detail. The recording unit can record diaries in text format. For example, the recording unit can record today's events and conversations in text format. The recording unit can also record diaries in audio format. For example, the recording unit can record conversations in audio format. Furthermore, the recording unit can record diaries with photos. For example, the recording unit can record daily events along with photos. This improves the accuracy of the brain training plan by recording daily events and conversations in detail. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input daily events and conversations into AI, and the AI ​​can automatically record them in diary format.

[0071] The recognition unit can recognize old photographs and create records of the distant past. The recognition unit can recognize, for example, family photographs, travel photographs, and photographs of specific events. The recognition unit can record events by era and details of specific events. For example, the recognition unit can recognize old family photographs and record family history. It can also recognize travel photographs and record travel memories. Furthermore, the recognition unit can recognize photographs of specific events and record details of those events. This allows for the creation of records of the distant past by recognizing old photographs, which can then be incorporated into brain training plans. Some or all of the above processing in the recognition unit may be performed using AI, or it may be performed without AI. For example, the recognition unit can input old photographs into an AI, which can automatically recognize the photographs and create records of the distant past.

[0072] The generation unit can use a generation AI to analyze the contents of a diary and photographs and generate a personalized brain training plan for the individual. For example, the generation unit can analyze the contents of a diary and generate a brain training plan that includes topics and questions that the individual might be interested in. The generation unit can also analyze the contents of photographs and generate a brain training plan that will evoke the individual's memories. The generation unit can use the generation AI to generate individually customized brain training plans. For example, the generation unit can input the contents of a diary into the generation AI, which will automatically analyze and generate a brain training plan. Alternatively, the generation unit can input the contents of a photograph into the generation AI, which will automatically analyze and generate a brain training plan. This allows for the generation of individually customized brain training plans using the generation AI. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the generation unit can input the contents of a diary and photographs into the generation AI, which will automatically analyze and generate a brain training plan.

[0073] The user unit can conduct conversations based on the generated brain training plan. The user unit can provide content that the user will want to talk about, for example, during day care services or conversations with family members. The user unit can conduct conversations based on the generated brain training plan using methods such as questioning, dialogue, and storytelling. For example, the user unit can ask questions to the user based on the brain training plan. The user unit can also engage in dialogue with the user based on the brain training plan. Furthermore, the user unit can engage in storytelling based on the brain training plan. In this way, by conducting conversations based on the generated brain training plan, it is possible to provide content that the user will want to talk about. Some or all of the above processing in the user unit may be performed using AI or not. For example, the user unit can input the generated brain training plan into AI, and the AI ​​can automatically conduct the conversation.

[0074] The user unit can provide content that encourages the individual to talk during day service sessions or conversations with family members. For example, the user unit can provide content that encourages the individual to talk by having a caregiver at the day service center guide the conversation based on the brain training plan. The user unit can also encourage the individual to speak more actively during conversations with family members by guiding the conversation based on the brain training plan. The user unit can ask the individual questions based on the brain training plan. For example, the user unit can provide topics that are likely to interest the individual based on the brain training plan. The user unit can also engage in dialogue with the individual based on the brain training plan. For example, the user unit can engage in storytelling with the individual based on the brain training plan. This allows the individual to speak more actively during day service sessions or conversations with family members. Some or all of the above processing in the user unit may be performed using AI or not. For example, the user unit can input the generated brain training plan into AI, which can then automatically guide the conversation.

[0075] The recording unit can estimate the user's emotions and adjust the level of detail in the recorded content based on the estimated emotions. For example, if the user is relaxed, the recording unit will record detailed events and conversations. If the user is stressed, the recording unit can record only concise details. Furthermore, if the user is excited, the recording unit can make detailed recordings that reflect the heightened emotions. This allows for more appropriate recording by adjusting the level of detail in the recorded content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's emotions into the AI, which can then automatically estimate the emotions and adjust the level of detail in the recorded content.

[0076] The recording unit can select the optimal recording method by referring to the user's past conversation history during recording. For example, the recording unit may prioritize recording topics that the user has enjoyed discussing in the past. It can also exclude topics that the user has avoided in the past from the recording. Furthermore, the recording unit can analyze the user's past conversation patterns and select the optimal recording format. This allows the optimal recording method to be selected by referring to the user's past conversation history. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's past conversation history into the AI, which can then automatically select the optimal recording method.

