system

Abstract

The system according to the embodiment aims to estimate the mental state of a dementia patient and provide an appropriate method of response to the patient's caregiver. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a suggestion unit. The collection unit collects facial expressions and behaviors of dementia patients. The analysis unit analyzes the data collected by the collection unit and estimates the mental state of the patient. The provision unit provides information to a caregiver based on the analysis results obtained by the analysis unit. The suggestion unit proposes an appropriate response method based on the information provided by the provision unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately grasp the mental state of dementia patients and provide appropriate support.

[0005] The system according to the embodiment aims to estimate the mental state of a dementia patient and provide an appropriate method of response to the patient's caregiver. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a suggestion unit. The collection unit collects facial expressions and behaviors of a dementia patient. The analysis unit analyzes the data collected by the collection unit and estimates the mental state of the patient. The provision unit provides information to a caregiver based on the analysis results obtained by the analysis unit. The suggestion unit suggests an appropriate response method based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can estimate the mental state of a dementia patient and provide an appropriate response method to a caregiver. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A dementia patient support system according to an embodiment of the present invention uses AI to interpret the minds of dementia patients and create an environment in which caregivers can respond appropriately. This system uses sensors to collect facial expressions and behaviors of dementia patients, and AI analyzes the collected data to estimate the patient's mental state. For example, when a patient smiles, the AI ​​analyzes the emotion behind the smile and conveys it to the caregiver. This allows the caregiver to understand the patient's mental state and respond appropriately. Furthermore, the AI ​​learns from the patient's past data and suggests optimal responses for each patient. For example, if a patient finds listening to certain music relaxing, the AI ​​suggests playing that music. This helps stabilize the patient's mental state. This mechanism allows the system to understand the mental state of dementia patients and respond appropriately, thereby improving the quality of life for both patients and caregivers. AI not only possesses advanced technology, but can also serve as an intermediary and assist those suffering from the disease. This allows the dementia patient support system to understand the patient's mental state and respond appropriately.

[0029] A dementia patient support system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a suggestion unit. The collection unit collects facial expressions and behaviors of a dementia patient. The collection unit can collect the patient's facial expressions and behaviors using, for example, a facial expression recognition camera or a motion sensor. The facial expression recognition camera captures the patient's facial expressions in real time, and the motion sensor detects the patient's movements. For example, when a patient smiles, the collection unit can collect the emotion behind the smile. The collection unit can also detect the patient's movements when walking and collect that data. The analysis unit analyzes the data collected by the collection unit and infers the patient's state of mind. The analysis unit can analyze the collected data using, for example, a facial expression recognition algorithm or a behavior analysis algorithm. The facial expression recognition algorithm infers the emotion from the patient's facial expression, and the behavior analysis algorithm infers the patient's state of mind from the patient's movements. For example, when a patient smiles, the analysis unit can analyze the emotion behind the smile. The analysis unit can also analyze the patient's movements when walking and infer the patient's state of mind from the movements. The providing unit provides information to the caregiver based on the analysis results obtained by the analysis unit. The providing unit can provide the information to the caregiver through, for example, a smartphone app or a dedicated display. The smartphone app allows the caregiver to check the information anytime and anywhere, and the dedicated display is used to display the information in nursing facilities, etc. For example, when a patient smiles, the providing unit can convey to the caregiver the emotion behind the smile. The providing unit can also analyze the patient's walking movements and convey to the caregiver the patient's mental state estimated from the movements. The suggestion unit proposes an optimal response method based on the information provided by the providing unit. The suggestion unit can, for example, learn the patient's past facial expressions and behavioral data and propose an optimal response method for each individual patient. For example, if a patient finds that listening to certain music relaxes them, the suggestion unit can suggest playing that music. If a patient finds that performing a certain movement relaxes them, the suggestion unit can also suggest performing that movement. This allows the dementia patient support system according to the embodiment to understand the patient's mental state and take appropriate measures.Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the patient's past facial expressions and behavior data into the generation AI and have the generation AI execute a suggestion of the optimal response method.

[0030] The collection unit can collect the facial expressions and behavior of the dementia patient using a facial expression recognition camera or a motion sensor. The collection unit, for example, collects the patient's facial expressions using a facial expression recognition camera. For example, the facial expression recognition camera captures the patient's facial expressions in real time and collects the data. The collection unit can also collect the patient's behavior using a motion sensor. For example, the motion sensor detects the patient's movements and collects the data. In this way, by using the facial expression recognition camera or the motion sensor, the facial expressions and behavior of the dementia patient can be accurately collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired by the facial expression recognition camera into a generation AI and have the generation AI analyze the facial expression data.

[0031] The analysis unit can analyze the collected data using a facial expression recognition algorithm or a behavior analysis algorithm to estimate the patient's mental state. The analysis unit, for example, uses a facial expression recognition algorithm to analyze the patient's facial expression. For example, the facial expression recognition algorithm estimates emotions from the patient's facial expressions and analyzes the data. The analysis unit can also analyze the patient's behavior using a behavior analysis algorithm. For example, the behavior analysis algorithm estimates the patient's mental state from the patient's movements and analyzes the data. In this way, by using the facial expression recognition algorithm or the behavior analysis algorithm, the patient's mental state can be accurately estimated. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data acquired by the facial expression recognition algorithm into a generation AI and have the generation AI estimate emotions.

[0032] The providing unit can provide information to the caregiver through a smartphone app or a dedicated display. The providing unit provides information to the caregiver using, for example, a smartphone app. For example, the smartphone app allows the caregiver to check the information anytime, anywhere and provides the data. The providing unit can also provide information to the caregiver using a dedicated display. For example, a dedicated display is used to display information in a care facility, etc., and provides the data. By providing information through the smartphone app or the dedicated display, the caregiver can easily receive the information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data acquired by the smartphone app into the generation AI and cause the generation AI to provide the information.

[0033] The suggestion unit can learn the patient's past facial expressions and behavioral data and suggest an appropriate response method for each individual patient. The suggestion unit, for example, learns the patient's past facial expressions and behavioral data and suggests the optimal response method. For example, if a patient feels relaxed when listening to specific music, the suggestion unit can suggest playing that music. Also, if a patient feels relaxed when performing a specific movement, the suggestion unit can suggest performing that movement. In this way, by learning the patient's past data, the optimal response method for each individual patient can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the patient's past facial expressions and behavioral data into the generation AI and have the generation AI execute a suggestion of the optimal response method.

