System

Abstract

An object of the system according to the embodiment is to detect a possibility of senior dementia or an abnormality at an early stage and take an appropriate action.SOLUTION: A system according to an embodiment includes a dementia detector, a movement checker, an anomaly detector, and a notifier. The dementia detector detects a likelihood of dementia from the speech of the senior. The movement checker checks the abnormality detected by the dementia detector. The abnormality detection unit detects the abnormality confirmed by the movement confirmation unit. The notifier notifies the senior of the abnormality detected by the abnormality detector from the call center, and reports the abnormality to a designated contact destination when the abnormality is confirmed.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology is not sufficient for early detection of dementia or abnormalities in seniors, and there is room for improvement.

[0005] The system according to the embodiment aims to detect the possibility of dementia or abnormalities in seniors at an early stage and to take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a dementia detection unit, a movement confirmation unit, an anomaly detection unit, and a communication unit. The dementia detection unit detects the possibility of dementia from the senior's conversation. The movement confirmation unit confirms the abnormality detected by the dementia detection unit. The anomaly detection unit detects the abnormality confirmed by the movement confirmation unit. The communication unit notifies the senior of the abnormality detected by the anomaly detection unit from a call center, and reports the abnormality to a designated contact if it is confirmed. [Effects of the Invention]

[0007] The system according to the embodiment can detect the possibility of dementia or abnormalities in seniors at an early stage and take appropriate measures. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The senior support system according to an embodiment of the present invention is a system that detects the possibility of dementia from a senior's conversation, and if an abnormality is confirmed, supports the senior by using the services of a mobile phone company (GPS function and call center). This enables the senior support system to detect dementia in seniors early and respond quickly when an abnormality occurs.

[0029] A senior support system according to an embodiment includes a dementia detection unit, a movement confirmation unit, an anomaly detection unit, and a communication unit. The dementia detection unit detects the possibility of dementia from a senior's conversation. For example, the generation AI collects conversation content through the voice assistant function of a smartphone used daily by the senior and analyzes the data. The generation AI has learned criteria for dementia assessment and detects abnormalities by analyzing the content and patterns of conversation. For example, it detects statements such as "I've been forgetting things a lot lately" or when the flow of conversation becomes unnatural. The movement confirmation unit uses the smartphone's GPS function to check whether the senior has moved to confirm the abnormality detected by the dementia detection unit. For example, it detects when the senior has not moved at all for a certain period of time or when the senior is staying in an unusual location for a long time. The anomaly detection unit detects abnormalities confirmed by the movement confirmation unit. For example, it detects when the senior does not answer the phone or when an abnormality is confirmed from the content of the conversation. The communication unit notifies the senior of the abnormality detected by the anomaly detection unit via a call center, and if an abnormality is confirmed, reports it to a designated contact. For example, if a senior does not return home or if it is determined from the content of the conversation that the symptoms of dementia are progressing, the system will contact a family member or guardian, etc. This allows the senior support system according to the embodiment to detect dementia in seniors early and respond quickly when an abnormality occurs.

[0030] The dementia detection unit analyzes not only a senior's conversation, but also their facial expressions and tone of voice, enabling multifaceted detection of the possibility of dementia. For example, when a senior is talking through a smartphone camera, the generation AI analyzes their facial expressions in real time and detects subtle changes in their expression. For example, it analyzes the frequency of smiles and eye movements to find signs of dementia. The dementia detection unit also analyzes the tone of the senior's voice and detects the possibility of dementia based on the pitch, strength, and rhythm of the voice. For example, it detects sudden changes in voice tone or unnatural speaking styles. This enables multifaceted detection of the possibility of dementia in seniors.

[0031] The dementia detection unit learns the daily behavioral patterns of seniors and can detect the possibility of dementia if abnormal behavioral patterns are observed. For example, the dementia detection unit collects the daily behavioral patterns of seniors through a smartphone sensor, and the generation AI analyzes the data. For example, it learns the time and route of daily walks and issues an alert if there are any abnormal changes. The dementia detection unit also learns the eating and sleeping patterns of seniors and detects the possibility of dementia if any abnormalities are observed. For example, it detects a decrease in the number of meals or extreme fluctuations in sleep time. This makes it possible to learn the daily behavioral patterns of seniors and detect abnormal behavioral patterns.

[0032] The dementia detection unit can integrate a senior's conversation data with other health data to assess their overall health condition. For example, the dementia detection unit integrates a senior's conversation data with heart rate data, and the generation AI evaluates their overall health condition. For example, it analyzes fluctuations in heart rate during conversation and issues an alert if any abnormalities are detected. The dementia detection unit also integrates a senior's sleep pattern and dietary data to assess their overall health condition. For example, it detects when their sleep time becomes extremely short or when they eat less frequently. This makes it possible to integrate a senior's conversation data with other health data to assess their overall health condition.