[0077] The recording unit can filter recordings based on the user's current lifestyle and areas of interest. For example, the recording unit can prioritize recording topics that the user is currently interested in. The recording unit can also record content that is highly relevant to the user's lifestyle. Furthermore, the recording unit can select recording content based on the user's areas of interest. This allows for recordings that are highly relevant by selecting recording content based on the user's current lifestyle and areas of interest. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's current lifestyle and areas of interest into the AI, which can then automatically perform the filtering.

[0078] The recording unit can estimate the user's emotions and determine the priority of content to record based on the estimated emotions. For example, if the user is relaxed, the recording unit may prioritize recording detailed events and conversations. If the user is stressed, the recording unit may also prioritize recording concise content. Furthermore, if the user is excited, the recording unit may prioritize recording content that reflects heightened emotions. This allows for more appropriate recording by prioritizing content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's emotions into an AI, which can then automatically estimate the emotions and determine the priority of content to record.

[0079] The recording unit can prioritize recording highly relevant content by considering the user's geographical location information during recording. For example, if the user is in a specific location, the recording unit can record events and conversations related to that location. The recording unit can also prioritize recording highly relevant content based on the user's geographical location information. Furthermore, if the user is on the move, the recording unit can record content related to their destination. This allows the recording unit to prioritize recording highly relevant content based on the user's geographical location information. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's geographical location information into the AI, which can then automatically prioritize recording highly relevant content.

[0080] The recording unit can analyze the user's social media activity and record relevant content during recording. For example, the recording unit can record content that the user has shared on social media. The recording unit can also record highly relevant topics from the user's social media activity. Furthermore, the recording unit can analyze the user's interests on social media and select content to record. This allows for the recording of highly relevant content by analyzing the user's social media activity. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's social media activity into AI, and the AI ​​can automatically record relevant content.

[0081] The recognition unit can estimate the user's emotions and adjust the criteria for selecting photos to recognize based on the estimated emotions. For example, if the user is relaxed, the recognition unit will prioritize recognizing photos of happy memories. If the user is stressed, the recognition unit can also prioritize recognizing photos with calming content. Furthermore, if the user is excited, the recognition unit can also prioritize recognizing photos with stimulating content. By adjusting the criteria for selecting photos to recognize according to the user's emotions, more appropriate photos can be recognized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input the user's emotions into the AI, which can then automatically estimate the emotions and adjust the photo selection criteria.

[0082] The recognition unit can improve the accuracy of recognition based on the time and location where the photograph was taken. For example, the recognition unit can recognize related events based on the time the photograph was taken. The recognition unit can also recognize related information based on the location where the photograph was taken. Furthermore, the recognition unit can improve the accuracy of recognition by combining the time and location where the photograph was taken. This allows for more accurate recognition by improving the accuracy of recognition based on the time and location where the photograph was taken. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input the time and location where the photograph was taken into the AI, and the AI ​​can automatically improve the accuracy of recognition.

[0083] The recognition unit can apply different recognition algorithms depending on the content of the photograph during recognition. For example, the recognition unit can apply a face recognition algorithm to photographs of people. It can also apply a landscape recognition algorithm to photographs of landscapes. Furthermore, it can apply an event recognition algorithm to photographs of events. By applying different recognition algorithms depending on the content of the photograph, the accuracy of recognition is improved. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input the content of the photograph into the AI, and the AI ​​can automatically apply an appropriate recognition algorithm.

[0084] The recognition unit can estimate the user's emotions and determine the priority of photos to recognize based on the estimated emotions. For example, if the user is relaxed, the recognition unit will prioritize recognizing photos of happy memories. If the user is stressed, the recognition unit can also prioritize recognizing photos with calming content. Furthermore, if the user is excited, the recognition unit can prioritize recognizing photos with stimulating content. This allows for the recognition of more appropriate photos by prioritizing photos according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input the user's emotions into an AI, which can then automatically estimate the emotions and determine the priority of photos.

[0085] The recognition unit can perform recognition while considering the attribute information of the photographer of the photograph. For example, if the photographer is a family member, the recognition unit will prioritize recognizing family photos. Similarly, if the photographer is a friend, the recognition unit can prioritize recognizing photos of friends. Furthermore, the recognition unit can improve the accuracy of recognition based on the attribute information of the photographer. Thus, considering the attribute information of the photographer improves the accuracy of recognition. Some or all of the above processing in the recognition unit may be performed using AI, or without AI. For example, the recognition unit can input the attribute information of the photographer into the AI, which can then perform recognition automatically.