[0034] The suggestion unit can suggest that a patient who feels relaxed when listening to specific music play that music. For example, the suggestion unit suggests that a patient who feels relaxed when listening to specific music play that music. For example, the suggestion unit suggests playing classical music to help the patient relax. The suggestion unit can also suggest playing natural sounds to help the patient relax. In this way, playing specific music can promote relaxation for the patient. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input music suitable for relaxing the patient into the generation AI and cause the generation AI to play the music.

[0035] The collection unit can analyze the patient's past behavioral patterns and select an appropriate collection timing. For example, if the patient is active during a specific time period, the collection unit can concentrate collection during that time period. For example, if the patient is active in the morning, the collection unit can concentrate collection of facial expression and behavioral data during that time period. The collection unit can also reduce the frequency of data collection during the night if the patient is calmer at night. For example, the collection unit can reduce the frequency of data collection when the patient is relaxing at night. The collection unit can also collect data during a time period when the patient exhibits a specific behavior while eating. For example, the collection unit collects behavioral data when the patient exhibits a specific behavior while eating. This allows the optimal collection timing to be selected by analyzing the past behavioral patterns. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the patient's past behavioral data into the generation AI and cause the generation AI to select the optimal collection timing.

[0036] When collecting facial expressions and behaviors, the collection unit can filter the data based on the patient's current health condition and environment. For example, if the patient is in poor health, the collection unit filters the collected data to collect only important data. For example, when the patient is in poor health, the collection unit filters facial expression data to collect only important data. The collection unit can also improve the accuracy of the collected data by removing environmental noise when the patient is out. For example, the collection unit removes environmental noise from data collected while the patient is out to collect highly accurate data. The collection unit can also analyze the collected data in detail when the patient is in a relaxed environment. For example, the collection unit analyzes data collected in a relaxed environment in detail. This allows filtering based on the health condition and environment to collect only important data. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the patient's health condition and environmental data into the generation AI and have the generation AI perform data filtering.

[0037] When collecting facial expressions and behaviors, the collection unit can prioritize collecting highly relevant data by taking into account the patient's geographical location information. For example, when the patient is at home, the collection unit prioritizes collecting daily behavior data. For example, when the patient is at home, the collection unit prioritizes collecting daily behavior data. Furthermore, when the patient is out, the collection unit can also prioritize collecting data related to the external environment. For example, the collection unit prioritizes collecting data collected while the patient is out. Furthermore, when the patient is in a medical facility, the collection unit can also prioritize collecting medical-related behavior data. For example, the collection unit prioritizes collecting medical-related behavior data when the patient is in a medical facility. In this way, highly relevant data can be prioritized by taking the geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the patient's geographical location information to the generation AI and cause the generation AI to prioritize collecting highly relevant data.

[0038] The collection unit can analyze the patient's social media activity when collecting facial expressions and behaviors and collect related data. For example, the collection unit collects data when the patient expresses emotions on social media. For example, the collection unit collects data when the patient expresses emotions on social media. The collection unit can also collect behavioral data when the patient participates in a specific event. For example, the collection unit collects behavioral data when the patient participates in a specific event. The collection unit can also prioritize other data collection methods when the patient is less active on social media. For example, the collection unit prioritizes other data collection methods when the patient is less active on social media. In this way, related data can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's social media activity data into the generation AI and cause the generation AI to collect related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, when there is a lot of important data, the analysis unit performs a detailed analysis. For example, when there is a lot of important data, the analysis unit performs a detailed analysis. Furthermore, when there is a lot of data of low importance, the analysis unit can also perform a simplified analysis. For example, when there is a lot of data of low importance, the analysis unit performs a simplified analysis. Furthermore, when there is a lot of data of medium importance, the analysis unit can also perform an analysis with a moderate level of detail. For example, when there is a lot of data of medium importance, the analysis unit performs an analysis with a moderate level of detail. In this way, by adjusting the level of detail of the analysis based on the importance of the data, efficient analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the collected data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a facial expression recognition algorithm to facial expression data. For example, the analysis unit applies a facial expression recognition algorithm to facial expression data and analyzes the data. The analysis unit can also apply a behavioral analysis algorithm to behavioral data. For example, the analysis unit applies a behavioral analysis algorithm to behavioral data and analyzes the data. The analysis unit can also apply a voice analysis algorithm to voice data. For example, the analysis unit applies a voice analysis algorithm to voice data and analyzes the data. This enables more appropriate analysis by applying different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also perform analysis while referring to past data. For example, the analysis unit performs analysis while referring to past data. The analysis unit can also prioritize analyzing data from a specific time period. For example, the analysis unit prioritizes analyzing data from a specific time period. This enables efficient analysis by determining the priority of analysis based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the priority of analysis.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of low-relevance data. For example, the analysis unit postpones analysis of low-relevance data. The analysis unit can also moderately analyze data with medium relevance. For example, the analysis unit moderately analyzes data with medium relevance. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0043] When providing information, the providing unit can select an appropriate information provision method by referring to the caregiver's past response history. The providing unit, for example, provides information based on response methods that have been successful in the past by the caregiver. For example, the providing unit provides information based on response methods that have been successful in the past by the caregiver. The providing unit can also provide information by avoiding response methods that have failed in the past by the caregiver. For example, the providing unit provides information by avoiding response methods that have failed in the past by the caregiver. The providing unit can also analyze the caregiver's past response history and select an optimal information provision method. For example, the providing unit analyzes the caregiver's past response history and selects an optimal information provision method. In this way, the optimal information provision method can be selected by referring to the caregiver's past response history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the caregiver's past response history into the generation AI and cause the generation AI to select an information provision method.

[0044] The providing unit can customize the means of providing information based on the caregiver's current situation when providing information. For example, when the caregiver is busy, the providing unit provides concise information. For example, when the caregiver is busy, the providing unit provides concise information. The providing unit can also provide detailed information when the caregiver is relaxed. For example, when the caregiver is relaxed, the providing unit provides detailed information. The providing unit can also provide information in a calm manner when the caregiver is nervous. For example, when the caregiver is nervous, the providing unit provides information in a calm manner. This enables more appropriate information to be provided by customizing the means of providing information based on the caregiver's current situation. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the caregiver's current situation into the generating AI and cause the generating AI to customize the means of providing information.

[0045] When providing information, the providing unit can select an appropriate information provision method by taking into account the geographical location information of the caregiver. For example, when the caregiver is at home, the providing unit provides detailed information. For example, the providing unit provides detailed information when the caregiver is at home. The providing unit can also provide concise information when the caregiver is out. For example, the providing unit provides concise information when the caregiver is out. The providing unit can also prioritize providing medical-related information when the caregiver is in a medical facility. For example, the providing unit prioritizes providing medical-related information when the caregiver is in a medical facility. This makes it possible to select an optimal information provision method by taking into account the geographical location information of the caregiver. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the geographical location information of the caregiver to the generation AI and cause the generation AI to select an information provision method.

