system

Abstract

The system according to this embodiment aims to predict early on the possibility of being at risk of developing dementia and to encourage appropriate treatment. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and an alert unit. The data collection unit collects data on the user's daily activities. The analysis unit analyzes the data collected by the data collection unit and predicts the likelihood of the user being at risk of developing dementia. The alert unit issues an alert based on the prediction results obtained by the analysis unit.

Description

[[Technical Field]]

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

[0002] Patent Document 1 discloses a persona chatbot control method executed by at least one processor, the method comprising the steps of: receiving a user utterance; adding the user utterance to a prompt including an instruction associated with a description of a character of a chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [[Prior Art Document]] [[Patent Document]]

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

[0004] In the conventional technology, early detection of dementia is difficult, and there is a risk that appropriate treatment for delaying the progression may be delayed.

[0005] An object of the system according to an embodiment is to early predict a possibility that a subject falls into the pre-dementia group and promote appropriate treatment. [[Means for Solving the Problem]]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and an alert unit. The collection unit collects daily activity data of a user. The analysis unit analyzes the data collected by the collection unit and predicts a possibility that the user falls into the pre-dementia group. The alert unit issues an alert based on the prediction result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can predict early on whether a person is at risk of developing dementia and encourage appropriate treatment. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in the present specification, when three or more matters are expressed by connecting with "and / or", the same concept as "A and / or B" applies.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing apparatus 12 and a smart device 14. A server can be named as an example of the data processing apparatus 12.

[0018] The data processing apparatus 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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 WAN (Wide Area Network) and / or 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 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that collects behavioral data and combines it with AI prediction data to issue an alert if the user is likely to be at risk of developing dementia, thereby leading to early treatment. This system collects the user's daily behavioral data, and the AI ​​analyzes it to predict the likelihood of the user being at risk of developing dementia. Based on the prediction results, if there is a high probability that the user is at risk of developing dementia, an alert is issued. This alert allows the user to visit a medical institution early and receive appropriate treatment. For example, when collecting the user's daily behavioral data, various devices are used to collect data such as the user's walking patterns, conversation content, and frequency of meals. This allows for a detailed understanding of the user's daily behavior. Next, the AI ​​analyzes the collected behavioral data. Based on the collected data, the AI ​​analyzes the user's behavioral patterns and predicts the likelihood of the user being at risk of developing dementia. For example, it analyzes changes in walking patterns, conversation content, and frequency of meals to detect signs of dementia. This makes it possible to predict the likelihood of the user being at risk of developing dementia from the user's behavioral data. Based on the prediction results, if there is a high probability that the user is at risk of developing dementia, an alert is issued. For example, an alert is sent to the user using a notification function. This alert includes information indicating that the user may be at risk of developing dementia and recommends seeking medical attention early. This allows users to seek medical attention early and receive appropriate treatment, enabling early detection and treatment of dementia. Users can identify signs of dementia based on their own behavioral data and take early action. For example, noticing changes in walking patterns or conversation content and seeking medical attention can slow the progression of dementia. Family members and caregivers can also share the user's behavioral data to notice signs of dementia early and take appropriate action. By collecting behavioral data and combining it with AI prediction data, it becomes possible to issue alerts about the possibility of being at risk of developing dementia, leading to early treatment.

[0029] The dementia-predisposing detection system according to this embodiment comprises a data collection unit, an analysis unit, and an alert unit. The data collection unit collects data on the user's daily activities. This data includes, but is not limited to, walking patterns, conversation content, and meal frequency. The data collection unit collects the user's walking patterns using, for example, a pedometer or an accelerometer. The data collection unit can also collect the user's conversation content using speech recognition technology. Furthermore, the data collection unit can collect the user's meal frequency using a meal logging app. For example, the data collection unit records the user's daily steps using a pedometer and analyzes the walking rhythm and speed using an accelerometer. Speech recognition technology converts the user's conversation content into text data and analyzes the conversation content using natural language processing technology. The meal logging app records meal frequency by allowing the user to input the content and number of meals. The analysis unit analyzes the data collected by the data collection unit and predicts the likelihood of the user being at risk of dementia. The analysis unit analyzes the collected data using, for example, AI. The AI ​​analyzes the user's behavior patterns based on collected data and detects signs of dementia. For example, the AI ​​analyzes changes in walking patterns, conversation content, and meal frequency to predict the likelihood of being at risk of dementia. For instance, to detect changes in walking patterns, the AI ​​analyzes changes in walking rhythm and speed. To detect changes in conversation content, it analyzes changes in conversation content and tone. To detect changes in meal frequency, it analyzes changes in the number and content of meals. The alert unit issues alerts based on the prediction results obtained by the analysis unit. The alert unit issues alerts using methods such as smartphone notifications, email, and voice notifications. The alerts include information indicating the possibility of being at risk of dementia and recommending early medical consultation. For example, the alert unit can issue alerts to the user using smartphone notifications. Alerts can also be sent via email or voice notification. Thus, the dementia risk detection system according to this embodiment enables early detection and treatment of individuals at risk of dementia by collecting, analyzing, and issuing alerts based on the user's daily behavior data.

[0030] The data collection unit can collect data such as the user's walking patterns, conversation content, and meal frequency. For example, the data collection unit can collect the user's walking patterns using a pedometer or accelerometer. For instance, it can record the user's daily steps using a pedometer and analyze the walking rhythm and speed using an accelerometer. The data collection unit can also collect the user's conversation content using speech recognition technology. For example, it can convert the user's conversation content into text data using speech recognition technology and analyze the conversation content using natural language processing technology. Furthermore, the data collection unit can collect the user's meal frequency using a meal logging app. For example, the data collection unit records meal frequency when the user inputs the content and number of meals using a meal logging app. This allows the data collection unit to collect detailed data on the user's daily activities and more accurately identify signs of dementia.

