system

Abstract

The system according to this embodiment aims to improve the symptoms of dementia patients by recalling their past memories. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects past photographs and stories from the patient and their family. The generation unit generates video and audio content that evokes past memories based on the data collected by the collection unit. The provision unit provides the content generated by the generation unit to the patient in a friendly voice.

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 a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that means for effectively evoking the past memories of dementia patients are limited and do not sufficiently contribute to the improvement of symptoms and the reduction of the care burden on family members.

[0005] The system according to the embodiment aims to evoke the past memories of dementia patients and improve symptoms.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects past photographs and stories from the patient and their family. The generation unit generates video and audio content that evokes past memories based on the data collected by the collection unit. The provision unit provides the content generated by the generation unit to the patient in a friendly voice. [Effects of the Invention]

[0007] The system according to this embodiment can help recall past memories in dementia patients and improve their symptoms. [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, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 dementia patient support system according to an embodiment of the present invention is a system that utilizes AI technology to recall past memories of dementia patients and improve their symptoms, thereby reducing the burden of care on families and alleviating the shortage of caregivers. The dementia patient support system collects past photos and stories from patients and their families, and based on the collected data, the AI ​​generates video and audio content that recalls past memories. The generated content is provided in the form of the AI ​​speaking to the patient in a voice that is easy for them to understand. This mechanism is expected to improve dementia symptoms and reduce the burden of care on families, both physically and psychologically. Furthermore, it contributes to alleviating the shortage of caregivers. For example, by having the AI ​​interact with the patient, caregivers can concentrate on other tasks for longer. This program contributes to improving the quality of life for dementia patients and their families and contributes to the healthy development of an aging society. In terms of market size, there are currently about 4.5 million dementia patients, and the increase in the aging population and the demand for early diagnosis are expected. With the advancement of AI technology, the feasibility is increasing, and demand from the perspective of social contribution and SDGs is also growing. This allows the dementia patient support system to help dementia patients recall past memories and improve their symptoms, thereby reducing the burden of care on families and addressing the shortage of caregivers.

[0029] The dementia patient support system according to this embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects past photographs and stories from the patient and their family. The collection unit can collect, for example, family photographs, travel stories, and school memories. The collection unit can also use AI to automatically classify the types of photographs and stories to be collected and select appropriate data. The generation unit generates video and audio content that evokes past memories based on the data collected by the collection unit. The generation unit can, for example, use natural language processing technology to understand the patient's story and generate an appropriate response. The generation unit can also use image recognition technology to analyze past photographs and evoke related memories. Furthermore, the generation unit can use machine learning to learn the patient's responses and provide optimal rehabilitation content. The provision unit provides the content generated by the generation unit in a friendly voice to the patient. The provision unit can, for example, use speech synthesis technology to speak to the patient in a friendly voice. As a result, the dementia patient support system according to this embodiment can evoke past memories of dementia patients and improve their symptoms, thereby reducing the burden of care on families and alleviating the shortage of personnel.

[0030] The data collection unit collects past photographs and stories from patients and their families. Specifically, it can collect family photos, travel stories, school memories, and more. The data collection unit can also use AI to automatically classify the types of photos and stories to be collected and select the appropriate data. For example, it can use facial recognition technology to identify specific individuals in family photos, and natural language processing technology to extract locations and events from travel stories. This allows the data collection unit to efficiently extract information important to patients from a vast amount of data. Furthermore, the data collection unit can also collect detailed episodes and emotionally charged stories through interviews with patients and their families. As a result, the collected data becomes rich in content, including not only factual information but also the depth of emotions and memories. The data collection unit stores this data in digital format and makes it available for subsequent processing. For example, collected photos and stories are stored in cloud storage and made accessible to the generation and provision units. In addition, the data collection unit protects the data using encryption technology to ensure data privacy and security. This allows the data collection unit to efficiently collect necessary data while protecting the privacy of patients and their families, and to improve the overall performance of the system.

[0031] The generation unit generates video and audio content that evokes past memories based on data collected by the collection unit. Specifically, it uses natural language processing technology to understand the patient's speech and generate appropriate responses. For example, if a patient says, "I went to the beach with my family a long time ago," the generation unit analyzes the speech, searches for relevant photos and videos, and displays them. The generation unit can also use image recognition technology to analyze past photos and evoke related memories. For example, it can analyze family photos, recognize specific people or places, and generate episodes related to them. Furthermore, the generation unit can use machine learning to learn the patient's responses and provide optimal rehabilitation content. For example, if a patient shows a strong response to a particular topic, it will prioritize providing content related to that topic. By combining these technologies, the generation unit can generate the most effective rehabilitation content for the patient. The generated content is provided in video and audio formats, helping the patient recall memories visually and aurally. This allows the generation unit to effectively recall the patient's past memories and maximize the effectiveness of rehabilitation.

[0032] The delivery unit provides content generated by the generation unit to the patient in a friendly voice. Specifically, it can speak to the patient in a friendly voice using speech synthesis technology. For example, if the patient is talking about a past family trip, the delivery unit can display photos and videos related to that story and use speech synthesis technology to say, "This is a picture of the beach you went to with your family." This allows the patient to receive visual and auditory information simultaneously, making memory recall more effective. Furthermore, the delivery unit can monitor the patient's responses in real time and provide appropriate feedback. For example, if the patient shows interest in a particular topic, it can provide additional content related to that topic. The delivery unit can also analyze the patient's emotions and responses to evaluate the progress of rehabilitation. This allows the delivery unit to provide the patient with optimal rehabilitation content and maximize the effectiveness of rehabilitation. In addition, the delivery unit can provide feedback to the patient's family and caregivers, sharing the progress and effectiveness of rehabilitation. This allows the delivery unit to provide useful information not only to the patient but also to those around them, enhancing the overall support effect.