[0033] The dementia detection unit also collects data from other devices used by seniors, enabling multifaceted detection of the possibility of dementia. For example, the dementia detection unit collects data from a smartwatch used by a senior, and the generation AI analyzes that data. For example, it analyzes heart rate and step count data and issues an alert if an abnormality is detected. The dementia detection unit also collects data from fitness trackers used by seniors to detect the possibility of dementia. For example, it detects an extreme decrease in exercise volume or abnormal movements. This allows data to be collected from other devices used by seniors, enabling multifaceted detection of the possibility of dementia.

[0034] The movement confirmation unit can learn the movement patterns of seniors over a long period of time and issue a warning if an abnormal movement pattern is detected. For example, the movement confirmation unit collects the movement patterns of seniors over a long period of time, and the generation AI analyzes the data. For example, it learns daily walking routes and frequently visited places, and issues a warning if an abnormality is detected. The movement confirmation unit also analyzes the frequency and distance of seniors' movements and issues a warning if an abnormality is detected. For example, it detects when movements become extremely infrequent or when the senior stays in an unusual place for a long time. This makes it possible to learn the movement patterns of seniors over a long period of time and detect abnormal movement patterns.

[0035] The movement confirmation unit can analyze the accelerometer and gyroscope data of the senior's smartphone in addition to GPS data to detect falls or sudden movements. For example, the movement confirmation unit collects the accelerometer and gyroscope data of the senior's smartphone, and the generation AI analyzes that data. For example, it detects falls or sudden movements and issues a warning. The movement confirmation unit also analyzes the senior's walking pattern and issues a warning if an abnormality is detected. For example, it detects a sudden change in walking speed or an unnatural walking rhythm. This makes it possible to detect falls or sudden movements of the senior.

[0036] The movement confirmation unit can compare the movement data of a senior with the movement data of other seniors and create a benchmark for detecting abnormal patterns. The movement confirmation unit, for example, compares the movement data of a senior with the movement data of other seniors and uses the generation AI to create a benchmark for detecting abnormal patterns. For example, if an abnormality is found when compared with an average movement pattern, an alert is issued. The movement confirmation unit also compares the movement data of seniors by region and detects abnormal patterns. For example, an alert is issued if the movement pattern in a specific region is abnormal. This makes it possible to compare the movement data of a senior with the movement data of other seniors and create a benchmark for detecting abnormal patterns.

[0037] The movement confirmation unit can provide an app that shares the senior's movement data with family members and caregivers and allows location information to be checked in real time. The movement confirmation unit can, for example, develop an app that shares the senior's movement data with family members and caregivers and allows location information to be checked in real time. For example, it can provide a function that displays the senior's current location and movement history. The movement confirmation unit can also provide a function that sends notifications to family members and caregivers based on the senior's movement data. For example, it can send a notification if the senior stays in an unusual location for a long period of time. This makes it possible to provide an app that shares the senior's movement data with family members and caregivers and allows location information to be checked in real time.

[0038] The anomaly detection unit uses the generation AI to analyze the content of a conversation in real time when a call center operator is conversing with a senior, and can detect any abnormalities. For example, when a call center operator is conversing with a senior, the generation AI analyzes the content of the conversation in real time and detects any abnormalities. For example, it issues a warning if the flow of the conversation becomes unnatural. The anomaly detection unit also analyzes the tone of the senior's voice and facial expression to detect any abnormalities. For example, it detects a sudden change in voice tone or facial expression. This allows the generation AI to analyze the content of a conversation in real time when a call center operator is conversing with a senior, and can detect any abnormalities.

[0039] The anomaly detection unit allows a call center operator to refer to a senior's past conversation history and issue a warning if an abnormal change is detected. The anomaly detection unit, for example, builds a system in which a call center operator refers to a senior's past conversation history and issues a warning if an abnormal change is detected. For example, the anomaly detection unit issues a warning if an abnormality is detected by comparing the content of past conversations. The anomaly detection unit also analyzes the senior's conversation history and detects abnormal patterns. For example, it detects when the content of the conversation suddenly changes or when the speaking style becomes unnatural. This allows a call center operator to refer to a senior's past conversation history and issue a warning if an abnormal change is detected.

[0040] The anomaly detection unit uses the generation AI to summarize the conversation content when a call center operator converses with a senior, enabling a quick response. For example, when a call center operator converses with a senior, the generation AI summarizes the conversation content in real time, enabling a quick response. For example, it extracts important information and presents it to the operator. The anomaly detection unit also analyzes the conversation content of the senior and issues a warning if an abnormality is detected. For example, it issues a warning if the flow of the conversation becomes unnatural. This allows the call center operator to use the generation AI to summarize the conversation content when a call center operator converses with a senior, enabling a quick response.