[0031] The analysis unit can analyze user behavior patterns based on collected data and detect signs of dementia. For example, the analysis unit uses AI to analyze the collected data. The AI ​​analyzes user behavior patterns based on the collected data and detects signs of dementia. For example, the AI ​​analyzes changes in walking patterns, conversation content, and meal frequency to predict the likelihood of being at risk of dementia. For example, to detect changes in walking patterns, the AI ​​analyzes changes in walking rhythm and speed. To detect changes in conversation content, it analyzes changes in conversation content and tone. To detect changes in meal frequency, it analyzes changes in the number and content of meals. In this way, the analysis unit can detect signs of dementia early by analyzing the collected data.

[0032] The alert unit can send alerts using smartphone notification functions, email, voice notifications, etc. For example, the alert unit can send alerts to users using smartphone notification functions. For example, the alert unit can send alerts to users using smartphone notification functions. It can also send alerts using email. For example, the alert unit can send alerts to users using email. It can also send alerts using voice notifications. For example, the alert unit can send alerts to users using voice notifications. In this way, the alert unit can encourage users to seek medical attention early by sending alerts.

[0033] The data collection unit anonymizes the collected data and ensures it is not provided to third parties. For example, the data collection unit anonymizes the collected data. For example, the data collection unit removes personal information from the collected data and masks the data. This anonymizes the collected data and ensures it is not provided to third parties. The data collection unit ensures that the data is not provided to third parties by, for example, establishing data management methods and access restrictions. This anonymizes the collected data and protects user privacy.

[0034] The data collection unit can estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. For example, the data collection unit estimates the user's emotions using facial recognition technology. For example, the data collection unit adjusts the timing of behavioral data collection based on the user's emotions. For example, if the user is stressed, the data collection unit reduces the collection timing to alleviate the user's burden. For example, if the user is relaxed, the data collection unit increases the collection timing to collect more detailed data. For example, if the user is in a hurry, the data collection unit adjusts the collection timing to collect only important data. In this way, the data collection unit can reduce the user's burden by adjusting the collection timing according to the user's emotions.

[0035] The data collection unit can analyze users' past behavioral data and select the optimal collection method. For example, the data collection unit analyzes users' past behavioral data. For example, the data collection unit analyzes past records and historical data. For example, the data collection unit customizes the collection method based on actions that users frequently performed in the past. For example, the data collection unit selects the most efficient collection method from users' past behavioral data. For example, the data collection unit analyzes users' past behavioral patterns and optimizes the collection method. Thus, the data collection unit can select the optimal collection method by analyzing past behavioral data.

[0036] The data collection unit can filter behavioral data based on the user's current health status and living environment. For example, the data collection unit can assess the user's health status. For example, the data collection unit can assess the user's health status based on medical data or self-reported data. For example, the data collection unit can assess the user's living environment. For example, the data collection unit can assess the user's living environment based on their living environment and lifestyle. For example, the data collection unit can filter the collected data based on the user's health status and living environment. For example, if the user's health status is poor, the data collection unit can reduce the amount of data collected to alleviate the user's burden. For example, if the user's living environment changes, the data collection unit can collect data adapted to the new environment. For example, the data collection unit can filter the collected data based on the user's health status and living environment. In this way, the data collection unit can reduce the user's burden by filtering the data based on the user's health status and living environment.

[0037] The data collection unit can estimate the user's emotions and determine the priority of behavioral data to collect based on the estimated user emotions. For example, the data collection unit estimates the user's emotions using facial recognition technology. For example, the data collection unit determines the priority of behavioral data to collect based on the user's emotions. For example, if the user is stressed, the data collection unit prioritizes collecting only important data. For example, if the user is relaxed, the data collection unit prioritizes collecting detailed data. For example, if the user is in a hurry, the data collection unit adjusts the priority of the data to be collected. In this way, the data collection unit can prioritize the collection of important data by determining the priority of the data to be collected according to the user's emotions.

[0038] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting behavioral data. For example, the data collection unit collects the user's geographical location information. For example, the data collection unit collects the user's geographical location information using GPS data or location information services. For example, the data collection unit prioritizes the collection of highly relevant data based on the user's geographical location information. For example, if the user is in a specific location, the data collection unit prioritizes the collection of data related to that location. For example, the data collection unit collects highly relevant data based on the user's geographical location information. For example, if the user is on the move, the data collection unit prioritizes the collection of data related to the destination. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information.

[0039] The data collection unit can analyze users' social media activity and collect relevant data when collecting behavioral data. For example, the data collection unit analyzes users' social media activity. For example, the data collection unit analyzes the content of posts and the frequency of activity. For example, the data collection unit analyzes users' social media activity and collects relevant behavioral data. For example, the data collection unit selects data to collect based on information shared by users on social media. For example, the data collection unit collects highly relevant data from users' social media activity. In this way, the data collection unit can collect highly relevant data by analyzing users' social media activity.

[0040] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions. For example, the analysis unit estimates the user's emotions using facial recognition technology. For example, the analysis unit adjusts the presentation of the analysis based on the user's emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is stressed, the analysis unit provides concise analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis according to the user's emotions.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data during the analysis. For example, the analysis unit evaluates the importance of the behavioral data. For example, the analysis unit evaluates the importance of the behavioral data based on the impact of the data or the priority of the analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the behavioral data. For example, the analysis unit performs a detailed analysis for important behavioral data. For example, the analysis unit performs a concise analysis for less important behavioral data. For example, the analysis unit adjusts the level of detail of the analysis according to the importance of the behavioral data. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis according to the importance of the behavioral data.