[0033] The data collection unit can analyze the patient's past life history and select the optimal data collection method. For example, the data collection unit can analyze the patient's past travel history and collect photos and stories from travel destinations. It can also analyze the patient's hobbies and interests and collect related photos and stories. Furthermore, the data collection unit can analyze the patient's family structure and friendships and collect memories with close people. By selecting the optimal data collection method based on the patient's past life history, more effective memory retrieval becomes possible. 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 input the patient's past life history data into a generating AI and have the generating AI select the optimal data collection method.

[0034] The data collection unit can filter data based on the patient's current health status and areas of interest during collection. For example, if the patient is in good health, the collection unit will collect active photos and stories. If the patient is in poor health, the collection unit can also collect calm photos and stories. Furthermore, the collection unit can collect photos and stories that are of interest to the patient based on their areas of interest. This allows for more effective memory recall by collecting appropriate photos and stories according to the patient's health status and areas of interest. Some or all of the processing described above in the collection unit may be performed using AI or not. For example, the collection unit can input the patient's health status data into a generating AI and have the generating AI perform the filtering.

[0035] The data collection unit can prioritize the collection of highly relevant data, taking into account the patient's geographical location information. For example, the data collection unit can collect past photographs and stories of the area where the patient currently lives. It can also collect photographs and stories of areas where the patient has lived in the past. Furthermore, it can collect photographs and stories of places the patient frequently visits. This allows for more effective memory recall by collecting highly relevant data based on the patient's geographical location information. 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 input the patient's geographical location information data into a generating AI and have the generating AI select highly relevant data.

[0036] The data collection unit can analyze the patient's social media activity and collect relevant data during the collection process. For example, the data collection unit can collect photos and stories that the patient has shared on social media. It can also collect posts that the patient has "liked" on social media. Furthermore, it can collect posts from accounts that the patient follows on social media. This allows for more effective memory recall by collecting relevant data based on the patient's social media activity. 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 input the patient's social media data into a generating AI and have the generating AI select the relevant data.

[0037] The generation unit can understand the patient's speech using natural language processing and generate appropriate responses. For example, the generation unit analyzes the patient's speech using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. Furthermore, the generation unit can generate responses that reflect the patient's emotions or based on past conversation history. This makes it possible to understand the patient's speech and generate appropriate responses using natural language processing. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input text data of the patient's speech into a generation AI and have the generation AI generate appropriate responses.

[0038] The generation unit can analyze past photographs using image recognition technology and recall associated memories. For example, the generation unit analyzes past photographs using image recognition technologies such as face recognition, object recognition, and scene analysis. The generation unit can also recall episodes related to people and places depicted in the photographs. This makes it possible to analyze past photographs and recall associated memories by using image recognition technology. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input past photographic data into a generation AI and have the generation AI perform the recall of associated memories.

[0039] The generation unit can learn patient responses using machine learning and provide optimal rehabilitation content. The generation unit learns patient responses using machine learning techniques such as supervised learning, unsupervised learning, and reinforcement learning. Furthermore, the generation unit can customize content based on patient responses and select rehabilitation content based on past effectiveness. This makes it possible to learn patient responses and provide optimal rehabilitation content by using machine learning. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input patient response data into a generation AI and have the generation AI provide optimal rehabilitation content.

[0040] The service provider can speak to patients in a friendly voice using speech synthesis technology. For example, the service provider can generate a friendly voice using speech synthesis technologies such as text-to-speech synthesis or voice cloning technology. Furthermore, the service provider can generate speech while considering friendly voice characteristics such as tone, speaking style, and voice quality. This makes it possible to speak to patients in a friendly voice using speech synthesis technology. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input text data into a generation AI and have the generation AI generate a friendly voice.

[0041] The delivery unit can analyze the patient's past responses and select the optimal delivery method at the time of delivery. For example, the delivery unit can prioritize delivery methods that the patient has preferred in the past. It can also avoid delivery methods that the patient has disliked in the past. Furthermore, the delivery unit can learn and provide the optimal delivery method based on the patient's past responses. This makes it possible to deliver content more effectively by selecting the optimal delivery method based on the patient's past responses. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the patient's past response data into a generating AI and have the generating AI select the optimal delivery method.

[0042] The service provider can customize the means of delivery based on the patient's current health condition at the time of delivery. For example, if the patient is in good health, the service provider can provide active content. If the patient is in poor health, the service provider can also provide calming content. Furthermore, the service provider can provide visual or auditory content depending on the patient's health condition. This allows for more effective content delivery by customizing the means of delivery according to the patient's health condition. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input patient health data into a generating AI and have the generating AI perform the customization of the means of delivery.

[0043] The content delivery unit can select the optimal delivery method by considering the patient's geographical location information at the time of delivery. For example, the delivery unit can provide past photos and stories of the area where the patient currently lives. It can also provide photos and stories of areas where the patient has lived in the past. Furthermore, it can provide photos and stories of places the patient frequently visits. By selecting the optimal delivery method based on the patient's geographical location information, more effective content delivery becomes possible. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the patient's geographical location information data into a generating AI and have the generating AI select the optimal delivery method.

[0044] The content delivery unit can analyze the patient's social media activity and propose delivery methods at the time of delivery. For example, the delivery unit can provide photos and stories that the patient has shared on social media. It can also provide posts that the patient has "liked" on social media. Furthermore, it can provide posts from accounts that the patient follows on social media. This enables more effective content delivery by proposing delivery methods based on the patient's social media activity. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the patient's social media data into a generating AI and have the generating AI execute the proposal of delivery methods.

