system

Abstract

An object of a system according to an embodiment is to alleviate a symptom due to BPSD by providing content based on preferences and interests of a patient.SOLUTION: A system includes a collection part, a generation part, a provision part, and a relaxation part. The collection unit collects information based on the preference and interest of the patient. The generation unit generates a photograph or a moving image based on the information collected by the collection unit. The providing unit provides the patient with the photograph or the moving image generated by the generating unit. The mitigator mitigates the symptom of the patient based on the content provided by the provider.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have limited means to effectively alleviate the behavioral and psychological symptoms of BPSD, placing a significant burden on caregivers.

[0005] The system according to the embodiment aims to alleviate symptoms of BPSD by providing content based on the patient's preferences and interests. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, a provision unit, and a mitigation unit. The collection unit collects information based on the patient's preferences and interests. The generation unit generates photos and videos based on the information collected by the collection unit. The provision unit provides the photos and videos generated by the generation unit to the patient. The mitigation unit alleviates the patient's symptoms based on the content provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can alleviate symptoms of BPSD by providing content based on the patient's preferences and interests. [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 system according to an embodiment of the present invention utilizes generative AI to alleviate behavioral and psychological symptoms associated with BPSD (Behavioral and Psychological Symptoms of Dementia). This system collects information reflecting a patient's preferences and interests, generates photos and videos based on the collected information, and provides the generated photos and videos to the patient, thereby alleviating BPSD symptoms. Furthermore, in the future, we are considering using VR images for patients with wandering symptoms. This allows patients to feel as if they are actually present and wandering. This prevents them from actually moving around on their own. This reduces the burden on nursing home staff and relatives and improves the patient's quality of life (QOL). For example, the system collects information such as the patient's past visits, favorite scenery, and family photos, and the generative AI generates photos and videos based on this information. Showing the generated photos and videos to the patient can alleviate BPSD symptoms. Furthermore, if a patient is in an agitated state, showing them videos of relaxing scenery can alleviate their symptoms. Furthermore, if a patient is feeling anxious, showing them photos of their family can provide a sense of security. In the future, VR images may be used to help patients with wandering disorders satisfy their desire to wander and prevent them from actually moving around.

[0029] A BPSD alleviation system according to an embodiment includes a collection unit, a generation unit, a provision unit, and a mitigation unit. The collection unit collects information based on a patient's preferences and interests. For example, the collection unit collects information such as places the patient has visited in the past, favorite scenery, and family photos. The collection unit can also collect information using means such as questionnaires, sensors, and social media analysis. The generation unit generates photos and videos based on the collected information using a generation AI. For example, the generation unit generates photos and videos of places the patient has visited in the past. The generation unit can also use the generation AI to generate content in the form of still images, slideshows, short videos, and the like. The provision unit provides the generated photos and videos to the patient. For example, the provision unit provides the content using a smartphone app, tablet, VR device, or the like. The provision unit can alleviate BPSD symptoms by showing the generated content to the patient. The mitigation unit alleviates the patient's symptoms based on the provided content. For example, the mitigation unit can show a video of a relaxing scenery if the patient is excited. The mitigation unit can also show photos of family members if the patient is feeling anxious. Furthermore, the mitigation department is considering using VR images for patients with symptoms of wandering in the future. As a result, the BPSD mitigation system according to the embodiment can alleviate BPSD symptoms and reduce the burden on care facility staff and relatives by providing content based on the patient's preferences and interests.

[0030] The collection unit can collect photos of places the patient has visited in the past, favorite scenery, and family. The collection unit, for example, collects information on places the patient has visited in the past. For example, the collection unit collects information such as the patient's travel destinations, hometowns, and places of memories. The collection unit can also collect the patient's favorite scenery. For example, the collection unit collects scenery such as the ocean, mountains, and cityscapes that the patient likes. The collection unit can also collect photos of the patient's family. For example, the collection unit collects photos of family trips and daily life. This allows for more effective content to be provided by collecting information based on the patient's past experiences and preferences. 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 information on places the patient has visited in the past into AI, and the AI ​​can analyze and collect the information.

[0031] The generation unit can generate photos and videos based on the collected information. For example, the generation unit generates photos based on the collected information. For example, the generation unit generates photos of places the patient has visited in the past. The generation unit can also generate videos based on the collected information. For example, the generation unit generates videos of scenery the patient likes. The generation unit can also generate content in the form of still images, slideshows, short videos, etc. based on the collected information. For example, the generation unit uses a generation AI to generate still images tailored to the patient's preferences. The generation unit can also use a generation AI to generate slideshows based on the patient's interests. In this way, content tailored to the patient can be provided by generating content based on the collected information. Some or all of the above-described processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit can input the collected information into a generation AI, which then generates photos and videos based on the information.

[0032] The providing unit can provide the generated photos and videos to the patient. The providing unit, for example, provides the generated photos to the patient. For example, the providing unit displays the photos using a smartphone app. The providing unit can also provide the generated videos to the patient. For example, the providing unit plays the videos using a tablet. The providing unit can also provide the generated content using a VR device. For example, the providing unit provides the patient with a virtual reality experience using a VR device. In this way, providing the generated content to the patient can alleviate the symptoms of BPSD. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the generated photos and videos to AI, and the AI ​​can select a method for providing the content to the patient.

[0033] The mitigation unit can alleviate the patient's symptoms based on the provided content. For example, if the patient is agitated, the mitigation unit can show them videos of relaxing scenery. For example, the mitigation unit can show videos of ocean waves, forest scenes, or stars in the night sky. If the patient is feeling anxious, the mitigation unit can also show them photos of family members. For example, the mitigation unit can show them photos of family trips or everyday life. Furthermore, the mitigation unit is considering using VR images for patients with symptoms of wandering in the future. For example, the mitigation unit can satisfy the patient's desire to wander by showing them VR images of places the patient has visited in the past. This can alleviate the patient's symptoms based on the provided content, thereby reducing the burden on nursing home staff and relatives. Some or all of the above-mentioned processing in the mitigation unit may be performed using AI, or may be performed without AI. For example, the mitigation unit can input the patient's symptoms into AI, which can then select appropriate content based on the symptoms.

[0034] The alleviation unit can show a video of a relaxing scene when the patient is in an excited state. For example, when the patient is in an excited state, the alleviation unit shows a video of a relaxing scene. For example, the alleviation unit shows a video of ocean waves, a forest scene, or stars in the night sky. In this way, by showing a video of a relaxing scene when the patient is in an excited state, symptoms can be alleviated. Some or all of the above-mentioned processing in the alleviation unit may be performed using AI, or may be performed without using AI. For example, the alleviation unit can input the patient's excited state into AI, and the AI ​​can select a video of a relaxing scene based on that state.

[0035] The alleviation unit can show the patient photos of family members if the patient is feeling anxious. For example, the alleviation unit can show the patient photos of family members if the patient is feeling anxious. For example, the alleviation unit can show photos of family trips or daily life photos. In this way, if the patient is feeling anxious, showing the patient photos of family members can provide a sense of security. Some or all of the above-mentioned processing in the alleviation unit may be performed using AI, or may be performed without using AI. For example, the alleviation unit can input the patient's anxiety state into AI, and the AI ​​can select photos of family members based on that state.

[0036] The mitigation unit can use VR images for patients with symptoms of wandering. The mitigation unit uses VR images for patients with symptoms of wandering. For example, the mitigation unit can satisfy the patient's desire to wander by showing them VR images of places they have visited in the past. In this way, by using VR images for patients with symptoms of wandering, the patient's desire to wander can be satisfied and they can be prevented from actually moving around. Some or all of the above-mentioned processing in the mitigation unit may be performed using AI, or may be performed without using AI. For example, the mitigation unit can input the patient's desire to wander into AI, and the AI ​​can select appropriate VR images based on that desire.

