system

Abstract

The system according to this embodiment aims to help dementia patients achieve emotional stability by recreating conversations with deceased loved ones. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a provision unit, and an adjustment unit. The collection unit collects photographs taken and audio recordings made of the subject during their lifetime. The generation unit analyzes the photographs and audio collected by the collection unit and reconstructs the content of conversations and stories from before their death. The provision unit provides the patient with a specific video generated by the generation unit. The adjustment unit monitors the patient's reaction based on the video provided by the provision unit and adjusts the specific video as necessary.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that dementia patients could not recognize important people who had passed away, and their mental state remained unstable.

[0005] The system according to the embodiment aims to reproduce the conversation with important people who have passed away for dementia patients and achieve mental stability.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, a provision unit, and an adjustment unit. The collection unit collects photographs taken and audio recordings made of the subject during their lifetime. The generation unit analyzes the photographs and audio collected by the collection unit and reconstructs the content of conversations and stories from the subject's lifetime. The provision unit provides the patient with a specific video generated by the generation unit. The adjustment unit monitors the patient's reaction based on the video provided by the provision unit and adjusts the specific video as needed. [Effects of the Invention]

[0007] The system according to this embodiment can help dementia patients find emotional stability by recreating conversations with deceased loved ones. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The care support system according to an embodiment of the present invention is a system for caregivers (family members and care facilities) of dementia patients and dementia patients who have lost a loved one. This system addresses the problem that dementia patients cannot recognize the fact of death, repeatedly ask questions of their caregivers, and fall into a state of emotional instability due to their inability to recognize the death. To solve this, the system utilizes a generative AI to create a video that allows for dialogue and conversation, incorporating photographs, audio recordings, and conversations and stories from the time the person was alive. First, photographs, audio recordings, and conversations of the person from before their death are collected. For example, photo albums, audio recordings, and past conversation records held by family members or care facilities are collected. This collected data is input into the generative AI. Next, the generative AI analyzes the collected data and generates a video to recreate conversations and stories from before the person's death. Based on the photographs and audio recordings, the generative AI recreates conversations and episodes that the person had before their death. For example, it can recreate memorable scenes spent with family or conversations at specific events. The generated video is provided to the dementia patient. By watching this video, the dementia patient can experience dialogue and conversations with their deceased loved one. This is expected to alleviate the emotional instability of dementia patients and reduce the repetitive questioning of caregivers. For example, if a dementia patient repeatedly asks, "Where is Mom?", watching the generated video allows them to experience a conversation with their mother and gain a sense of security. Furthermore, recreating specific events or memorable scenes can help stabilize the emotional state of dementia patients. This mechanism can alleviate the emotional instability of dementia patients and reduce the burden on caregivers. By utilizing generation AI, personalized dialogues and conversations tailored to individual patients become possible, leading to more effective care support. As a result, the care support system can alleviate the emotional instability of dementia patients and reduce the burden on caregivers.

[0029] The care support system according to this embodiment comprises a collection unit, a generation unit, a provision unit, and an adjustment unit. The collection unit collects photographs taken and audio recordings made of the subject during their lifetime. The collection unit can collect, for example, photo albums, audio recordings, and past conversation records held by family members or care facilities. The collection unit can, for example, evaluate the reliability of photographs and audio provided by family members and prioritize the collection of highly reliable data. The collection unit can also estimate the patient's emotions and select the types of data to collect based on the estimated emotions. For example, if the patient is sad, the collection unit can prioritize the collection of photographs and audio recordings of happy memories. The generation unit analyzes the photographs and audio collected by the collection unit and reconstructs the content of conversations and stories from the subject's lifetime. The generation unit can, for example, analyze the collected photographs and audio and reconstruct conversations and episodes that the subject had during their lifetime. The generation unit uses a generation AI to reconstruct conversations and episodes based on the collected data. For example, the generation AI can reconstruct memorable scenes that the subject spent with their family or the content of conversations at specific events. The generation unit can estimate the patient's emotions and adjust the content of the generated video based on the estimated emotions. For example, if the patient is sad, the unit can generate a video that includes many scenes of happy memories. The provision unit provides the specific video generated by the generation unit to the patient. The provision unit can, for example, provide the generated video to a patient with dementia. The provision unit can also estimate the patient's emotions and adjust the method of providing the video based on the estimated emotions. For example, if the patient is sad, the video can be provided in a quiet environment. The adjustment unit monitors the patient's reaction based on the video provided by the provision unit and adjusts the specific video as needed. The adjustment unit can, for example, monitor the patient's reaction and adjust the video as needed. The adjustment unit can also estimate the patient's emotions and determine the content of the video adjustments based on the estimated emotions. For example, if the patient is sad, the video can be adjusted to include many scenes of happy memories. As a result, the care support system according to this embodiment can alleviate the emotional instability of patients with dementia and reduce the burden on caregivers.

[0030] The data collection unit can collect photo albums, audio recordings, and past conversation records held by families and care facilities. For example, the data collection unit can collect photo albums held by families and care facilities. For example, photo albums provided by families may include photos of family trips and daily life. The data collection unit can also collect audio recordings. For example, these may include recordings of daily conversations or conversations at specific events recorded by families. Furthermore, the data collection unit can also collect past conversation records. For example, these may include conversations recorded by care facilities or conversations recorded by families. This improves the accuracy of the generated videos by collecting highly reliable data. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can use AI to automatically evaluate the reliability of the data provided by families and care facilities and select highly reliable data.

[0031] The generation unit can analyze collected photographs and audio and recreate specific conversations and episodes that the subject had during their lifetime. For example, the generation unit can analyze collected photographs and recreate conversations and episodes that the subject had during their lifetime. For example, the generation unit can recreate memorable scenes spent with family. The generation unit can also analyze collected audio and recreate conversations that the subject had during their lifetime. For example, the generation unit can recreate the content of conversations at specific events. Furthermore, the generation unit can recreate episodes that the subject had during their lifetime based on the collected data. For example, the generation unit can recreate memorable scenes spent with family or the content of conversations at specific events. By recreating conversations and episodes from their lifetime, it is possible to provide a sense of security to dementia patients. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input collected photographs and audio into a generation AI and have the generation AI perform the recreation of conversations and episodes.