[0045] The generation unit can understand the patient's speech using natural language processing and generate appropriate responses. For example, the generation unit analyzes the patient's speech using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. Furthermore, the generation unit can generate responses that reflect the patient's emotions or based on past conversation history. This makes it possible to understand the patient's speech and generate appropriate responses using natural language processing. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input text data of the patient's speech into a generation AI and have the generation AI generate appropriate responses.

[0046] The generation unit can analyze past photographs using image recognition technology and recall associated memories. For example, the generation unit analyzes past photographs using image recognition technologies such as face recognition, object recognition, and scene analysis. The generation unit can also recall episodes related to people and places depicted in the photographs. This makes it possible to analyze past photographs and recall associated memories by using image recognition technology. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input past photographic data into a generation AI and have the generation AI perform the recall of associated memories.

[0047] The generation unit can learn patient responses using machine learning and provide optimal rehabilitation content. The generation unit learns patient responses using machine learning techniques such as supervised learning, unsupervised learning, and reinforcement learning. Furthermore, the generation unit can customize content based on patient responses and select rehabilitation content based on past effectiveness. This makes it possible to learn patient responses and provide optimal rehabilitation content by using machine learning. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input patient response data into a generation AI and have the generation AI provide optimal rehabilitation content.

[0048] The service provider can speak to patients in a friendly voice using speech synthesis technology. For example, the service provider can generate a friendly voice using speech synthesis technologies such as text-to-speech synthesis or voice cloning technology. Furthermore, the service provider can generate speech while considering friendly voice characteristics such as tone, speaking style, and voice quality. This makes it possible to speak to patients in a friendly voice using speech synthesis technology. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input text data into a generation AI and have the generation AI generate a friendly voice.

[0049] The delivery unit can analyze the patient's past responses and select the optimal delivery method at the time of delivery. For example, the delivery unit can prioritize delivery methods that the patient has preferred in the past. It can also avoid delivery methods that the patient has disliked in the past. Furthermore, the delivery unit can learn and provide the optimal delivery method based on the patient's past responses. This makes it possible to deliver content more effectively by selecting the optimal delivery method based on the patient's past responses. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the patient's past response data into a generating AI and have the generating AI select the optimal delivery method.

[0050] The service provider can customize the means of delivery based on the patient's current health condition at the time of delivery. For example, if the patient is in good health, the service provider can provide active content. If the patient is in poor health, the service provider can also provide calming content. Furthermore, the service provider can provide visual or auditory content depending on the patient's health condition. This allows for more effective content delivery by customizing the means of delivery according to the patient's health condition. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input patient health data into a generating AI and have the generating AI perform the customization of the means of delivery.

[0051] The content delivery unit can select the optimal delivery method by considering the patient's geographical location information at the time of delivery. For example, the delivery unit can provide past photos and stories of the area where the patient currently lives. It can also provide photos and stories of areas where the patient has lived in the past. Furthermore, it can provide photos and stories of places the patient frequently visits. By selecting the optimal delivery method based on the patient's geographical location information, more effective content delivery becomes possible. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the patient's geographical location information data into a generating AI and have the generating AI select the optimal delivery method.

[0052] The content delivery unit can analyze the patient's social media activity and propose delivery methods at the time of delivery. For example, the delivery unit can provide photos and stories that the patient has shared on social media. It can also provide posts that the patient has "liked" on social media. Furthermore, it can provide posts from accounts that the patient follows on social media. This enables more effective content delivery by proposing delivery methods based on the patient's social media activity. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the patient's social media data into a generating AI and have the generating AI execute the proposal of delivery methods.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The dementia patient support system can also be equipped with a biometrics collection unit to collect biometric data. This unit collects vital signs such as the patient's heart rate, blood pressure, and body temperature, and uses this data to monitor the patient's health in real time. For example, if the heart rate rises sharply, the system can determine that the patient may be experiencing stress and provide relaxing content. Similarly, if blood pressure is high, providing calming music or images of scenery can help lower it. Furthermore, if body temperature is abnormally high, the system can determine that the patient may have a fever and issue an alert prompting them to contact a medical institution. This allows for the provision of more personalized care by utilizing biometric data.

[0055] The dementia patient support system can also be equipped with a lifestyle rhythm monitoring unit that monitors the patient's daily rhythm. This unit records the patient's sleep patterns and meal times, and based on this data, can determine the optimal timing for content delivery. For example, if a patient frequently wakes up at night, the system can improve sleep quality by providing relaxing content at night. It can also provide videos and audio that stimulate appetite at meal times. Furthermore, by analyzing lifestyle rhythm data, if an abnormal pattern is detected, the system can issue an alert prompting the patient to contact a medical institution. This makes it possible to provide individualized care based on the patient's daily rhythm.

[0056] The dementia patient support system can also be equipped with an exercise data collection unit to collect the patient's exercise data. This unit records the patient's daily exercise volume, steps taken, and activity time, and based on this data, can provide an optimal rehabilitation program. For example, if a patient is not getting enough exercise, the system can suggest a light stretching or walking program. If a patient is overexerting themselves, it can also provide a relaxation program to encourage rest. Furthermore, by analyzing the exercise data, if an abnormal pattern is detected, the system can issue an alert prompting the patient to contact a medical institution. This makes it possible to provide personalized rehabilitation care based on the patient's exercise data.

[0057] The dementia patient support system may also include a social interaction facilitator to promote the patient's social interaction. This facilitator can support communication with the patient's friends and family, preventing social isolation. For example, it could suggest regular video calls to facilitate conversations with family and friends. It could also suggest participation in online communities or hobby groups that the patient is interested in. Furthermore, the social interaction facilitator could analyze the patient's past relationships and suggest reconnections. This would promote the patient's social interaction and maintain their mental health. Some or all of the above processes in the social interaction facilitator may be performed using AI or not. For example, the social interaction facilitator could input the patient's relationship data into a generating AI, which could then suggest optimal ways of interaction.