[0086] The recognition unit can improve the accuracy of recognition by referring to relevant literature for the photograph during recognition. For example, the recognition unit can improve the accuracy of recognition by referring to literature related to the photograph. The recognition unit can also obtain information related to the content of the photograph from the literature and reflect it in the recognition. Furthermore, the recognition unit can improve the accuracy of recognition by referring to literature related to the time and place where the photograph was taken. In this way, the accuracy of recognition is improved by referring to relevant literature for the photograph. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input relevant literature for the photograph into the AI, and the AI ​​can automatically improve the accuracy of recognition.

[0087] The generation unit can estimate the user's emotions and adjust the content of the brain training plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a brain training plan with relaxing content. It can also generate a brain training plan with stress-reducing content if the user is stressed. Furthermore, if the user is excited, the generation unit can generate a brain training plan with content that maintains excitement. This allows for the provision of a more appropriate plan by adjusting the brain training plan content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generation AI. For example, the generation unit can input the user's emotions into a generation AI, which can automatically estimate the emotions and adjust the content of the brain training plan.

[0088] The generation unit can generate an optimal brain training plan by thoroughly analyzing the contents of the diary and photos during the generation process. For example, the generation unit can analyze the contents of the diary and generate a brain training plan that includes topics likely to interest the user. The generation unit can also analyze the contents of photos and generate a brain training plan that will evoke the user's memories. Furthermore, the generation unit can combine the contents of the diary and photos to generate an optimal brain training plan. In this way, a more effective brain training plan can be generated by thoroughly analyzing the contents of the diary and photos. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the contents of the diary and photos into a generation AI, which can then automatically analyze them in detail and generate an optimal brain training plan.

[0089] The generation unit can optimize its generation algorithm by referring to the user's past brain training plan history during generation. For example, the generation unit can refer to the user's past brain training plan history and generate a plan that includes effective content. The generation unit can also analyze the user's past brain training plan history and apply the optimal generation algorithm. Furthermore, the generation unit can generate a customized plan based on the user's past brain training plan history. This allows the generation algorithm to be optimized by referring to the past brain training plan history, providing a more effective plan. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's past brain training plan history into a generation AI, which can then automatically optimize the generation algorithm.

[0090] The generation unit can estimate the user's emotions and determine the priority of brain training plans based on the estimated emotions. For example, if the user is relaxed, the generation unit will prioritize generating brain training plans that promote relaxation. It can also prioritize generating brain training plans that reduce stress if the user is stressed. Furthermore, if the user is excited, the generation unit can prioritize generating brain training plans that maintain excitement. This allows for the provision of more appropriate plans by prioritizing brain training plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generation AI. For example, the generation unit can input the user's emotions into a generation AI, which can automatically estimate the emotions and determine the priority of brain training plans.

[0091] The generation unit can customize brain training plans during generation, taking into account the user's lifestyle and schedule. For example, the generation unit can generate brain training plans that can be performed at the optimal time, according to the user's lifestyle. The generation unit can also generate plans that can be easily implemented, taking into account the user's schedule. Furthermore, the generation unit can combine the user's lifestyle and schedule to generate customized plans. This allows the generation unit to provide plans that can be easily implemented, taking into account the user's lifestyle and schedule. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's lifestyle and schedule into a generation AI, which can then automatically generate a customized plan.

[0092] The generation unit can adjust the content of the brain training plan based on the user's hobbies and interests during the generation process. For example, the generation unit can generate a brain training plan that includes content related to the user's hobbies. The generation unit can also generate a plan with interesting content based on the user's interests. Furthermore, the generation unit can combine the user's hobbies and interests to generate an optimal plan. This allows for the provision of more engaging plans by adjusting the plan content based on the user's hobbies and interests. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's hobbies and interests into the generation AI, which can then automatically adjust the plan content.

[0093] The user unit can estimate the user's emotions and adjust how the brain training plan is used based on the estimated emotions. For example, if the user is relaxed, the user unit will implement the brain training plan in a way that promotes relaxation. If the user is stressed, the user unit can also implement the brain training plan in a way that reduces stress. Furthermore, if the user is excited, the user unit can implement the brain training plan in a way that maintains excitement. This allows for more effective implementation of the brain training plan by adjusting the usage method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the user unit may be performed using AI or not. For example, the user unit can input the user's emotions into the AI, which can then automatically estimate the emotions and adjust the usage method.

