System

Abstract

An object of a system according to an embodiment is to provide a reminiscence scenario personalized for a dementia patient.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects specific information related to the age of the patient, the degree of dementia, and the interest or past experience. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a personalized reminiscence scenario based on the information analyzed by the analysis unit. The providing unit provides the reminiscence information generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that reminiscence therapy for dementia patients is uniform, making it difficult to provide a reminiscence scenario optimized for each individual patient.

[0005] The system according to the embodiment aims to provide a personalized reminiscence scenario for a dementia patient. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects specific information regarding the patient's age, dementia progression, interests, and past experiences. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a personalized recollection scenario based on the information analyzed by the analysis unit. The provision unit provides the recollection scenario generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized reminiscence scenarios for dementia patients. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A reminiscence therapy system according to an embodiment of the present invention provides reminiscence therapy using images and audio generated by a generative AI based on the patient's personal life history. The reminiscence therapy system collects information about the patient's age, dementia stage, interests, and past experiences. The generative AI analyzes this information to automatically generate a personalized reminiscence scenario. This scenario includes photos and videos of nostalgic memories and aims to stimulate the patient's memories. The generated reminiscence scenario aims to maintain and improve the patient's cognitive function while also promoting communication with family and caregivers. For example, the reminiscence therapy system collects information such as places the patient visited in their youth, favorite music, and memories with their family. The generative AI then analyzes the input information and automatically generates a personalized reminiscence scenario. Based on the collected information, the generative AI recreates photos and videos of the patient's nostalgic memories. For example, it generates photos of places the patient visited in their youth and videos of memories with their family. The generated reminiscence scenario stimulates the patient's memory and aims to maintain and improve cognitive function. For example, by viewing nostalgic photos and videos, the patient can recall past events and activate their memories. Furthermore, the generated reminiscence scenarios promote communication with family and caregivers. For example, looking at nostalgic photos and videos together with family members can create common topics of conversation and stimulate communication, thereby improving the patient's emotional well-being. In this way, by utilizing cutting-edge generative AI technology, we can provide an immersive reminiscence experience tailored to each individual, bringing innovation to dementia care. This allows the reminiscence therapy system to stimulate patients' memories and maintain and improve their cognitive function. It also promotes communication with family and caregivers, improving the patient's emotional well-being.

[0029] A reminiscence therapy system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information about a patient's age, dementia progression, interests, and past experiences. The collection unit collects information such as places the patient visited when they were younger, their favorite music, and memories with their family. The collection unit can also collect information about the patient's living situation and daily activity patterns. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected information using, for example, data mining or statistical analysis. The analysis unit provides data for generating a personalized reminiscence scenario based on the collected information. The generation unit uses a generation AI to generate photos and videos of nostalgic memories based on the collected information. For example, the generation unit generates photos of places the patient visited when they were younger and videos of memories with their family. The generation unit uses the generation AI to recreate photos and videos of nostalgic memories for the patient. The provision unit provides the generated reminiscence scenario to the patient to stimulate the patient's memories. The providing unit stimulates the patient's memory, for example, by showing the generated reminiscence scenario to the patient. The providing unit also promotes communication by sharing the generated reminiscence scenario with family members and caregivers. For example, the providing unit may create a common topic of conversation by viewing nostalgic photos and videos with family members, thereby stimulating communication. This allows the reminiscence therapy system according to the embodiment to stimulate the patient's memory and maintain and improve cognitive function. Furthermore, it can promote communication with family members and caregivers, improving the patient's emotional well-being.

[0030] The collection unit can analyze the patient's past behavioral history and select the optimal information collection method. For example, the collection unit prioritizes collecting information about places the patient has frequently visited in the past. The collection unit can also collect information about topics the patient has shown interest in in the past. The collection unit can also analyze the patient's past behavioral patterns and select the most effective information collection method. This enables more effective information collection by selecting the optimal information collection method based on the patient's past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's behavioral history data into the generation AI and cause the generation AI to select the optimal information collection method.

[0031] When collecting information, the collection unit can filter the information based on the patient's current living situation and areas of interest. For example, the collection unit prioritizes collecting information related to hobbies in which the patient is currently interested. The collection unit can also filter out highly relevant information according to the patient's current living situation. The collection unit can also exclude unnecessary information based on the patient's areas of interest. This allows more relevant information to be collected by filtering information based on the patient's current living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the patient's living situation data into the generation AI and have the generation AI perform information filtering.

[0032] When collecting information, the collection unit can select an appropriate collection means depending on the patient's input method. For example, if the patient prefers voice input, the collection unit can collect information by voice. Also, if the patient prefers text input, the collection unit can collect information by text. Also, if the patient prefers image input, the collection unit can collect information by image. This improves the efficiency of information collection by selecting the optimal collection means depending on the patient's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's input data into the generation AI and have the generation AI select the optimal collection means.