[0042] The analysis unit can apply different analysis algorithms depending on the category of behavioral data during analysis. For example, the analysis unit defines categories of behavioral data. For example, the analysis unit defines categories based on the type of behavioral data and the classification of the data to be analyzed. For example, the analysis unit applies different analysis algorithms depending on the category of behavioral data. For example, the analysis unit applies a gait analysis algorithm to gait pattern data. For example, the analysis unit applies a natural language processing algorithm to conversation content data. For example, the analysis unit applies a meal analysis algorithm to meal frequency data. In this way, the analysis unit can improve the accuracy of the analysis by applying an appropriate analysis algorithm according to the category of behavioral data.

[0043] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions. For example, the analysis unit estimates the user's emotions using facial recognition technology. For example, the analysis unit adjusts the length of the analysis based on the user's emotions. For example, the analysis unit performs a detailed analysis when the user is relaxed. For example, the analysis unit performs a concise analysis when the user is stressed. For example, the analysis unit performs a to-the-point analysis when the user is in a hurry. In this way, the analysis unit can provide the user with appropriate analysis results by adjusting the length of the analysis according to the user's emotions.

[0044] The analysis unit can determine the priority of analysis based on the timing of behavioral data collection during analysis. For example, the analysis unit evaluates the timing of behavioral data collection. For example, the analysis unit evaluates the timing of behavioral data collection based on the freshness of the data and the timing of collection. For example, the analysis unit determines the priority of analysis based on the timing of behavioral data collection. For example, the analysis unit prioritizes the analysis of the most recent behavioral data. For example, the analysis unit gives importance to the latest data while referring to past behavioral data. For example, the analysis unit determines the priority of analysis based on the timing of behavioral data collection. As a result, the analysis unit can prioritize the analysis of the most recent data by determining the priority of analysis based on the timing of behavioral data collection.

[0045] The analysis unit can adjust the order of analysis based on the relevance of behavioral data during analysis. For example, the analysis unit evaluates the relevance of behavioral data. For example, the analysis unit evaluates the relevance of behavioral data based on data correlation or relevance scoring. For example, the analysis unit adjusts the order of analysis based on the relevance of behavioral data. For example, the analysis unit prioritizes the analysis of highly relevant behavioral data. For example, the analysis unit postpones the analysis of less relevant behavioral data. For example, the analysis unit adjusts the order of analysis based on the relevance of behavioral data. In this way, the analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of behavioral data.

[0046] The alert unit can estimate the user's emotions and adjust how it delivers alerts based on those emotions. For example, the alert unit estimates the user's emotions using facial recognition technology. The alert unit adjusts how it delivers alerts based on the user's emotions. For example, if the user is relaxed, the alert unit delivers a gentle alert. If the user is stressed, the alert unit delivers a concise and clear alert. If the user is in a hurry, the alert unit delivers an alert that allows for a quick response. In this way, the alert unit can deliver appropriate alerts to the user by adjusting how it delivers alerts according to the user's emotions.

[0047] The alert unit can adjust the level of detail of an alert based on the importance of the prediction result when issuing an alert. For example, the alert unit evaluates the importance of the prediction result. For example, the alert unit evaluates the importance of the prediction result based on the impact of the prediction result or the priority of the alert. For example, the alert unit adjusts the level of detail of the alert based on the importance of the prediction result. For example, the alert unit issues a detailed alert for important prediction results. For example, the alert unit issues a concise alert for less important prediction results. For example, the alert unit adjusts the level of detail of the alert according to the importance of the prediction result. In this way, the alert unit can appropriately convey important information by adjusting the level of detail of the alert according to the importance of the prediction result.

[0048] The alert unit can apply different alert delivery methods depending on the user's attribute information when issuing an alert. For example, the alert unit collects user attribute information. For example, the alert unit collects attribute information such as age, gender, and occupation. For example, the alert unit selects the optimal alert delivery method based on the user's attribute information. For example, the alert unit prioritizes voice notifications for elderly users. For example, the alert unit uses smartphone notification functions to send alerts to younger users. The alert unit selects the optimal alert delivery method based on the user's attribute information. As a result, the alert unit can deliver alerts in a way that is appropriate for the user by selecting the optimal alert delivery method based on the user's attribute information.

[0049] The alert unit can estimate the user's emotions and determine the priority of alerts based on the estimated emotions. For example, the alert unit estimates the user's emotions using facial recognition technology. For example, the alert unit determines the priority of alerts based on the user's emotions. For example, if the user is relaxed, the alert unit will prioritize important alerts. For example, if the user is stressed, the alert unit will prioritize concise alerts. For example, if the user is in a hurry, the alert unit will prioritize alerts that can be addressed quickly. In this way, the alert unit can prioritize important alerts by determining the priority of alerts according to the user's emotions.

[0050] The alert unit can select the optimal alert delivery method when issuing an alert, taking into account the user's geographical location information. For example, the alert unit collects the user's geographical location information. For example, the alert unit collects the user's geographical location information using GPS data or location information services. For example, the alert unit selects the optimal alert delivery method based on the user's geographical location information. For example, if the user is at home, the alert unit sends an alert using the smartphone's notification function. For example, if the user is out, the alert unit prioritizes sending an alert via voice notification. For example, the alert unit selects the optimal alert delivery method based on the user's geographical location information. As a result, the alert unit can send alerts in a way that is appropriate for the user by selecting the optimal alert delivery method based on the user's geographical location information.