[0032] The service provider can provide the generated video to dementia patients. For example, the service provider can provide the generated video to dementia patients through devices such as televisions and tablets. The service provider can also provide the generated video through monitors in care facilities. Furthermore, the service provider can provide the generated video through smartphones and computers owned by family members. By providing the generated video, it is possible to alleviate the emotional instability of dementia patients. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can estimate the patient's emotions and adjust the method of providing the video based on the estimated emotions of the patient. For example, if the patient is sad, the video can be provided in a quiet environment.

[0033] The adjustment unit can monitor the patient's reactions and adjust the video as needed. For example, the adjustment unit can capture the patient's facial expressions with a camera and estimate the patient's emotions using facial recognition technology. It can also record the patient's voice and estimate the patient's emotions using voice analysis technology. Furthermore, the adjustment unit can collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate the patient's emotions using an emotion estimation algorithm. This allows for more effective care support by adjusting the video according to the patient's reactions. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can estimate the patient's emotions and determine the content of the video adjustment based on the estimated emotions. For example, if the patient is sad, the video can be adjusted to include more scenes of happy memories.

[0034] The data collection unit can evaluate the reliability of data provided by families and care facilities and prioritize the collection of highly reliable data. For example, the data collection unit can evaluate the reliability of photos and audio provided by families and prioritize the collection of highly reliable data. For example, to evaluate the reliability of photos and audio provided by families, it can verify the source and quality of the data. The data collection unit can also evaluate the reliability of conversation records provided by care facilities and prioritize the collection of highly reliable data. For example, to evaluate the reliability of conversation records provided by care facilities, it can verify the accuracy and consistency of the records. Furthermore, the data collection unit can also have AI automatically evaluate the reliability of the provided data and select highly reliable data. For example, to evaluate the reliability of the provided data, the AI ​​can calculate a reliability score based on the source and quality of the data. This improves the accuracy of the generated videos by collecting highly reliable data. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0035] The data collection unit can determine the priority of data collection by considering its freshness and relevance. For example, the data collection unit can prioritize the collection of recently taken photos and audio recordings. It can also prioritize the collection of conversation records related to the patient's current condition. Furthermore, the data collection unit can also have AI automatically evaluate the freshness and relevance of the data and determine the priority of collection. For example, the AI ​​can calculate a priority score based on the timing and relevance of the data collection and determine the priority of collection. This allows for the generation of more effective videos by prioritizing the collection of fresh and highly relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0036] The data collection unit can prioritize the collection of highly relevant data by considering the patient's living environment and daily behavioral patterns. For example, the data collection unit can prioritize the collection of photos and audio recordings from places where the patient frequently spends time. It can also prioritize the collection of conversational records related to the patient's daily behavioral patterns. Furthermore, the data collection unit can have AI automatically evaluate data related to the patient's living environment and select highly relevant data. For example, the AI ​​can calculate a relevance score based on the patient's living environment and daily behavioral patterns and select highly relevant data. This allows for the generation of more effective videos by collecting data related to the patient's living environment and behavioral patterns. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0037] The data collection unit can analyze a patient's social media activity and collect relevant data. For example, the data collection unit can collect photos and audio shared by the patient on social media. The data collection unit can also analyze the content of the patient's social media conversations and collect relevant data. Furthermore, the data collection unit can have AI automatically analyze the patient's social media activity and select relevant data. For example, the AI ​​can calculate a relevance score based on the patient's social media activity and select relevant data. This allows for the collection of more diverse data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0038] The generation unit can adjust the level of detail of the generated video based on the importance of the collected data. For example, the generation unit can reproduce scenes containing important dialogue in detail. It can also reproduce less important scenes in a simplified manner. Furthermore, the generation unit can have AI automatically evaluate the importance of the data and adjust the level of detail of the video. For example, the AI ​​can calculate a detail score based on the importance of the data and adjust the level of detail of the video. This allows for the generation of videos with a level of detail appropriate to the importance of the data, thereby providing more effective videos. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0039] The generation unit can apply different generation algorithms depending on the subject's activities and hobbies during their lifetime. For example, the generation unit can generate scenes that recreate the activities the subject enjoyed as hobbies. The generation unit can also apply a specific generation algorithm based on the subject's activities during their lifetime. Furthermore, the generation unit can have AI automatically analyze the subject's hobbies and activities and select the optimal generation algorithm. For example, the AI ​​can select a generation algorithm based on the subject's hobbies and activities and generate a video. This allows for the provision of more personalized videos by applying a generation algorithm tailored to the subject's activities and hobbies. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without using a generation AI.

[0040] The generation unit can determine the priority of videos to generate based on the submission date of the collected data. For example, the generation unit can prioritize generating videos based on the submission date of the collected data. Alternatively, the generation unit can postpone generating videos for older data. Furthermore, the generation unit can have an AI automatically evaluate the data submission date and determine the video priority. For example, the AI ​​can calculate a priority score based on the data submission date and determine the video priority. This allows for the provision of videos that reflect the latest data by prioritizing videos based on the submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0041] The generation unit can adjust the order of the generated videos based on the relevance of the collected data. For example, the generation unit can reproduce highly relevant scenes first. It can also reproduce less relevant scenes later. Furthermore, the generation unit can have AI automatically evaluate the relevance of the data and adjust the order of the videos. For example, the AI ​​can calculate a relevance score based on the degree of similarity of the data content and related topics, and adjust the order of the videos. This allows for the provision of more effective videos by adjusting the order of videos based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0042] The delivery unit can select the optimal delivery method by referring to the patient's past response history. For example, the delivery unit can refer to the delivery method of videos that the patient has previously enjoyed watching. The delivery unit can also have AI automatically analyze the patient's past response history and select the optimal delivery method. Furthermore, the delivery unit can select the most effective delivery method from the patient's past response history. For example, AI can select the optimal delivery method based on the patient's past viewing history and response data. This allows for the selection of a more effective delivery method by referring to past response history. Some or all of the above processing in the delivery unit may be performed using AI or not.

[0043] The delivery unit can adjust the timing of delivery based on the patient's current health condition and lifestyle. For example, the delivery unit can deliver videos when the patient is relaxed. It can also deliver videos when the patient is in good health. Furthermore, the delivery unit can have AI automatically evaluate the patient's lifestyle and select the optimal delivery timing. For example, the AI ​​can select the optimal delivery timing based on the patient's health condition and lifestyle. This allows for the delivery of more effective videos by adjusting the delivery timing according to the patient's health condition and lifestyle. Some or all of the above processing in the delivery unit may be performed using AI or not.