[0037] The collection unit can analyze the patient's past behavioral history and select the optimal information collection method. The collection unit, for example, analyzes the patient's past behavioral history and selects the optimal information collection method. For example, the collection unit prioritizes collecting information on places the patient has frequently visited in the past. The collection unit can also collect information based on scenery and photos that the patient has liked to see in the past. Furthermore, the collection unit can analyze the patient's past behavioral patterns and select the most effective information collection method. In this way, the optimal information collection method can be selected by analyzing the patient's past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the patient's past behavioral data into AI, and the AI ​​can select the optimal information collection method based on that data.

[0038] The collection unit can filter information based on the patient's current psychological state and health state when collecting information. For example, the collection unit can filter information based on the patient's current psychological state and health state when collecting information. For example, when the patient is relaxed, the collection unit can collect information of relaxing scenery. Furthermore, when the patient is excited, the collection unit can collect less stimulating information. Furthermore, when the patient is feeling anxious, the collection unit can collect information that provides a sense of security. In this way, by filtering information based on the patient's current psychological state and health state, more appropriate information can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input data on the patient's psychological state and health state into AI, and the AI ​​can filter information based on that data.

[0039] The collection unit can select the optimal collection means depending on the patient's input method when collecting information. For example, when collecting information, the collection unit selects the optimal 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. Furthermore, if the patient prefers image input, the collection unit can collect information by image. In this way, by selecting the optimal collection means depending on the patient's input method, the efficiency of information collection is improved. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input data on the patient's input method into AI, and the AI ​​can select the optimal collection means based on that data.

[0040] The collection unit can prioritize collecting highly relevant information by taking into account the patient's geographical location information when collecting information. For example, the collection unit prioritizes collecting highly relevant information by taking into account the patient's geographical location information when collecting 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. Furthermore, the collection unit can also prioritize collecting information related to places the patient plans to visit in the future. In this way, by collecting information by taking into account the patient's geographical location information, more relevant information can be provided. Some or all of the above-described processing by the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the patient's geographical location information into AI, and the AI ​​can collect highly relevant information based on that information.

[0041] The collection unit can analyze the patient's social media activity and collect relevant information when collecting information. For example, the collection unit can analyze the patient's social media activity and collect relevant information when collecting information. For example, the collection unit can collect information related to places where the patient has checked in on social media. The collection unit can also analyze the content of the patient's social media posts and collect relevant information. Furthermore, the collection unit can also collect relevant information by referring 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-mentioned processing in the collection unit may be performed using AI or may be performed without using AI. For example, the collection unit can input data on the patient's social media activity into AI, and the AI ​​can collect relevant information based on that data.

[0042] The collection unit can customize the collection method by reflecting the patient's past feedback when collecting information. For example, the collection unit customizes the collection method by reflecting the patient's past feedback when collecting information. For example, the collection unit customizes the collection method based on the patient's past preferences. The collection unit can also analyze the patient's past feedback and select the optimal collection method. Furthermore, the collection unit can improve the collection method based on feedback provided by the patient in the past. This allows the collection method to be customized by reflecting the patient's past feedback, enabling more effective information collection. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the patient's past feedback data into AI, and the AI ​​can customize the collection method based on that data.

[0043] The generation unit can adjust the level of detail of the content generated based on the importance of the information when generating the content. For example, the generation unit can adjust the level of detail of the content generated based on the importance of the information when generating the content. For example, the generation unit can generate a video including a detailed explanation for important information. The generation unit can also generate a simple photo for less important information. Furthermore, the generation unit can adjust the length and level of detail of the content to be generated according to the importance of the information. In this way, by adjusting the level of detail of the content generated based on the importance of the information, more effective content can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit can input information importance data to the generation AI, and the generation AI can adjust the level of detail of the content generated based on that data.

[0044] The generation unit can apply different generation algorithms depending on the category of information when generating content. For example, the generation unit can apply different generation algorithms depending on the category of information when generating content. For example, when generating a landscape photo, the generation unit can use a generation algorithm specialized for landscapes. Furthermore, when generating a family photo, the generation unit can use a generation algorithm specialized for people. Furthermore, when generating a video, the generation unit can use a generation algorithm specialized for videos. In this way, by applying different generation algorithms depending on the category of information, more appropriate content can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit can input information category data into a generation AI, and the generation AI can select an appropriate generation algorithm based on that data.

[0045] The generation unit can improve the accuracy of generation by referring to the patient's past generation results when generating content. For example, the generation unit can improve the accuracy of generation by referring to the patient's past generation results when generating content. For example, the generation unit can improve the accuracy of generation based on content that the patient has previously preferred. The generation unit can also analyze the patient's past generation results and select the optimal generation method. Furthermore, the generation unit can improve the accuracy of generation based on feedback provided by the patient in the past. In this way, the accuracy of generation can be 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 a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit can input the patient's past generation result data into the generation AI, and the generation AI can improve the accuracy of generation based on that data.

[0046] The generation unit can determine the generation priority based on the time of information submission when generating content. The generation unit can determine the generation priority based on the time of information submission when generating content. For example, the generation unit can prioritize generation of information with high urgency. The generation unit can also prioritize generation of information with an approaching submission deadline. Furthermore, the generation unit can adjust the generation priority based on the time of submission. In this way, by determining the generation priority based on the time of information submission, it is possible to prioritize generation of information with high urgency. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit can input information submission time data into the generation AI, and the generation AI can determine the generation priority based on that data.

[0047] The generation unit can adjust the order of generation based on the relevance of information when generating content. The generation unit, for example, adjusts the order of generation based on the relevance of information when generating content. For example, the generation unit prioritizes generation of highly relevant information. The generation unit can also postpone generation of less relevant information. Furthermore, the generation unit can adjust the order of generation based on the relevance of information. In this way, by adjusting the order of generation based on the relevance of information, more relevant information can be generated preferentially. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit can input information relevance data into the generation AI, and the generation AI can adjust the order of generation based on that data.

[0048] The generation unit can adjust the use of technical terminology in the generated content according to the patient's level of expertise when generating content. For example, the generation unit can adjust the use of technical terminology in the generated content according to the patient's level of expertise when generating content. For example, if the patient has technical expertise, the generation unit can generate content that uses a lot of technical terminology. Also, if the patient does not have technical expertise, the generation unit can generate content that avoids technical terminology. Furthermore, the generation unit can adjust the use of technical terminology in the generated content according to the patient's level of expertise. This makes it possible to provide content that is easier to understand by adjusting the use of technical terminology according to the patient's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. For example, the generation unit can input patient expertise level data into the generation AI, and the generation AI can adjust the use of technical terminology based on that data.

[0049] The providing unit can select the optimal delivery method by referring to the patient's past responses when providing content. For example, the providing unit selects the optimal delivery method by referring to the patient's past responses when providing content. For example, the providing unit selects the optimal delivery method based on the delivery method that the patient preferred in the past. The providing unit can also analyze the patient's past responses and select the optimal delivery method. Furthermore, the providing unit can improve the delivery method based on feedback provided by the patient in the past. This makes it possible to select the optimal delivery method by referring to the patient's past responses and provide content more effectively. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the patient's past response data into AI, and the AI ​​can select the optimal delivery method based on that data.