[0041] The anomaly detection unit can provide a system that allows call center operators to check the health data of seniors in real time. The anomaly detection unit, for example, builds a system that allows call center operators to check the health data of seniors in real time. For example, heart rate and blood pressure data is displayed to the operator. The anomaly detection unit also analyzes the senior's health data and issues a warning if an abnormality is detected. For example, it detects a sudden increase in heart rate or abnormally high blood pressure. This makes it possible to provide a system that allows call center operators to check the health data of seniors in real time.

[0042] If an abnormality is detected, the communication department can automatically generate report content using the generation AI and provide a quick and accurate report. For example, if an abnormality is detected, the communication department can automatically generate report content using the generation AI and provide a quick and accurate report to family members or guardians. For example, it can automatically generate a report that includes details of the abnormality and countermeasures. The communication department also checks the senior's situation in real time and immediately notifies them if an abnormality is detected. For example, it can send a notification if a senior is staying in an unusual place for a long period of time. As a result, if an abnormality is detected, the communication department can automatically generate report content using the generation AI and provide a quick and accurate report.

[0043] The communication unit provides a dashboard that allows family members and guardians to check the senior's status in real time, and can immediately notify if an abnormality is detected. The communication unit, for example, provides a dashboard that allows family members and guardians to check the senior's status in real time, and builds a system that immediately notifies if an abnormality is detected. For example, it displays the senior's current location and health data. The communication unit also monitors the senior's status in real time, and sends a notification if an abnormality is detected. For example, it sends a notification if the senior is staying in an unusual place for a long period of time. This provides a dashboard that allows family members and guardians to check the senior's status in real time, and can immediately notify if an abnormality is detected.

[0044] If an abnormality is detected, the communication department can use the generating AI to suggest specific countermeasures to family members or guardians. For example, the communication department will build a system in which, if an abnormality is detected, the generating AI will suggest specific countermeasures to family members or guardians. For example, it will suggest first aid methods if a senior falls. The communication department will also analyze the senior's situation and suggest appropriate countermeasures. For example, it will suggest relaxation methods if the senior is feeling stressed. In this way, if an abnormality is detected, the generating AI can suggest specific countermeasures to family members or guardians.

[0045] The communication department can use the generating AI to analyze past data and report long-term trends when family members or guardians check on the senior's status. For example, the communication department will build a system in which the generating AI analyzes past data and reports long-term trends when family members or guardians check on the senior's status. For example, it will report on changes in the senior's health condition and trends in behavioral patterns. The communication department will also issue a warning if an abnormality is detected based on the senior's past data. For example, it will detect a sudden deterioration in health condition or a sudden change in behavioral patterns. This allows the generating AI to analyze past data and report long-term trends when family members or guardians check on the senior's status.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The senior support system can further include a health monitoring unit. The health monitoring unit collects vital data such as the senior's heart rate, blood pressure, and body temperature in real time and issues a warning if an abnormality is detected. For example, it detects a sudden increase in heart rate or an abnormally high body temperature. The health monitoring unit also analyzes the senior's sleep patterns and issues a warning if an abnormality is detected. For example, it detects an extremely short sleep time or a decline in sleep quality. This allows for comprehensive monitoring of the senior's health condition and allows for prompt action if an abnormality is detected.

[0048] The senior support system can further include an environmental monitoring unit. The environmental monitoring unit monitors the temperature, humidity, illuminance, etc. of the senior's living environment in real time and issues an alert if an abnormality is detected. For example, it detects when the room temperature becomes extremely high or the humidity becomes abnormally low. The environmental monitoring unit also analyzes changes in volume and light in the senior's living environment and issues an alert if an abnormality is detected. For example, it detects when an abnormal sound is heard at night or when the lights suddenly become brighter. This allows for comprehensive monitoring of the senior's living environment and allows for quick response if an abnormality is detected.

[0049] The senior support system can further include a communication support unit. The communication support unit helps seniors communicate smoothly with family and friends. For example, when a senior makes a video call via smartphone, the generation AI summarizes the content of the conversation and presents important information. In addition, when a senior sends an email or message, the generation AI checks grammar and expressions and suggests appropriate corrections. This allows seniors to communicate smoothly with family and friends.

[0050] The senior support system can also be equipped with a reminder function. The reminder function notifies seniors of tasks and appointments that they tend to forget in their daily lives. For example, it can remind them to take their medicine or make an appointment with a doctor. The reminder function also analyzes the senior's schedule and notifies them of important events and activities. For example, it can remind them to make a video call with their family or to participate in a hobby club. This helps seniors remember to carry out important tasks and appointments in their daily lives.