[0044] The delivery unit can select the optimal delivery method considering the patient's device information. For example, if the patient is using a smartphone, the delivery unit can provide a video sized to fit the screen. If the patient is using a tablet, the delivery unit can provide a video optimized for a larger screen. Furthermore, if the patient is using a smartwatch, the delivery unit can provide a concise and highly visible video. This improves the viewing experience by providing videos optimized for the patient's device. Some or all of the above processing in the delivery unit may be performed using AI or not.

[0045] The service provider can adjust the content of the service based on feedback from the patient's family or caregiver. For example, the service provider can adjust the content of the video based on feedback from the family or caregiver. The service provider can also have AI automatically analyze the opinions of the family or caregiver and select the most appropriate content. Furthermore, the service provider can adjust the method of delivering the video based on feedback from the family or caregiver. For example, the AI ​​can select the most appropriate content based on feedback from the family or caregiver and deliver the video. This allows for the delivery of more effective videos by incorporating feedback from the family or caregiver. Some or all of the above-described processes in the service provider may be performed using AI or not.

[0046] The adjustment unit can select the optimal adjustment method by referring to the patient's past response data. For example, the adjustment unit can refer to adjustment methods of videos that the patient has enjoyed watching in the past. The adjustment unit can also have AI automatically analyze the patient's past response data and select the optimal adjustment method. Furthermore, the adjustment unit can select the most effective adjustment method from the patient's past response data. For example, AI can select the optimal adjustment method based on the patient's past viewing history and response data. This allows for the selection of a more effective adjustment method by referring to past response data. Some or all of the above processing in the adjustment unit may be performed using AI or not.

[0047] The adjustment unit can determine the timing of adjustments based on the patient's current psychological state and living environment. For example, the adjustment unit can adjust the video during times when the patient is relaxed. It can also adjust the video when the patient's psychological state is stable. Furthermore, the adjustment unit can have AI automatically evaluate the patient's living environment and select the optimal adjustment timing. For example, the AI ​​can select the optimal adjustment timing based on the patient's psychological state and living environment. This allows for the provision of more effective videos by determining the adjustment timing according to the psychological state and living environment. Some or all of the above processing in the adjustment unit may be performed using AI or not.

[0048] The adjustment unit can determine the adjustments based on feedback from the patient's family or caregiver. For example, the adjustment unit can determine the adjustments based on feedback from the patient's family or caregiver. For example, the adjustment unit can determine the adjustments to the video based on feedback from the family or caregiver. The adjustment unit can also have AI automatically analyze the opinions of the family or caregiver and select the optimal adjustments. Furthermore, the adjustment unit can determine how to adjust the video based on feedback from the family or caregiver. For example, the AI ​​can select the optimal adjustments based on feedback from the family or caregiver and adjust the video. This allows for the provision of more effective videos by reflecting feedback from the family or caregiver. Some or all of the above processing in the adjustment unit may be performed using AI or not.

[0049] The adjustment unit can determine the timing of adjustments by considering the patient's daily rhythm and behavioral patterns. For example, the adjustment unit can adjust the video during times when the patient is typically relaxed. The adjustment unit can also determine the optimal adjustment timing based on the patient's daily behavioral patterns. Furthermore, the adjustment unit can have AI automatically analyze the patient's daily rhythm and select the optimal adjustment timing. For example, the AI ​​can select the optimal adjustment timing based on the patient's daily rhythm and behavioral patterns. This allows for the provision of more effective videos by determining the adjustment timing according to the patient's daily rhythm and behavioral patterns. Some or all of the above-described processes in the adjustment unit may be performed using AI or not.

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

[0051] The data collection unit can also collect data about the patient's living environment. For example, it can collect photos of the room the patient spends time in daily, as well as photos of furniture and decorations that the patient frequently uses. This allows the generated videos to reflect the patient's living environment, providing a more realistic experience. The data collection unit can also receive feedback from family members and caregivers when collecting data about the patient's living environment. For example, based on information provided by family members, it can prioritize collecting data on places and items that the patient particularly likes. Furthermore, the data collection unit can use AI to automatically evaluate the data about the patient's living environment and select the most relevant data. This allows for the generation of more effective videos by collecting data based on the patient's living environment.

[0052] The service provider can adjust the color tone of the generated video based on the patient's visual preferences when providing it. For example, if the patient prefers bright colors, the service provider can adjust the video's color tone to be brighter. Similarly, if the patient prefers muted colors, the service provider can adjust the video's color tone to be more muted. Furthermore, the service provider can adjust the video's contrast and brightness based on the patient's visual preferences. This improves the viewing experience by providing videos tailored to the patient's visual preferences. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can use visual analysis to adjust the video's color tone based on the patient's visual preferences.

[0053] The data collection unit can collect data on the patient's past hobbies and interests. For example, it can collect photos and audio related to hobbies and interests the patient enjoyed in the past. This allows the generated videos to reflect the patient's past hobbies and interests, providing a more personalized experience. The data collection unit can also receive feedback from family members and caregivers when collecting data on the patient's past hobbies and interests. For example, based on information provided by family members, it can prioritize collecting activities and events that the patient particularly enjoyed. Furthermore, the data collection unit can use AI to automatically evaluate the data on the patient's past hobbies and interests and select the most relevant data. This allows for the generation of more effective videos by collecting data based on the patient's past hobbies and interests.

[0054] The generation unit can adjust the content of the generated video based on the reliability of the collected data. For example, it can reproduce detailed scenes based on highly reliable data. It can also reproduce less reliable data in a simplified form. Furthermore, the generation unit can have AI automatically evaluate the reliability of the data and adjust the video content accordingly. For example, the AI ​​can calculate a detail score based on the reliability of the data and adjust the video content based on that score. This allows for the generation of videos with content appropriate to the reliability of the data, thereby providing more effective videos. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI.