[0051] The alert unit can analyze the user's social media activity and adjust the content of the alert when issuing an alert. For example, the alert unit analyzes the user's social media activity. For example, the alert unit analyzes the content of posts and the frequency of activity. For example, the alert unit analyzes the user's social media activity and issues relevant alerts. For example, the alert unit adjusts the alert content based on the information the user has shared on social media. For example, the alert unit selects the most appropriate alert content from the user's social media activity. In this way, the alert unit can issue highly relevant alerts by analyzing the user's social media activity.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The data collection unit can collect not only user behavioral data but also user physiological data. For example, by collecting physiological data such as heart rate, blood pressure, and body temperature, it is possible to understand the user's health status in more detail. The analysis unit can analyze the collected physiological data and combine it with behavioral data to predict the likelihood of being at risk of dementia. For example, it can analyze fluctuations in heart rate, changes in blood pressure, and abnormal body temperature to detect signs of dementia. The alert unit can issue alerts regarding the user's health status based on the analysis results. For example, if the heart rate is abnormally high or blood pressure fluctuates rapidly, it can issue an alert recommending a visit to a medical institution. This allows users to take early measures not only for dementia but also for other health problems.

[0054] The analysis unit can detect changes in a user's social activities based on their behavioral data. For example, it can detect signs of social isolation if a user goes out less frequently or interacts less with friends. The analysis unit can evaluate these changes as signs of dementia and reflect them in the prediction results. The alert unit can issue alerts to encourage the user to increase their social activities if signs of social isolation are detected. For example, it can issue alerts recommending participation in local events or community activities. This helps the user prevent social isolation and reduce their risk of dementia.

[0055] The data collection unit can collect user sleep data in addition to user behavior data. For example, by collecting data such as sleep duration, sleep quality, and the number of times a user turns over in their sleep, it is possible to understand the user's sleep state in detail. The analysis unit can analyze the collected sleep data and combine it with behavior data to predict the likelihood of being at risk of dementia. For example, it can analyze a decrease in sleep duration, a decline in sleep quality, and an increase in the number of times a user turns over in their sleep to detect signs of dementia. The alert unit can issue alerts regarding the user's sleep state based on the analysis results. For example, if the sleep duration is short or the quality of sleep is poor, it can issue an alert recommending improvements to the sleep environment. This allows the user to improve their sleep state and reduce the risk of dementia.

[0056] The analysis unit can detect changes in a user's cognitive function based on their behavioral data. For example, it can detect a decline in cognitive function by analyzing the time it takes a user to complete tasks they perform daily and the frequency of errors. The analysis unit can evaluate these changes as signs of dementia and reflect them in the prediction results. The alert unit can send alerts to the user recommending training or activities to maintain cognitive function if a decline in cognitive function is detected. For example, it can send alerts recommending activities that stimulate cognitive function, such as using brain training apps, reading, or solving puzzles. This allows the user to maintain cognitive function and reduce the risk of dementia.

[0057] The data collection unit can collect not only user behavior data but also user dietary data. For example, by collecting data such as meal content, calorie intake, and nutritional balance, it is possible to understand the user's eating habits in detail. The analysis unit can analyze the collected dietary data and combine it with behavioral data to predict the likelihood of being at risk of dementia. For example, it can analyze imbalances in nutrition and excesses or deficiencies in calorie intake to detect signs of dementia. The alert unit can issue alerts regarding the user's eating habits based on the analysis results. For example, it can issue alerts recommending improvements to eating habits if the nutritional balance is skewed or if calorie intake is excessive or insufficient. This allows the user to improve their eating habits and reduce their risk of dementia.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data collection unit collects the user's daily activity data. This data includes, for example, walking patterns, conversation content, and meal frequency. The data collection unit collects the user's walking patterns using a pedometer and accelerometer, collects the user's conversation content using speech recognition technology, and collects the user's meal frequency using a meal logging app. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts the likelihood of the individual being at risk of developing dementia. The analysis unit uses AI to analyze the collected data and detect signs of dementia by analyzing the user's behavior patterns. For example, it analyzes changes in walking patterns, changes in conversation content, and changes in the frequency of meals. Step 3: The alert unit issues an alert based on the prediction results obtained by the analysis unit. The alert unit issues alerts using smartphone notification functions, email, voice notifications, etc., and includes information such as the possibility of being at risk of dementia and a recommendation to seek medical attention early.

[0060] (Example of form 2) The system according to an embodiment of the present invention is a system that collects behavioral data and combines it with AI prediction data to issue an alert if the user is likely to be at risk of developing dementia, thereby leading to early treatment. This system collects the user's daily behavioral data, and the AI ​​analyzes it to predict the likelihood of the user being at risk of developing dementia. Based on the prediction results, if there is a high probability that the user is at risk of developing dementia, an alert is issued. This alert allows the user to visit a medical institution early and receive appropriate treatment. For example, when collecting the user's daily behavioral data, various devices are used to collect data such as the user's walking patterns, conversation content, and frequency of meals. This allows for a detailed understanding of the user's daily behavior. Next, the AI ​​analyzes the collected behavioral data. Based on the collected data, the AI ​​analyzes the user's behavioral patterns and predicts the likelihood of the user being at risk of developing dementia. For example, it analyzes changes in walking patterns, conversation content, and frequency of meals to detect signs of dementia. This makes it possible to predict the likelihood of the user being at risk of developing dementia from the user's behavioral data. Based on the prediction results, if there is a high probability that the user is at risk of developing dementia, an alert is issued. For example, an alert is sent to the user using a notification function. This alert includes information indicating that the user may be at risk of developing dementia and recommends seeking medical attention early. This allows users to seek medical attention early and receive appropriate treatment, enabling early detection and treatment of dementia. Users can identify signs of dementia based on their own behavioral data and take early action. For example, noticing changes in walking patterns or conversation content and seeking medical attention can slow the progression of dementia. Family members and caregivers can also share the user's behavioral data to notice signs of dementia early and take appropriate action. By collecting behavioral data and combining it with AI prediction data, it becomes possible to issue alerts about the possibility of being at risk of developing dementia, leading to early treatment.