[0046] When providing information, the providing unit can analyze the caregiver's social media activity and suggest a means of providing the information. For example, if the caregiver shares information on social media, the providing unit suggests the means. For example, if the caregiver shares information on social media, the providing unit suggests the means. Furthermore, if the caregiver is less active on social media, the providing unit can suggest other means of providing information. For example, if the caregiver is less active on social media, the providing unit can suggest other means of providing information. Furthermore, if the caregiver frequently uses a specific social media platform, the providing unit can provide information through that platform. For example, if the caregiver frequently uses a specific social media platform, the providing unit provides information through that platform. In this way, by analyzing the caregiver's social media activity, the optimal means of providing information can be suggested. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the caregiver's social media activity data into the generation AI and cause the generation AI to suggest means of providing information.

[0047] When making a proposal, the suggestion unit can analyze the patient's past behavioral data and select an appropriate proposal method. The suggestion unit, for example, makes a proposal based on the patient's past relaxing behavior. For example, the suggestion unit makes a proposal based on the patient's past relaxing behavior. The suggestion unit can also make a proposal while avoiding the patient's past excited behavior. For example, the suggestion unit makes a proposal while avoiding the patient's past excited behavior. The suggestion unit can also analyze the patient's past behavioral data and select an optimal proposal method. For example, the suggestion unit analyzes the patient's past behavioral data and selects an optimal proposal method. In this way, the optimal proposal method can be selected by analyzing the patient's past behavioral data. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the patient's past behavioral data to the generation AI and cause the generation AI to select a proposal method.

[0048] When making a suggestion, the suggestion unit can customize the suggestion means based on the patient's current living situation. For example, when the patient is at home, the suggestion unit makes daily suggestions. For example, when the patient is at home, the suggestion unit makes daily suggestions. The suggestion unit can also make suggestions related to the external environment when the patient is out. For example, when the patient is out, the suggestion unit can make suggestions related to the external environment. The suggestion unit can also make medical-related suggestions when the patient is in a medical facility. For example, when the patient is in a medical facility, the suggestion unit makes medical-related suggestions. This enables more appropriate suggestions by customizing the suggestion means based on the patient's current living situation. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data about the patient's current living situation into the generation AI and cause the generation AI to customize the suggestion means.

[0049] When making a proposal, the suggestion unit can select an appropriate proposal method by taking into consideration the patient's geographical location information. For example, when the patient is at home, the suggestion unit suggests activities that can be done at home. For example, when the patient is at home, the suggestion unit suggests activities that can be done at home. Furthermore, when the patient is out, the suggestion unit can also suggest activities that can be done while away from home. For example, when the patient is out, the suggestion unit suggests activities that can be done while away from home. Furthermore, when the patient is in a medical facility, the suggestion unit can also suggest activities that can be done within the medical facility. For example, when the patient is in a medical facility, the suggestion unit suggests activities that can be done within the medical facility. In this way, the optimal proposal method can be selected by taking into consideration the patient's geographical location information. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the patient's geographical location information to the generation AI and cause the generation AI to select a proposal method.

[0050] When making a suggestion, the suggestion unit can analyze the patient's social media activity and suggest a suggestion method. For example, when the patient expresses emotions on social media, the suggestion unit makes a suggestion based on that data. For example, when the patient expresses emotions on social media, the suggestion unit makes a suggestion based on that data. In addition, when the patient participates in a specific event, the suggestion unit can make a suggestion based on behavioral data. For example, when the patient participates in a specific event, the suggestion unit can make a suggestion based on that behavioral data. In addition, when the patient is less active on social media, the suggestion unit can make a suggestion based on other data collection methods. For example, when the patient is less active on social media, the suggestion unit makes a suggestion based on other data collection methods. In this way, by analyzing the patient's social media activity, the optimal suggestion method can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the patient's social media activity data into the generation AI and cause the generation AI to suggest a suggestion method.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The collection unit may further include an environmental sensor. The environmental sensor may collect environmental data such as the temperature, humidity, and illuminance around the patient. For example, the environmental sensor may suggest an appropriate temperature adjustment if the temperature in the patient's room is too high. The environmental sensor may also suggest adjusting the lighting if the illuminance is low. Furthermore, the environmental sensor may suggest using a humidifier if the humidity is low. In this way, the environmental data may be utilized to maintain a comfortable living environment for the patient.

[0053] The providing unit can further include a voice feedback function. The voice feedback function can communicate analysis results and suggestions to caregivers by voice. For example, even if a caregiver is busy, they can receive information by voice. The voice feedback function can also directly communicate information to patients. For example, advice on how to relax can be provided to patients by voice. Furthermore, the voice feedback function can also support multiple languages. This makes it possible to utilize voice to improve the convenience of providing information.

[0054] The suggestion unit can further incorporate game elements. Game elements are used to attract the patient's interest and make rehabilitation and daily activities more enjoyable. For example, the suggestion unit can propose a game in which the patient accumulates points by performing specific movements. The suggestion unit can also propose a game in which the patient receives rewards by completing daily tasks. Furthermore, the suggestion unit can propose a game in which the patient is motivated by competing with other patients. In this way, incorporating game elements can increase the patient's motivation to be active.

[0055] The collection unit can further collect dietary data of the patient. The dietary data is used to understand the patient's nutritional status and dietary preferences. For example, the collection unit can record the contents and amount of food the patient has eaten. If the patient has a preference for a particular ingredient, the collection unit can also suggest recipes that include that ingredient. Furthermore, the collection unit can record the time of day when the patient eats meals and suggest appropriate meal times. In this way, by utilizing the dietary data, the patient's nutritional management can be more effective.

[0056] The providing unit can further include a visual feedback function. The visual feedback function can visually communicate the analysis results and suggestions to the caregiver. For example, the providing unit can visually display the patient's condition using graphs and charts. The providing unit can also use animations to clearly communicate the suggestions. Furthermore, the providing unit can visually indicate the importance of information using colors and icons. As a result, utilizing visual feedback makes it easier to understand the information.