[0055] The service provider can adjust the content of the service based on feedback from the patient's family or caregiver. For example, the service provider can adjust the video content based on feedback from family members or caregivers. The service provider can also have AI automatically analyze the opinions of family members or caregivers and select the most appropriate content. Furthermore, the service provider can adjust the method of delivering the video based on feedback from family members or caregivers. For example, the AI ​​can select the most appropriate content based on feedback from family members or caregivers and deliver the video. This allows for the delivery of more effective videos by incorporating feedback from family members or caregivers. Some or all of the above-described processes in the service provider may be performed using AI or not.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects photographs and audio recordings taken of the subject during their lifetime. For example, it can collect photo albums, audio recordings, and past conversation records held by family members or care facilities. The data collection unit can also evaluate the reliability of the photographs and audio provided by the family and prioritize the collection of reliable data. Furthermore, the data collection unit can estimate the patient's emotions and select the types of data to collect based on the estimated emotions. For example, if the patient is sad, it will prioritize the collection of photographs and audio recordings of happy memories. Step 2: The generation unit analyzes the photos and audio collected by the collection unit to recreate conversations and stories from the person's lifetime. The generation unit can analyze the collected photos and audio to recreate conversations and episodes that the person had during their lifetime. Using generation AI, it recreates conversations and episodes based on the collected data. For example, it can recreate memorable scenes of the person spending time with their family or conversations from specific events. The generation unit can also estimate the patient's emotions and adjust the content of the generated video based on the estimated emotions. For example, if the patient is sad, it will generate a video that includes many scenes of happy memories. Step 3: The delivery unit provides the patient with the specific video generated by the generation unit. For example, the generated video can be provided to a patient with dementia. The delivery unit can also estimate the patient's emotions and adjust the way the video is provided based on the estimated emotions. For example, if the patient is sad, the video can be provided in a quiet environment. Step 4: The adjustment unit monitors the patient's reaction based on the video provided by the supply unit and adjusts the specific video as needed. For example, it can monitor the patient's reaction and adjust the video as necessary. The adjustment unit can also estimate the patient's emotions and decide how to adjust the video based on the estimated emotions. For example, if the patient is sad, the adjustment unit will adjust the video to include more scenes of happy memories.

[0058] (Example of form 2) The care support system according to an embodiment of the present invention is a system for caregivers (family members and care facilities) of dementia patients and dementia patients who have lost a loved one. This system addresses the problem that dementia patients cannot recognize the fact of death, repeatedly ask questions of their caregivers, and fall into a state of emotional instability due to their inability to recognize the death. To solve this, the system utilizes a generative AI to create a video that allows for dialogue and conversation, incorporating photographs, audio recordings, and conversations and stories from the time the person was alive. First, photographs, audio recordings, and conversations of the person from before their death are collected. For example, photo albums, audio recordings, and past conversation records held by family members or care facilities are collected. This collected data is input into the generative AI. Next, the generative AI analyzes the collected data and generates a video to recreate conversations and stories from before the person's death. Based on the photographs and audio recordings, the generative AI recreates conversations and episodes that the person had before their death. For example, it can recreate memorable scenes spent with family or conversations at specific events. The generated video is provided to the dementia patient. By watching this video, the dementia patient can experience dialogue and conversations with their deceased loved one. This is expected to alleviate the emotional instability of dementia patients and reduce the repetitive questioning of caregivers. For example, if a dementia patient repeatedly asks, "Where is Mom?", watching the generated video allows them to experience a conversation with their mother and gain a sense of security. Furthermore, recreating specific events or memorable scenes can help stabilize the emotional state of dementia patients. This mechanism can alleviate the emotional instability of dementia patients and reduce the burden on caregivers. By utilizing generation AI, personalized dialogues and conversations tailored to individual patients become possible, leading to more effective care support. As a result, the care support system can alleviate the emotional instability of dementia patients and reduce the burden on caregivers.

[0059] The care support system according to this embodiment comprises a collection unit, a generation unit, a provision unit, and an adjustment unit. The collection unit collects photographs taken and audio recordings made of the subject during their lifetime. The collection unit can collect, for example, photo albums, audio recordings, and past conversation records held by family members or care facilities. The collection unit can, for example, evaluate the reliability of photographs and audio provided by family members and prioritize the collection of highly reliable data. The collection unit can also estimate the patient's emotions and select the types of data to collect based on the estimated emotions. For example, if the patient is sad, the collection unit can prioritize the collection of photographs and audio recordings of happy memories. The generation unit analyzes the photographs and audio collected by the collection unit and reconstructs the content of conversations and stories from the subject's lifetime. The generation unit can, for example, analyze the collected photographs and audio and reconstruct conversations and episodes that the subject had during their lifetime. The generation unit uses a generation AI to reconstruct conversations and episodes based on the collected data. For example, the generation AI can reconstruct memorable scenes that the subject spent with their family or the content of conversations at specific events. The generation unit can estimate the patient's emotions and adjust the content of the generated video based on the estimated emotions. For example, if the patient is sad, the unit can generate a video that includes many scenes of happy memories. The provision unit provides the specific video generated by the generation unit to the patient. The provision unit can, for example, provide the generated video to a patient with dementia. The provision unit can also estimate the patient's emotions and adjust the method of providing the video based on the estimated emotions. For example, if the patient is sad, the video can be provided in a quiet environment. The adjustment unit monitors the patient's reaction based on the video provided by the provision unit and adjusts the specific video as needed. The adjustment unit can, for example, monitor the patient's reaction and adjust the video as needed. The adjustment unit can also estimate the patient's emotions and determine the content of the video adjustments based on the estimated emotions. For example, if the patient is sad, the video can be adjusted to include many scenes of happy memories. As a result, the care support system according to this embodiment can alleviate the emotional instability of patients with dementia and reduce the burden on caregivers.

[0060] The data collection unit can collect photo albums, audio recordings, and past conversation records held by families and care facilities. For example, the data collection unit can collect photo albums held by families and care facilities. For example, photo albums provided by families may include photos of family trips and daily life. The data collection unit can also collect audio recordings. For example, these may include recordings of daily conversations or conversations at specific events recorded by families. Furthermore, the data collection unit can also collect past conversation records. For example, these may include conversations recorded by care facilities or conversations recorded by families. This improves the accuracy of the generated videos by collecting highly reliable data. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can use AI to automatically evaluate the reliability of the data provided by families and care facilities and select highly reliable data.