[0033] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the patient's geographical location information. For example, the collection unit prioritizes collecting information related to the patient's current location. The collection unit can also prioritize collecting information related to places the patient has visited in the past. The collection unit can also filter highly relevant information based on the patient's geographical location information. This makes it possible to collect more relevant information by taking the patient's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the patient's location information data into the generation AI and cause the generation AI to collect highly relevant information.

[0034] When collecting information, the collection unit can analyze the patient's social media activity and collect relevant information. For example, the collection unit collects information about places where the patient has checked in on social media. The collection unit can also analyze the patient's social media posts and collect relevant information. The collection unit can also refer to the activities of the patient's friends on social media. In this way, more relevant information can be collected by analyzing the patient's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's social media data into the generation AI and cause the generation AI to collect relevant information.

[0035] The collection unit can customize the collection method by reflecting the patient's past feedback when collecting information. For example, the collection unit preferentially uses an information collection method that the patient has previously preferred. The collection unit can also adjust the collection method based on the patient's past feedback. The collection unit can also select the optimal collection method by reflecting the patient's past feedback. This enables more effective information collection by reflecting the patient's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input patient feedback data into the generation AI and cause the generation AI to customize the collection method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on information with high importance. The analysis unit can also perform a concise analysis on information with low importance. The analysis unit can also adjust the level of detail of the analysis according to the importance of the information. This allows for more effective analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply an image analysis algorithm to photos. The analysis unit can also apply an audio analysis algorithm to audio. The analysis unit can also apply a natural language processing algorithm to text. This allows for more effective analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the patient's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the patient's past analysis results. The analysis unit can also select the optimal analysis method by reflecting the patient's past analysis results. In this way, the accuracy of the analysis is improved by referring to the patient's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was collected. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also postpone analysis of older information. The analysis unit can also determine the priority of analysis based on the time when the information was collected. This enables more effective analysis by determining the priority of analysis based on the time when the information was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the information was collected into the generation AI and have the generation AI determine the priority of analysis.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the relevance of the information. This allows for more effective analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the patient's level of expertise. For example, if the patient has technical expertise, the analysis unit uses a lot of technical terms. Also, if the patient does not have technical expertise, the analysis unit can avoid technical terms. Also, the analysis unit can adjust the use of technical terms in the analysis according to the patient's level of expertise. This enables more effective analysis by adjusting the use of technical terms in the analysis according to the patient's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the patient's level of expertise data into the generation AI and have the generation AI execute the use of technical terms.

[0042] The generation unit can adjust the level of detail of the generated scenario based on the importance of the information at the time of generation. For example, the generation unit generates a detailed scenario for information with high importance. The generation unit can also generate a concise scenario for information with low importance. The generation unit can also adjust the level of detail of the generated scenario according to the importance of the information. In this way, by adjusting the level of detail of the scenario based on the importance of the information, a more effective recollection scenario can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the scenario.

[0043] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, the generation unit applies an image generation algorithm to photos. The generation unit can also apply a voice generation algorithm to voice. The generation unit can also apply a natural language generation algorithm to text. In this way, by applying different generation algorithms depending on the category of information, a more effective reminiscence scenario can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information category data into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0044] During generation, the generation unit can improve the accuracy of generation by referring to the patient's past generation results. The generation unit, for example, adjusts the generation algorithm based on the patient's past generation results. The generation unit can also improve the accuracy of generation by referring to the patient's past generation results. The generation unit can also select the optimal generation method by reflecting the patient's past generation results. In this way, the accuracy of generation is improved by referring to the patient's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0045] At the time of generation, the generation unit can determine the priority of the generated scenarios based on the time when the information was collected. For example, the generation unit generates the most recent information with priority. The generation unit can also generate older information later. The generation unit can also determine the priority of the generated scenarios based on the time when the information was collected. In this way, by determining the priority of the scenarios based on the time when the information was collected, a more effective recollection scenario can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information collection time data into the generation AI and have the generation AI determine the priority of the scenarios.

[0046] The generation unit can adjust the order of generated scenarios based on the relevance of information during generation. For example, the generation unit prioritizes generating highly relevant information. The generation unit can also postpone generating less relevant information. The generation unit can also adjust the order of generated scenarios based on the relevance of information. In this way, by adjusting the order of scenarios based on the relevance of information, a more effective recollection scenario can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information relevance data into the generation AI and have the generation AI adjust the order of the scenarios.