[0057] The collection unit can further collect exercise data of the patient. The exercise data is used to understand the patient's physical activity level and exercise patterns. For example, the collection unit can record the type and duration of exercise performed by the patient. The collection unit can also record the patient's heart rate and calories burned when performing a specific exercise. Furthermore, the collection unit can suggest an appropriate exercise program based on the patient's exercise data. In this way, by utilizing the exercise data, the patient's health can be managed more effectively.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects the facial expressions and behaviors of the dementia patient. The collection unit can collect the facial expressions and behaviors of the patient using, for example, a facial expression recognition camera or a movement sensor. The facial expression recognition camera captures the patient's facial expressions in real time, and the movement sensor detects the patient's movements. For example, when the patient smiles, the collection unit can collect the emotion behind the smile. The collection unit can also detect the patient's movements when walking and collect that data. Step 2: The analysis unit analyzes the data collected by the collection unit and estimates the patient's mental state. The analysis unit can analyze the collected data using, for example, a facial expression recognition algorithm or a behavior analysis algorithm. The facial expression recognition algorithm estimates emotions from the patient's facial expressions, and the behavior analysis algorithm estimates the patient's mental state from their movements. For example, if the patient smiles, the analysis unit can analyze the emotions behind the smile. The analysis unit can also analyze the patient's movements when walking and estimate the patient's mental state from those movements. Step 3: The providing unit provides information to the caregiver based on the analysis results obtained by the analyzing unit. The providing unit can provide information to the caregiver, for example, through a smartphone app or a dedicated display. The smartphone app allows the caregiver to check the information anytime, anywhere, and the dedicated display is used to display the information in nursing facilities, etc. For example, when a patient smiles, the providing unit can convey to the caregiver the emotion behind the smile. The providing unit can also analyze the patient's movements when walking and convey to the caregiver the patient's mental state estimated from those movements. Step 4: The suggestion unit suggests an optimal response method based on the information provided by the provision unit. The suggestion unit can, for example, learn the patient's past facial expressions and behavioral data and suggest an optimal response method for each individual patient. For example, if a patient feels relaxed when listening to specific music, the suggestion unit can suggest playing that music. Also, if a patient feels relaxed when performing a specific action, the suggestion unit can suggest performing that action. This allows the dementia patient support system according to the embodiment to understand the patient's mental state and take appropriate action. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the patient's past facial expressions and behavioral data into the generation AI and have the generation AI execute a suggestion of an optimal response method.

[0060] (Example 2) A dementia patient support system according to an embodiment of the present invention uses AI to interpret the minds of dementia patients and create an environment in which caregivers can respond appropriately. This system uses sensors to collect facial expressions and behaviors of dementia patients, and AI analyzes the collected data to estimate the patient's mental state. For example, when a patient smiles, the AI ​​analyzes the emotion behind the smile and conveys it to the caregiver. This allows the caregiver to understand the patient's mental state and respond appropriately. Furthermore, the AI ​​learns from the patient's past data and suggests optimal responses for each patient. For example, if a patient finds listening to certain music relaxing, the AI ​​suggests playing that music. This helps stabilize the patient's mental state. This mechanism allows the system to understand the mental state of dementia patients and respond appropriately, thereby improving the quality of life for both patients and caregivers. AI not only possesses advanced technology, but can also serve as an intermediary and assist those suffering from the disease. This allows the dementia patient support system to understand the patient's mental state and respond appropriately.

[0061] A dementia patient support system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a suggestion unit. The collection unit collects facial expressions and behaviors of a dementia patient. The collection unit can collect the patient's facial expressions and behaviors using, for example, a facial expression recognition camera or a motion sensor. The facial expression recognition camera captures the patient's facial expressions in real time, and the motion sensor detects the patient's movements. For example, when a patient smiles, the collection unit can collect the emotion behind the smile. The collection unit can also detect the patient's movements when walking and collect that data. The analysis unit analyzes the data collected by the collection unit and infers the patient's state of mind. The analysis unit can analyze the collected data using, for example, a facial expression recognition algorithm or a behavior analysis algorithm. The facial expression recognition algorithm infers the emotion from the patient's facial expression, and the behavior analysis algorithm infers the patient's state of mind from the patient's movements. For example, when a patient smiles, the analysis unit can analyze the emotion behind the smile. The analysis unit can also analyze the patient's movements when walking and infer the patient's state of mind from the movements. The providing unit provides information to the caregiver based on the analysis results obtained by the analysis unit. The providing unit can provide the information to the caregiver through, for example, a smartphone app or a dedicated display. The smartphone app allows the caregiver to check the information anytime and anywhere, and the dedicated display is used to display the information in nursing facilities, etc. For example, when a patient smiles, the providing unit can convey to the caregiver the emotion behind the smile. The providing unit can also analyze the patient's walking movements and convey to the caregiver the patient's mental state estimated from the movements. The suggestion unit proposes an optimal response method based on the information provided by the providing unit. The suggestion unit can, for example, learn the patient's past facial expressions and behavioral data and propose an optimal response method for each individual patient. For example, if a patient finds that listening to certain music relaxes them, the suggestion unit can suggest playing that music. If a patient finds that performing a certain movement relaxes them, the suggestion unit can also suggest performing that movement. This allows the dementia patient support system according to the embodiment to understand the patient's mental state and take appropriate measures.Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the patient's past facial expressions and behavior data into the generation AI and have the generation AI execute a suggestion of the optimal response method.

[0062] The collection unit can collect the facial expressions and behavior of the dementia patient using a facial expression recognition camera or a motion sensor. The collection unit, for example, collects the patient's facial expressions using a facial expression recognition camera. For example, the facial expression recognition camera captures the patient's facial expressions in real time and collects the data. The collection unit can also collect the patient's behavior using a motion sensor. For example, the motion sensor detects the patient's movements and collects the data. In this way, by using the facial expression recognition camera or the motion sensor, the facial expressions and behavior of the dementia patient can be accurately collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired by the facial expression recognition camera into a generation AI and have the generation AI analyze the facial expression data.

[0063] The analysis unit can analyze the collected data using a facial expression recognition algorithm or a behavior analysis algorithm to estimate the patient's mental state. The analysis unit, for example, uses a facial expression recognition algorithm to analyze the patient's facial expression. For example, the facial expression recognition algorithm estimates emotions from the patient's facial expressions and analyzes the data. The analysis unit can also analyze the patient's behavior using a behavior analysis algorithm. For example, the behavior analysis algorithm estimates the patient's mental state from the patient's movements and analyzes the data. In this way, by using the facial expression recognition algorithm or the behavior analysis algorithm, the patient's mental state can be accurately estimated. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data acquired by the facial expression recognition algorithm into a generation AI and have the generation AI estimate emotions.

[0064] The providing unit can provide information to the caregiver through a smartphone app or a dedicated display. The providing unit provides information to the caregiver using, for example, a smartphone app. For example, the smartphone app allows the caregiver to check the information anytime, anywhere and provides the data. The providing unit can also provide information to the caregiver using a dedicated display. For example, a dedicated display is used to display information in a care facility, etc., and provides the data. By providing information through the smartphone app or the dedicated display, the caregiver can easily receive the information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data acquired by the smartphone app into the generation AI and cause the generation AI to provide the information.