[0050] The providing unit can customize the provided content according to the patient's current psychological state when providing content. For example, the providing unit customizes the provided content according to the patient's current psychological state when providing content. For example, if the patient is relaxed, the providing unit can provide content with a relaxing landscape. Furthermore, if the patient is excited, the providing unit can provide content with calming colors. Furthermore, if the patient is feeling anxious, the providing unit can provide photos of family members that give a sense of security. This allows for more effective content provision by customizing the provided content according to the patient's current psychological state. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input data on the patient's current psychological state into AI, and the AI ​​can customize the provided content based on that data.

[0051] The providing unit can improve the content delivery method by reflecting patient feedback when providing content. For example, the providing unit can improve the content delivery method by reflecting patient feedback when providing content. For example, the providing unit can improve the content delivery method based on feedback previously provided by the patient. The providing unit can also analyze patient feedback and select the optimal content delivery method. Furthermore, the providing unit can customize the content to be provided based on feedback provided by the patient. This allows the content delivery method to be improved by reflecting patient feedback, enabling more effective content delivery. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input patient feedback data into AI, and the AI ​​can improve the content delivery method based on that data.

[0052] The providing unit can select the optimal delivery method by taking into account the patient's geographical location information when providing content. For example, the providing unit selects the optimal delivery method by taking into account the patient's geographical location information when providing content. For example, the providing unit provides content related to the patient's current location. The providing unit can also provide content related to places the patient has visited in the past. Furthermore, the providing unit can also provide content related to places the patient plans to visit in the future. This enables more effective content delivery by selecting the optimal delivery method by taking into account the patient's geographical location information. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the patient's geographical location information into AI, and the AI ​​can select the optimal delivery method based on that information.

[0053] The providing unit can customize the content to be provided by analyzing the patient's social media activity when providing content. For example, the providing unit can customize the content to be provided by analyzing the patient's social media activity when providing content. For example, the providing unit can provide content related to places 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 content. Furthermore, the providing unit can provide related content by referring to the activity of the patient's friends on social media. In this way, more relevant content can be provided by analyzing the patient's social media activity. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input data on the patient's social media activity into AI, and the AI ​​can customize the content to be provided based on that data.

[0054] The providing unit can customize the delivery method by reflecting the patient's past feedback when providing content. For example, the providing unit customizes the delivery method by reflecting the patient's past feedback when providing content. For example, the providing unit customizes the delivery method based on the delivery method that the patient previously preferred. The providing unit can also analyze the patient's past feedback and select the optimal delivery method. Furthermore, the providing unit can customize the content to be provided based on feedback provided by the patient in the past. This allows the delivery method to be customized by reflecting the patient's past feedback, enabling more effective content delivery. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the patient's past feedback data into AI, and the AI ​​can customize the delivery method based on that data.

[0055] The mitigation unit can analyze the patient's past behavior and select the optimal mitigation method when alleviating symptoms. For example, the mitigation unit analyzes the patient's past behavior and selects the optimal mitigation method when alleviating symptoms. For example, the mitigation unit selects the optimal mitigation method based on methods that the patient has previously preferred. The mitigation unit can also analyze the patient's past behavioral patterns and select the optimal mitigation method. Furthermore, the mitigation unit can improve the mitigation method based on feedback provided by the patient in the past. In this way, by analyzing the patient's past behavior, the optimal mitigation method can be selected and more effective symptom alleviation is possible. Some or all of the above-mentioned processing in the mitigation unit may be performed using AI, or may be performed without using AI. For example, the mitigation unit can input the patient's past behavioral data into AI, and the AI ​​can select the optimal mitigation method based on that data.

[0056] The alleviation unit can customize alleviation measures based on the patient's current psychological state when alleviating symptoms. For example, the alleviation unit customizes alleviation measures based on the patient's current psychological state when alleviating symptoms. For example, if the patient is relaxed, the alleviation unit can show a video of a relaxing scene. If the patient is excited, the alleviation unit can show a photo with calming colors. Furthermore, if the patient is feeling anxious, the alleviation unit can show a photo of family members that gives a sense of security. In this way, customizing alleviation measures based on the patient's current psychological state enables more effective symptom alleviation. Some or all of the above-mentioned processing in the alleviation unit may be performed using AI, or may be performed without using AI. For example, the alleviation unit can input data on the patient's current psychological state into AI, and the AI ​​can customize alleviation measures based on that data.

[0057] The mitigation unit can improve the mitigation method by reflecting patient feedback when alleviating symptoms. For example, the mitigation unit improves the mitigation method by reflecting patient feedback when alleviating symptoms. For example, the mitigation unit improves the mitigation method based on feedback previously provided by the patient. The mitigation unit can also analyze the patient's feedback and select the optimal mitigation method. Furthermore, the mitigation unit can customize the mitigation means based on feedback provided by the patient. In this way, by reflecting the patient's feedback, the mitigation method can be improved and more effective symptom alleviation is possible. Some or all of the above-mentioned processing in the mitigation unit may be performed using AI or without AI. For example, the mitigation unit can input patient feedback data into AI, and the AI ​​can improve the mitigation method based on that data.

[0058] The mitigation unit can select the optimal mitigation method by taking into account the patient's geographical location information when alleviating symptoms. For example, the ... display content related to the patient's current location. The mitigation unit can also display content related to places the patient has visited in the past. Furthermore, the mitigation unit can display content related to places the patient plans to visit in the future. In this way, more effective symptom alleviation is possible by selecting the optimal mitigation method by taking into account the patient's geographical location information. Some or all of the above-described processing in the mitigation unit may be performed using AI, or may be performed without using AI. For example, the mitigation unit can input the patient's geographical location information into AI, and the AI ​​can select the optimal mitigation method based on that information.

[0059] The mitigation unit can analyze the patient's social media activity and suggest mitigation measures when symptoms are alleviated. For example, the mitigation unit can analyze the patient's social media activity and suggest mitigation measures when symptoms are alleviated. For example, the mitigation unit can display content related to places where the patient has checked in on social media. The mitigation unit can also analyze the patient's social media posts and show related content. Furthermore, the mitigation unit can also show related content based on the activities of the patient's friends on social media. In this way, by analyzing the patient's social media activity, more relevant mitigation measures can be suggested. Some or all of the above-mentioned processing in the mitigation unit may be performed using AI, or may be performed without using AI. For example, the mitigation unit can input data on the patient's social media activity into AI, and the AI ​​can suggest mitigation measures based on that data.

[0060] The mitigation unit can customize the mitigation method by reflecting the patient's past feedback when alleviating symptoms. For example, the mitigation unit customizes the mitigation method by reflecting the patient's past feedback when alleviating symptoms. For example, the mitigation unit customizes the mitigation method based on the patient's past preferred method. The mitigation unit can also analyze the patient's past feedback and select the optimal mitigation method. Furthermore, the mitigation unit can improve the mitigation method based on feedback provided by the patient in the past. In this way, the mitigation method can be customized by reflecting the patient's past feedback, enabling more effective symptom alleviation. Some or all of the above-mentioned processing in the mitigation unit may be performed using AI, or may be performed without using AI. For example, the mitigation unit can input the patient's past feedback data into AI, and the AI ​​can customize the mitigation method based on that data.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The collection unit can also analyze the patient's past behavioral history and select the optimal information collection method. For example, the collection unit can prioritize collecting information on places the patient has frequently visited in the past. It can also collect information based on scenery and photos that the patient has liked to see in the past. It can also analyze the patient's past behavioral patterns and select the most effective information collection method. In this way, the optimal information collection method can be selected by analyzing the patient's past behavioral history.