[0061] The generation unit can analyze collected photographs and audio and recreate specific conversations and episodes that the subject had during their lifetime. For example, the generation unit can analyze collected photographs and recreate conversations and episodes that the subject had during their lifetime. For example, the generation unit can recreate memorable scenes spent with family. The generation unit can also analyze collected audio and recreate conversations that the subject had during their lifetime. For example, the generation unit can recreate the content of conversations at specific events. Furthermore, the generation unit can recreate episodes that the subject had during their lifetime based on the collected data. For example, the generation unit can recreate memorable scenes spent with family or the content of conversations at specific events. By recreating conversations and episodes from their lifetime, it is possible to provide a sense of security to dementia patients. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input collected photographs and audio into a generation AI and have the generation AI perform the recreation of conversations and episodes.

[0062] The service provider can provide the generated video to dementia patients. For example, the service provider can provide the generated video to dementia patients through devices such as televisions and tablets. The service provider can also provide the generated video through monitors in care facilities. Furthermore, the service provider can provide the generated video through smartphones and computers owned by family members. By providing the generated video, it is possible to alleviate the emotional instability of dementia patients. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can estimate the patient's emotions and adjust the method of providing the video based on the estimated emotions of the patient. For example, if the patient is sad, the video can be provided in a quiet environment.

[0063] The adjustment unit can monitor the patient's reactions and adjust the video as needed. For example, the adjustment unit can capture the patient's facial expressions with a camera and estimate the patient's emotions using facial recognition technology. It can also record the patient's voice and estimate the patient's emotions using voice analysis technology. Furthermore, the adjustment unit can collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate the patient's emotions using an emotion estimation algorithm. This allows for more effective care support by adjusting the video according to the patient's reactions. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can estimate the patient's emotions and determine the content of the video adjustment based on the estimated emotions. For example, if the patient is sad, the video can be adjusted to include more scenes of happy memories.

[0064] The data collection unit can estimate the patient's emotions and select the types of data to collect based on the estimated emotions. For example, if the patient is sad, the data collection unit can prioritize collecting photos and audio of happy memories. If the patient is feeling anxious, the data collection unit can also collect recordings of conversations with family members that provide reassurance. Furthermore, if the patient is feeling lonely, the data collection unit can collect photos and audio of interactions with friends and family. This allows for the generation of more appropriate videos by collecting data tailored to the patient's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use facial expression analysis or voice analysis to estimate the patient's emotions and select the types of data to collect based on the estimated emotions.

[0065] The data collection unit can evaluate the reliability of data provided by families and care facilities and prioritize the collection of highly reliable data. For example, the data collection unit can evaluate the reliability of photos and audio provided by families and prioritize the collection of highly reliable data. For example, to evaluate the reliability of photos and audio provided by families, it can verify the source and quality of the data. The data collection unit can also evaluate the reliability of conversation records provided by care facilities and prioritize the collection of highly reliable data. For example, to evaluate the reliability of conversation records provided by care facilities, it can verify the accuracy and consistency of the records. Furthermore, the data collection unit can also have AI automatically evaluate the reliability of the provided data and select highly reliable data. For example, to evaluate the reliability of the provided data, the AI ​​can calculate a reliability score based on the source and quality of the data. This improves the accuracy of the generated videos by collecting highly reliable data. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0066] The data collection unit can determine the priority of data collection by considering its freshness and relevance. For example, the data collection unit can prioritize the collection of recently taken photos and audio recordings. It can also prioritize the collection of conversation records related to the patient's current condition. Furthermore, the data collection unit can also have AI automatically evaluate the freshness and relevance of the data and determine the priority of collection. For example, the AI ​​can calculate a priority score based on the timing and relevance of the data collection and determine the priority of collection. This allows for the generation of more effective videos by prioritizing the collection of fresh and highly relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0067] The data collection unit can estimate the patient's emotions and adjust the amount of data collected based on the estimated emotions. For example, if the patient is sad, the data collection unit can collect more photos and audio of happy memories. If the patient is feeling anxious, the data collection unit can collect more recordings of conversations with family members that provide reassurance. Furthermore, if the patient is feeling lonely, the data collection unit can collect more photos and audio of interactions with friends and family. By adjusting the amount of data according to the patient's emotions, a more appropriate video can be generated. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use facial expression analysis or voice analysis to estimate the patient's emotions and adjust the amount of data collected based on the estimated emotions.

[0068] The data collection unit can prioritize the collection of highly relevant data by considering the patient's living environment and daily behavioral patterns. For example, the data collection unit can prioritize the collection of photos and audio recordings from places where the patient frequently spends time. It can also prioritize the collection of conversational records related to the patient's daily behavioral patterns. Furthermore, the data collection unit can have AI automatically evaluate data related to the patient's living environment and select highly relevant data. For example, the AI ​​can calculate a relevance score based on the patient's living environment and daily behavioral patterns and select highly relevant data. This allows for the generation of more effective videos by collecting data related to the patient's living environment and behavioral patterns. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0069] The data collection unit can analyze a patient's social media activity and collect relevant data. For example, the data collection unit can collect photos and audio shared by the patient on social media. The data collection unit can also analyze the content of the patient's social media conversations and collect relevant data. Furthermore, the data collection unit can have AI automatically analyze the patient's social media activity and select relevant data. For example, the AI ​​can calculate a relevance score based on the patient's social media activity and select relevant data. This allows for the collection of more diverse data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0070] The generation unit can estimate the patient's emotions and adjust the content of the generated video based on the estimated emotions. For example, if the patient is sad, the generation unit can generate a video that includes many scenes of happy memories. Also, if the patient is feeling anxious, the generation unit can generate a video that includes many scenes of reassuring conversations with family. Furthermore, if the patient is feeling lonely, the generation unit can generate a video that includes many scenes of interactions with friends and family. By adjusting the video content according to the patient's emotions, a more effective video can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can use facial expression analysis or voice analysis to estimate the patient's emotions and adjust the content of the generated video based on the estimated emotions.