[0094] The user unit can select the optimal usage method by referring to the user's past conversation history during use. For example, the user unit can implement a brain training plan based on topics the user has enjoyed talking about in the past. The user unit can also implement a brain training plan while avoiding topics the user has avoided in the past. Furthermore, the user unit can analyze the user's past conversation patterns and select the optimal usage method. In this way, the optimal usage method can be selected by referring to the user's past conversation history. Some or all of the above processing in the user unit may be performed using AI or not. For example, the user unit can input the user's past conversation history into AI, and the AI ​​can automatically select the optimal usage method.

[0095] The user unit can customize the method of using the brain training plan based on the user's current lifestyle when in use. For example, the user unit can implement the brain training plan in a way that is manageable for the user, according to their lifestyle. The user unit can also implement the brain training plan at the optimal time based on the user's lifestyle. Furthermore, the user unit can implement the brain training plan in a customized way, taking into account the user's lifestyle. This allows the user unit to provide a plan that is manageable by customizing the method of use based on the user's current lifestyle. Some or all of the above processing in the user unit may be performed using AI or not. For example, the user unit can input the user's lifestyle into the AI, which can then automatically customize the method of use.

[0096] The user unit can estimate the user's emotions and determine the priority of brain training plans based on the estimated emotions. For example, if the user is relaxed, the user unit will prioritize brain training plans that promote relaxation. If the user is stressed, the user unit can also prioritize brain training plans that reduce stress. Furthermore, if the user is excited, the user unit can prioritize brain training plans that maintain excitement. This allows for more effective plan implementation by determining the priority of plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the user unit may be performed using AI or not. For example, the user unit can input the user's emotions into an AI, which can then automatically estimate the emotions and determine the priority of plans.

[0097] The user unit can select the optimal usage method when in use, taking into account the user's geographical location information. For example, if the user is in a specific location, the user unit will implement the brain training plan in a way that is appropriate for that location. The user unit can also select the optimal usage method based on the user's geographical location information. Furthermore, if the user is on the move, the user unit can implement the brain training plan in a way that is appropriate for the destination. This allows for the implementation of a more effective plan by selecting the optimal usage method based on the user's geographical location information. Some or all of the above processing in the user unit may be performed using AI or not. For example, the user unit can input the user's geographical location information into the AI, which can then automatically select the optimal usage method.

[0098] The user unit can analyze the user's social media activity during use and suggest ways to use the brain training plan. For example, the user unit can suggest a brain training plan containing highly relevant content based on the user's social media activity. The user unit can also analyze the user's social media activity and suggest the most suitable way to use it. Furthermore, the user unit can suggest ways to use the brain training plan based on the user's interests on social media. In this way, by analyzing the user's social media activity, it is possible to suggest ways to use a plan containing highly relevant content. Some or all of the above processing in the user unit may be performed using AI or not. For example, the user unit can input the user's social media activity into AI, and the AI ​​can automatically suggest ways to use it.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The recording unit can estimate the user's emotions and adjust the level of detail in the recorded content based on the estimated emotions. For example, if the user is relaxed, it can record detailed events and conversations. If the user is stressed, it can record only concise details. Furthermore, if the user is excited, it can record detailed information that reflects the heightened emotions. By adjusting the level of detail in the recorded content according to the user's emotions, more appropriate recording becomes possible. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the recording unit may be performed using AI or not.

[0101] The recording unit can select the optimal recording method by referring to the user's past conversation history during recording. For example, it can prioritize recording topics that the user has enjoyed talking about in the past. It can also exclude topics that the user has avoided in the past from the recording. Furthermore, it can analyze the user's past conversation patterns and select the optimal recording format. In this way, the optimal recording method can be selected by referring to the user's past conversation history. Some or all of the above processing in the recording unit may be performed using AI or not.

[0102] The recording unit can filter recordings based on the user's current lifestyle and areas of interest. For example, it can prioritize recording topics the user is currently interested in. It can also record content that is highly relevant to the user's lifestyle. Furthermore, it can select recording content based on the user's areas of interest. This allows for recordings that are highly relevant by selecting recording content based on the user's current lifestyle and areas of interest. Some or all of the above processing in the recording unit may be performed using AI, or it may be performed without using AI.