[0061] The dementia-predisposing detection system according to this embodiment comprises a data collection unit, an analysis unit, and an alert unit. The data collection unit collects data on the user's daily activities. This data includes, but is not limited to, walking patterns, conversation content, and meal frequency. The data collection unit collects the user's walking patterns using, for example, a pedometer or an accelerometer. The data collection unit can also collect the user's conversation content using speech recognition technology. Furthermore, the data collection unit can collect the user's meal frequency using a meal logging app. For example, the data collection unit records the user's daily steps using a pedometer and analyzes the walking rhythm and speed using an accelerometer. Speech recognition technology converts the user's conversation content into text data and analyzes the conversation content using natural language processing technology. The meal logging app records meal frequency by allowing the user to input the content and number of meals. The analysis unit analyzes the data collected by the data collection unit and predicts the likelihood of the user being at risk of dementia. The analysis unit analyzes the collected data using, for example, AI. The AI ​​analyzes the user's behavior patterns based on collected data and detects signs of dementia. For example, the AI ​​analyzes changes in walking patterns, conversation content, and meal frequency to predict the likelihood of being at risk of dementia. For instance, to detect changes in walking patterns, the AI ​​analyzes changes in walking rhythm and speed. To detect changes in conversation content, it analyzes changes in conversation content and tone. To detect changes in meal frequency, it analyzes changes in the number and content of meals. The alert unit issues alerts based on the prediction results obtained by the analysis unit. The alert unit issues alerts using methods such as smartphone notifications, email, and voice notifications. The alerts include information indicating the possibility of being at risk of dementia and recommending early medical consultation. For example, the alert unit can issue alerts to the user using smartphone notifications. Alerts can also be sent via email or voice notification. Thus, the dementia risk detection system according to this embodiment enables early detection and treatment of individuals at risk of dementia by collecting, analyzing, and issuing alerts based on the user's daily behavior data.

[0062] The data collection unit can collect data such as the user's walking patterns, conversation content, and meal frequency. For example, the data collection unit can collect the user's walking patterns using a pedometer or accelerometer. For instance, it can record the user's daily steps using a pedometer and analyze the walking rhythm and speed using an accelerometer. The data collection unit can also collect the user's conversation content using speech recognition technology. For example, it can convert the user's conversation content into text data using speech recognition technology and analyze the conversation content using natural language processing technology. Furthermore, the data collection unit can collect the user's meal frequency using a meal logging app. For example, the data collection unit records meal frequency when the user inputs the content and number of meals using a meal logging app. This allows the data collection unit to collect detailed data on the user's daily activities and more accurately identify signs of dementia.

[0063] The analysis unit can analyze user behavior patterns based on collected data and detect signs of dementia. For example, the analysis unit uses AI to analyze the collected data. The AI ​​analyzes user behavior patterns based on the collected data and detects signs of dementia. For example, the AI ​​analyzes changes in walking patterns, conversation content, and meal frequency to predict the likelihood of being at risk of dementia. For example, to detect changes in walking patterns, the AI ​​analyzes changes in walking rhythm and speed. To detect changes in conversation content, it analyzes changes in conversation content and tone. To detect changes in meal frequency, it analyzes changes in the number and content of meals. In this way, the analysis unit can detect signs of dementia early by analyzing the collected data.

[0064] The alert unit can send alerts using smartphone notification functions, email, voice notifications, etc. For example, the alert unit can send alerts to users using smartphone notification functions. For example, the alert unit can send alerts to users using smartphone notification functions. It can also send alerts using email. For example, the alert unit can send alerts to users using email. It can also send alerts using voice notifications. For example, the alert unit can send alerts to users using voice notifications. In this way, the alert unit can encourage users to seek medical attention early by sending alerts.

[0065] The data collection unit anonymizes the collected data and ensures it is not provided to third parties. For example, the data collection unit anonymizes the collected data. For example, the data collection unit removes personal information from the collected data and masks the data. This anonymizes the collected data and ensures it is not provided to third parties. The data collection unit ensures that the data is not provided to third parties by, for example, establishing data management methods and access restrictions. This anonymizes the collected data and protects user privacy.

[0066] The data collection unit can estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. For example, the data collection unit estimates the user's emotions using facial recognition technology. For example, the data collection unit adjusts the timing of behavioral data collection based on the user's emotions. For example, if the user is stressed, the data collection unit reduces the collection timing to alleviate the user's burden. For example, if the user is relaxed, the data collection unit increases the collection timing to collect more detailed data. For example, if the user is in a hurry, the data collection unit adjusts the collection timing to collect only important data. In this way, the data collection unit can reduce the user's burden by adjusting the collection timing according to the user's emotions.

[0067] The data collection unit can analyze users' past behavioral data and select the optimal collection method. For example, the data collection unit analyzes users' past behavioral data. For example, the data collection unit analyzes past records and historical data. For example, the data collection unit customizes the collection method based on actions that users frequently performed in the past. For example, the data collection unit selects the most efficient collection method from users' past behavioral data. For example, the data collection unit analyzes users' past behavioral patterns and optimizes the collection method. Thus, the data collection unit can select the optimal collection method by analyzing past behavioral data.

[0068] The data collection unit can filter behavioral data based on the user's current health status and living environment. For example, the data collection unit can assess the user's health status. For example, the data collection unit can assess the user's health status based on medical data or self-reported data. For example, the data collection unit can assess the user's living environment. For example, the data collection unit can assess the user's living environment based on their living environment and lifestyle. For example, the data collection unit can filter the collected data based on the user's health status and living environment. For example, if the user's health status is poor, the data collection unit can reduce the amount of data collected to alleviate the user's burden. For example, if the user's living environment changes, the data collection unit can collect data adapted to the new environment. For example, the data collection unit can filter the collected data based on the user's health status and living environment. In this way, the data collection unit can reduce the user's burden by filtering the data based on the user's health status and living environment.