[0058] The dementia patient support system can further include a hobby support unit to support the patient's hobbies and interests. The hobby support unit can analyze the patient's past hobbies and interests and suggest new hobbies and activities based on that analysis. For example, if the patient enjoyed painting in the past, the system can suggest online painting classes. If the patient was interested in music, it can also provide opportunities to play musical instruments or listen to music. Furthermore, if the patient enjoyed gardening, it can offer advice on home gardening and how to care for plants. This makes it possible to improve the patient's quality of life by supporting their hobbies and interests. Some or all of the above processing in the hobby support unit may be performed using AI or not. For example, the hobby support unit can input the patient's hobby data into a generating AI and have the generating AI suggest the most suitable hobbies and activities.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects past photos and stories from patients and their families. For example, it can collect family photos, travel stories, school memories, etc. The data collection unit can also use AI to automatically categorize the types of photos and stories to be collected and select the appropriate data. Step 2: The generation unit generates video and audio content that evokes past memories based on the data collected by the collection unit. For example, it can use natural language processing technology to understand the patient's speech and generate appropriate responses. It can also use image recognition technology to analyze past photographs and evoke related memories. Furthermore, it can use machine learning to learn the patient's responses and provide optimal rehabilitation content. Step 3: The delivery unit delivers the content generated by the generation unit in a friendly voice to the patient. For example, speech synthesis technology can be used to speak to the patient in a friendly voice.

[0061] (Example of form 2) The dementia patient support system according to an embodiment of the present invention is a system that utilizes AI technology to recall past memories of dementia patients and improve their symptoms, thereby reducing the burden of care on families and alleviating the shortage of caregivers. The dementia patient support system collects past photos and stories from patients and their families, and based on the collected data, the AI ​​generates video and audio content that recalls past memories. The generated content is provided in the form of the AI ​​speaking to the patient in a voice that is easy for them to understand. This mechanism is expected to improve dementia symptoms and reduce the burden of care on families, both physically and psychologically. Furthermore, it contributes to alleviating the shortage of caregivers. For example, by having the AI ​​interact with the patient, caregivers can concentrate on other tasks for longer. This program contributes to improving the quality of life for dementia patients and their families and contributes to the healthy development of an aging society. In terms of market size, there are currently about 4.5 million dementia patients, and the increase in the aging population and the demand for early diagnosis are expected. With the advancement of AI technology, the feasibility is increasing, and demand from the perspective of social contribution and SDGs is also growing. This allows the dementia patient support system to help dementia patients recall past memories and improve their symptoms, thereby reducing the burden of care on families and addressing the shortage of caregivers.

[0062] The dementia patient support system according to this embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects past photographs and stories from the patient and their family. The collection unit can collect, for example, family photographs, travel stories, and school memories. The collection unit can also use AI to automatically classify the types of photographs and stories to be collected and select appropriate data. The generation unit generates video and audio content that evokes past memories based on the data collected by the collection unit. The generation unit can, for example, use natural language processing technology to understand the patient's story and generate an appropriate response. The generation unit can also use image recognition technology to analyze past photographs and evoke related memories. Furthermore, the generation unit can use machine learning to learn the patient's responses and provide optimal rehabilitation content. The provision unit provides the content generated by the generation unit in a friendly voice to the patient. The provision unit can, for example, use speech synthesis technology to speak to the patient in a friendly voice. As a result, the dementia patient support system according to this embodiment can evoke past memories of dementia patients and improve their symptoms, thereby reducing the burden of care on families and alleviating the shortage of personnel.

[0063] The data collection unit collects past photographs and stories from patients and their families. Specifically, it can collect family photos, travel stories, school memories, and more. The data collection unit can also use AI to automatically classify the types of photos and stories to be collected and select the appropriate data. For example, it can use facial recognition technology to identify specific individuals in family photos, and natural language processing technology to extract locations and events from travel stories. This allows the data collection unit to efficiently extract information important to patients from a vast amount of data. Furthermore, the data collection unit can also collect detailed episodes and emotionally charged stories through interviews with patients and their families. As a result, the collected data becomes rich in content, including not only factual information but also the depth of emotions and memories. The data collection unit stores this data in digital format and makes it available for subsequent processing. For example, collected photos and stories are stored in cloud storage and made accessible to the generation and provision units. In addition, the data collection unit protects the data using encryption technology to ensure data privacy and security. This allows the data collection unit to efficiently collect necessary data while protecting the privacy of patients and their families, and to improve the overall performance of the system.

[0064] The generation unit generates video and audio content that evokes past memories based on data collected by the collection unit. Specifically, it uses natural language processing technology to understand the patient's speech and generate appropriate responses. For example, if a patient says, "I went to the beach with my family a long time ago," the generation unit analyzes the speech, searches for relevant photos and videos, and displays them. The generation unit can also use image recognition technology to analyze past photos and evoke related memories. For example, it can analyze family photos, recognize specific people or places, and generate episodes related to them. Furthermore, the generation unit can use machine learning to learn the patient's responses and provide optimal rehabilitation content. For example, if a patient shows a strong response to a particular topic, it will prioritize providing content related to that topic. By combining these technologies, the generation unit can generate the most effective rehabilitation content for the patient. The generated content is provided in video and audio formats, helping the patient recall memories visually and aurally. This allows the generation unit to effectively recall the patient's past memories and maximize the effectiveness of rehabilitation.