[0051] The senior support system can further include an exercise support unit. The exercise support unit provides exercise programs for seniors to maintain their health. For example, the generation AI suggests an exercise menu based on the senior's health condition and physical strength. The exercise support unit also instructs seniors on the correct form and movements when exercising. For example, it analyzes the senior's movements using a smartphone camera and suggests appropriate modifications. This allows seniors to exercise safely and effectively to maintain their health.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The dementia detection unit detects the possibility of dementia from the senior's conversation. For example, the generation AI collects the content of conversations through the voice assistant function of the senior's smartphone that they use daily and analyzes the data. The generation AI has learned the criteria for determining dementia and detects abnormalities by analyzing the content and patterns of conversations. For example, it detects statements such as "I've been forgetting things a lot lately" or when the flow of conversation becomes unnatural. Step 2: The movement confirmation unit uses the smartphone's GPS function to check whether the senior has moved or not in order to confirm the abnormality detected by the dementia detection unit. For example, it detects when the senior has not moved at all for a certain period of time, or when the senior has stayed in an unusual place for a long time. Step 3: The anomaly detection unit detects the anomaly confirmed by the movement confirmation unit. For example, it detects when the senior does not answer the phone or when an anomaly is confirmed from the content of the conversation. Step 4: The communication unit will notify the senior via the call center of any abnormalities detected by the anomaly detection unit, and if an abnormality is confirmed, will report it to the designated contact. For example, if the senior does not return home, or if the content of the conversation indicates that the symptoms of dementia are progressing, the communication unit will contact a family member or guardian.

[0054] (Example 2) The senior support system according to an embodiment of the present invention is a system that detects the possibility of dementia from a senior's conversation, and if an abnormality is confirmed, supports the senior by using the services of a mobile phone company (GPS function and call center). This enables the senior support system to detect dementia in seniors early and respond quickly when an abnormality occurs.

[0055] A senior support system according to an embodiment includes a dementia detection unit, a movement confirmation unit, an anomaly detection unit, and a communication unit. The dementia detection unit detects the possibility of dementia from a senior's conversation. For example, the generation AI collects conversation content through the voice assistant function of a smartphone used daily by the senior and analyzes the data. The generation AI has learned criteria for dementia assessment and detects abnormalities by analyzing the content and patterns of conversation. For example, it detects statements such as "I've been forgetting things a lot lately" or when the flow of conversation becomes unnatural. The movement confirmation unit uses the smartphone's GPS function to check whether the senior has moved to confirm the abnormality detected by the dementia detection unit. For example, it detects when the senior has not moved at all for a certain period of time or when the senior is staying in an unusual location for a long time. The anomaly detection unit detects abnormalities confirmed by the movement confirmation unit. For example, it detects when the senior does not answer the phone or when an abnormality is confirmed from the content of the conversation. The communication unit notifies the senior of the abnormality detected by the anomaly detection unit via a call center, and if an abnormality is confirmed, reports it to a designated contact. For example, if a senior does not return home or if it is determined from the content of the conversation that the symptoms of dementia are progressing, the system will contact a family member or guardian, etc. This allows the senior support system according to the embodiment to detect dementia in seniors early and respond quickly when an abnormality occurs.

[0056] The dementia detection unit analyzes not only a senior's conversation, but also their facial expressions and tone of voice, enabling multifaceted detection of the possibility of dementia. For example, when a senior is talking through a smartphone camera, the generation AI analyzes their facial expressions in real time and detects subtle changes in their expression. For example, it analyzes the frequency of smiles and eye movements to find signs of dementia. The dementia detection unit also analyzes the tone of the senior's voice and detects the possibility of dementia based on the pitch, strength, and rhythm of the voice. For example, it detects sudden changes in voice tone or unnatural speaking styles. This enables multifaceted detection of the possibility of dementia in seniors.

[0057] The dementia detection unit learns the daily behavioral patterns of seniors and can detect the possibility of dementia if abnormal behavioral patterns are observed. For example, the dementia detection unit collects the daily behavioral patterns of seniors through a smartphone sensor, and the generation AI analyzes the data. For example, it learns the time and route of daily walks and issues an alert if there are any abnormal changes. The dementia detection unit also learns the eating and sleeping patterns of seniors and detects the possibility of dementia if any abnormalities are observed. For example, it detects a decrease in the number of meals or extreme fluctuations in sleep time. This makes it possible to learn the daily behavioral patterns of seniors and detect abnormal behavioral patterns.

[0058] The dementia detection unit uses the emotion estimation function to detect emotional changes from the senior's conversation and determine whether emotional instability is a sign of dementia. The dementia detection unit, for example, analyzes the content of the senior's conversation and detects emotional changes using the emotion estimation function. For example, it issues a warning if emotions such as anger or sadness suddenly appear during a conversation. The dementia detection unit also analyzes the senior's tone of voice and facial expression to detect emotional instability. For example, it detects a sudden change in tone of voice or a sudden change in facial expression. This makes it possible to detect emotional changes in the senior and determine whether emotional instability is a sign of dementia.