[0069] The data collection unit can estimate the user's emotions and determine the priority of behavioral data to collect based on the estimated user emotions. For example, the data collection unit estimates the user's emotions using facial recognition technology. For example, the data collection unit determines the priority of behavioral data to collect based on the user's emotions. For example, if the user is stressed, the data collection unit prioritizes collecting only important data. For example, if the user is relaxed, the data collection unit prioritizes collecting detailed data. For example, if the user is in a hurry, the data collection unit adjusts the priority of the data to be collected. In this way, the data collection unit can prioritize the collection of important data by determining the priority of the data to be collected according to the user's emotions.

[0070] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting behavioral data. For example, the data collection unit collects the user's geographical location information. For example, the data collection unit collects the user's geographical location information using GPS data or location information services. For example, the data collection unit prioritizes the collection of highly relevant data based on the user's geographical location information. For example, if the user is in a specific location, the data collection unit prioritizes the collection of data related to that location. For example, the data collection unit collects highly relevant data based on the user's geographical location information. For example, if the user is on the move, the data collection unit prioritizes the collection of data related to the destination. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information.

[0071] The data collection unit can analyze users' social media activity and collect relevant data when collecting behavioral data. For example, the data collection unit analyzes users' social media activity. For example, the data collection unit analyzes the content of posts and the frequency of activity. For example, the data collection unit analyzes users' social media activity and collects relevant behavioral data. For example, the data collection unit selects data to collect based on information shared by users on social media. For example, the data collection unit collects highly relevant data from users' social media activity. In this way, the data collection unit can collect highly relevant data by analyzing users' social media activity.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions. For example, the analysis unit estimates the user's emotions using facial recognition technology. For example, the analysis unit adjusts the presentation of the analysis based on the user's emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is stressed, the analysis unit provides concise analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis according to the user's emotions.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data during the analysis. For example, the analysis unit evaluates the importance of the behavioral data. For example, the analysis unit evaluates the importance of the behavioral data based on the impact of the data or the priority of the analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the behavioral data. For example, the analysis unit performs a detailed analysis for important behavioral data. For example, the analysis unit performs a concise analysis for less important behavioral data. For example, the analysis unit adjusts the level of detail of the analysis according to the importance of the behavioral data. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis according to the importance of the behavioral data.

[0074] The analysis unit can apply different analysis algorithms depending on the category of behavioral data during analysis. For example, the analysis unit defines categories of behavioral data. For example, the analysis unit defines categories based on the type of behavioral data and the classification of the data to be analyzed. For example, the analysis unit applies different analysis algorithms depending on the category of behavioral data. For example, the analysis unit applies a gait analysis algorithm to gait pattern data. For example, the analysis unit applies a natural language processing algorithm to conversation content data. For example, the analysis unit applies a meal analysis algorithm to meal frequency data. In this way, the analysis unit can improve the accuracy of the analysis by applying an appropriate analysis algorithm according to the category of behavioral data.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions. For example, the analysis unit estimates the user's emotions using facial recognition technology. For example, the analysis unit adjusts the length of the analysis based on the user's emotions. For example, the analysis unit performs a detailed analysis when the user is relaxed. For example, the analysis unit performs a concise analysis when the user is stressed. For example, the analysis unit performs a to-the-point analysis when the user is in a hurry. In this way, the analysis unit can provide the user with appropriate analysis results by adjusting the length of the analysis according to the user's emotions.

[0076] The analysis unit can determine the priority of analysis based on the timing of behavioral data collection during analysis. For example, the analysis unit evaluates the timing of behavioral data collection. For example, the analysis unit evaluates the timing of behavioral data collection based on the freshness of the data and the timing of collection. For example, the analysis unit determines the priority of analysis based on the timing of behavioral data collection. For example, the analysis unit prioritizes the analysis of the most recent behavioral data. For example, the analysis unit gives importance to the latest data while referring to past behavioral data. For example, the analysis unit determines the priority of analysis based on the timing of behavioral data collection. As a result, the analysis unit can prioritize the analysis of the most recent data by determining the priority of analysis based on the timing of behavioral data collection.

[0077] The analysis unit can adjust the order of analysis based on the relevance of behavioral data during analysis. For example, the analysis unit evaluates the relevance of behavioral data. For example, the analysis unit evaluates the relevance of behavioral data based on data correlation or relevance scoring. For example, the analysis unit adjusts the order of analysis based on the relevance of behavioral data. For example, the analysis unit prioritizes the analysis of highly relevant behavioral data. For example, the analysis unit postpones the analysis of less relevant behavioral data. For example, the analysis unit adjusts the order of analysis based on the relevance of behavioral data. In this way, the analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of behavioral data.

[0078] The alert unit can estimate the user's emotions and adjust how it delivers alerts based on those emotions. For example, the alert unit estimates the user's emotions using facial recognition technology. The alert unit adjusts how it delivers alerts based on the user's emotions. For example, if the user is relaxed, the alert unit delivers a gentle alert. If the user is stressed, the alert unit delivers a concise and clear alert. If the user is in a hurry, the alert unit delivers an alert that allows for a quick response. In this way, the alert unit can deliver appropriate alerts to the user by adjusting how it delivers alerts according to the user's emotions.