[0065] The delivery unit provides content generated by the generation unit to the patient in a friendly voice. Specifically, it can speak to the patient in a friendly voice using speech synthesis technology. For example, if the patient is talking about a past family trip, the delivery unit can display photos and videos related to that story and use speech synthesis technology to say, "This is a picture of the beach you went to with your family." This allows the patient to receive visual and auditory information simultaneously, making memory recall more effective. Furthermore, the delivery unit can monitor the patient's responses in real time and provide appropriate feedback. For example, if the patient shows interest in a particular topic, it can provide additional content related to that topic. The delivery unit can also analyze the patient's emotions and responses to evaluate the progress of rehabilitation. This allows the delivery unit to provide the patient with optimal rehabilitation content and maximize the effectiveness of rehabilitation. In addition, the delivery unit can provide feedback to the patient's family and caregivers, sharing the progress and effectiveness of rehabilitation. This allows the delivery unit to provide useful information not only to the patient but also to those around them, enhancing the overall support effect.

[0066] The collection unit can estimate the patient's emotions and adjust the types of photos and stories to collect based on the estimated emotions. For example, if the patient is relaxed, the collection unit will prioritize collecting photos and stories of happy memories. If the patient is anxious, the collection unit can also collect photos and stories of reassuring family members. Furthermore, if the patient is agitated, the collection unit can collect calming photos and stories to soothe the agitation. This allows for more effective memory recall by collecting appropriate photos and stories according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the patient's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0067] The data collection unit can analyze the patient's past life history and select the optimal data collection method. For example, the data collection unit can analyze the patient's past travel history and collect photos and stories from travel destinations. It can also analyze the patient's hobbies and interests and collect related photos and stories. Furthermore, the data collection unit can analyze the patient's family structure and friendships and collect memories with close people. By selecting the optimal data collection method based on the patient's past life history, more effective memory retrieval becomes possible. 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 input the patient's past life history data into a generating AI and have the generating AI select the optimal data collection method.

[0068] The data collection unit can filter data based on the patient's current health status and areas of interest during collection. For example, if the patient is in good health, the collection unit will collect active photos and stories. If the patient is in poor health, the collection unit can also collect calm photos and stories. Furthermore, the collection unit can collect photos and stories that are of interest to the patient based on their areas of interest. This allows for more effective memory recall by collecting appropriate photos and stories according to the patient's health status and areas of interest. Some or all of the processing described above in the collection unit may be performed using AI or not. For example, the collection unit can input the patient's health status data into a generating AI and have the generating AI perform the filtering.

[0069] The data collection unit can estimate the patient's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the patient is relaxed, the data collection unit may prioritize collecting data related to pleasant memories. If the patient is anxious, the data collection unit may also prioritize collecting data that provides a sense of security. Furthermore, if the patient is agitated, the data collection unit may prioritize collecting data that alleviates agitation. By prioritizing data according to the patient's emotions, more effective memory recall becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the patient's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0070] The data collection unit can prioritize the collection of highly relevant data, taking into account the patient's geographical location information. For example, the data collection unit can collect past photographs and stories of the area where the patient currently lives. It can also collect photographs and stories of areas where the patient has lived in the past. Furthermore, it can collect photographs and stories of places the patient frequently visits. This allows for more effective memory recall by collecting highly relevant data based on the patient's geographical location information. 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 input the patient's geographical location information data into a generating AI and have the generating AI select highly relevant data.

[0071] The data collection unit can analyze the patient's social media activity and collect relevant data during the collection process. For example, the data collection unit can collect photos and stories that the patient has shared on social media. It can also collect posts that the patient has "liked" on social media. Furthermore, it can collect posts from accounts that the patient follows on social media. This allows for more effective memory recall by collecting relevant data based on the patient's social media activity. 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 input the patient's social media data into a generating AI and have the generating AI select the relevant data.

[0072] The generation unit can understand the patient's speech using natural language processing and generate appropriate responses. For example, the generation unit analyzes the patient's speech using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. Furthermore, the generation unit can generate responses that reflect the patient's emotions or based on past conversation history. This makes it possible to understand the patient's speech and generate appropriate responses using natural language processing. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input text data of the patient's speech into a generation AI and have the generation AI generate appropriate responses.

[0073] The generation unit can analyze past photographs using image recognition technology and recall associated memories. For example, the generation unit analyzes past photographs using image recognition technologies such as face recognition, object recognition, and scene analysis. The generation unit can also recall episodes related to people and places depicted in the photographs. This makes it possible to analyze past photographs and recall associated memories by using image recognition technology. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input past photographic data into a generation AI and have the generation AI perform the recall of associated memories.

[0074] The generation unit can learn patient responses using machine learning and provide optimal rehabilitation content. The generation unit learns patient responses using machine learning techniques such as supervised learning, unsupervised learning, and reinforcement learning. Furthermore, the generation unit can customize content based on patient responses and select rehabilitation content based on past effectiveness. This makes it possible to learn patient responses and provide optimal rehabilitation content by using machine learning. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input patient response data into a generation AI and have the generation AI provide optimal rehabilitation content.

[0075] The service provider can speak to patients in a friendly voice using speech synthesis technology. For example, the service provider can generate a friendly voice using speech synthesis technologies such as text-to-speech synthesis or voice cloning technology. Furthermore, the service provider can generate speech while considering friendly voice characteristics such as tone, speaking style, and voice quality. This makes it possible to speak to patients in a friendly voice using speech synthesis technology. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input text data into a generation AI and have the generation AI generate a friendly voice.