[0059] The dementia detection unit can integrate a senior's conversation data with other health data to assess their overall health condition. For example, the dementia detection unit integrates a senior's conversation data with heart rate data, and the generation AI evaluates their overall health condition. For example, it analyzes fluctuations in heart rate during conversation and issues an alert if any abnormalities are detected. The dementia detection unit also integrates a senior's sleep pattern and dietary data to assess their overall health condition. For example, it detects when their sleep time becomes extremely short or when they eat less frequently. This makes it possible to integrate a senior's conversation data with other health data to assess their overall health condition.

[0060] The dementia detection unit also collects data from other devices used by seniors, enabling multifaceted detection of the possibility of dementia. For example, the dementia detection unit collects data from a smartwatch used by a senior, and the generation AI analyzes that data. For example, it analyzes heart rate and step count data and issues an alert if an abnormality is detected. The dementia detection unit also collects data from fitness trackers used by seniors to detect the possibility of dementia. For example, it detects an extreme decrease in exercise volume or abnormal movements. This allows data to be collected from other devices used by seniors, enabling multifaceted detection of the possibility of dementia.

[0061] The dementia detection unit uses the emotion estimation function to analyze the emotions of seniors during conversation in real time and suggest conversation content that will elicit positive emotions. For example, the dementia detection unit analyzes the emotions of seniors during conversation in real time, and the AI ​​generation suggests conversation content that will elicit positive emotions. For example, it suggests topics that make seniors happy. The dementia detection unit also analyzes the emotional state of seniors and suggests topics and activities that will help them relax. For example, it suggests topics that are related to seniors' favorite hobbies or interests. This makes it possible to analyze the emotions of seniors during conversation in real time and suggest conversation content that will elicit positive emotions.

[0062] The movement confirmation unit can learn the movement patterns of seniors over a long period of time and issue a warning if an abnormal movement pattern is detected. For example, the movement confirmation unit collects the movement patterns of seniors over a long period of time, and the generation AI analyzes the data. For example, it learns daily walking routes and frequently visited places, and issues a warning if an abnormality is detected. The movement confirmation unit also analyzes the frequency and distance of seniors' movements and issues a warning if an abnormality is detected. For example, it detects when movements become extremely infrequent or when the senior stays in an unusual place for a long time. This makes it possible to learn the movement patterns of seniors over a long period of time and detect abnormal movement patterns.

[0063] The movement confirmation unit can analyze the accelerometer and gyroscope data of the senior's smartphone in addition to GPS data to detect falls or sudden movements. For example, the movement confirmation unit collects the accelerometer and gyroscope data of the senior's smartphone, and the generation AI analyzes that data. For example, it detects falls or sudden movements and issues a warning. The movement confirmation unit also analyzes the senior's walking pattern and issues a warning if an abnormality is detected. For example, it detects a sudden change in walking speed or an unnatural walking rhythm. This makes it possible to detect falls or sudden movements of the senior.

[0064] The movement confirmation unit can use the emotion estimation function to estimate the emotion of the senior while moving and issue a warning if anxiety or confusion is observed. The movement confirmation unit, for example, constructs a system that estimates the emotion of the senior while moving and issues a warning if anxiety or confusion is observed. For example, it issues a warning if the senior feels anxious while moving. The movement confirmation unit also analyzes the senior's facial expression and tone of voice while moving and detects changes in emotion. For example, it detects when the facial expression becomes anxious or when the tone of voice suddenly changes. This makes it possible to estimate the emotion of the senior while moving and issue a warning if anxiety or confusion is observed.

[0065] The movement confirmation unit can compare the movement data of a senior with the movement data of other seniors and create a benchmark for detecting abnormal patterns. The movement confirmation unit, for example, compares the movement data of a senior with the movement data of other seniors and uses the generation AI to create a benchmark for detecting abnormal patterns. For example, if an abnormality is found when compared with an average movement pattern, an alert is issued. The movement confirmation unit also compares the movement data of seniors by region and detects abnormal patterns. For example, an alert is issued if the movement pattern in a specific region is abnormal. This makes it possible to compare the movement data of a senior with the movement data of other seniors and create a benchmark for detecting abnormal patterns.

[0066] The movement confirmation unit can provide an app that shares the senior's movement data with family members and caregivers and allows location information to be checked in real time. The movement confirmation unit can, for example, develop an app that shares the senior's movement data with family members and caregivers and allows location information to be checked in real time. For example, it can provide a function that displays the senior's current location and movement history. The movement confirmation unit can also provide a function that sends notifications to family members and caregivers based on the senior's movement data. For example, it can send a notification if the senior stays in an unusual location for a long period of time. This makes it possible to provide an app that shares the senior's movement data with family members and caregivers and allows location information to be checked in real time.