[0071] The generation unit can adjust the level of detail of the generated video based on the importance of the collected data. For example, the generation unit can reproduce scenes containing important dialogue in detail. It can also reproduce less important scenes in a simplified manner. Furthermore, the generation unit can have AI automatically evaluate the importance of the data and adjust the level of detail of the video. For example, the AI ​​can calculate a detail score based on the importance of the data and adjust the level of detail of the video. This allows for the generation of videos with a level of detail appropriate to the importance of the data, thereby providing more effective videos. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0072] The generation unit can apply different generation algorithms depending on the subject's activities and hobbies during their lifetime. For example, the generation unit can generate scenes that recreate the activities the subject enjoyed as hobbies. The generation unit can also apply a specific generation algorithm based on the subject's activities during their lifetime. Furthermore, the generation unit can have AI automatically analyze the subject's hobbies and activities and select the optimal generation algorithm. For example, the AI ​​can select a generation algorithm based on the subject's hobbies and activities and generate a video. This allows for the provision of more personalized videos by applying a generation algorithm tailored to the subject's activities and hobbies. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without using a generation AI.

[0073] The generation unit can estimate the patient's emotions and adjust the length of the generated video based on the estimated emotions. For example, if the patient is sad, the generation unit can generate a short, concise video. If the patient is relaxed, the generation unit can generate a longer video with detailed explanations. Furthermore, if the patient is excited, the generation unit can generate a video with visually stimulating effects. By adjusting the video length according to the patient's emotions, a more effective video can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can use facial expression analysis or voice analysis to estimate the patient's emotions and adjust the length of the generated video based on the estimated emotions.

[0074] The generation unit can determine the priority of videos to generate based on the submission date of the collected data. For example, the generation unit can prioritize generating videos based on the submission date of the collected data. Alternatively, the generation unit can postpone generating videos for older data. Furthermore, the generation unit can have an AI automatically evaluate the data submission date and determine the video priority. For example, the AI ​​can calculate a priority score based on the data submission date and determine the video priority. This allows for the provision of videos that reflect the latest data by prioritizing videos based on the submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0075] The generation unit can adjust the order of the generated videos based on the relevance of the collected data. For example, the generation unit can reproduce highly relevant scenes first. It can also reproduce less relevant scenes later. Furthermore, the generation unit can have AI automatically evaluate the relevance of the data and adjust the order of the videos. For example, the AI ​​can calculate a relevance score based on the degree of similarity of the data content and related topics, and adjust the order of the videos. This allows for the provision of more effective videos by adjusting the order of videos based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0076] The delivery unit can estimate the patient's emotions and adjust the video delivery method based on the estimated emotions. For example, if the patient is sad, the delivery unit can deliver the video in a quiet environment. If the patient is feeling anxious, the delivery unit can deliver the video in a reassuring environment. Furthermore, if the patient is feeling lonely, the delivery unit can offer the video for viewing with family or friends. By adjusting the delivery method according to the patient's emotions, a more effective video can be delivered. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can use facial expression analysis or voice analysis to estimate the patient's emotions and adjust the video delivery method based on the estimated emotions.

[0077] The delivery unit can select the optimal delivery method by referring to the patient's past response history. For example, the delivery unit can refer to the delivery method of videos that the patient has previously enjoyed watching. The delivery unit can also have AI automatically analyze the patient's past response history and select the optimal delivery method. Furthermore, the delivery unit can select the most effective delivery method from the patient's past response history. For example, AI can select the optimal delivery method based on the patient's past viewing history and response data. This allows for the selection of a more effective delivery method by referring to past response history. Some or all of the above processing in the delivery unit may be performed using AI or not.

[0078] The delivery unit can adjust the timing of delivery based on the patient's current health condition and lifestyle. For example, the delivery unit can deliver videos when the patient is relaxed. It can also deliver videos when the patient is in good health. Furthermore, the delivery unit can have AI automatically evaluate the patient's lifestyle and select the optimal delivery timing. For example, the AI ​​can select the optimal delivery timing based on the patient's health condition and lifestyle. This allows for the delivery of more effective videos by adjusting the delivery timing according to the patient's health condition and lifestyle. Some or all of the above processing in the delivery unit may be performed using AI or not.

[0079] The service provider can estimate the patient's emotions and adjust the frequency of video delivery based on the estimated emotions. For example, if the patient is sad, the service provider can deliver videos more frequently. If the patient is relaxed, the service provider can deliver videos at a moderate frequency. Furthermore, if the patient is agitated, the service provider can reduce the frequency of video delivery. By adjusting the delivery frequency according to the patient's emotions, more effective videos can be delivered. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can use facial expression analysis or voice analysis to estimate the patient's emotions and adjust the frequency of video delivery based on the estimated emotions.

[0080] The delivery unit can select the optimal delivery method considering the patient's device information. For example, if the patient is using a smartphone, the delivery unit can provide a video sized to fit the screen. If the patient is using a tablet, the delivery unit can provide a video optimized for a larger screen. Furthermore, if the patient is using a smartwatch, the delivery unit can provide a concise and highly visible video. This improves the viewing experience by providing videos optimized for the patient's device. Some or all of the above processing in the delivery unit may be performed using AI or not.

[0081] The service provider can adjust the content of the service based on feedback from the patient's family or caregiver. For example, the service provider can adjust the content of the video based on feedback from the family or caregiver. The service provider can also have AI automatically analyze the opinions of the family or caregiver and select the most appropriate content. Furthermore, the service provider can adjust the method of delivering the video based on feedback from the family or caregiver. For example, the AI ​​can select the most appropriate content based on feedback from the family or caregiver and deliver the video. This allows for the delivery of more effective videos by incorporating feedback from the family or caregiver. Some or all of the above-described processes in the service provider may be performed using AI or not.

[0082] The adjustment unit can estimate the patient's emotions and determine the content of the video adjustments based on the estimated emotions. For example, if the patient is sad, the adjustment unit can adjust the video to include more scenes of happy memories. If the patient is feeling anxious, the adjustment unit can adjust the video to include more scenes of reassuring conversations with family. Furthermore, if the patient is feeling lonely, the adjustment unit can adjust the video to include more scenes of interactions with friends and family. This allows for the provision of more effective videos by determining adjustments that correspond to the patient's emotions. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can use facial expression analysis or voice analysis to estimate the patient's emotions and determine the content of the video adjustments based on the estimated emotions.

[0083] The adjustment unit can select the optimal adjustment method by referring to the patient's past response data. For example, the adjustment unit can refer to adjustment methods of videos that the patient has enjoyed watching in the past. The adjustment unit can also have AI automatically analyze the patient's past response data and select the optimal adjustment method. Furthermore, the adjustment unit can select the most effective adjustment method from the patient's past response data. For example, AI can select the optimal adjustment method based on the patient's past viewing history and response data. This allows for the selection of a more effective adjustment method by referring to past response data. Some or all of the above processing in the adjustment unit may be performed using AI or not.