[0063] When generating content, the generation unit can also adjust the level of detail of the content based on the importance of the information. For example, the generation unit can generate a video including a detailed explanation for important information. For less important information, the generation unit can generate a simple photo. Furthermore, the length and level of detail of the content to be generated can be adjusted depending on the importance of the information. In this way, by adjusting the level of detail of the content based on the importance of the information, more effective content can be provided.

[0064] When providing content, the providing unit can also select the optimal delivery method by referring to the patient's past responses. For example, the providing unit selects the optimal delivery method based on delivery methods that the patient has preferred in the past. The providing unit can also analyze the patient's past responses and select the optimal delivery method. Furthermore, the delivery method can also be improved based on feedback provided by the patient in the past. In this way, by referring to the patient's past responses, the optimal delivery method can be selected, enabling more effective content delivery.

[0065] The alleviation unit can also select the optimal alleviation method by analyzing the patient's past behavior when alleviating symptoms. For example, the alleviation unit selects the optimal alleviation method based on methods that the patient has preferred in the past. The alleviation unit can also select the optimal alleviation method by analyzing the patient's past behavior patterns. Furthermore, the alleviation method can be improved based on feedback provided by the patient in the past. In this way, by analyzing the patient's past behavior, the optimal alleviation method can be selected, enabling more effective symptom alleviation.

[0066] When collecting information, the collection unit can also filter the information based on the patient's current psychological state and health condition. For example, when the patient is relaxed, the collection unit can collect information of relaxing scenery. When the patient is excited, the collection unit can collect less stimulating information. Furthermore, when the patient is feeling anxious, the collection unit can collect information that gives a sense of security. In this way, by filtering information based on the patient's current psychological state and health condition, more appropriate information can be collected.

[0067] When providing content, the providing unit can also analyze the patient's social media activity to customize the content to be provided. For example, the providing unit can provide content related to places where the patient has checked in on social media. The providing unit can also analyze the patient's social media posts to provide relevant content. Furthermore, the providing unit can provide relevant content based on the activities of the patient's friends on social media. In this way, more relevant content can be provided by analyzing the patient's social media activity.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The collection unit collects information based on the patient's preferences and interests. For example, the collection unit collects information such as places the patient has visited in the past, favorite scenery, and family photos. The collection unit can also collect information using methods such as questionnaires, sensors, and social media analysis. Step 2: The generator uses the AI ​​to generate photos and videos based on the collected information. For example, the generator generates photos and videos of places the patient has visited in the past. The generator can also use the AI ​​to generate content in the form of still images, slideshows, short videos, and other formats. Step 3: The providing unit provides the generated photos and videos to the patient. For example, the providing unit provides the content using a smartphone app, tablet, VR device, etc. The providing unit can also reduce BPSD symptoms by showing the generated content to the patient. Step 4: The mitigation department alleviates the patient's symptoms based on the provided content. For example, if the patient is excited, the mitigation department can show them a video of a relaxing scene. If the patient is feeling anxious, the mitigation department can also show them photos of their family. In addition, the mitigation department is considering using VR images for patients with symptoms of wandering in the future.

[0070] (Example 2) A system according to an embodiment of the present invention utilizes generative AI to alleviate behavioral and psychological symptoms associated with BPSD (Behavioral and Psychological Symptoms of Dementia). This system collects information reflecting a patient's preferences and interests, generates photos and videos based on the collected information, and provides the generated photos and videos to the patient, thereby alleviating BPSD symptoms. Furthermore, in the future, we are considering using VR images for patients with wandering symptoms. This allows patients to feel as if they are actually present and wandering. This prevents them from actually moving around on their own. This reduces the burden on nursing home staff and relatives and improves the patient's quality of life (QOL). For example, the system collects information such as the patient's past visits, favorite scenery, and family photos, and the generative AI generates photos and videos based on this information. Showing the generated photos and videos to the patient can alleviate BPSD symptoms. Furthermore, if a patient is in an agitated state, showing them videos of relaxing scenery can alleviate their symptoms. Furthermore, if a patient is feeling anxious, showing them photos of their family can provide a sense of security. In the future, VR images may be used to help patients with wandering disorders satisfy their desire to wander and prevent them from actually moving around.

[0071] A BPSD alleviation system according to an embodiment includes a collection unit, a generation unit, a provision unit, and a mitigation unit. The collection unit collects information based on a patient's preferences and interests. For example, the collection unit collects information such as places the patient has visited in the past, favorite scenery, and family photos. The collection unit can also collect information using means such as questionnaires, sensors, and social media analysis. The generation unit generates photos and videos based on the collected information using a generation AI. For example, the generation unit generates photos and videos of places the patient has visited in the past. The generation unit can also use the generation AI to generate content in the form of still images, slideshows, short videos, and the like. The provision unit provides the generated photos and videos to the patient. For example, the provision unit provides the content using a smartphone app, tablet, VR device, or the like. The provision unit can alleviate BPSD symptoms by showing the generated content to the patient. The mitigation unit alleviates the patient's symptoms based on the provided content. For example, the mitigation unit can show a video of a relaxing scenery if the patient is excited. The mitigation unit can also show photos of family members if the patient is feeling anxious. Furthermore, the mitigation department is considering using VR images for patients with symptoms of wandering in the future. As a result, the BPSD mitigation system according to the embodiment can alleviate BPSD symptoms and reduce the burden on care facility staff and relatives by providing content based on the patient's preferences and interests.

[0072] The collection unit can collect photos of places the patient has visited in the past, favorite scenery, and family. The collection unit, for example, collects information on places the patient has visited in the past. For example, the collection unit collects information such as the patient's travel destinations, hometowns, and places of memories. The collection unit can also collect the patient's favorite scenery. For example, the collection unit collects scenery such as the ocean, mountains, and cityscapes that the patient likes. The collection unit can also collect photos of the patient's family. For example, the collection unit collects photos of family trips and daily life. This allows for more effective content to be provided by collecting information based on the patient's past experiences and preferences. 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 information on places the patient has visited in the past into AI, and the AI ​​can analyze and collect the information.

[0073] The generation unit can generate photos and videos based on the collected information. For example, the generation unit generates photos based on the collected information. For example, the generation unit generates photos of places the patient has visited in the past. The generation unit can also generate videos based on the collected information. For example, the generation unit generates videos of scenery the patient likes. The generation unit can also generate content in the form of still images, slideshows, short videos, etc. based on the collected information. For example, the generation unit uses a generation AI to generate still images tailored to the patient's preferences. The generation unit can also use a generation AI to generate slideshows based on the patient's interests. In this way, content tailored to the patient can be provided by generating content based on the collected information. Some or all of the above-described processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit can input the collected information into a generation AI, which then generates photos and videos based on the information.

[0074] The providing unit can provide the generated photos and videos to the patient. The providing unit, for example, provides the generated photos to the patient. For example, the providing unit displays the photos using a smartphone app. The providing unit can also provide the generated videos to the patient. For example, the providing unit plays the videos using a tablet. The providing unit can also provide the generated content using a VR device. For example, the providing unit provides the patient with a virtual reality experience using a VR device. In this way, providing the generated content to the patient can alleviate the symptoms of BPSD. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the generated photos and videos to AI, and the AI ​​can select a method for providing the content to the patient.