[0067] The movement confirmation unit can use the emotion estimation function to analyze the emotions of seniors while they are moving in real time and suggest movement routes that will bring out positive emotions. For example, the movement confirmation unit can analyze the emotions of seniors while they are moving in real time, and the generation AI can suggest movement routes that will bring out positive emotions. For example, it can suggest routes that take seniors to their favorite places or have beautiful scenery. The movement confirmation unit can also analyze the emotional state of seniors and suggest movement routes that will help them relax. For example, if a senior is feeling stressed, it can suggest quiet parks or places with lots of nature. This makes it possible to analyze the emotions of seniors while they are moving in real time and suggest movement routes that will bring out positive emotions.

[0068] The anomaly detection unit uses the generation AI to analyze the content of a conversation in real time when a call center operator is conversing with a senior, and can detect any abnormalities. For example, when a call center operator is conversing with a senior, the generation AI analyzes the content of the conversation in real time and detects any abnormalities. For example, it issues a warning if the flow of the conversation becomes unnatural. The anomaly detection unit also analyzes the tone of the senior's voice and facial expression to detect any abnormalities. For example, it detects a sudden change in voice tone or facial expression. This allows the generation AI to analyze the content of a conversation in real time when a call center operator is conversing with a senior, and can detect any abnormalities.

[0069] The anomaly detection unit allows a call center operator to refer to a senior's past conversation history and issue a warning if an abnormal change is detected. The anomaly detection unit, for example, builds a system in which a call center operator refers to a senior's past conversation history and issues a warning if an abnormal change is detected. For example, the anomaly detection unit issues a warning if an abnormality is detected by comparing the content of past conversations. The anomaly detection unit also analyzes the senior's conversation history and detects abnormal patterns. For example, it detects when the content of the conversation suddenly changes or when the speaking style becomes unnatural. This allows a call center operator to refer to a senior's past conversation history and issue a warning if an abnormal change is detected.

[0070] The anomaly detection unit uses the emotion estimation function to analyze emotions during a conversation with a senior in real time, and can issue a warning if emotional instability is detected. For example, when a call center operator is conversing with a senior, the anomaly detection unit uses the emotion estimation function to analyze changes in emotions in real time, and can issue a warning if emotional instability is detected. For example, the anomaly detection unit issues a warning if the senior suddenly becomes angry. The anomaly detection unit also analyzes the senior's tone of voice and facial expression to detect emotional instability. For example, it detects a sudden change in tone of voice or a sudden change in facial expression. This makes it possible to analyze emotions during a conversation with a senior in real time, and can issue a warning if emotional instability is detected.

[0071] The anomaly detection unit uses the generation AI to summarize the conversation content when a call center operator converses with a senior, enabling a quick response. For example, when a call center operator converses with a senior, the generation AI summarizes the conversation content in real time, enabling a quick response. For example, it extracts important information and presents it to the operator. The anomaly detection unit also analyzes the conversation content of the senior and issues a warning if an abnormality is detected. For example, it issues a warning if the flow of the conversation becomes unnatural. This allows the call center operator to use the generation AI to summarize the conversation content when a call center operator converses with a senior, enabling a quick response.

[0072] The anomaly detection unit can provide a system that allows call center operators to check the health data of seniors in real time. The anomaly detection unit, for example, builds a system that allows call center operators to check the health data of seniors in real time. For example, heart rate and blood pressure data is displayed to the operator. The anomaly detection unit also analyzes the senior's health data and issues a warning if an abnormality is detected. For example, it detects a sudden increase in heart rate or abnormally high blood pressure. This makes it possible to provide a system that allows call center operators to check the health data of seniors in real time.

[0073] The anomaly detection unit uses the emotion estimation function to analyze emotions during conversations with seniors in real time and suggest conversation content that will elicit positive emotions. For example, when a call center operator is conversing with a senior, the anomaly detection unit uses the emotion estimation function to analyze changes in emotions in real time, and the AI ​​suggests conversation content that will elicit positive emotions. For example, it suggests topics that will make the senior feel happy. The anomaly detection unit also analyzes the senior's emotional state and suggests topics and activities that will help them relax. For example, it suggests topics that are related to the senior's favorite hobbies or interests. This makes it possible to analyze emotions during conversations with seniors in real time and suggest conversation content that will elicit positive emotions.

[0074] If an abnormality is detected, the communication department can automatically generate report content using the generation AI and provide a quick and accurate report. For example, if an abnormality is detected, the communication department can automatically generate report content using the generation AI and provide a quick and accurate report to family members or guardians. For example, it can automatically generate a report that includes details of the abnormality and countermeasures. The communication department also checks the senior's situation in real time and immediately notifies them if an abnormality is detected. For example, it can send a notification if a senior is staying in an unusual place for a long period of time. As a result, if an abnormality is detected, the communication department can automatically generate report content using the generation AI and provide a quick and accurate report.