[0047] During generation, the generation unit can adjust the use of technical terminology in the generated scenario according to the patient's level of expertise. For example, if the patient has specialized knowledge, the generation unit uses a lot of technical terminology. Alternatively, if the patient does not have specialized knowledge, the generation unit can avoid technical terminology. The generation unit can also adjust the use of technical terminology in the generated scenario according to the patient's level of expertise. This allows for a more effective recollection scenario to be provided by adjusting the use of technical terminology in the scenario according to the patient's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the patient's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0048] At the time of provision, the provision unit can select an appropriate provision method by analyzing the patient's past responses. For example, the provision unit preferentially uses a provision method that the patient has previously preferred. The provision unit can also adjust the provision method based on the patient's past responses. The provision unit can also select an optimal provision method by reflecting the patient's past responses. In this way, a more effective provision method can be selected by analyzing the patient's past responses. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the patient's response data into the generation AI and have the generation AI select a delivery method.

[0049] The providing unit can customize the content to be provided based on the patient's current living situation at the time of providing. The providing unit, for example, provides highly relevant content according to the patient's current living situation. The providing unit can also customize the content to be provided based on the patient's current living situation. The providing unit can also exclude unnecessary content based on the patient's current living situation. In this way, by customizing the content to be provided based on the patient's current living situation, a more effective reminiscence scenario can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the patient's living situation data into the generating AI and cause the generating AI to customize the content to be provided.

[0050] The providing unit can improve the providing method by reflecting patient feedback at the time of providing. The providing unit, for example, adjusts the providing method based on patient feedback. The providing unit can also improve the provided content by reflecting patient feedback. The providing unit can also select the optimal providing method by referring to patient feedback. In this way, a more effective providing method can be selected by reflecting patient feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input patient feedback data into the generating AI and cause the generating AI to improve the providing method.

[0051] At the time of provision, the provision unit can select the optimal provision method by taking into account the patient's geographical location information. For example, the provision unit can prioritize providing information related to the patient's current location. The provision unit can also prioritize providing information related to places the patient has visited in the past. The provision unit can also provide highly relevant information based on the patient's geographical location information. This makes it possible to select a more effective provision method by taking the patient's geographical location information into consideration. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the patient's location information data into the generation AI and cause the generation AI to select the optimal provision method.

[0052] At the time of provision, the providing unit can customize the content to be provided by analyzing the patient's social media activity. For example, the providing unit provides information about locations where the patient has checked in on social media. The providing unit can also analyze the content posted by the patient on social media and provide related information. The providing unit can also provide related information by referring to the activities of the patient's friends on social media. In this way, by analyzing the patient's social media activity, more effective content to be provided can be selected. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the patient's social media data into a generating AI and have the generating AI customize the content to be provided.

[0053] The providing unit can customize the delivery method by reflecting the patient's past feedback when providing the drug. For example, the providing unit preferentially uses a delivery method that the patient has previously preferred. The providing unit can also adjust the delivery method based on the patient's past feedback. The providing unit can also select the optimal delivery method by reflecting the patient's past feedback. In this way, a more effective delivery method can be selected by reflecting the patient's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input patient feedback data into the generating AI and cause the generating AI to customize the delivery method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The analysis unit can analyze the patient's past medical data and select an analysis method according to the progression of dementia. For example, a detailed analysis can be performed on patients in the early stages, and a simple analysis can be performed on patients with more advanced dementia. The analysis unit can also select an optimal analysis algorithm based on the patient's past medical data. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the patient's past medical data. This enables more effective analysis by selecting an analysis method according to the progression of the patient's dementia. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the patient's medical data into the generation AI and have the generation AI select an analysis method.

[0056] The collection unit can collect feedback from the patient's family and caregivers and improve the information collection method. For example, the patient's interests and concerns can be more accurately understood based on information provided by the family. The timing and method of information collection can also be adjusted based on feedback from the caregiver. Furthermore, the accuracy of information collection can be improved by reflecting feedback from the family and caregiver. As a result, more effective information collection is possible by collecting feedback from the family and caregiver. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input feedback data from the family and caregiver into the generation AI and cause the generation AI to improve the information collection method.

[0057] The providing unit can customize the scenario to be provided based on the patient's past hobbies and interests. For example, it can provide a scenario related to hobbies that the patient was interested in in the past. It can also provide a scenario related to places the patient has visited in the past. It can also provide a scenario related to activities the patient has enjoyed in the past. In this way, by customizing the scenario based on the patient's past hobbies and interests, a more effective reminiscence scenario can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the patient's hobby and interest data into the generating AI and cause the generating AI to customize the scenario.

[0058] The generation unit can adjust the content of the scenario to be generated based on the patient's current health condition. For example, if the patient is in good health, an active scenario can be generated. On the other hand, if the patient is in poor health, a calm scenario can be generated. Furthermore, the length and detail of the scenario can be adjusted according to the patient's health condition. In this way, by adjusting the content of the scenario based on the patient's current health condition, a more effective reminiscence scenario can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the patient's health condition data into the generation AI and have the generation AI adjust the content of the scenario.