[0076] The service provider can estimate the patient's emotions and adjust the way content is delivered based on the estimated emotions. For example, if the patient is relaxed, the service provider can deliver content in a calm voice. If the patient is anxious, the service provider can deliver content in a reassuring voice. Furthermore, if the patient is agitated, the service provider can deliver content with visually stimulating effects. By adjusting the delivery method according to the patient's emotions, more effective content delivery becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input patient facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The delivery unit can analyze the patient's past responses and select the optimal delivery method at the time of delivery. For example, the delivery unit can prioritize delivery methods that the patient has preferred in the past. It can also avoid delivery methods that the patient has disliked in the past. Furthermore, the delivery unit can learn and provide the optimal delivery method based on the patient's past responses. This makes it possible to deliver content more effectively by selecting the optimal delivery method based on the patient's past responses. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the patient's past response data into a generating AI and have the generating AI select the optimal delivery method.

[0078] The service provider can customize the means of delivery based on the patient's current health condition at the time of delivery. For example, if the patient is in good health, the service provider can provide active content. If the patient is in poor health, the service provider can also provide calming content. Furthermore, the service provider can provide visual or auditory content depending on the patient's health condition. This allows for more effective content delivery by customizing the means of delivery according to the patient's health condition. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input patient health data into a generating AI and have the generating AI perform the customization of the means of delivery.

[0079] The content delivery unit can estimate the patient's emotions and prioritize the content to be delivered based on the estimated emotions. For example, if the patient is relaxed, the delivery unit may prioritize content related to pleasant memories. If the patient is feeling anxious, the delivery unit may also prioritize content that provides a sense of security. Furthermore, if the patient is agitated, the delivery unit may prioritize content that soothes their agitation. This allows for more effective content delivery by prioritizing content according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input patient facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The content delivery unit can select the optimal delivery method by considering the patient's geographical location information at the time of delivery. For example, the delivery unit can provide past photos and stories of the area where the patient currently lives. It can also provide photos and stories of areas where the patient has lived in the past. Furthermore, it can provide photos and stories of places the patient frequently visits. By selecting the optimal delivery method based on the patient's geographical location information, more effective content delivery becomes possible. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the patient's geographical location information data into a generating AI and have the generating AI select the optimal delivery method.

[0081] The content delivery unit can analyze the patient's social media activity and propose delivery methods at the time of delivery. For example, the delivery unit can provide photos and stories that the patient has shared on social media. It can also provide posts that the patient has "liked" on social media. Furthermore, it can provide posts from accounts that the patient follows on social media. This enables more effective content delivery by proposing delivery methods based on the patient's social media activity. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the patient's social media data into a generating AI and have the generating AI execute the proposal of delivery methods.

[0082] The generation unit can understand the patient's speech using natural language processing and generate appropriate responses. For example, the generation unit analyzes the patient's speech using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. Furthermore, the generation unit can generate responses that reflect the patient's emotions or based on past conversation history. This makes it possible to understand the patient's speech and generate appropriate responses using natural language processing. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input text data of the patient's speech into a generation AI and have the generation AI generate appropriate responses.

[0083] The generation unit can analyze past photographs using image recognition technology and recall associated memories. For example, the generation unit analyzes past photographs using image recognition technologies such as face recognition, object recognition, and scene analysis. The generation unit can also recall episodes related to people and places depicted in the photographs. This makes it possible to analyze past photographs and recall associated memories by using image recognition technology. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input past photographic data into a generation AI and have the generation AI perform the recall of associated memories.

[0084] The generation unit can learn patient responses using machine learning and provide optimal rehabilitation content. The generation unit learns patient responses using machine learning techniques such as supervised learning, unsupervised learning, and reinforcement learning. Furthermore, the generation unit can customize content based on patient responses and select rehabilitation content based on past effectiveness. This makes it possible to learn patient responses and provide optimal rehabilitation content by using machine learning. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input patient response data into a generation AI and have the generation AI provide optimal rehabilitation content.

[0085] The service provider can speak to patients in a friendly voice using speech synthesis technology. For example, the service provider can generate a friendly voice using speech synthesis technologies such as text-to-speech synthesis or voice cloning technology. Furthermore, the service provider can generate speech while considering friendly voice characteristics such as tone, speaking style, and voice quality. This makes it possible to speak to patients in a friendly voice using speech synthesis technology. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input text data into a generation AI and have the generation AI generate a friendly voice.

[0086] The service provider can estimate the patient's emotions and adjust the way content is delivered based on the estimated emotions. For example, if the patient is relaxed, the service provider can deliver content in a calm voice. If the patient is anxious, the service provider can deliver content in a reassuring voice. Furthermore, if the patient is agitated, the service provider can deliver content with visually stimulating effects. By adjusting the delivery method according to the patient's emotions, more effective content delivery becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input patient facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The delivery unit can analyze the patient's past responses and select the optimal delivery method at the time of delivery. For example, the delivery unit can prioritize delivery methods that the patient has preferred in the past. It can also avoid delivery methods that the patient has disliked in the past. Furthermore, the delivery unit can learn and provide the optimal delivery method based on the patient's past responses. This makes it possible to deliver content more effectively by selecting the optimal delivery method based on the patient's past responses. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the patient's past response data into a generating AI and have the generating AI select the optimal delivery method.

[0088] The service provider can customize the means of delivery based on the patient's current health condition at the time of delivery. For example, if the patient is in good health, the service provider can provide active content. If the patient is in poor health, the service provider can also provide calming content. Furthermore, the service provider can provide visual or auditory content depending on the patient's health condition. This allows for more effective content delivery by customizing the means of delivery according to the patient's health condition. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input patient health data into a generating AI and have the generating AI perform the customization of the means of delivery.