[0075] The communication unit provides a dashboard that allows family members and guardians to check the senior's status in real time, and can immediately notify if an abnormality is detected. The communication unit, for example, provides a dashboard that allows family members and guardians to check the senior's status in real time, and builds a system that immediately notifies if an abnormality is detected. For example, it displays the senior's current location and health data. The communication unit also monitors the senior's status in real time, and sends a notification if an abnormality is detected. For example, it sends a notification if the senior is staying in an unusual place for a long period of time. This provides a dashboard that allows family members and guardians to check the senior's status in real time, and can immediately notify if an abnormality is detected.

[0076] The communication unit can use the emotion estimation function to include the senior's emotional state in the report content, allowing family members and guardians to understand the senior's emotional state. The communication unit, for example, uses the emotion estimation function to build a system that includes the senior's emotional state in the report content. For example, if the senior is feeling anxious, the details of that are included in the report. The communication unit also analyzes the senior's emotional state and notifies the family members and guardians. For example, if the senior is feeling stressed, the details are notified. In this way, the emotion estimation function can be used to include the senior's emotional state in the report content, allowing family members and guardians to understand the senior's emotional state.

[0077] If an abnormality is detected, the communication department can use the generating AI to suggest specific countermeasures to family members or guardians. For example, the communication department will build a system in which, if an abnormality is detected, the generating AI will suggest specific countermeasures to family members or guardians. For example, it will suggest first aid methods if a senior falls. The communication department will also analyze the senior's situation and suggest appropriate countermeasures. For example, it will suggest relaxation methods if the senior is feeling stressed. In this way, if an abnormality is detected, the generating AI can suggest specific countermeasures to family members or guardians.

[0078] The communication department can use the generating AI to analyze past data and report long-term trends when family members or guardians check on the senior's status. For example, the communication department will build a system in which the generating AI analyzes past data and reports long-term trends when family members or guardians check on the senior's status. For example, it will report on changes in the senior's health condition and trends in behavioral patterns. The communication department will also issue a warning if an abnormality is detected based on the senior's past data. For example, it will detect a sudden deterioration in health condition or a sudden change in behavioral patterns. This allows the generating AI to analyze past data and report long-term trends when family members or guardians check on the senior's status.

[0079] The communication unit can use the emotion estimation function to analyze the emotional state of the senior in real time and suggest countermeasures to elicit positive emotions from family members and guardians. For example, the communication unit can use the emotion estimation function to analyze the emotional state of the senior in real time and have the generation AI suggest countermeasures to elicit positive emotions from family members and guardians. For example, it can suggest topics or activities that will help the senior relax. The communication unit also analyzes the emotional state of the senior and suggests appropriate countermeasures. For example, it can suggest ways to relax if the senior is feeling stressed. In this way, the emotion estimation function can be used to analyze the emotional state of the senior in real time and suggest countermeasures to elicit positive emotions from family members and guardians.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The senior support system can further include a health monitoring unit. The health monitoring unit collects vital data such as the senior's heart rate, blood pressure, and body temperature in real time and issues a warning if an abnormality is detected. For example, it detects a sudden increase in heart rate or an abnormally high body temperature. The health monitoring unit also analyzes the senior's sleep patterns and issues a warning if an abnormality is detected. For example, it detects an extremely short sleep time or a decline in sleep quality. This allows for comprehensive monitoring of the senior's health condition and allows for prompt action if an abnormality is detected.

[0082] The senior support system can further include an environmental monitoring unit. The environmental monitoring unit monitors the temperature, humidity, illuminance, etc. of the senior's living environment in real time and issues an alert if an abnormality is detected. For example, it detects when the room temperature becomes extremely high or the humidity becomes abnormally low. The environmental monitoring unit also analyzes changes in volume and light in the senior's living environment and issues an alert if an abnormality is detected. For example, it detects when an abnormal sound is heard at night or when the lights suddenly become brighter. This allows for comprehensive monitoring of the senior's living environment and allows for quick response if an abnormality is detected.

[0083] The senior support system can further include a communication support unit. The communication support unit helps seniors communicate smoothly with family and friends. For example, when a senior makes a video call via smartphone, the generation AI summarizes the content of the conversation and presents important information. In addition, when a senior sends an email or message, the generation AI checks grammar and expressions and suggests appropriate corrections. This allows seniors to communicate smoothly with family and friends.

[0084] The senior support system can also be equipped with a reminder function. The reminder function notifies seniors of tasks and appointments that they tend to forget in their daily lives. For example, it can remind them to take their medicine or make an appointment with a doctor. The reminder function also analyzes the senior's schedule and notifies them of important events and activities. For example, it can remind them to make a video call with their family or to participate in a hobby club. This helps seniors remember to carry out important tasks and appointments in their daily lives.

[0085] The senior support system can further include an exercise support unit. The exercise support unit provides exercise programs for seniors to maintain their health. For example, the generation AI suggests an exercise menu based on the senior's health condition and physical strength. The exercise support unit also instructs seniors on the correct form and movements when exercising. For example, it analyzes the senior's movements using a smartphone camera and suggests appropriate modifications. This allows seniors to exercise safely and effectively to maintain their health.