[0065] The suggestion unit can learn the patient's past facial expressions and behavioral data and suggest an appropriate response method for each individual patient. The suggestion unit, for example, learns the patient's past facial expressions and behavioral data and suggests the optimal response method. For example, if a patient feels relaxed when listening to specific music, the suggestion unit can suggest playing that music. Also, if a patient feels relaxed when performing a specific movement, the suggestion unit can suggest performing that movement. In this way, by learning the patient's past data, the optimal response method for each individual patient can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the patient's past facial expressions and behavioral data into the generation AI and have the generation AI execute a suggestion of the optimal response method.

[0066] The suggestion unit can suggest that a patient who feels relaxed when listening to specific music play that music. For example, the suggestion unit suggests that a patient who feels relaxed when listening to specific music play that music. For example, the suggestion unit suggests playing classical music to help the patient relax. The suggestion unit can also suggest playing natural sounds to help the patient relax. In this way, playing specific music can promote relaxation for the patient. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input music suitable for relaxing the patient into the generation AI and cause the generation AI to play the music.

[0067] The collection unit can estimate the patient's emotions and adjust the frequency of collection of facial expressions and behavioral data based on the estimated patient emotions. For example, when the patient is feeling anxious, the collection unit increases the collection frequency to collect data in real time. For example, when the patient is feeling anxious, the collection unit frequently collects facial expression data. Furthermore, when the patient is relaxed, the collection unit can also reduce the collection frequency to reduce the burden of data collection. For example, when the patient is relaxed, the collection unit reduces the frequency of collection of behavioral data. Furthermore, when the patient is excited, the collection unit can set the collection frequency to a medium level to efficiently collect necessary data. For example, when the patient is excited, the collection unit collects voice data at a medium frequency. This allows for more appropriate data collection by adjusting the collection frequency based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input patient emotion data to the generation AI and cause the generation AI to adjust the collection frequency.

[0068] The collection unit can analyze the patient's past behavioral patterns and select an appropriate collection timing. For example, if the patient is active during a specific time period, the collection unit can concentrate collection during that time period. For example, if the patient is active in the morning, the collection unit can concentrate collection of facial expression and behavioral data during that time period. The collection unit can also reduce the frequency of data collection during the night if the patient is calmer at night. For example, the collection unit can reduce the frequency of data collection when the patient is relaxing at night. The collection unit can also collect data during a time period when the patient exhibits a specific behavior while eating. For example, the collection unit collects behavioral data when the patient exhibits a specific behavior while eating. This allows the optimal collection timing to be selected by analyzing the past behavioral patterns. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the patient's past behavioral data into the generation AI and cause the generation AI to select the optimal collection timing.

[0069] When collecting facial expressions and behaviors, the collection unit can filter the data based on the patient's current health condition and environment. For example, if the patient is in poor health, the collection unit filters the collected data to collect only important data. For example, when the patient is in poor health, the collection unit filters facial expression data to collect only important data. The collection unit can also improve the accuracy of the collected data by removing environmental noise when the patient is out. For example, the collection unit removes environmental noise from data collected while the patient is out to collect highly accurate data. The collection unit can also analyze the collected data in detail when the patient is in a relaxed environment. For example, the collection unit analyzes data collected in a relaxed environment in detail. This allows filtering based on the health condition and environment to collect only important data. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the patient's health condition and environmental data into the generation AI and have the generation AI perform data filtering.

[0070] The collection unit can estimate the patient's emotions and determine the priority of data to be collected based on the estimated patient's emotions. For example, when the patient is feeling anxious, the collection unit prioritizes collecting facial expression data. For example, when the patient is feeling anxious, the collection unit prioritizes collecting facial expression data. The collection unit can also prioritize collecting behavioral data when the patient is relaxed. For example, when the patient is relaxed, the collection unit prioritizes collecting behavioral data. The collection unit can also prioritize collecting voice data when the patient is excited. For example, when the patient is excited, the collection unit prioritizes collecting voice data. In this way, by determining the priority of data based on the patient's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input patient emotion data into the generation AI and have the generation AI determine the priority of the data.

[0071] When collecting facial expressions and behaviors, the collection unit can prioritize collecting highly relevant data by taking into account the patient's geographical location information. For example, when the patient is at home, the collection unit prioritizes collecting daily behavior data. For example, when the patient is at home, the collection unit prioritizes collecting daily behavior data. Furthermore, when the patient is out, the collection unit can also prioritize collecting data related to the external environment. For example, the collection unit prioritizes collecting data collected while the patient is out. Furthermore, when the patient is in a medical facility, the collection unit can also prioritize collecting medical-related behavior data. For example, the collection unit prioritizes collecting medical-related behavior data when the patient is in a medical facility. In this way, highly relevant data can be prioritized by taking the geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the patient's geographical location information to the generation AI and cause the generation AI to prioritize collecting highly relevant data.

[0072] The collection unit can analyze the patient's social media activity when collecting facial expressions and behaviors and collect related data. For example, the collection unit collects data when the patient expresses emotions on social media. For example, the collection unit collects data when the patient expresses emotions on social media. The collection unit can also collect behavioral data when the patient participates in a specific event. For example, the collection unit collects behavioral data when the patient participates in a specific event. The collection unit can also prioritize other data collection methods when the patient is less active on social media. For example, the collection unit prioritizes other data collection methods when the patient is less active on social media. In this way, related data can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's social media activity data into the generation AI and cause the generation AI to collect related data.