[0075] The mitigation unit can alleviate the patient's symptoms based on the provided content. For example, if the patient is agitated, the mitigation unit can show them videos of relaxing scenery. For example, the mitigation unit can show videos of ocean waves, forest scenes, or stars in the night sky. If the patient is feeling anxious, the mitigation unit can also show them photos of family members. For example, the mitigation unit can show them photos of family trips or everyday life. Furthermore, the mitigation unit is considering using VR images for patients with symptoms of wandering in the future. For example, the mitigation unit can satisfy the patient's desire to wander by showing them VR images of places the patient has visited in the past. This can alleviate the patient's symptoms based on the provided content, thereby reducing the burden on nursing home staff and relatives. Some or all of the above-mentioned processing in the mitigation unit may be performed using AI, or may be performed without AI. For example, the mitigation unit can input the patient's symptoms into AI, which can then select appropriate content based on the symptoms.

[0076] The alleviation unit can show a video of a relaxing scene when the patient is in an excited state. For example, when the patient is in an excited state, the alleviation unit shows a video of a relaxing scene. For example, the alleviation unit shows a video of ocean waves, a forest scene, or stars in the night sky. In this way, by showing a video of a relaxing scene when the patient is in an excited state, symptoms can be alleviated. Some or all of the above-mentioned processing in the alleviation unit may be performed using AI, or may be performed without using AI. For example, the alleviation unit can input the patient's excited state into AI, and the AI ​​can select a video of a relaxing scene based on that state.

[0077] The alleviation unit can show the patient photos of family members if the patient is feeling anxious. For example, the alleviation unit can show the patient photos of family members if the patient is feeling anxious. For example, the alleviation unit can show photos of family trips or daily life photos. In this way, if the patient is feeling anxious, showing the patient photos of family members can provide a sense of security. Some or all of the above-mentioned processing in the alleviation unit may be performed using AI, or may be performed without using AI. For example, the alleviation unit can input the patient's anxiety state into AI, and the AI ​​can select photos of family members based on that state.

[0078] The mitigation unit can use VR images for patients with symptoms of wandering. The mitigation unit uses VR images for patients with symptoms of wandering. For example, the mitigation unit can satisfy the patient's desire to wander by showing them VR images of places they have visited in the past. In this way, by using VR images for patients with symptoms of wandering, the patient's desire to wander can be satisfied and they can be prevented from actually moving around. Some or all of the above-mentioned processing in the mitigation unit may be performed using AI, or may be performed without using AI. For example, the mitigation unit can input the patient's desire to wander into AI, and the AI ​​can select appropriate VR images based on that desire.

[0079] 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 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 can collect information related to past memories when the patient is relaxed. The collection unit can also temporarily suspend information collection when the patient is excited and resume it when the patient has calmed down. Furthermore, when the patient is feeling anxious, the collection unit can prioritize collecting information that provides a sense of security. This allows for more effective information collection by adjusting the timing of information collection based on the patient's emotions. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input patient emotion data into AI, and the AI ​​can adjust the timing of information collection based on the data.

[0080] The collection unit can analyze the patient's past behavioral history and select the optimal information collection method. The collection unit, for example, analyzes the patient's past behavioral history and selects the optimal information collection method. For example, the collection unit prioritizes collecting information on places the patient has frequently visited in the past. The collection unit can also collect information based on scenery and photos that the patient has liked to see in the past. Furthermore, the collection unit can analyze the patient's past behavioral patterns and select the most effective information collection method. In this way, the optimal information collection method can be selected by analyzing the patient's past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the patient's past behavioral data into AI, and the AI ​​can select the optimal information collection method based on that data.

[0081] The collection unit can filter information based on the patient's current psychological state and health state when collecting information. For example, the collection unit can filter information based on the patient's current psychological state and health state when collecting information. For example, when the patient is relaxed, the collection unit can collect information of relaxing scenery. Furthermore, when the patient is excited, the collection unit can collect less stimulating information. Furthermore, when the patient is feeling anxious, the collection unit can collect information that provides a sense of security. In this way, by filtering information based on the patient's current psychological state and health state, more appropriate information can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input data on the patient's psychological state and health state into AI, and the AI ​​can filter information based on that data.

[0082] The collection unit can select the optimal collection means depending on the patient's input method when collecting information. For example, when collecting information, the collection unit selects the optimal 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. Furthermore, if the patient prefers image input, the collection unit can collect information by image. In this way, by selecting the optimal collection means depending on the patient's input method, the efficiency of information collection is improved. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input data on the patient's input method into AI, and the AI ​​can select the optimal collection means based on that data.

[0083] 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. The collection unit, for example, estimates the patient's emotions and determines 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 information of relaxing scenery. Furthermore, when the patient is excited, the collection unit can prioritize collecting less stimulating information. Furthermore, when the patient is feeling anxious, the collection unit can prioritize collecting information that provides a sense of security. This enables more effective information collection by determining the priority of information to be collected based on the patient's emotions. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input patient emotion data into AI, and the AI ​​can determine the priority of information based on the data.

[0084] The collection unit can prioritize collecting highly relevant information by taking into account the patient's geographical location information when collecting information. For example, the collection unit prioritizes collecting highly relevant information by taking into account the patient's geographical location information when collecting 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. Furthermore, the collection unit can also prioritize collecting information related to places the patient plans to visit in the future. In this way, by collecting information by taking into account the patient's geographical location information, more relevant information can be provided. Some or all of the above-described processing by the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the patient's geographical location information into AI, and the AI ​​can collect highly relevant information based on that information.

[0085] The collection unit can analyze the patient's social media activity and collect relevant information when collecting information. For example, the collection unit can analyze the patient's social media activity and collect relevant information when collecting information. For example, the collection unit can collect information related to places where the patient has checked in on social media. The collection unit can also analyze the content of the patient's social media posts and collect relevant information. Furthermore, the collection unit can also collect relevant information by referring 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-mentioned processing in the collection unit may be performed using AI or may be performed without using AI. For example, the collection unit can input data on the patient's social media activity into AI, and the AI ​​can collect relevant information based on that data.

[0086] The collection unit can customize the collection method by reflecting the patient's past feedback when collecting information. For example, the collection unit customizes the collection method by reflecting the patient's past feedback when collecting information. For example, the collection unit customizes the collection method based on the patient's past preferences. The collection unit can also analyze the patient's past feedback and select the optimal collection method. Furthermore, the collection unit can improve the collection method based on feedback provided by the patient in the past. This allows the collection method to be customized by reflecting the patient's past feedback, enabling more effective information collection. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the patient's past feedback data into AI, and the AI ​​can customize the collection method based on that data.

[0087] The generation unit can estimate the patient's emotions and adjust the presentation method of the generated content based on the estimated patient's emotions. For example, the generation unit can estimate the patient's emotions and adjust the presentation method of the generated content based on the estimated patient's emotions. For example, if the patient is relaxed, the generation unit can generate a video of a relaxing landscape accompanied by calm music. If the patient is excited, the generation unit can also generate a photo in calm colors. Furthermore, if the patient is feeling anxious, the generation unit can generate a photo of family members that gives a sense of security. In this way, by adjusting the presentation method of the content based on the patient's emotions, more effective content can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. For example, the generation unit can input patient emotion data into the generation AI, and the generation AI can adjust the presentation method of the content based on the data.

[0088] The generation unit can adjust the level of detail of the content generated based on the importance of the information when generating the content. For example, the generation unit can adjust the level of detail of the content generated based on the importance of the information when generating the content. For example, the generation unit can generate a video including a detailed explanation for important information. The generation unit can also generate a simple photo for less important information. Furthermore, the generation unit can adjust the length and level of detail of the content to be generated according to the importance of the information. In this way, by adjusting the level of detail of the content generated based on the importance of the information, more effective content can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit can input information importance data to the generation AI, and the generation AI can adjust the level of detail of the content generated based on that data.