[0089] The content delivery unit can estimate the patient's emotions and prioritize the content to be delivered based on the estimated emotions. For example, if the patient is relaxed, the delivery unit may prioritize content related to pleasant memories. If the patient is feeling anxious, the delivery unit may also prioritize content that provides a sense of security. Furthermore, if the patient is agitated, the delivery unit may prioritize content that soothes their agitation. This allows for more effective content delivery by prioritizing content according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input patient facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0090] The content delivery unit can select the optimal delivery method by considering the patient's geographical location information at the time of delivery. For example, the delivery unit can provide past photos and stories of the area where the patient currently lives. It can also provide photos and stories of areas where the patient has lived in the past. Furthermore, it can provide photos and stories of places the patient frequently visits. By selecting the optimal delivery method based on the patient's geographical location information, more effective content delivery becomes possible. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the patient's geographical location information data into a generating AI and have the generating AI select the optimal delivery method.

[0091] The content delivery unit can analyze the patient's social media activity and propose delivery methods at the time of delivery. For example, the delivery unit can provide photos and stories that the patient has shared on social media. It can also provide posts that the patient has "liked" on social media. Furthermore, it can provide posts from accounts that the patient follows on social media. This enables more effective content delivery by proposing delivery methods based on the patient's social media activity. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the patient's social media data into a generating AI and have the generating AI execute the proposal of delivery methods.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The dementia patient support system can also be equipped with a biometrics collection unit to collect biometric data. This unit collects vital signs such as the patient's heart rate, blood pressure, and body temperature, and uses this data to monitor the patient's health in real time. For example, if the heart rate rises sharply, the system can determine that the patient may be experiencing stress and provide relaxing content. Similarly, if blood pressure is high, providing calming music or images of scenery can help lower it. Furthermore, if body temperature is abnormally high, the system can determine that the patient may have a fever and issue an alert prompting them to contact a medical institution. This allows for the provision of more personalized care by utilizing biometric data.

[0094] The dementia patient support system can also include a music selection unit that estimates the patient's emotions and selects music based on those emotions. For example, if the patient is relaxed, the music selection unit can select calming classical music. If the patient is feeling anxious, it can select reassuring jazz or bossa nova. Furthermore, if the patient is agitated, it can select ambient music to soothe their energy. This allows for emotional stabilization by providing music that matches the patient's emotions. The music selection unit can implement emotion estimation using an emotion engine or generative AI. For example, the patient's facial expression data can be input into the generative AI, and the generative AI can perform emotion estimation.

[0095] The dementia patient support system can also be equipped with a lifestyle rhythm monitoring unit that monitors the patient's daily rhythm. This unit records the patient's sleep patterns and meal times, and based on this data, can determine the optimal timing for content delivery. For example, if a patient frequently wakes up at night, the system can improve sleep quality by providing relaxing content at night. It can also provide videos and audio that stimulate appetite at meal times. Furthermore, by analyzing lifestyle rhythm data, if an abnormal pattern is detected, the system can issue an alert prompting the patient to contact a medical institution. This makes it possible to provide individualized care based on the patient's daily rhythm.

[0096] The dementia patient support system can also include an aroma selection unit that estimates the patient's emotions and selects an aroma based on those emotions. For example, if the patient is relaxed, the aroma selection unit can select lavender or chamomile. If the patient is anxious, it can select bergamot or orange to provide a sense of calm. Furthermore, if the patient is agitated, it can select sandalwood or frankincense to soothe the agitation. This allows for emotional stabilization by providing aromas that match the patient's emotions. The aroma selection unit can implement emotion estimation using an emotion engine or generative AI. For example, the patient's facial expression data can be input into the generative AI, and the generative AI can perform emotion estimation.

[0097] The dementia patient support system may also include a lighting adjustment unit that estimates the patient's emotions and adjusts the lighting based on those emotions. For example, if the patient is relaxed, the lighting adjustment unit may provide warm, soft lighting. If the patient is feeling anxious, it may provide neutral-colored lighting to provide a sense of security. Furthermore, if the patient is agitated, it may provide cool blue lighting to soothe the agitation. This allows for emotional stabilization by providing lighting that matches the patient's emotions. The lighting adjustment unit can implement emotion estimation functionality using an emotion engine or generative AI. For example, patient facial expression data can be input into the generative AI, and the generative AI can perform emotion estimation.

[0098] The dementia patient support system can also be equipped with an exercise data collection unit to collect the patient's exercise data. This unit records the patient's daily exercise volume, steps taken, and activity time, and based on this data, can provide an optimal rehabilitation program. For example, if a patient is not getting enough exercise, the system can suggest a light stretching or walking program. If a patient is overexerting themselves, it can also provide a relaxation program to encourage rest. Furthermore, by analyzing the exercise data, if an abnormal pattern is detected, the system can issue an alert prompting the patient to contact a medical institution. This makes it possible to provide personalized rehabilitation care based on the patient's exercise data.

[0099] The dementia patient support system can also include a meal suggestion unit that estimates the patient's emotions and suggests meals based on those emotions. For example, if the patient is relaxed, the meal suggestion unit may suggest a light salad or fruit. If the patient is feeling anxious, it may suggest a warm soup or stew to provide comfort. Furthermore, if the patient is agitated, it may suggest herbal tea or yogurt to calm the agitation. This allows for emotional stabilization by suggesting meals that match the patient's emotions. The meal suggestion unit can implement emotion estimation using an emotion engine or generative AI. For example, the patient's facial expression data can be input into the generative AI, and the generative AI can perform emotion estimation.

[0100] The dementia patient support system may also include a social interaction facilitator to promote the patient's social interaction. This facilitator can support communication with the patient's friends and family, preventing social isolation. For example, it could suggest regular video calls to facilitate conversations with family and friends. It could also suggest participation in online communities or hobby groups that the patient is interested in. Furthermore, the social interaction facilitator could analyze the patient's past relationships and suggest reconnections. This would promote the patient's social interaction and maintain their mental health. Some or all of the above processes in the social interaction facilitator may be performed using AI or not. For example, the social interaction facilitator could input the patient's relationship data into a generating AI, which could then suggest optimal ways of interaction.