[0073] The analysis unit can estimate the patient's emotions and adjust the parameters of the analysis algorithm based on the estimated patient's emotions. For example, if the patient is feeling anxious, the analysis unit sets the emotion analysis parameters to be sensitive. For example, if the patient is feeling anxious, the analysis unit sets the emotion analysis parameters to be sensitive. The analysis unit can also set the emotion analysis parameters to be gentle when the patient is relaxed. For example, if the patient is relaxed, the analysis unit sets the emotion analysis parameters to be gentle. The analysis unit can also set the emotion analysis parameters to be moderate when the patient is excited. For example, if the patient is excited, the analysis unit sets the emotion analysis parameters to be moderate. This enables more accurate analysis by adjusting the parameters of the analysis algorithm based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input patient emotion data into the generation AI and have the generation AI adjust the parameters of the analysis algorithm.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, when there is a lot of important data, the analysis unit performs a detailed analysis. For example, when there is a lot of important data, the analysis unit performs a detailed analysis. Furthermore, when there is a lot of data of low importance, the analysis unit can also perform a simplified analysis. For example, when there is a lot of data of low importance, the analysis unit performs a simplified analysis. Furthermore, when there is a lot of data of medium importance, the analysis unit can also perform an analysis with a moderate level of detail. For example, when there is a lot of data of medium importance, the analysis unit performs an analysis with a moderate level of detail. In this way, by adjusting the level of detail of the analysis based on the importance of the data, efficient analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the collected data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0075] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a facial expression recognition algorithm to facial expression data. For example, the analysis unit applies a facial expression recognition algorithm to facial expression data and analyzes the data. The analysis unit can also apply a behavioral analysis algorithm to behavioral data. For example, the analysis unit applies a behavioral analysis algorithm to behavioral data and analyzes the data. The analysis unit can also apply a voice analysis algorithm to voice data. For example, the analysis unit applies a voice analysis algorithm to voice data and analyzes the data. This enables more appropriate analysis by applying different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0076] The analysis unit can estimate the patient's emotions and adjust the display method of the analysis results based on the estimated patient's emotions. For example, when the patient is feeling anxious, the analysis unit provides a simple, highly visible display method. For example, when the patient is feeling anxious, the analysis unit provides a simple, highly visible display method. The analysis unit can also provide a display method including detailed information when the patient is relaxed. For example, when the patient is relaxed, the analysis unit provides a display method including detailed information. The analysis unit can also provide a visually stimulating display method when the patient is excited. For example, when the patient is excited, the analysis unit provides a visually stimulating display method. This allows for adjusting the display method based on the patient's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or without AI. For example, the analysis unit can input patient emotion data into the generation AI and have the generation AI adjust the display method.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also perform analysis while referring to past data. For example, the analysis unit performs analysis while referring to past data. The analysis unit can also prioritize analyzing data from a specific time period. For example, the analysis unit prioritizes analyzing data from a specific time period. This enables efficient analysis by determining the priority of analysis based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the priority of analysis.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of low-relevance data. For example, the analysis unit postpones analysis of low-relevance data. The analysis unit can also moderately analyze data with medium relevance. For example, the analysis unit moderately analyzes data with medium relevance. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0079] The providing unit can estimate the patient's emotions and adjust the way information is presented based on the estimated patient's emotions. For example, if the patient is feeling anxious, the providing unit provides information in a calm manner. For example, if the patient is feeling anxious, the providing unit provides information in a calm manner. Furthermore, if the patient is relaxed, the providing unit can also provide information in a cheerful manner. For example, if the patient is relaxed, the providing unit provides information in a cheerful manner. Furthermore, if the patient is excited, the providing unit can also provide information in a visually stimulating manner. For example, if the patient is excited, the providing unit provides information in a visually stimulating manner. This allows for adjusting the way information is presented based on the patient's emotions, thereby enabling more appropriate information to be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can input the patient's emotional data into the generating AI and have the generating AI adjust the way the information is presented.

[0080] When providing information, the providing unit can select an appropriate information provision method by referring to the caregiver's past response history. The providing unit, for example, provides information based on response methods that have been successful in the past by the caregiver. For example, the providing unit provides information based on response methods that have been successful in the past by the caregiver. The providing unit can also provide information by avoiding response methods that have failed in the past by the caregiver. For example, the providing unit provides information by avoiding response methods that have failed in the past by the caregiver. The providing unit can also analyze the caregiver's past response history and select an optimal information provision method. For example, the providing unit analyzes the caregiver's past response history and selects an optimal information provision method. In this way, the optimal information provision method can be selected by referring to the caregiver's past response history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the caregiver's past response history into the generation AI and cause the generation AI to select an information provision method.

[0081] The providing unit can customize the means of providing information based on the caregiver's current situation when providing information. For example, when the caregiver is busy, the providing unit provides concise information. For example, when the caregiver is busy, the providing unit provides concise information. The providing unit can also provide detailed information when the caregiver is relaxed. For example, when the caregiver is relaxed, the providing unit provides detailed information. The providing unit can also provide information in a calm manner when the caregiver is nervous. For example, when the caregiver is nervous, the providing unit provides information in a calm manner. This enables more appropriate information to be provided by customizing the means of providing information based on the caregiver's current situation. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the caregiver's current situation into the generating AI and cause the generating AI to customize the means of providing information.

[0082] The providing unit can estimate the patient's emotions and determine the priority of information provision based on the estimated patient's emotions. For example, when the patient is feeling anxious, the providing unit can prioritize providing important information. For example, when the patient is feeling anxious, the providing unit can prioritize providing important information. The providing unit can also prioritize providing detailed information when the patient is relaxed. For example, when the patient is relaxed, the providing unit can prioritize providing detailed information. The providing unit can also prioritize providing visually stimulating information when the patient is excited. For example, when the patient is excited, the providing unit prioritizes providing visually stimulating information. In this way, by determining the priority of information provision based on the patient's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or without AI. For example, the providing unit can input patient emotion data into the generating AI and have the generating AI determine the priority of information provision.

[0083] When providing information, the providing unit can select an appropriate information provision method by taking into account the geographical location information of the caregiver. For example, when the caregiver is at home, the providing unit provides detailed information. For example, the providing unit provides detailed information when the caregiver is at home. The providing unit can also provide concise information when the caregiver is out. For example, the providing unit provides concise information when the caregiver is out. The providing unit can also prioritize providing medical-related information when the caregiver is in a medical facility. For example, the providing unit prioritizes providing medical-related information when the caregiver is in a medical facility. This makes it possible to select an optimal information provision method by taking into account the geographical location information of the caregiver. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the geographical location information of the caregiver to the generation AI and cause the generation AI to select an information provision method.

[0084] When providing information, the providing unit can analyze the caregiver's social media activity and suggest a means of providing the information. For example, if the caregiver shares information on social media, the providing unit suggests the means. For example, if the caregiver shares information on social media, the providing unit suggests the means. Furthermore, if the caregiver is less active on social media, the providing unit can suggest other means of providing information. For example, if the caregiver is less active on social media, the providing unit can suggest other means of providing information. Furthermore, if the caregiver frequently uses a specific social media platform, the providing unit can provide information through that platform. For example, if the caregiver frequently uses a specific social media platform, the providing unit provides information through that platform. In this way, by analyzing the caregiver's social media activity, the optimal means of providing information can be suggested. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the caregiver's social media activity data into the generation AI and cause the generation AI to suggest means of providing information.