[0089] The generation unit can apply different generation algorithms depending on the category of information when generating content. For example, the generation unit can apply different generation algorithms depending on the category of information when generating content. For example, when generating a landscape photo, the generation unit can use a generation algorithm specialized for landscapes. Furthermore, when generating a family photo, the generation unit can use a generation algorithm specialized for people. Furthermore, when generating a video, the generation unit can use a generation algorithm specialized for videos. In this way, by applying different generation algorithms depending on the category of information, more appropriate content can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit can input information category data into a generation AI, and the generation AI can select an appropriate generation algorithm based on that data.

[0090] The generation unit can improve the accuracy of generation by referring to the patient's past generation results when generating content. For example, the generation unit can improve the accuracy of generation by referring to the patient's past generation results when generating content. For example, the generation unit can improve the accuracy of generation based on content that the patient has previously preferred. The generation unit can also analyze the patient's past generation results and select the optimal generation method. Furthermore, the generation unit can improve the accuracy of generation based on feedback provided by the patient in the past. In this way, the accuracy of generation can be 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 a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit can input the patient's past generation result data into the generation AI, and the generation AI can improve the accuracy of generation based on that data.

[0091] The generation unit can estimate the patient's emotions and adjust the length of the generated content based on the estimated patient's emotions. The generation unit, for example, estimates the patient's emotions and adjusts the length of the generated content based on the estimated patient's emotions. For example, if the patient is relaxed, the generation unit can generate a longer, relaxing video. If the patient is excited, the generation unit can also generate a shorter, calming video. Furthermore, if the patient is anxious, the generation unit can generate a short video that provides a sense of security. This allows for more effective content to be provided by adjusting the length of the content based on the patient's emotions. Some or all of the above-described processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. For example, the generation unit can input patient emotion data into the generation AI, and the generation AI can adjust the length of the content based on that data.

[0092] The generation unit can determine the generation priority based on the time of information submission when generating content. The generation unit can determine the generation priority based on the time of information submission when generating content. For example, the generation unit can prioritize generation of information with high urgency. The generation unit can also prioritize generation of information with an approaching submission deadline. Furthermore, the generation unit can adjust the generation priority based on the time of submission. In this way, by determining the generation priority based on the time of information submission, it is possible to prioritize generation of information with high urgency. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit can input information submission time data into the generation AI, and the generation AI can determine the generation priority based on that data.

[0093] The generation unit can adjust the order of generation based on the relevance of information when generating content. The generation unit, for example, adjusts the order of generation based on the relevance of information when generating content. For example, the generation unit prioritizes generation of highly relevant information. The generation unit can also postpone generation of less relevant information. Furthermore, the generation unit can adjust the order of generation based on the relevance of information. In this way, by adjusting the order of generation based on the relevance of information, more relevant information can be generated preferentially. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit can input information relevance data into the generation AI, and the generation AI can adjust the order of generation based on that data.

[0094] The generation unit can adjust the use of technical terminology in the generated content according to the patient's level of expertise when generating content. For example, the generation unit can adjust the use of technical terminology in the generated content according to the patient's level of expertise when generating content. For example, if the patient has technical expertise, the generation unit can generate content that uses a lot of technical terminology. Also, if the patient does not have technical expertise, the generation unit can generate content that avoids technical terminology. Furthermore, the generation unit can adjust the use of technical terminology in the generated content according to the patient's level of expertise. This makes it possible to provide content that is easier to understand by adjusting the use of technical terminology according to the patient's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. For example, the generation unit can input patient expertise level data into the generation AI, and the generation AI can adjust the use of technical terminology based on that data.

[0095] The providing unit can estimate the patient's emotions and adjust the content provision method based on the estimated patient's emotions. The providing unit, for example, estimates the patient's emotions and adjusts the content provision method based on the estimated patient's emotions. For example, if the patient is relaxed, the providing unit can provide content with calming music. Also, if the patient is excited, the providing unit can provide content in calming colors. Furthermore, if the patient is feeling anxious, the providing unit can provide photos of family members that give a sense of security. This allows for more effective content provision by adjusting the content provision method based on the patient's emotions. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input patient emotion data into AI, and the AI ​​can adjust the content provision method based on the data.

[0096] The providing unit can select the optimal delivery method by referring to the patient's past responses when providing content. For example, the providing unit selects the optimal delivery method by referring to the patient's past responses when providing content. For example, the providing unit selects the optimal delivery method based on the delivery method that the patient preferred in the past. The providing unit can also analyze the patient's past responses and select the optimal delivery method. Furthermore, the providing unit can improve the delivery method based on feedback provided by the patient in the past. This makes it possible to select the optimal delivery method by referring to the patient's past responses and provide content more effectively. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the patient's past response data into AI, and the AI ​​can select the optimal delivery method based on that data.

[0097] The providing unit can customize the provided content according to the patient's current psychological state when providing content. For example, the providing unit customizes the provided content according to the patient's current psychological state when providing content. For example, if the patient is relaxed, the providing unit can provide content with a relaxing landscape. Furthermore, if the patient is excited, the providing unit can provide content with calming colors. Furthermore, if the patient is feeling anxious, the providing unit can provide photos of family members that give a sense of security. This allows for more effective content provision by customizing the provided content according to the patient's current psychological state. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input data on the patient's current psychological state into AI, and the AI ​​can customize the provided content based on that data.

[0098] The providing unit can improve the content delivery method by reflecting patient feedback when providing content. For example, the providing unit can improve the content delivery method by reflecting patient feedback when providing content. For example, the providing unit can improve the content delivery method based on feedback previously provided by the patient. The providing unit can also analyze patient feedback and select the optimal content delivery method. Furthermore, the providing unit can customize the content to be provided based on feedback provided by the patient. This allows the content delivery method to be improved by reflecting patient feedback, enabling more effective content delivery. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input patient feedback data into AI, and the AI ​​can improve the content delivery method based on that data.

[0099] The providing unit can estimate the patient's emotions and determine the priority of content to be provided based on the estimated patient's emotions. The providing unit, for example, estimates the patient's emotions and determines the priority of content to be provided based on the estimated patient's emotions. For example, if the patient is relaxed, the providing unit can prioritize providing content with relaxing scenery. Also, if the patient is excited, the providing unit can prioritize providing content with calming colors. Furthermore, if the patient is feeling anxious, the providing unit can prioritize providing photos of family members that give a sense of security. This enables more effective content provision by determining the priority of content to be provided based on the patient's emotions. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input patient emotion data into AI, and the AI ​​can determine the priority of content based on the data.

[0100] The providing unit can select the optimal delivery method by taking into account the patient's geographical location information when providing content. For example, the providing unit selects the optimal delivery method by taking into account the patient's geographical location information when providing content. For example, the providing unit provides content related to the patient's current location. The providing unit can also provide content related to places the patient has visited in the past. Furthermore, the providing unit can also provide content related to places the patient plans to visit in the future. This enables more effective content delivery by selecting the optimal delivery method by taking into account the patient's geographical location information. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the patient's geographical location information into AI, and the AI ​​can select the optimal delivery method based on that information.

[0101] The providing unit can customize the content to be provided by analyzing the patient's social media activity when providing content. For example, the providing unit can customize the content to be provided by analyzing the patient's social media activity when providing content. For example, the providing unit can provide content related to places 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 content. Furthermore, the providing unit can provide related content by referring to the activity of the patient's friends on social media. In this way, more relevant content can be provided by analyzing the patient's social media activity. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input data on the patient's social media activity into AI, and the AI ​​can customize the content to be provided based on that data.