[0059] The analysis unit can adjust the analysis method based on the patient's living environment. For example, if the patient lives in an urban area, it can prioritize analyzing information related to the city. Also, if the patient lives in a rural area, it can prioritize analyzing information related to the rural area. Furthermore, the level of detail and method of analysis can be adjusted depending on the patient's living environment. This allows for more effective analysis by adjusting the analysis method based on the patient's living environment. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the patient's living environment data into the generation AI and have the generation AI adjust the analysis method.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection department collects information about the patient's age, stage of dementia, interests, and past experiences. Specifically, they collect information about places the patient visited when they were younger, their favorite music, memories with their family, etc. They can also collect information about the patient's living situation and daily activity patterns. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected information using data mining and statistical analysis to provide data for generating personalized recollection scenarios. Step 3: The generator uses AI to generate photos and videos of nostalgic memories based on the collected information, such as photos of places the patient visited when they were younger or videos of memories with their family. Step 4: The providing unit provides the generated recollection scenario to the patient to stimulate the patient's memory. For example, the generated recollection scenario is shown to the patient to stimulate the patient's memory. The generated recollection scenario is also shared with family members and caregivers to promote communication.

[0062] (Example 2) A reminiscence therapy system according to an embodiment of the present invention provides reminiscence therapy using images and audio generated by a generative AI based on the patient's personal life history. The reminiscence therapy system collects information about the patient's age, dementia stage, interests, and past experiences. The generative AI analyzes this information to automatically generate a personalized reminiscence scenario. This scenario includes photos and videos of nostalgic memories and aims to stimulate the patient's memories. The generated reminiscence scenario aims to maintain and improve the patient's cognitive function while also promoting communication with family and caregivers. For example, the reminiscence therapy system collects information such as places the patient visited in their youth, favorite music, and memories with their family. The generative AI then analyzes the input information and automatically generates a personalized reminiscence scenario. Based on the collected information, the generative AI recreates photos and videos of the patient's nostalgic memories. For example, it generates photos of places the patient visited in their youth and videos of memories with their family. The generated reminiscence scenario stimulates the patient's memory and aims to maintain and improve cognitive function. For example, by viewing nostalgic photos and videos, the patient can recall past events and activate their memories. Furthermore, the generated reminiscence scenarios promote communication with family and caregivers. For example, looking at nostalgic photos and videos together with family members can create common topics of conversation and stimulate communication, thereby improving the patient's emotional well-being. In this way, by utilizing cutting-edge generative AI technology, we can provide an immersive reminiscence experience tailored to each individual, bringing innovation to dementia care. This allows the reminiscence therapy system to stimulate patients' memories and maintain and improve their cognitive function. It also promotes communication with family and caregivers, improving the patient's emotional well-being.

[0063] A reminiscence therapy system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information about a patient's age, dementia progression, interests, and past experiences. The collection unit collects information such as places the patient visited when they were younger, their favorite music, and memories with their family. The collection unit can also collect information about the patient's living situation and daily activity patterns. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected information using, for example, data mining or statistical analysis. The analysis unit provides data for generating a personalized reminiscence scenario based on the collected information. The generation unit uses a generation AI to generate photos and videos of nostalgic memories based on the collected information. For example, the generation unit generates photos of places the patient visited when they were younger and videos of memories with their family. The generation unit uses the generation AI to recreate photos and videos of nostalgic memories for the patient. The provision unit provides the generated reminiscence scenario to the patient to stimulate the patient's memories. The providing unit stimulates the patient's memory, for example, by showing the generated reminiscence scenario to the patient. The providing unit also promotes communication by sharing the generated reminiscence scenario with family members and caregivers. For example, the providing unit may create a common topic of conversation by viewing nostalgic photos and videos with family members, thereby stimulating communication. This allows the reminiscence therapy system according to the embodiment to stimulate the patient's memory and maintain and improve cognitive function. Furthermore, it can promote communication with family members and caregivers, improving the patient's emotional well-being.

[0064] The collection unit can estimate the patient's emotions and adjust the timing of information collection based on the estimated patient's emotions. For example, the collection unit collects information about the patient's past experiences when the patient is relaxed. The collection unit can also temporarily suspend information collection when the patient is feeling stressed and resume it later. The collection unit can also refrain from collecting information when the patient is excited until the patient calms down. This allows for more effective information collection by adjusting the timing of information collection according to the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the patient's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0065] The collection unit can analyze the patient's past behavioral history and select the optimal information collection method. For example, the collection unit prioritizes collecting information about places the patient has frequently visited in the past. The collection unit can also collect information about topics the patient has shown interest in in the past. The collection unit can also analyze the patient's past behavioral patterns and select the most effective information collection method. This enables more effective information collection by selecting the optimal information collection method based on the patient's past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's behavioral history data into the generation AI and cause the generation AI to select the optimal information collection method.