[0079] The alert unit can adjust the level of detail of an alert based on the importance of the prediction result when issuing an alert. For example, the alert unit evaluates the importance of the prediction result. For example, the alert unit evaluates the importance of the prediction result based on the impact of the prediction result or the priority of the alert. For example, the alert unit adjusts the level of detail of the alert based on the importance of the prediction result. For example, the alert unit issues a detailed alert for important prediction results. For example, the alert unit issues a concise alert for less important prediction results. For example, the alert unit adjusts the level of detail of the alert according to the importance of the prediction result. In this way, the alert unit can appropriately convey important information by adjusting the level of detail of the alert according to the importance of the prediction result.

[0080] The alert unit can apply different alert delivery methods depending on the user's attribute information when issuing an alert. For example, the alert unit collects user attribute information. For example, the alert unit collects attribute information such as age, gender, and occupation. For example, the alert unit selects the optimal alert delivery method based on the user's attribute information. For example, the alert unit prioritizes voice notifications for elderly users. For example, the alert unit uses smartphone notification functions to send alerts to younger users. The alert unit selects the optimal alert delivery method based on the user's attribute information. As a result, the alert unit can deliver alerts in a way that is appropriate for the user by selecting the optimal alert delivery method based on the user's attribute information.

[0081] The alert unit can estimate the user's emotions and determine the priority of alerts based on the estimated emotions. For example, the alert unit estimates the user's emotions using facial recognition technology. For example, the alert unit determines the priority of alerts based on the user's emotions. For example, if the user is relaxed, the alert unit will prioritize important alerts. For example, if the user is stressed, the alert unit will prioritize concise alerts. For example, if the user is in a hurry, the alert unit will prioritize alerts that can be addressed quickly. In this way, the alert unit can prioritize important alerts by determining the priority of alerts according to the user's emotions.

[0082] The alert unit can select the optimal alert delivery method when issuing an alert, taking into account the user's geographical location information. For example, the alert unit collects the user's geographical location information. For example, the alert unit collects the user's geographical location information using GPS data or location information services. For example, the alert unit selects the optimal alert delivery method based on the user's geographical location information. For example, if the user is at home, the alert unit sends an alert using the smartphone's notification function. For example, if the user is out, the alert unit prioritizes sending an alert via voice notification. For example, the alert unit selects the optimal alert delivery method based on the user's geographical location information. As a result, the alert unit can send alerts in a way that is appropriate for the user by selecting the optimal alert delivery method based on the user's geographical location information.

[0083] The alert unit can analyze the user's social media activity and adjust the content of the alert when issuing an alert. For example, the alert unit analyzes the user's social media activity. For example, the alert unit analyzes the content of posts and the frequency of activity. For example, the alert unit analyzes the user's social media activity and issues relevant alerts. For example, the alert unit adjusts the alert content based on the information the user has shared on social media. For example, the alert unit selects the most appropriate alert content from the user's social media activity. In this way, the alert unit can issue highly relevant alerts by analyzing the user's social media activity.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The data collection unit can collect not only user behavioral data but also user physiological data. For example, by collecting physiological data such as heart rate, blood pressure, and body temperature, it is possible to understand the user's health status in more detail. The analysis unit can analyze the collected physiological data and combine it with behavioral data to predict the likelihood of being at risk of dementia. For example, it can analyze fluctuations in heart rate, changes in blood pressure, and abnormal body temperature to detect signs of dementia. The alert unit can issue alerts regarding the user's health status based on the analysis results. For example, if the heart rate is abnormally high or blood pressure fluctuates rapidly, it can issue an alert recommending a visit to a medical institution. This allows users to take early measures not only for dementia but also for other health problems.

[0086] The analysis unit can detect changes in a user's social activities based on their behavioral data. For example, it can detect signs of social isolation if a user goes out less frequently or interacts less with friends. The analysis unit can evaluate these changes as signs of dementia and reflect them in the prediction results. The alert unit can issue alerts to encourage the user to increase their social activities if signs of social isolation are detected. For example, it can issue alerts recommending participation in local events or community activities. This helps the user prevent social isolation and reduce their risk of dementia.

[0087] The data collection unit can collect user sleep data in addition to user behavior data. For example, by collecting data such as sleep duration, sleep quality, and the number of times a user turns over in their sleep, it is possible to understand the user's sleep state in detail. The analysis unit can analyze the collected sleep data and combine it with behavior data to predict the likelihood of being at risk of dementia. For example, it can analyze a decrease in sleep duration, a decline in sleep quality, and an increase in the number of times a user turns over in their sleep to detect signs of dementia. The alert unit can issue alerts regarding the user's sleep state based on the analysis results. For example, if the sleep duration is short or the quality of sleep is poor, it can issue an alert recommending improvements to the sleep environment. This allows the user to improve their sleep state and reduce the risk of dementia.

[0088] The analysis unit can detect changes in a user's cognitive function based on their behavioral data. For example, it can detect a decline in cognitive function by analyzing the time it takes a user to complete tasks they perform daily and the frequency of errors. The analysis unit can evaluate these changes as signs of dementia and reflect them in the prediction results. The alert unit can send alerts to the user recommending training or activities to maintain cognitive function if a decline in cognitive function is detected. For example, it can send alerts recommending activities that stimulate cognitive function, such as using brain training apps, reading, or solving puzzles. This allows the user to maintain cognitive function and reduce the risk of dementia.

[0089] The data collection unit can collect not only user behavior data but also user dietary data. For example, by collecting data such as meal content, calorie intake, and nutritional balance, it is possible to understand the user's eating habits in detail. The analysis unit can analyze the collected dietary data and combine it with behavioral data to predict the likelihood of being at risk of dementia. For example, it can analyze imbalances in nutrition and excesses or deficiencies in calorie intake to detect signs of dementia. The alert unit can issue alerts regarding the user's eating habits based on the analysis results. For example, it can issue alerts recommending improvements to eating habits if the nutritional balance is skewed or if calorie intake is excessive or insufficient. This allows the user to improve their eating habits and reduce their risk of dementia.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis results can be presented in a concise summary. If the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is in a hurry, the analysis results can be presented in a concise manner. This allows the user to receive appropriate information according to their emotional state and to utilize the analysis results more effectively.