[0102] The providing unit can customize the delivery method by reflecting the patient's past feedback when providing content. For example, the providing unit customizes the delivery method by reflecting the patient's past feedback when providing content. For example, the providing unit customizes the delivery method based on the delivery method that the patient previously preferred. The providing unit can also analyze the patient's past feedback and select the optimal delivery method. Furthermore, the providing unit can customize the content to be provided based on feedback provided by the patient in the past. This allows the delivery method to be customized by reflecting the patient's past feedback, enabling more effective content delivery. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the patient's past feedback data into AI, and the AI ​​can customize the delivery method based on that data.

[0103] The alleviation unit can estimate the patient's emotions and adjust the symptom alleviation method based on the estimated patient's emotions. The alleviation unit can, for example, estimate the patient's emotions and adjust the symptom alleviation method based on the estimated patient's emotions. For example, if the patient is relaxed, the alleviation unit can show a video of a relaxing scene. If the patient is excited, the alleviation unit can also show a photo with calming colors. Furthermore, if the patient is feeling anxious, the alleviation unit can also show a photo of family members that gives a sense of security. In this way, by adjusting the symptom alleviation method based on the patient's emotions, more effective symptom alleviation is possible. Some or all of the above-mentioned processing in the alleviation unit may be performed using AI, or may be performed without using AI. For example, the alleviation unit can input patient emotion data into AI, and the AI ​​can adjust the symptom alleviation method based on that data.

[0104] The mitigation unit can analyze the patient's past behavior and select the optimal mitigation method when alleviating symptoms. For example, the mitigation unit analyzes the patient's past behavior and selects the optimal mitigation method when alleviating symptoms. For example, the mitigation unit selects the optimal mitigation method based on methods that the patient has previously preferred. The mitigation unit can also analyze the patient's past behavioral patterns and select the optimal mitigation method. Furthermore, the mitigation unit can improve the mitigation method based on feedback provided by the patient in the past. In this way, by analyzing the patient's past behavior, the optimal mitigation method can be selected and more effective symptom alleviation is possible. Some or all of the above-mentioned processing in the mitigation unit may be performed using AI, or may be performed without using AI. For example, the mitigation unit can input the patient's past behavioral data into AI, and the AI ​​can select the optimal mitigation method based on that data.

[0105] The alleviation unit can customize alleviation measures based on the patient's current psychological state when alleviating symptoms. For example, the alleviation unit customizes alleviation measures based on the patient's current psychological state when alleviating symptoms. For example, if the patient is relaxed, the alleviation unit can show a video of a relaxing scene. If the patient is excited, the alleviation unit can show a photo with calming colors. Furthermore, if the patient is feeling anxious, the alleviation unit can show a photo of family members that gives a sense of security. In this way, customizing alleviation measures based on the patient's current psychological state enables more effective symptom alleviation. Some or all of the above-mentioned processing in the alleviation unit may be performed using AI, or may be performed without using AI. For example, the alleviation unit can input data on the patient's current psychological state into AI, and the AI ​​can customize alleviation measures based on that data.

[0106] The mitigation unit can improve the mitigation method by reflecting patient feedback when alleviating symptoms. For example, the mitigation unit improves the mitigation method by reflecting patient feedback when alleviating symptoms. For example, the mitigation unit improves the mitigation method based on feedback previously provided by the patient. The mitigation unit can also analyze the patient's feedback and select the optimal mitigation method. Furthermore, the mitigation unit can customize the mitigation means based on feedback provided by the patient. In this way, by reflecting the patient's feedback, the mitigation method can be improved and more effective symptom alleviation is possible. Some or all of the above-mentioned processing in the mitigation unit may be performed using AI or without AI. For example, the mitigation unit can input patient feedback data into AI, and the AI ​​can improve the mitigation method based on that data.

[0107] The alleviation unit can estimate the patient's emotions and determine the priority of symptom alleviation based on the estimated patient's emotions. The alleviation unit can, for example, estimate the patient's emotions and determine the priority of symptom alleviation based on the estimated patient's emotions. For example, if the patient is relaxed, the alleviation unit can prioritize showing videos of relaxing scenery. Also, if the patient is excited, the alleviation unit can prioritize showing photos with calming colors. Furthermore, if the patient is feeling anxious, the alleviation unit can prioritize showing photos of family members that give a sense of security. In this way, by determining the priority of symptom alleviation based on the patient's emotions, more effective symptom alleviation is possible. Some or all of the above-mentioned processing in the alleviation unit may be performed using AI, or may be performed without using AI. For example, the alleviation unit can input patient emotion data into AI, and the AI ​​can determine the priority of symptom alleviation based on that data.

[0108] The mitigation unit can select the optimal mitigation method by taking into account the patient's geographical location information when alleviating symptoms. For example, the ... display content related to the patient's current location. The mitigation unit can also display content related to places the patient has visited in the past. Furthermore, the mitigation unit can display content related to places the patient plans to visit in the future. In this way, more effective symptom alleviation is possible by selecting the optimal mitigation method by taking into account the patient's geographical location information. Some or all of the above-described processing in the mitigation unit may be performed using AI, or may be performed without using AI. For example, the mitigation unit can input the patient's geographical location information into AI, and the AI ​​can select the optimal mitigation method based on that information.

[0109] The mitigation unit can analyze the patient's social media activity and suggest mitigation measures when symptoms are alleviated. For example, the mitigation unit can analyze the patient's social media activity and suggest mitigation measures when symptoms are alleviated. For example, the mitigation unit can display content related to places where the patient has checked in on social media. The mitigation unit can also analyze the patient's social media posts and show related content. Furthermore, the mitigation unit can also show related content based on the activities of the patient's friends on social media. In this way, by analyzing the patient's social media activity, more relevant mitigation measures can be suggested. Some or all of the above-mentioned processing in the mitigation unit may be performed using AI, or may be performed without using AI. For example, the mitigation unit can input data on the patient's social media activity into AI, and the AI ​​can suggest mitigation measures based on that data.