[0086] The senior support system can also use an emotion estimation function to analyze the emotional state of seniors in real time and support stress management. For example, it can analyze the content of a senior's conversation and facial expressions to detect signs of stress. It can also use the emotion estimation function to suggest activities and environments that will help seniors relax. For example, it can play the senior's favorite music or suggest places where they can relax. This allows the system to analyze the senior's emotional state in real time and support stress management.

[0087] The senior support system can further use the emotion estimation function to provide personalized entertainment based on the emotional state of the senior. For example, the system can analyze the emotional state of the senior and suggest movies or music that match the senior's mood at that time. The emotion estimation function can also be used to suggest games or activities that the senior can enjoy. For example, if the senior wants to relax, a relaxing puzzle game can be suggested. This makes it possible to provide personalized entertainment based on the senior's emotional state.

[0088] The senior support system can further use the emotion estimation function to propose a meal plan based on the senior's emotional state. For example, it can analyze the senior's emotional state and propose a meal menu that suits their mood at that time. It can also use the emotion estimation function to suggest a meal environment that will help the senior relax. For example, if the senior is feeling stressed, it can suggest playing relaxing music while eating. This makes it possible to propose a meal plan based on the senior's emotional state.

[0089] The senior support system can further use its emotion estimation function to suggest a sleeping environment based on the senior's emotional state. For example, it can analyze the senior's emotional state and suggest a sleeping environment that suits their mood at that time. It can also use the emotion estimation function to provide advice on creating a sleeping environment that helps the senior relax. For example, if the senior is feeling stressed, it can suggest relaxing music or aromas. This makes it possible to suggest a sleeping environment based on the senior's emotional state.

[0090] The senior support system can further use its emotion estimation function to propose a rehabilitation plan based on the emotional state of the senior. For example, it can analyze the emotional state of the senior and propose a rehabilitation menu that suits their mood at that time. It can also use the emotion estimation function to propose a rehabilitation environment that helps the senior relax. For example, if the senior is feeling stressed, it can suggest playing relaxing music while performing rehabilitation. This makes it possible to propose a rehabilitation plan based on the senior's emotional state.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The dementia detection unit detects the possibility of dementia from the senior's conversation. For example, the generation AI collects the content of conversations through the voice assistant function of the senior's smartphone that they use daily and analyzes the data. The generation AI has learned the criteria for determining dementia and detects abnormalities by analyzing the content and patterns of conversations. For example, it detects statements such as "I've been forgetting things a lot lately" or when the flow of conversation becomes unnatural. Step 2: The movement confirmation unit uses the smartphone's GPS function to check whether the senior has moved or not in order to confirm the abnormality detected by the dementia detection unit. For example, it detects when the senior has not moved at all for a certain period of time, or when the senior has stayed in an unusual place for a long time. Step 3: The anomaly detection unit detects the anomaly confirmed by the movement confirmation unit. For example, it detects when the senior does not answer the phone or when an anomaly is confirmed from the content of the conversation. Step 4: The communication unit will notify the senior via the call center of any abnormalities detected by the anomaly detection unit, and if an abnormality is confirmed, will report it to the designated contact. For example, if the senior does not return home, or if the content of the conversation indicates that the symptoms of dementia are progressing, the communication unit will contact a family member or guardian.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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. [Explanation of symbols]

[0160] 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 dementia detection unit that detects the possibility of dementia from the conversation of seniors, and a movement confirmation unit that confirms the abnormality detected by the dementia detection unit; an anomaly detection unit that detects an abnormality confirmed by the movement confirmation unit; a contact unit that notifies the senior of the abnormality detected by the abnormality detection unit from a call center and reports the abnormality to a designated contact point when the abnormality is confirmed; A system characterized by:

2. The dementia detection unit Detecting emotional changes from the conversation of the senior and determining whether the emotional instability is a sign of dementia.

2. The system of claim 1.

3. The dementia detection unit Data from other devices used by the senior will also be collected to detect the possibility of dementia from multiple angles.

2. The system of claim 1.

4. The movement confirmation unit Learning the movement patterns of the senior over a long period of time and issuing a warning if an abnormal movement pattern is observed.

2. The system of claim 1.

5. The anomaly detection unit When the call center operator talks with the senior, the conversation content is analyzed in real time using generative AI to detect abnormalities.

2. The system of claim 1.

6. The communication unit When the abnormality is detected, the report content will be automatically generated using generation AI, and the report will be made quickly and accurately.

2. The system of claim 1.

7. The movement confirmation unit Estimate the senior's emotions while traveling and issue a warning if anxiety or confusion is detected.

2. The system of claim 1.

8. The communication unit Include the senior's emotional state in the report to allow family members or guardians to understand the senior's emotional state.

2. The system of claim 1.

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