[0101] The dementia patient support system can further include a relaxation provision unit that estimates the patient's emotions and provides relaxation techniques based on those estimated emotions. For example, if the patient is relaxed, the relaxation provision unit can provide guidance on deep breathing or meditation. If the patient is feeling anxious, it can also provide a mindfulness session to provide a sense of security. Furthermore, if the patient is agitated, it can provide guidance on yoga or stretching to alleviate the agitation. This allows for emotional stabilization by providing relaxation techniques tailored to the patient's emotions. The relaxation provision unit can implement emotion estimation using an emotion engine or generative AI. For example, patient facial expression data can be input into the generative AI, which can then perform emotion estimation.

[0102] The dementia patient support system can further include a hobby support unit to support the patient's hobbies and interests. The hobby support unit can analyze the patient's past hobbies and interests and suggest new hobbies and activities based on that analysis. For example, if the patient enjoyed painting in the past, the system can suggest online painting classes. If the patient was interested in music, it can also provide opportunities to play musical instruments or listen to music. Furthermore, if the patient enjoyed gardening, it can offer advice on home gardening and how to care for plants. This makes it possible to improve the patient's quality of life by supporting their hobbies and interests. Some or all of the above processing in the hobby support unit may be performed using AI or not. For example, the hobby support unit can input the patient's hobby data into a generating AI and have the generating AI suggest the most suitable hobbies and activities.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The data collection unit collects past photos and stories from patients and their families. For example, it can collect family photos, travel stories, school memories, etc. The data collection unit can also use AI to automatically categorize the types of photos and stories to be collected and select the appropriate data. Step 2: The generation unit generates video and audio content that evokes past memories based on the data collected by the collection unit. For example, it can use natural language processing technology to understand the patient's speech and generate appropriate responses. It can also use image recognition technology to analyze past photographs and evoke related memories. Furthermore, it can use machine learning to learn the patient's responses and provide optimal rehabilitation content. Step 3: The delivery unit delivers the content generated by the generation unit in a friendly voice to the patient. For example, speech synthesis technology can be used to speak to the patient in a friendly voice.

[0105] 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.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0107] 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.

[0108] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect past photos and stories from the patient and their family, and transmits them to the data processing unit 12 via the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates video and audio content that evokes past memories based on the collected data. The provision unit uses, for example, the output device 40 of the smart device 14 to provide the generated content to the patient in a friendly voice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] 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.

[0124] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect past photos and stories from the patient and their family, and transmits them to the data processing unit 12 via the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates video and audio content that evokes past memories based on the collected data. The provision unit uses, for example, the speaker 240 of the smart glasses 214 to provide the generated content to the patient in a friendly voice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect past photos and stories from the patient and their family, and transmits them to the data processing unit 12 via the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates video and audio content that evokes past memories based on the collected data. The provision unit uses the speaker 240 of the headset terminal 314 to provide the generated content to the patient in a friendly voice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] 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.

[0157] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect past photographs and stories from the patient and their family, and transmits them to the data processing unit 12 via the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates video and audio content that evokes past memories based on the collected data. The provision unit uses the speaker 240 of the robot 414 to provide the generated content to the patient in a friendly voice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] (Note 1) The collection department collects past photos and stories from patients and their families, A generation unit generates video and audio content that evokes past memories based on the data collected by the aforementioned collection unit, The system includes a providing unit that delivers the content generated by the generation unit to the patient in a friendly voice. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system estimates the patient's emotions and adjusts the types of photos and stories collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the patient's past lifestyle history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is During data collection, filtering is performed based on the patient's current health status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, analyze patients' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is Using natural language processing, we understand what the patient is saying and generate appropriate responses. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is Using image recognition technology, past photographs are analyzed to evoke associated memories. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is Using machine learning, we learn from patients' responses and provide optimal rehabilitation content. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supply unit is, Using speech synthesis technology, we speak to patients in a friendly voice. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is The system estimates the patient's emotions and adjusts the types of photos and stories collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Analyze the patient's past lifestyle history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, filtering is performed based on the patient's current health status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is During data collection, analyze patients' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is Using natural language processing, we understand what the patient is saying and generate appropriate responses. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is Using image recognition technology, past photographs are analyzed to evoke associated memories. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is Using machine learning, we learn from patients' responses and provide optimal rehabilitation content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, Using speech synthesis technology, we speak to patients in a friendly voice. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, We estimate the patient's emotions and adjust the way we deliver content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, At the time of delivery, the patient's past responses are analyzed to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, At the time of delivery, the delivery method will be customized based on the patient's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the patient's emotions and prioritizes the content provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, we analyze the patient's social media activity and propose a method of delivery. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0177] 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 past photos and stories from patients and their families, A generation unit generates video and audio content that evokes past memories based on the data collected by the aforementioned collection unit, The system includes a providing unit that delivers the content generated by the generation unit to the patient in a friendly voice. A system characterized by the following features.

2. The aforementioned collection unit is The system estimates the patient's emotions and adjusts the types of photos and stories collected based on those estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze the patient's past lifestyle history and select the optimal data collection method. The system according to feature 1.

4. The aforementioned collection unit is During data collection, filtering is performed based on the patient's current health status and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the patient's geographical location. The system according to feature 1.

7. The aforementioned collection unit is During data collection, analyze patients' social media activity and collect relevant data. The system according to feature 1.

8. The generating unit is Using natural language processing, we understand what the patient is saying and generate appropriate responses. The system according to feature 1.

Images