[0110] The mitigation unit can customize the mitigation method by reflecting the patient's past feedback when alleviating symptoms. For example, the mitigation unit customizes the mitigation method by reflecting the patient's past feedback when alleviating symptoms. For example, the mitigation unit customizes the mitigation method based on the patient's past preferred method. The mitigation unit can also analyze the patient's past feedback and select the optimal mitigation method. Furthermore, the mitigation unit can improve the mitigation method based on feedback provided by the patient in the past. In this way, the mitigation method can be customized by reflecting the patient's past feedback, enabling more effective symptom alleviation. Some or all of the above-mentioned processing in the mitigation unit may be performed using AI, or may be performed without using AI. For example, the mitigation unit can input the patient's past feedback data into AI, and the AI ​​can customize the mitigation method based on that data. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, generation unit, provision unit, and alleviation 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 can collect information based on the patient's preferences and interests using the camera 42 and microphone 38B of the smart device 14. The collection unit can also collect information using means such as questionnaires and social media analysis via the specific processing unit 290 of the data processing device 12. The generation unit generates photos and videos based on the information collected using the generation AI via the specific processing unit 290 of the data processing device 12. The provision unit provides the generated photos and videos to the patient using the control unit 46A of the smart device 14. The alleviation unit alleviates the patient's symptoms based on the provided content using the display 40A and speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, provision unit, and alleviation 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 can collect information based on the patient's preferences and interests using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also collect information using means such as questionnaires and social media analysis via the specific processing unit 290 of the data processing device 12. The generation unit generates photos and videos based on the information collected using the generation AI via the specific processing unit 290 of the data processing device 12. The provision unit provides the patient with the photos and videos generated using the control unit 46A of the smart glasses 214. The alleviation unit alleviates the patient's symptoms based on the provided content using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, generation unit, provision unit, and alleviation unit described above 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 can collect information based on the patient's preferences and interests using the camera 42 and microphone 238 of the headset-type terminal 314. The collection unit can also collect information using means such as questionnaires and social media analysis by the specific processing unit 290 of the data processing device 12. The generation unit generates photos and videos based on the information collected using the generation AI by the specific processing unit 290 of the data processing device 12. The provision unit provides the patient with the photos and videos generated using the control unit 46A of the headset-type terminal 314. The alleviation unit alleviates the patient's symptoms based on the provided content using the display 343 and speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, generation unit, provision unit, and alleviation 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 can collect information based on the patient's preferences and interests using the camera 42 and microphone 238 of the robot 414. The collection unit can also collect information using means such as questionnaires and social media analysis by the specific processing unit 290 of the data processing device 12. The generation unit generates photos and videos based on the information collected by the specific processing unit 290 of the data processing device 12 using a generation AI. The provision unit provides the patient with the photos and videos generated using the control unit 46A of the robot 414. The alleviation unit alleviates the patient's symptoms based on the provided content using the speaker 240 and display device of the robot 414.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The collection unit can also estimate the patient's emotions and adjust the type of information to be collected based on the estimated emotions. For example, if the patient is relaxed, the collection unit can prioritize collecting information about relaxing scenery and music. If the patient is excited, the collection unit can collect less stimulating information. Furthermore, if the patient is feeling anxious, the collection unit can collect information about family photos and memorable places that give a sense of security. In this way, adjusting the type of information to be collected based on the patient's emotions enables more effective information collection.

[0113] The generation unit can also adjust the format of the content to be generated based on the collected information. For example, if the patient is relaxed, the generation unit can generate a video of a relaxing landscape accompanied by calm music. If the patient is excited, the generation unit can generate a photo with calming colors. Furthermore, if the patient is feeling anxious, the generation unit can generate a photo of a family member that gives a sense of security. In this way, by adjusting the format of the content based on the collected information, more effective content can be provided.

[0114] When providing the generated content, the providing unit can estimate the patient's emotions and adjust the method of providing the content based on the estimated emotions. For example, if the patient is relaxed, the providing unit can provide content with calming music. If the patient is excited, the providing unit can provide content in calming colors. Furthermore, if the patient is feeling anxious, the providing unit can provide a photo of family members that gives a sense of security. In this way, by adjusting the method of providing content based on the patient's emotions, more effective content provision is possible.

[0115] When alleviating the patient's symptoms based on the provided content, the alleviation unit can also estimate the patient's emotions and adjust the alleviation method based on the estimated emotions. For example, if the patient is relaxed, the alleviation unit can show a video of a relaxing scene. If the patient is excited, the alleviation unit can show a photo with calming colors. Furthermore, if the patient is feeling anxious, the alleviation unit can show a photo of a family member that gives a sense of security. This allows for more effective symptom alleviation by adjusting the alleviation method based on the patient's emotions.

[0116] The collection unit can also analyze the patient's past behavioral history and select the optimal information collection method. For example, the collection unit can prioritize collecting information on places the patient has frequently visited in the past. It can also collect information based on scenery and photos that the patient has liked to see in the past. It can also analyze the patient's past behavioral patterns and select the most effective information collection method. In this way, the optimal information collection method can be selected by analyzing the patient's past behavioral history.

[0117] When generating content, the generation unit can also adjust the level of detail of the content based on the importance of the information. For example, the generation unit can generate a video including a detailed explanation for important information. For less important information, the generation unit can generate a simple photo. Furthermore, the length and level of detail of the content to be generated can be adjusted depending on the importance of the information. In this way, by adjusting the level of detail of the content based on the importance of the information, more effective content can be provided.

[0118] When providing content, the providing unit can also select the optimal delivery method by referring to the patient's past responses. For example, the providing unit selects the optimal delivery method based on delivery methods that the patient has preferred in the past. The providing unit can also analyze the patient's past responses and select the optimal delivery method. Furthermore, the delivery method can also be improved based on feedback provided by the patient in the past. In this way, by referring to the patient's past responses, the optimal delivery method can be selected, enabling more effective content delivery.

[0119] The alleviation unit can also select the optimal alleviation method by analyzing the patient's past behavior when alleviating symptoms. For example, the alleviation unit selects the optimal alleviation method based on methods that the patient has preferred in the past. The alleviation unit can also select the optimal alleviation method by analyzing the patient's past behavior patterns. Furthermore, the alleviation method can be improved based on feedback provided by the patient in the past. In this way, by analyzing the patient's past behavior, the optimal alleviation method can be selected, enabling more effective symptom alleviation.

[0120] When collecting information, the collection unit can also filter the information based on the patient's current psychological state and health condition. For example, when the patient is relaxed, the collection unit can collect information of relaxing scenery. When the patient is excited, the collection unit can collect less stimulating information. Furthermore, when the patient is feeling anxious, the collection unit can collect information that gives a sense of security. In this way, by filtering information based on the patient's current psychological state and health condition, more appropriate information can be collected.

[0121] When providing content, the providing unit can also analyze the patient's social media activity to customize the content to be provided. For example, the providing unit can provide content related to places where the patient has checked in on social media. The providing unit can also analyze the patient's social media posts to provide relevant content. Furthermore, the providing unit can provide relevant content based on the activities of the patient's friends on social media. In this way, more relevant content can be provided by analyzing the patient's social media activity.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The collection unit collects information based on the patient's preferences and interests. For example, the collection unit collects information such as places the patient has visited in the past, favorite scenery, and family photos. The collection unit can also collect information using methods such as questionnaires, sensors, and social media analysis. Step 2: The generator uses the AI ​​to generate photos and videos based on the collected information. For example, the generator generates photos and videos of places the patient has visited in the past. The generator can also use the AI ​​to generate content in the form of still images, slideshows, short videos, and other formats. Step 3: The providing unit provides the generated photos and videos to the patient. For example, the providing unit provides the content using a smartphone app, tablet, VR device, etc. The providing unit can also reduce BPSD symptoms by showing the generated content to the patient. Step 4: The mitigation department alleviates the patient's symptoms based on the provided content. For example, if the patient is excited, the mitigation department can show them a video of a relaxing scene. If the patient is feeling anxious, the mitigation department can also show them photos of their family. In addition, the mitigation department is considering using VR images for patients with symptoms of wandering in the future.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects information based on patient preferences and interests; a generation unit that generates photos and videos based on the information collected by the collection unit; a providing unit that provides the photographs and videos generated by the generating unit to patients; a relief unit that relieves symptoms of a patient based on the content provided by the provision unit. A system characterized by:

2. The collecting unit Collect photos of places the patient has visited, their favorite scenery, and their family.

2. The system of claim 1.

3. The generation unit Generate photos and videos based on collected information 2. The system of claim 1.

4. The providing unit Providing generated photos and videos to patients 2. The system of claim 1.

5. The relaxation portion is Relieve patient symptoms based on the content provided 2. The system of claim 1.

6. The relaxation portion is If the patient is agitated, show them videos of relaxing scenes.

2. The system of claim 1.

7. The relaxation portion is If the patient is anxious, show them pictures of their family 2. The system of claim 1.

8. The relaxation portion is Using VR images for patients with wandering symptoms 2. The system of claim 1.

Images