[0085] The suggestion unit can estimate the patient's emotions and adjust the suggestion method based on the estimated patient's emotions. For example, if the patient is feeling anxious, the suggestion unit provides a calm suggestion method. For example, if the patient is feeling anxious, the suggestion unit provides a calm suggestion method. The suggestion unit can also provide a cheerful suggestion method if the patient is relaxed. For example, if the patient is relaxed, the suggestion unit provides a cheerful suggestion method. The suggestion unit can also provide a visually stimulating suggestion method if the patient is excited. For example, if the patient is excited, the suggestion unit provides a visually stimulating suggestion method. This enables more appropriate suggestions by adjusting the suggestion method based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI, or without AI. For example, the suggestion unit can input patient emotion data into the generation AI and have the generation AI adjust the proposed method.

[0086] When making a proposal, the suggestion unit can analyze the patient's past behavioral data and select an appropriate proposal method. The suggestion unit, for example, makes a proposal based on the patient's past relaxing behavior. For example, the suggestion unit makes a proposal based on the patient's past relaxing behavior. The suggestion unit can also make a proposal while avoiding the patient's past excited behavior. For example, the suggestion unit makes a proposal while avoiding the patient's past excited behavior. The suggestion unit can also analyze the patient's past behavioral data and select an optimal proposal method. For example, the suggestion unit analyzes the patient's past behavioral data and selects an optimal proposal method. In this way, the optimal proposal method can be selected by analyzing the patient's past behavioral data. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the patient's past behavioral data to the generation AI and cause the generation AI to select a proposal method.

[0087] When making a suggestion, the suggestion unit can customize the suggestion means based on the patient's current living situation. For example, when the patient is at home, the suggestion unit makes daily suggestions. For example, when the patient is at home, the suggestion unit makes daily suggestions. The suggestion unit can also make suggestions related to the external environment when the patient is out. For example, when the patient is out, the suggestion unit can make suggestions related to the external environment. The suggestion unit can also make medical-related suggestions when the patient is in a medical facility. For example, when the patient is in a medical facility, the suggestion unit makes medical-related suggestions. This enables more appropriate suggestions by customizing the suggestion means based on the patient's current living situation. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data about the patient's current living situation into the generation AI and cause the generation AI to customize the suggestion means.

[0088] The suggestion unit can estimate the patient's emotions and prioritize suggestions based on the estimated patient's emotions. For example, if the patient is feeling anxious, the suggestion unit prioritizes suggestions to relax. For example, if the patient is feeling anxious, the suggestion unit prioritizes suggestions to relax. The suggestion unit can also prioritize active suggestions when the patient is relaxed. For example, if the patient is relaxed, the suggestion unit prioritizes active suggestions. The suggestion unit can also prioritize suggestions to calm down when the patient is excited. For example, if the patient is excited, the suggestion unit prioritizes suggestions to calm down. In this way, by prioritizing suggestions based on the patient's emotions, important suggestions can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input patient emotion data into the generation AI and have the generation AI determine the priority of the suggestions.

[0089] When making a proposal, the suggestion unit can select an appropriate proposal method by taking into consideration the patient's geographical location information. For example, when the patient is at home, the suggestion unit suggests activities that can be done at home. For example, when the patient is at home, the suggestion unit suggests activities that can be done at home. Furthermore, when the patient is out, the suggestion unit can also suggest activities that can be done while away from home. For example, when the patient is out, the suggestion unit suggests activities that can be done while away from home. Furthermore, when the patient is in a medical facility, the suggestion unit can also suggest activities that can be done within the medical facility. For example, when the patient is in a medical facility, the suggestion unit suggests activities that can be done within the medical facility. In this way, the optimal proposal method can be selected by taking into consideration the patient's geographical location information. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the patient's geographical location information to the generation AI and cause the generation AI to select a proposal method.

[0090] When making a suggestion, the suggestion unit can analyze the patient's social media activity and suggest a suggestion method. For example, when the patient expresses emotions on social media, the suggestion unit makes a suggestion based on that data. For example, when the patient expresses emotions on social media, the suggestion unit makes a suggestion based on that data. In addition, when the patient participates in a specific event, the suggestion unit can make a suggestion based on behavioral data. For example, when the patient participates in a specific event, the suggestion unit can make a suggestion based on that behavioral data. In addition, when the patient is less active on social media, the suggestion unit can make a suggestion based on other data collection methods. For example, when the patient is less active on social media, the suggestion unit makes a suggestion based on other data collection methods. In this way, by analyzing the patient's social media activity, the optimal suggestion method can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the patient's social media activity data into the generation AI and cause the generation AI to suggest a suggestion method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and suggestion unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the patient's facial expressions and behavior using the camera 42 and motion sensors of the smart device 14. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and estimates the patient's mental state. The provision unit provides information to the caregiver through the display 40A and speaker 40B of the smart device 14. The suggestion unit proposes an optimal response method using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and suggestion unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the patient's facial expressions and behavior using the camera 42 and motion sensors of the smart glasses 214. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing device 12 and estimates the patient's mental state. The provision unit provides information to the caregiver through the speaker 240 of the smart glasses 214. The suggestion unit proposes an optimal response method using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, and suggestion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects the patient's facial expressions and behavior using the camera 42 and motion sensors of the headset type terminal 314. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and estimates the patient's mental state. The provision unit provides information to the caregiver through the display 343 and speaker 240 of the headset type terminal 314. The suggestion unit proposes an optimal response method using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the patient's facial expressions and behavior using the camera 42 and motion sensors of the robot 414. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and estimates the patient's mental state. The provision unit provides information to the caregiver through the speaker 240 and eye LEDs of the robot 414. The suggestion unit proposes the optimal response method using the specific processing unit 290 of the data processing device 12.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The dementia patient support system can also be equipped with a voice recognition unit. The voice recognition unit can analyze the patient's speech in real time and infer their emotions and intentions. For example, if the patient is feeling anxious, it can infer their anxiety from the tone and content of their speech and notify the caregiver. In addition, when the patient utters a specific word, the voice recognition unit can refer to past data related to that word and suggest an appropriate response. Furthermore, the voice recognition unit can learn the patient's speech patterns and issue an alert if abnormal speech is detected. This makes it possible to utilize voice data to more comprehensively understand the patient's condition.

[0093] The collection unit may further include an environmental sensor. The environmental sensor may collect environmental data such as the temperature, humidity, and illuminance around the patient. For example, the environmental sensor may suggest an appropriate temperature adjustment if the temperature in the patient's room is too high. The environmental sensor may also suggest adjusting the lighting if the illuminance is low. Furthermore, the environmental sensor may suggest using a humidifier if the humidity is low. In this way, the environmental data may be utilized to maintain a comfortable living environment for the patient.