[0091] The alert system can estimate the user's emotions and adjust the content of the alert based on those emotions. For example, if the user is stressed, the alert can be concise and provide only the necessary information. If the user is relaxed, a more detailed alert can be provided. Furthermore, if the user is in a hurry, a focused alert can be sent to enable a quick response. This allows users to receive appropriate alerts tailored to their emotional state, enabling them to respond quickly and effectively.

[0092] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. If the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is in a hurry, only important data can be prioritized for collection. This allows users to receive appropriate data collection tailored to their emotional state, reducing their burden while ensuring they receive the necessary data.

[0093] The analysis unit can estimate the user's emotions and prioritize the analysis based on those emotions. For example, if the user is stressed, it can prioritize analyzing only the most important data and provide results quickly. If the user is relaxed, it can perform a detailed analysis and provide comprehensive results. Furthermore, if the user is in a hurry, it can perform a concise analysis to enable a quick response. This allows users to receive appropriate analysis results tailored to their emotional state and utilize them effectively.

[0094] The alert system can estimate the user's emotions and adjust the timing of alerts based on those emotions. For example, if the user is stressed, the alert can be delayed until the user is relaxed. If the user is relaxed, the alert can be sent immediately to encourage a quick response. Furthermore, if the user is in a hurry, only important alerts can be prioritized to enable a rapid response. This allows users to receive alerts at the appropriate time according to their emotional state and respond effectively.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The data collection unit collects the user's daily activity data. This data includes, for example, walking patterns, conversation content, and meal frequency. The data collection unit collects the user's walking patterns using a pedometer and accelerometer, collects the user's conversation content using speech recognition technology, and collects the user's meal frequency using a meal logging app. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts the likelihood of the individual being at risk of developing dementia. The analysis unit uses AI to analyze the collected data and detect signs of dementia by analyzing the user's behavior patterns. For example, it analyzes changes in walking patterns, changes in conversation content, and changes in the frequency of meals. Step 3: The alert unit issues an alert based on the prediction results obtained by the analysis unit. The alert unit issues alerts using smartphone notification functions, email, voice notifications, etc., and includes information such as the possibility of being at risk of dementia and a recommendation to seek medical attention early.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0100] Each of the multiple elements described above, including the data collection unit, analysis unit, and alert unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects the user's walking patterns using the pedometer and accelerometer of the smart device 14 and collects conversation content using voice recognition technology. The data collection unit also collects meal frequency using a meal recording app via the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 and detects signs of dementia using AI. The alert unit issues alerts using the notification function, email, or voice notification of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the data collection unit, analysis unit, and alert unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects the user's walking patterns using the pedometer and accelerometer of the smart glasses 214 and collects conversation content using voice recognition technology. The data collection unit also collects meal frequency using a meal recording app via the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and detects signs of dementia using AI. The alert unit issues alerts using, for example, the notification function of the smart glasses 214, email, or voice notification. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, and alert unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects the user's walking patterns using the pedometer and accelerometer of the headset terminal 314 and collects conversation content using voice recognition technology. The data collection unit also collects meal frequency using a meal recording application via the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 and detects signs of dementia using AI. The alert unit issues alerts using, for example, the notification function, email, or voice notification of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the data collection unit, analysis unit, and alert unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects the user's walking patterns using the robot 414's pedometer and accelerometer, and collects conversation content using speech recognition technology. The data collection unit also collects meal frequency using a meal recording app via the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 and detects signs of dementia using AI. The alert unit issues alerts using, for example, the robot 414's notification function, email, or voice notification. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0150] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] 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.

[0160] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) A data collection unit that collects data on the user's daily activities, An analysis unit analyzes the data collected by the aforementioned collection unit and predicts the likelihood of being at risk of developing dementia, The system includes an alert unit that issues an alert based on the prediction results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects data such as the user's walking patterns, conversation content, and meal frequency. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, user behavior patterns are analyzed to detect signs of dementia. The system described in Appendix 1, characterized by the features described herein. (Note 4) The alert unit is, Alerts are sent using smartphone notification functions, email, voice notifications, etc. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The collected data will be anonymized and will not be provided to third parties. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze users' past behavioral data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting behavioral data, filtering is performed based on the user's current health status and living environment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of behavioral data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting behavioral data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting behavioral data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the behavioral data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The alert unit is, It estimates the user's emotions and adjusts how alerts are sent based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The alert unit is, When an alert is issued, adjust the level of detail of the alert based on the importance of the prediction result. The system described in Appendix 1, characterized by the features described herein. (Note 20) The alert unit is, When an alert is issued, different alert methods are applied depending on the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The alert unit is, It estimates the user's emotions and determines the priority of alerts based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The alert unit is, When issuing an alert, the system selects the most suitable alert delivery method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The alert unit is, When issuing an alert, the system analyzes the user's social media activity to adjust the content of the alert. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data on the user's daily activities, An analysis unit analyzes the data collected by the aforementioned collection unit and predicts the likelihood of being at risk of developing dementia, The system includes an alert unit that issues an alert based on the prediction results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is The system collects data such as the user's walking patterns, conversation content, and meal frequency. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, user behavior patterns are analyzed to detect signs of dementia. The system according to feature 1.

4. The alert unit is, Alerts are sent using smartphone notification functions, email, voice notifications, etc. The system according to feature 1.

5. The aforementioned collection unit is The collected data will be anonymized and will not be provided to third parties. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze users' past behavioral data and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting behavioral data, filtering is performed based on the user's current health status and living environment. The system according to feature 1.

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