[0066] When collecting information, the collection unit can filter the information based on the patient's current living situation and areas of interest. For example, the collection unit prioritizes collecting information related to hobbies in which the patient is currently interested. The collection unit can also filter out highly relevant information according to the patient's current living situation. The collection unit can also exclude unnecessary information based on the patient's areas of interest. This allows more relevant information to be collected by filtering information based on the patient's current living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the patient's living situation data into the generation AI and have the generation AI perform information filtering.

[0067] When collecting information, the collection unit can select an appropriate collection means depending on the patient's input method. For example, if the patient prefers voice input, the collection unit can collect information by voice. Also, if the patient prefers text input, the collection unit can collect information by text. Also, if the patient prefers image input, the collection unit can collect information by image. This improves the efficiency of information collection by selecting the optimal collection means depending on the patient's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's input data into the generation AI and have the generation AI select the optimal collection means.

[0068] The collection unit can estimate the patient's emotions and determine the priority of information to be collected based on the estimated patient's emotions. For example, when the patient is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, when the patient is stressed, the collection unit can also prioritize collecting simple information. Furthermore, when the patient is excited, the collection unit can refrain from collecting information until the patient calms down. This enables more effective information collection by determining the priority of information based on the patient's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the patient's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0069] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the patient's geographical location information. For example, the collection unit prioritizes collecting information related to the patient's current location. The collection unit can also prioritize collecting information related to places the patient has visited in the past. The collection unit can also filter highly relevant information based on the patient's geographical location information. This makes it possible to collect more relevant information by taking the patient's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the patient's location information data into the generation AI and cause the generation AI to collect highly relevant information.

[0070] When collecting information, the collection unit can analyze the patient's social media activity and collect relevant information. For example, the collection unit collects information about places where the patient has checked in on social media. The collection unit can also analyze the patient's social media posts and collect relevant information. The collection unit can also refer to the activities of the patient's friends on social media. In this way, more relevant information can be collected by analyzing the patient's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's social media data into the generation AI and cause the generation AI to collect relevant information.

[0071] The collection unit can customize the collection method by reflecting the patient's past feedback when collecting information. For example, the collection unit preferentially uses an information collection method that the patient has previously preferred. The collection unit can also adjust the collection method based on the patient's past feedback. The collection unit can also select the optimal collection method by reflecting the patient's past feedback. This enables more effective information collection by reflecting the patient's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input patient feedback data into the generation AI and cause the generation AI to customize the collection method.

[0072] The analysis unit can estimate the patient's emotions and adjust the presentation method of the analysis based on the estimated patient's emotions. For example, the analysis unit can provide detailed analysis results when the patient is relaxed. The analysis unit can also provide concise analysis results when the patient is stressed. The analysis unit can also provide visually stimulating analysis results when the patient is excited. By adjusting the presentation method of the analysis according to the patient's emotions, more effective analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input facial expression data of the patient into the generation AI and have the generation AI perform emotion estimation.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on information with high importance. The analysis unit can also perform a concise analysis on information with low importance. The analysis unit can also adjust the level of detail of the analysis according to the importance of the information. This allows for more effective analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply an image analysis algorithm to photos. The analysis unit can also apply an audio analysis algorithm to audio. The analysis unit can also apply a natural language processing algorithm to text. This allows for more effective analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the patient's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the patient's past analysis results. The analysis unit can also select the optimal analysis method by reflecting the patient's past analysis results. In this way, the accuracy of the analysis is improved by referring to the patient's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0076] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated patient's emotions. For example, the analysis unit can perform a detailed analysis when the patient is relaxed. The analysis unit can also perform a brief analysis when the patient is stressed. The analysis unit can also perform a visually stimulating analysis when the patient is excited. This allows for more effective analysis by adjusting the length of the analysis according to the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the patient's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was collected. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also postpone analysis of older information. The analysis unit can also determine the priority of analysis based on the time when the information was collected. This enables more effective analysis by determining the priority of analysis based on the time when the information was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the information was collected into the generation AI and have the generation AI determine the priority of analysis.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the relevance of the information. This allows for more effective analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0079] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the patient's level of expertise. For example, if the patient has technical expertise, the analysis unit uses a lot of technical terms. Also, if the patient does not have technical expertise, the analysis unit can avoid technical terms. Also, the analysis unit can adjust the use of technical terms in the analysis according to the patient's level of expertise. This enables more effective analysis by adjusting the use of technical terms in the analysis according to the patient's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the patient's level of expertise data into the generation AI and have the generation AI execute the use of technical terms.