[0084] The adjustment unit can determine the timing of adjustments based on the patient's current psychological state and living environment. For example, the adjustment unit can adjust the video during times when the patient is relaxed. It can also adjust the video when the patient's psychological state is stable. Furthermore, the adjustment unit can have AI automatically evaluate the patient's living environment and select the optimal adjustment timing. For example, the AI ​​can select the optimal adjustment timing based on the patient's psychological state and living environment. This allows for the provision of more effective videos by determining the adjustment timing according to the psychological state and living environment. Some or all of the above processing in the adjustment unit may be performed using AI or not.

[0085] The adjustment unit can estimate the patient's emotions and determine the frequency of adjustments based on the estimated emotions. For example, the adjustment unit can adjust the video more frequently if the patient is sad. It can also adjust the video at a moderate frequency if the patient is relaxed. Furthermore, it can adjust the video at a reduced frequency if the patient is excited. This allows for the provision of more effective videos by determining the adjustment frequency according to the patient's emotions. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can use facial expression analysis or voice analysis to estimate the patient's emotions and determine the frequency of adjustments based on the estimated emotions.

[0086] The adjustment unit can determine the adjustments based on feedback from the patient's family or caregiver. For example, the adjustment unit can determine the adjustments based on feedback from the patient's family or caregiver. For example, the adjustment unit can determine the adjustments to the video based on feedback from the family or caregiver. The adjustment unit can also have AI automatically analyze the opinions of the family or caregiver and select the optimal adjustments. Furthermore, the adjustment unit can determine how to adjust the video based on feedback from the family or caregiver. For example, the AI ​​can select the optimal adjustments based on feedback from the family or caregiver and adjust the video. This allows for the provision of more effective videos by reflecting feedback from the family or caregiver. Some or all of the above processing in the adjustment unit may be performed using AI or not.

[0087] The adjustment unit can determine the timing of adjustments by considering the patient's daily rhythm and behavioral patterns. For example, the adjustment unit can adjust the video during times when the patient is typically relaxed. The adjustment unit can also determine the optimal adjustment timing based on the patient's daily behavioral patterns. Furthermore, the adjustment unit can have AI automatically analyze the patient's daily rhythm and select the optimal adjustment timing. For example, the AI ​​can select the optimal adjustment timing based on the patient's daily rhythm and behavioral patterns. This allows for the provision of more effective videos by determining the adjustment timing according to the patient's daily rhythm and behavioral patterns. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, generation unit, provision unit, and adjustment unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects photos and audio using the camera 42 and microphone 38B of the smart device 14 and transmits them to the data processing unit 12 via the control unit 46A. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and reproduces conversations and episodes using the generated AI. The provision unit provides the generated video to the dementia patient, for example, using the display 40A and speaker 40B of the smart device 14. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which monitors the patient's response and adjusts the content of the video as needed. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, generation unit, provision unit, and adjustment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects photos and audio using the camera 42 and microphone 238 of the smart glasses 214 and transmits them to the data processing unit 12 via the control unit 46A. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and reproduces conversations and episodes using generated AI. The provision unit provides the generated video to the dementia patient, for example, using the display and speaker 240 of the smart glasses 214. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which monitors the patient's response and adjusts the content of the video as needed. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, generation unit, provision unit, and adjustment unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects photos and audio using the camera 42 and microphone 238 of the headset terminal 314 and transmits them to the data processing unit 12 via the control unit 46A. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and reproduces conversations and episodes using the generated AI. The provision unit provides the generated video to the dementia patient, for example, using the display 343 and speaker 240 of the headset terminal 314. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which monitors the patient's response and adjusts the content of the video as needed. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, generation unit, provision unit, and adjustment unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects photos and audio using the camera 42 and microphone 238 of the robot 414 and transmits them to the data processing unit 12 via the control unit 46A. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and reproduces conversations and episodes using generated AI. The provision unit provides the generated video to the dementia patient, for example, using the display and speaker 240 of the robot 414. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which monitors the patient's response and adjusts the content of the video as needed.

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

[0089] The care support system can also be equipped with an emotional feedback unit. This unit can monitor changes in the patient's emotions in real time after watching a video and provide feedback to the data collection and generation units. For example, it can analyze facial expressions and voice while the patient is watching the video to detect changes in emotion. If the patient is relaxed after watching the video, the emotional feedback unit can instruct the data collection unit to prioritize collecting similar data. Similarly, if the patient feels anxious after watching the video, the unit can instruct the generation unit to include reassuring content in the next video generation. This creates a feedback loop based on the patient's emotions, resulting in more effective care support.

[0090] The data collection unit can also collect data about the patient's living environment. For example, it can collect photos of the room the patient spends time in daily, as well as photos of furniture and decorations that the patient frequently uses. This allows the generated videos to reflect the patient's living environment, providing a more realistic experience. The data collection unit can also receive feedback from family members and caregivers when collecting data about the patient's living environment. For example, based on information provided by family members, it can prioritize collecting data on places and items that the patient particularly likes. Furthermore, the data collection unit can use AI to automatically evaluate the data about the patient's living environment and select the most relevant data. This allows for the generation of more effective videos by collecting data based on the patient's living environment.

[0091] The generation unit can estimate the patient's emotions and adjust the audio tone of the video based on the estimated emotions. For example, if the patient is sad, the generation unit can generate a video using a calm and gentle tone of voice. If the patient is agitated, the generation unit can generate a video using a calm tone of voice. Furthermore, if the patient is relaxed, the generation unit can generate a video using a bright and cheerful tone of voice. This allows for the provision of more effective videos by adjusting the audio tone according to the patient's emotions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can use facial expression analysis or voice analysis to estimate the patient's emotions and adjust the audio tone based on the estimated emotions.

[0092] The service provider can adjust the color tone of the generated video based on the patient's visual preferences when providing it. For example, if the patient prefers bright colors, the service provider can adjust the video's color tone to be brighter. Similarly, if the patient prefers muted colors, the service provider can adjust the video's color tone to be more muted. Furthermore, the service provider can adjust the video's contrast and brightness based on the patient's visual preferences. This improves the viewing experience by providing videos tailored to the patient's visual preferences. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can use visual analysis to adjust the video's color tone based on the patient's visual preferences.