[0103] The recording unit can estimate the user's emotions and determine the priority of what to record based on the estimated emotions. For example, if the user is relaxed, detailed events and conversations may be prioritized for recording. If the user is stressed, concise content may be prioritized for recording. Furthermore, if the user is excited, content reflecting heightened emotions may be prioritized for recording. This allows for more appropriate recording by prioritizing the content according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the recording unit may be performed using AI or not.

[0104] The recording unit can prioritize recording highly relevant content by considering the user's geographical location information during recording. For example, if the user is in a specific location, it can record events and conversations related to that location. It can also prioritize recording highly relevant content based on the user's geographical location information. Furthermore, if the user is on the move, it can record content related to their destination. This allows for the prioritization of recording highly relevant content based on the user's geographical location information. Some or all of the above processing in the recording unit may be performed using AI, or it may be performed without using AI.

[0105] The recording unit can analyze the user's social media activity and record relevant content during recording. For example, it can record content shared by the user on social media. It can also record highly relevant topics from the user's social media activity. Furthermore, it can analyze the user's interests on social media and select content to record. This allows for the recording of highly relevant content by analyzing the user's social media activity. Some or all of the above processing in the recording unit may be performed using AI or not.

[0106] The recognition unit can estimate the user's emotions and adjust the criteria for selecting photos to recognize based on the estimated emotions. For example, if the user is relaxed, it can prioritize recognizing photos of happy memories. If the user is stressed, it can prioritize recognizing photos with calming content. Furthermore, if the user is excited, it can prioritize recognizing photos with stimulating content. In this way, by adjusting the criteria for selecting photos to recognize according to the user's emotions, more appropriate photos can be recognized. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the recognition unit may be performed using AI or not.

[0107] The recognition unit can improve the accuracy of recognition based on the time and location in which the photograph was taken. For example, it can recognize related events based on the time the photograph was taken. It can also recognize related information based on the location in which the photograph was taken. Furthermore, it can improve the accuracy of recognition by combining the time and location in which the photograph was taken. This allows for more accurate recognition by improving the accuracy of recognition based on the time and location in which the photograph was taken. Some or all of the above processing in the recognition unit may be performed using AI, or it may be performed without using AI.

[0108] The recognition unit can apply different recognition algorithms depending on the content of the photograph during recognition. For example, a face recognition algorithm can be applied to photographs of people. A landscape recognition algorithm can also be applied to photographs of landscapes. Furthermore, an event recognition algorithm can be applied to photographs of events. By applying different recognition algorithms depending on the content of the photograph, the accuracy of recognition is improved. Some or all of the above-described processing in the recognition unit may be performed using AI, or it may be performed without using AI.