[0080] The generation unit can estimate the patient's emotions and adjust the expression method of the generated scenario based on the estimated patient's emotions. For example, if the patient is relaxed, the generation unit uses a gentle expression method. Furthermore, if the patient is stressed, the generation unit can also use a simple expression method. Furthermore, if the patient is excited, the generation unit can also use a visually stimulating expression method. By adjusting the expression method of the scenario according to the patient's emotions, a more effective reminiscence scenario can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input facial expression data of the patient into the generation AI and cause the generation AI to estimate the emotion.

[0081] The generation unit can adjust the level of detail of the generated scenario based on the importance of the information at the time of generation. For example, the generation unit generates a detailed scenario for information with high importance. The generation unit can also generate a concise scenario for information with low importance. The generation unit can also adjust the level of detail of the generated scenario according to the importance of the information. In this way, by adjusting the level of detail of the scenario based on the importance of the information, a more effective recollection scenario can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the scenario.

[0082] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, the generation unit applies an image generation algorithm to photos. The generation unit can also apply a voice generation algorithm to voice. The generation unit can also apply a natural language generation algorithm to text. In this way, by applying different generation algorithms depending on the category of information, a more effective reminiscence scenario can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information category data into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0083] During generation, the generation unit can improve the accuracy of generation by referring to the patient's past generation results. The generation unit, for example, adjusts the generation algorithm based on the patient's past generation results. The generation unit can also improve the accuracy of generation by referring to the patient's past generation results. The generation unit can also select the optimal generation method by reflecting the patient's past generation results. In this way, the accuracy of generation is improved by referring to the patient's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0084] The generation unit can estimate the patient's emotions and adjust the length of the generated scenario based on the estimated patient's emotions. For example, if the patient is relaxed, the generation unit can generate a longer scenario. If the patient is stressed, the generation unit can also generate a shorter scenario. If the patient is excited, the generation unit can also generate a visually stimulating scenario. This allows for adjusting the length of the scenario according to the patient's emotions, thereby providing a more effective reminiscence scenario. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input facial expression data of the patient into the generation AI and cause the generation AI to estimate emotions.

[0085] At the time of generation, the generation unit can determine the priority of the generated scenarios based on the time when the information was collected. For example, the generation unit generates the most recent information with priority. The generation unit can also generate older information later. The generation unit can also determine the priority of the generated scenarios based on the time when the information was collected. In this way, by determining the priority of the scenarios based on the time when the information was collected, a more effective recollection scenario can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information collection time data into the generation AI and have the generation AI determine the priority of the scenarios.

[0086] The generation unit can adjust the order of generated scenarios based on the relevance of information during generation. For example, the generation unit prioritizes generating highly relevant information. The generation unit can also postpone generating less relevant information. The generation unit can also adjust the order of generated scenarios based on the relevance of information. In this way, by adjusting the order of scenarios based on the relevance of information, a more effective recollection scenario can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information relevance data into the generation AI and have the generation AI adjust the order of the scenarios.

[0087] During generation, the generation unit can adjust the use of technical terminology in the generated scenario according to the patient's level of expertise. For example, if the patient has specialized knowledge, the generation unit uses a lot of technical terminology. Alternatively, if the patient does not have specialized knowledge, the generation unit can avoid technical terminology. The generation unit can also adjust the use of technical terminology in the generated scenario according to the patient's level of expertise. This allows for a more effective recollection scenario to be provided by adjusting the use of technical terminology in the scenario according to the patient's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the patient's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0088] The providing unit can estimate the patient's emotions and adjust the presentation method of the scenario to be provided based on the estimated patient's emotions. For example, if the patient is relaxed, the providing unit uses a gentle presentation method. Furthermore, if the patient is stressed, the providing unit can also use a simple presentation method. Furthermore, if the patient is excited, the providing unit can also use a visually stimulating presentation method. By adjusting the presentation method of the scenario according to the patient's emotions, a more effective reminiscence scenario can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input facial expression data of the patient into the generation AI and cause the generation AI to estimate the emotion.

[0089] At the time of provision, the provision unit can select an appropriate provision method by analyzing the patient's past responses. For example, the provision unit preferentially uses a provision method that the patient has previously preferred. The provision unit can also adjust the provision method based on the patient's past responses. The provision unit can also select an optimal provision method by reflecting the patient's past responses. In this way, a more effective provision method can be selected by analyzing the patient's past responses. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the patient's response data into the generation AI and have the generation AI select a delivery method.

[0090] The providing unit can customize the content to be provided based on the patient's current living situation at the time of providing. The providing unit, for example, provides highly relevant content according to the patient's current living situation. The providing unit can also customize the content to be provided based on the patient's current living situation. The providing unit can also exclude unnecessary content based on the patient's current living situation. In this way, by customizing the content to be provided based on the patient's current living situation, a more effective reminiscence scenario can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the patient's living situation data into the generating AI and cause the generating AI to customize the content to be provided.