[0093] The adjustment unit can estimate the patient's emotions and adjust the video playback speed based on the estimated emotions. For example, if the patient is relaxed, the adjustment unit can adjust the video playback speed to be slower. If the patient is excited, the adjustment unit can adjust the video playback speed to be faster. Furthermore, if the patient is focused, the adjustment unit can provide the video at a normal playback speed. This allows for the provision of more effective videos by adjusting the playback speed according to the patient's emotions. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can use facial expression analysis or voice analysis to estimate the patient's emotions and adjust the video playback speed based on the estimated emotions.

[0094] The data collection unit can collect data on the patient's past hobbies and interests. For example, it can collect photos and audio related to hobbies and interests the patient enjoyed in the past. This allows the generated videos to reflect the patient's past hobbies and interests, providing a more personalized experience. The data collection unit can also receive feedback from family members and caregivers when collecting data on the patient's past hobbies and interests. For example, based on information provided by family members, it can prioritize collecting activities and events that the patient particularly enjoyed. Furthermore, the data collection unit can use AI to automatically evaluate the data on the patient's past hobbies and interests and select the most relevant data. This allows for the generation of more effective videos by collecting data based on the patient's past hobbies and interests.

[0095] The data collection unit can estimate the patient's emotions and select the format of data to collect based on those estimated emotions. For example, if the patient prefers visual information, the data collection unit can prioritize collecting photographs and video clips. If the patient prefers auditory information, the data collection unit can prioritize collecting audio and music. Furthermore, if the patient prefers text information, the data collection unit can prioritize collecting text messages and letters. By selecting a data format that matches the patient's emotions, a more appropriate video can be generated. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can use facial expression analysis or voice analysis to estimate the patient's emotions and select the data format to collect based on those estimated emotions.

[0096] The generation unit can adjust the content of the generated video based on the reliability of the collected data. For example, it can reproduce detailed scenes based on highly reliable data. It can also reproduce less reliable data in a simplified form. Furthermore, the generation unit can have AI automatically evaluate the reliability of the data and adjust the video content accordingly. For example, the AI ​​can calculate a detail score based on the reliability of the data and adjust the video content based on that score. This allows for the generation of videos with content appropriate to the reliability of the data, thereby providing more effective videos. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI.

[0097] The delivery unit can estimate the patient's emotions and adjust the video delivery method based on the estimated emotions. For example, if the patient is sad, the video can be delivered in a quiet environment. If the patient is feeling anxious, the video can be delivered in a reassuring environment. Furthermore, if the patient is feeling lonely, the video can be delivered in a way that encourages them to watch it with family or friends. By adjusting the delivery method according to the patient's emotions, a more effective video can be delivered. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can use facial expression analysis or voice analysis to estimate the patient's emotions and adjust the video delivery method based on the estimated emotions.

[0098] The service provider can adjust the content of the service based on feedback from the patient's family or caregiver. For example, the service provider can adjust the video content based on feedback from family members or caregivers. The service provider can also have AI automatically analyze the opinions of family members or caregivers and select the most appropriate content. Furthermore, the service provider can adjust the method of delivering the video based on feedback from family members or caregivers. For example, the AI ​​can select the most appropriate content based on feedback from family members or caregivers and deliver the video. This allows for the delivery of more effective videos by incorporating feedback from family members or caregivers. Some or all of the above-described processes in the service provider may be performed using AI or not.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The data collection unit collects photographs and audio recordings taken of the subject during their lifetime. For example, it can collect photo albums, audio recordings, and past conversation records held by family members or care facilities. The data collection unit can also evaluate the reliability of the photographs and audio provided by the family and prioritize the collection of reliable data. Furthermore, the data collection unit can estimate the patient's emotions and select the types of data to collect based on the estimated emotions. For example, if the patient is sad, it will prioritize the collection of photographs and audio recordings of happy memories. Step 2: The generation unit analyzes the photos and audio collected by the collection unit to recreate conversations and stories from the person's lifetime. The generation unit can analyze the collected photos and audio to recreate conversations and episodes that the person had during their lifetime. Using generation AI, it recreates conversations and episodes based on the collected data. For example, it can recreate memorable scenes of the person spending time with their family or conversations from specific events. The generation unit can also estimate the patient's emotions and adjust the content of the generated video based on the estimated emotions. For example, if the patient is sad, it will generate a video that includes many scenes of happy memories. Step 3: The delivery unit provides the patient with the specific video generated by the generation unit. For example, the generated video can be provided to a patient with dementia. The delivery unit can also estimate the patient's emotions and adjust the way the video is provided based on the estimated emotions. For example, if the patient is sad, the video can be provided in a quiet environment. Step 4: The adjustment unit monitors the patient's reaction based on the video provided by the supply unit and adjusts the specific video as needed. For example, it can monitor the patient's reaction and adjust the video as necessary. The adjustment unit can also estimate the patient's emotions and decide how to adjust the video based on the estimated emotions. For example, if the patient is sad, the adjustment unit will adjust the video to include more scenes of happy memories.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0154] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0164] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] [Explanation of Symbols]

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

Claims

1. The collection department collects photographs and audio recordings taken of the subject during their lifetime, The collection unit analyzes the photographs and audio collected by the collection unit and generates a generation unit that recreates the content of conversations and stories from when the person was alive. A providing unit that provides a specific video generated by the generation unit to the patient, The system includes an adjustment unit that monitors the patient's response based on the video provided by the aforementioned provisioning unit and adjusts the specific video as needed. A system characterized by the following features.

2. The aforementioned collection unit is Collect photo albums, audio recordings, and past conversation records owned by families and care facilities. The system according to feature 1.

3. The generating unit is By analyzing collected photographs and audio recordings, the researchers reconstruct specific conversations and episodes that the subjects had during their lifetime. The system according to feature 1.

4. The aforementioned supply unit is, The generated specific videos will be provided to dementia patients. The system according to feature 1.

5. The adjustment unit is, Monitor the patient's response and adjust specific videos as needed. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the patient's emotions and selects the types of data to collect based on the estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is We evaluate the reliability of data provided by families and care facilities, and prioritize the collection of highly reliable data. The system according to feature 1.

8. The aforementioned collection unit is Prioritize data collection based on its freshness and relevance. The system according to feature 1.

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