[0109] The recognition unit can estimate the user's emotions and determine the priority of photos to recognize based on the estimated emotions. For example, if the user is relaxed, it can prioritize recognizing photos of happy memories. If the user is stressed, it can prioritize recognizing photos with calming content. Furthermore, if the user is excited, it can prioritize recognizing photos with stimulating content. In this way, by determining the priority of photos to recognize according to the user's emotions, more appropriate photos can be recognized. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the recognition unit may be performed using AI or not.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The recording section records daily events and conversations. The recording section can record daily life events and conversations on specific topics, and can be done in text, audio, or photo diary format. Step 2: The recognition unit recognizes old photographs. The recognition unit can recognize family photos, travel photos, photos of specific events, etc., and record events by year or details of specific events. Step 3: The generation unit uses generation AI to generate brain training plans based on the data accumulated by the recording and recognition units. The generation unit can analyze the contents of diaries and photos to generate individually customized brain training plans. Step 4: The user uses the generated brain training plan. The user can provide content that the individual will want to talk about during day care services or conversations with family members, and can facilitate conversations using methods such as questions, dialogues, and storytelling.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the recording unit, recognition unit, generation unit, and usage unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recording unit is implemented by the control unit 46A of the smart device 14 and records the contents of daily events and conversations. The recognition unit recognizes old photographs using the camera 42 of the smart device 14. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a brain training plan based on the data accumulated by the recording unit and the recognition unit. The usage unit uses the brain training plan generated using the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the recording unit, recognition unit, generation unit, and usage unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the recording unit is implemented by the control unit 46A of the smart glasses 214 and records daily events and conversations. The recognition unit recognizes old photographs using the camera 42 of the smart glasses 214. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a brain training plan based on the data accumulated by the recording unit and the recognition unit. The usage unit uses the brain training plan generated using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the recording unit, recognition unit, generation unit, and usage unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recording unit is implemented by the control unit 46A of the headset terminal 314 and records daily events and conversations. The recognition unit recognizes old photographs using the camera 42 of the headset terminal 314. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a brain training plan based on the data accumulated by the recording unit and the recognition unit. The usage unit uses the brain training plan generated using the speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the recording unit, recognition unit, generation unit, and usage unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the recording unit is implemented by the control unit 46A of the robot 414 and records daily events and the content of conversations. The recognition unit recognizes old photographs using the camera 42 of the robot 414. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a brain training plan based on the data accumulated by the recording unit and the recognition unit. The usage unit uses the brain training plan generated using the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) The recording department records daily events and conversations, A recognition unit that recognizes old photographs, A generation unit that generates a brain training plan based on the data accumulated by the recording unit and the recognition unit, The system comprises a user unit that uses the brain training plan generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned recording unit is Record daily events and conversations in a diary format. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned recognition unit, Recognize old photographs and create records of the distant past. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The AI ​​analyzes the contents of the diary and photos to generate a personalized brain training plan for the individual. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned part is, The conversation proceeds based on the brain training plan that was generated. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned part is, Provide content that encourages the individual to talk during day service sessions or conversations with family members. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recording unit is It estimates the user's emotions and adjusts the level of detail in the recorded content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recording unit is During recording, the system selects the optimal recording method by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recording unit is During recording, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recording unit is It estimates the user's emotions and determines the priority of what to record based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recording unit is During recording, the system prioritizes recording highly relevant content, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recording unit is During recording, the system analyzes the user's social media activity and records relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recognition unit, It estimates the user's emotions and adjusts the criteria for selecting photos based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned recognition unit, During recognition, the accuracy of recognition is improved based on the time and location in which the photo was taken. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned recognition unit, During recognition, different recognition algorithms are applied depending on the content of the photograph. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned recognition unit, It estimates the user's emotions and determines the priority of photos to recognize based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned recognition unit, During recognition, the photographer's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recognition unit, During recognition, the accuracy of the recognition is improved by referring to related literature for the photograph. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system estimates the user's emotions and adjusts the brain training plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the content of the diary and photos are analyzed in detail to generate the optimal brain training plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the generation algorithm is optimized by referring to the user's past brain training plan history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and prioritizes brain training plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the brain training plan is customized to take into account the user's lifestyle and schedule. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the brain training plan content is adjusted based on the user's hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned part is, It estimates the user's emotions and adjusts how the brain training plan is used based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned part is, When used, the system selects the optimal usage method by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned part is, When used, the brain training plan is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned part is, It estimates the user's emotions and determines the priority of using brain training plans based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned part is, When using the system, the optimal usage method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned part is, During use, the system analyzes the user's social media activity and suggests ways to use the brain training plan. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The recording department records daily events and conversations, A recognition unit that recognizes old photographs, A generation unit that generates a brain training plan based on the data accumulated by the recording unit and the recognition unit, The system comprises a user unit that uses the brain training plan generated by the generation unit. A system characterized by the following features.

2. The aforementioned recording unit is Record daily events and conversations in a diary format. The system according to feature 1.

3. The recognition unit, Recognize old photographs and create records of the distant past. The system according to feature 1.

4. The generating unit is The AI ​​analyzes the contents of the diary and photos to generate a personalized brain training plan for the individual. The system according to feature 1.

5. The aforementioned part is, The conversation proceeds based on the brain training plan that was generated. The system according to feature 1.

6. The aforementioned part is, Provide content that encourages the individual to talk during day service sessions or conversations with family members. The system according to feature 1.

7. The aforementioned recording unit is It estimates the user's emotions and adjusts the level of detail in the recorded content based on the estimated user emotions. The system according to feature 1.

8. The aforementioned recording unit is During recording, the system selects the optimal recording method by referring to the user's past conversation history. The system according to feature 1.

9. The aforementioned recording unit is During recording, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned recording unit is It estimates the user's emotions and determines the priority of what to record based on those estimated emotions. The system according to feature 1.

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