[0094] The analysis unit can further be equipped with a vital data analysis function. The vital data analysis function can analyze vital data such as a patient's heart rate, blood pressure, and body temperature to estimate their health condition. For example, if the heart rate is abnormally high, it can estimate stress or anxiety and notify the caregiver. If the blood pressure is high, it can also suggest appropriate measures to take. Furthermore, if the body temperature is high, it can estimate the possibility of a fever and suggest contacting a medical institution. In this way, by utilizing vital data, it is possible to more accurately grasp the patient's health condition.

[0095] The providing unit can further include a voice feedback function. The voice feedback function can communicate analysis results and suggestions to caregivers by voice. For example, even if a caregiver is busy, they can receive information by voice. The voice feedback function can also directly communicate information to patients. For example, advice on how to relax can be provided to patients by voice. Furthermore, the voice feedback function can also support multiple languages. This makes it possible to utilize voice to improve the convenience of providing information.

[0096] The suggestion unit can further incorporate game elements. Game elements are used to attract the patient's interest and make rehabilitation and daily activities more enjoyable. For example, the suggestion unit can propose a game in which the patient accumulates points by performing specific movements. The suggestion unit can also propose a game in which the patient receives rewards by completing daily tasks. Furthermore, the suggestion unit can propose a game in which the patient is motivated by competing with other patients. In this way, incorporating game elements can increase the patient's motivation to be active.

[0097] The collection unit can further collect dietary data of the patient. The dietary data is used to understand the patient's nutritional status and dietary preferences. For example, the collection unit can record the contents and amount of food the patient has eaten. If the patient has a preference for a particular ingredient, the collection unit can also suggest recipes that include that ingredient. Furthermore, the collection unit can record the time of day when the patient eats meals and suggest appropriate meal times. In this way, by utilizing the dietary data, the patient's nutritional management can be more effective.

[0098] The analysis unit can further analyze the patient's sleep data. The sleep data is used to understand the patient's sleep quality and patterns. For example, the analysis unit can analyze the patient's sleep time and sleep depth to evaluate the quality of sleep. The analysis unit can also analyze the frequency with which the patient wakes up during the night to estimate the possibility of a sleep disorder. Furthermore, the analysis unit can suggest an appropriate sleeping environment and relaxation methods based on the patient's sleep data. In this way, the sleep data can be utilized to improve the patient's sleep quality.

[0099] The providing unit can further include a visual feedback function. The visual feedback function can visually communicate the analysis results and suggestions to the caregiver. For example, the providing unit can visually display the patient's condition using graphs and charts. The providing unit can also use animations to clearly communicate the suggestions. Furthermore, the providing unit can visually indicate the importance of information using colors and icons. As a result, utilizing visual feedback makes it easier to understand the information.

[0100] The suggestion unit can further make suggestions based on the patient's hobbies and interests. Suggestions based on hobbies and interests are used to improve the patient's quality of life. For example, if the patient likes music, the suggestion unit can suggest music-related activities. If the patient likes gardening, the suggestion unit can also suggest gardening-related activities. If the patient likes reading, the suggestion unit can also suggest reading-related activities. In this way, suggestions based on hobbies and interests can improve the patient's quality of life.

[0101] The collection unit can further collect exercise data of the patient. The exercise data is used to understand the patient's physical activity level and exercise patterns. For example, the collection unit can record the type and duration of exercise performed by the patient. The collection unit can also record the patient's heart rate and calories burned when performing a specific exercise. Furthermore, the collection unit can suggest an appropriate exercise program based on the patient's exercise data. In this way, by utilizing the exercise data, the patient's health can be managed more effectively.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects the facial expressions and behaviors of the dementia patient. The collection unit can collect the facial expressions and behaviors of the patient using, for example, a facial expression recognition camera or a movement sensor. The facial expression recognition camera captures the patient's facial expressions in real time, and the movement sensor detects the patient's movements. For example, when the patient smiles, the collection unit can collect the emotion behind the smile. The collection unit can also detect the patient's movements when walking and collect that data. Step 2: The analysis unit analyzes the data collected by the collection unit and estimates the patient's mental state. The analysis unit can analyze the collected data using, for example, a facial expression recognition algorithm or a behavior analysis algorithm. The facial expression recognition algorithm estimates emotions from the patient's facial expressions, and the behavior analysis algorithm estimates the patient's mental state from their movements. For example, if the patient smiles, the analysis unit can analyze the emotions behind the smile. The analysis unit can also analyze the patient's movements when walking and estimate the patient's mental state from those movements. Step 3: The providing unit provides information to the caregiver based on the analysis results obtained by the analyzing unit. The providing unit can provide information to the caregiver, for example, through a smartphone app or a dedicated display. The smartphone app allows the caregiver to check the information anytime, anywhere, and the dedicated display is used to display the information in nursing facilities, etc. For example, when a patient smiles, the providing unit can convey to the caregiver the emotion behind the smile. The providing unit can also analyze the patient's movements when walking and convey to the caregiver the patient's mental state estimated from those movements. Step 4: The suggestion unit suggests an optimal response method based on the information provided by the provision unit. The suggestion unit can, for example, learn the patient's past facial expressions and behavioral data and suggest an optimal response method for each individual patient. For example, if a patient feels relaxed when listening to specific music, the suggestion unit can suggest playing that music. Also, if a patient feels relaxed when performing a specific action, the suggestion unit can suggest performing that action. This allows the dementia patient support system according to the embodiment to understand the patient's mental state and take appropriate action. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the patient's past facial expressions and behavioral data into the generation AI and have the generation AI execute a suggestion of an optimal response method.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects facial expressions and behaviors of dementia patients, an analysis unit that analyzes the data collected by the collection unit and estimates the mental state of the patient; a providing unit that provides information to a caregiver based on the analysis result obtained by the analyzing unit; a suggestion unit that suggests an appropriate response method based on the information provided by the provision unit. A system characterized by:

2. The collecting unit Collect facial expressions and behaviors of dementia patients using facial recognition cameras or motion sensors 2. The system of claim 1.

3. The analysis unit Using facial expression recognition and behavior analysis algorithms, the collected data is analyzed to estimate the patient's mental state.

2. The system of claim 1.

4. The providing unit Provides information to caregivers through a smartphone app and dedicated display 2. The system of claim 1.

5. The proposal unit Learns from past patient facial expressions and behavioral data to suggest appropriate responses for each individual patient 2. The system of claim 1.

6. The proposal unit If a patient finds certain music relaxing, suggest playing that music.

2. The system of claim 1.

7. The collecting unit Estimate the patient's emotions and adjust the frequency of facial and behavioral collection based on the estimated patient emotions.

2. The system of claim 1.

8. The collecting unit Analyze the patient's past behavioral patterns and select the appropriate timing for collection 2. The system of claim 1.

Images