[0091] The providing unit can improve the providing method by reflecting patient feedback at the time of providing. The providing unit, for example, adjusts the providing method based on patient feedback. The providing unit can also improve the provided content by reflecting patient feedback. The providing unit can also select the optimal providing method by referring to patient feedback. In this way, a more effective providing method can be selected by reflecting patient feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input patient feedback data into the generating AI and cause the generating AI to improve the providing method.

[0092] The providing unit can estimate the patient's emotions and determine the priority of scenarios to provide based on the estimated patient's emotions. For example, if the patient is relaxed, the providing unit can prioritize providing detailed scenarios. Furthermore, if the patient is stressed, the providing unit can prioritize providing concise scenarios. Furthermore, if the patient is excited, the providing unit can prioritize providing visually stimulating scenarios. By determining the priority of scenarios based on the patient's emotions, more effective reminiscence scenarios can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input facial expression data of the patient into the generation AI and cause the generation AI to estimate the emotion.

[0093] At the time of provision, the provision unit can select the optimal provision method by taking into account the patient's geographical location information. For example, the provision unit can prioritize providing information related to the patient's current location. The provision unit can also prioritize providing information related to places the patient has visited in the past. The provision unit can also provide highly relevant information based on the patient's geographical location information. This makes it possible to select a more effective provision method by taking the patient's geographical location information into consideration. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the patient's location information data into the generation AI and cause the generation AI to select the optimal provision method.

[0094] At the time of provision, the providing unit can customize the content to be provided by analyzing the patient's social media activity. For example, the providing unit provides information about locations where the patient has checked in on social media. The providing unit can also analyze the content posted by the patient on social media and provide related information. The providing unit can also provide related information by referring to the activities of the patient's friends on social media. In this way, by analyzing the patient's social media activity, more effective content to be provided can be selected. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the patient's social media data into a generating AI and have the generating AI customize the content to be provided.

[0095] The providing unit can customize the delivery method by reflecting the patient's past feedback when providing the drug. For example, the providing unit preferentially uses a delivery method that the patient has previously preferred. The providing unit can also adjust the delivery method based on the patient's past feedback. The providing unit can also select the optimal delivery method by reflecting the patient's past feedback. In this way, a more effective delivery method can be selected by reflecting the patient's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input patient feedback data into the generating AI and cause the generating AI to customize the delivery method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects patient information using the camera 42 and microphone 38B of the smart device 14 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates photos and videos of nostalgic memories using a generation AI. The provision unit provides the generated reminiscence scenario to the patient using, for example, the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects patient information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates photos and videos of nostalgic memories using a generation AI. The provision unit provides the generated reminiscence scenario to the patient using, for example, the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects patient information using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates photos and videos of nostalgic memories using a generation AI. The provision unit provides the generated reminiscence scenario to the patient using, for example, the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects patient information using the camera 42 and microphone 238 of the robot 414 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates photos and videos of nostalgic memories using a generation AI. The provision unit provides the generated reminiscence scenario to the patient using, for example, the speaker 240 of the robot 414.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The analysis unit can estimate the patient's emotions and determine the priority of analysis based on the estimated patient's emotions. For example, if the patient is relaxed, detailed analysis can be prioritized. Also, if the patient is stressed, brief analysis can be prioritized. Furthermore, if the patient is excited, visually stimulating analysis can be prioritized. This enables more effective analysis by prioritizing analysis according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input facial expression data of the patient into the generation AI and have the generation AI perform emotion estimation.

[0098] The providing unit can estimate the patient's emotions and adjust the order of scenarios to be provided based on the estimated patient's emotions. For example, if the patient is relaxed, a calm scenario can be provided preferentially. Also, if the patient is stressed, a simple scenario can be provided preferentially. Furthermore, if the patient is excited, a visually stimulating scenario can be provided preferentially. By adjusting the order of scenarios according to the patient's emotions, more effective reminiscence scenarios can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input facial expression data of the patient into the generation AI and cause the generation AI to estimate the emotion.

[0099] The generation unit can estimate the patient's emotions and adjust the content of the scenario to be generated based on the estimated patient's emotions. For example, if the patient is relaxed, a calm scenario can be generated. If the patient is stressed, a simple scenario can be generated. Furthermore, if the patient is excited, a visually stimulating scenario can be generated. By adjusting the content of the scenario according to the patient's emotions, a more effective reminiscence scenario can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input facial expression data of the patient into the generation AI and cause the generation AI to estimate the emotion.

[0100] The collection unit can estimate the patient's emotions and determine the type of information to collect based on the estimated patient's emotions. For example, if the patient is relaxed, detailed information can be collected preferentially. Also, if the patient is stressed, concise information can be collected preferentially. Furthermore, if the patient is excited, visually stimulating information can be collected preferentially. This enables more effective information collection by determining the type of information to collect based on the patient's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the patient's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0101] The providing unit can estimate the patient's emotions and adjust the length of the scenario to be provided based on the estimated patient's emotions. For example, if the patient is relaxed, a longer scenario can be provided. Also, if the patient is stressed, a shorter scenario can be provided. Furthermore, if the patient is excited, a visually stimulating scenario can be provided. By adjusting the length of the scenario according to the patient's emotions, a more effective reminiscence scenario can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input facial expression data of the patient into the generation AI and cause the generation AI to estimate emotions.

[0102] The analysis unit can analyze the patient's past medical data and select an analysis method according to the progression of dementia. For example, a detailed analysis can be performed on patients in the early stages, and a simple analysis can be performed on patients with more advanced dementia. The analysis unit can also select an optimal analysis algorithm based on the patient's past medical data. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the patient's past medical data. This enables more effective analysis by selecting an analysis method according to the progression of the patient's dementia. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the patient's medical data into the generation AI and have the generation AI select an analysis method.

[0103] The collection unit can collect feedback from the patient's family and caregivers and improve the information collection method. For example, the patient's interests and concerns can be more accurately understood based on information provided by the family. The timing and method of information collection can also be adjusted based on feedback from the caregiver. Furthermore, the accuracy of information collection can be improved by reflecting feedback from the family and caregiver. As a result, more effective information collection is possible by collecting feedback from the family and caregiver. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input feedback data from the family and caregiver into the generation AI and cause the generation AI to improve the information collection method.

[0104] The providing unit can customize the scenario to be provided based on the patient's past hobbies and interests. For example, it can provide a scenario related to hobbies that the patient was interested in in the past. It can also provide a scenario related to places the patient has visited in the past. It can also provide a scenario related to activities the patient has enjoyed in the past. In this way, by customizing the scenario based on the patient's past hobbies and interests, a more effective reminiscence scenario can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the patient's hobby and interest data into the generating AI and cause the generating AI to customize the scenario.

[0105] The generation unit can adjust the content of the scenario to be generated based on the patient's current health condition. For example, if the patient is in good health, an active scenario can be generated. On the other hand, if the patient is in poor health, a calm scenario can be generated. Furthermore, the length and detail of the scenario can be adjusted according to the patient's health condition. In this way, by adjusting the content of the scenario based on the patient's current health condition, a more effective reminiscence scenario can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the patient's health condition data into the generation AI and have the generation AI adjust the content of the scenario.

[0106] The analysis unit can adjust the analysis method based on the patient's living environment. For example, if the patient lives in an urban area, it can prioritize analyzing information related to the city. Also, if the patient lives in a rural area, it can prioritize analyzing information related to the rural area. Furthermore, the level of detail and method of analysis can be adjusted depending on the patient's living environment. This allows for more effective analysis by adjusting the analysis method based on the patient's living environment. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the patient's living environment data into the generation AI and have the generation AI adjust the analysis method.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The collection department collects information about the patient's age, stage of dementia, interests, and past experiences. Specifically, they collect information about places the patient visited when they were younger, their favorite music, memories with their family, etc. They can also collect information about the patient's living situation and daily activity patterns. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected information using data mining and statistical analysis to provide data for generating personalized recollection scenarios. Step 3: The generator uses AI to generate photos and videos of nostalgic memories based on the collected information, such as photos of places the patient visited when they were younger or videos of memories with their family. Step 4: The providing unit provides the generated recollection scenario to the patient to stimulate the patient's memory. For example, the generated recollection scenario is shown to the patient to stimulate the patient's memory. The generated recollection scenario is also shared with family members and caregivers to promote communication.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] 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.

[0172] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0180] [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The collection department collects specific information about the patient's age, stage of dementia, interests, and past experiences. an analysis unit that analyzes the information collected by the collection unit; a generation unit that generates a personalized recollection scenario based on the information analyzed by the analysis unit; a providing unit that provides the recollection scenario generated by the generating unit. A system characterized by:

2. The collecting unit Estimate the patient's emotions and adjust the timing of information collection based on the estimated patient emotions 2. The system of claim 1.

3. The collecting unit Analyze the patient's past behavioral history and select the appropriate method of collecting information 2. The system of claim 1.

4. The collecting unit When gathering information, filter it based on the patient's current life situation or specific areas of interest.

2. The system of claim 1.

5. The collecting unit When collecting information, select the appropriate collection method depending on the patient's input method.

2. The system of claim 1.

6. The collecting unit Estimate the patient's feelings and prioritize the information to be collected based on the estimated patient's feelings.

2. The system of claim 1.

7. The collecting unit When collecting information, prioritize collection of specifically relevant information by taking into account the patient's geographic location.

2. The system of claim 1.

8. The collecting unit When collecting information, analyze the patient's social media activity and collect relevant information.

2. The system of claim 1.

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