system

Abstract

The system according to this embodiment aims to detect early signs of dementia and report them quickly to medical professionals. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a pattern detection unit, and a reporting unit. The collection unit collects the subject's health status or conversation patterns and medical information. The analysis unit analyzes the data collected by the collection unit. The pattern detection unit captures characteristic patterns or changes of dementia based on the analysis results obtained by the analysis unit. The reporting unit reports to a medical professional based on the patterns or changes captured by the pattern detection unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, early signs of dementia have not been efficiently detected and promptly reported to medical experts, leaving room for improvement.

[0005] The system according to the embodiment aims to detect early signs of dementia and promptly report them to medical experts.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a pattern detection unit, and a reporting unit. The data collection unit collects the subject's health status or conversation patterns and medical information. The analysis unit analyzes the data collected by the data collection unit. The pattern detection unit identifies characteristic patterns or changes of dementia based on the analysis results obtained by the analysis unit. The reporting unit reports to a medical professional based on the patterns or changes identified by the pattern detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect early signs of dementia and report them quickly to medical professionals. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The dementia early detection support system according to an embodiment of the present invention is a system that collects the subject's health status, conversation patterns, and medical information, and uses a generating AI to analyze this data to identify characteristic patterns and changes of dementia and provide early warnings to medical professionals. The dementia early detection support system collects the subject's health status, conversation patterns, and medical information, and the generating AI analyzes this data to evaluate the subject's language ability, memory, cognitive ability, etc. Furthermore, based on the analysis results, the generating AI identifies characteristic patterns and changes of dementia and provides early warnings to medical professionals. This mechanism enables the early detection of dementia and provides patients and their families with an opportunity to seek professional consultation and treatment. For example, the dementia early detection support system collects data such as the words the subject speaks, the sentences they write, and the pictures they draw on a daily basis. The generating AI analyzes this data to evaluate the subject's language ability, memory, cognitive ability, etc. The generating AI recognizes a decrease in the frequency of use of certain words in the subject's conversations or a change in the structure of sentences as signs of dementia. The generating AI integrates data and medical information of the subject to determine the progression and type of dementia, and reports the patient's condition, risk factors, and treatment options to medical professionals. This allows the dementia early detection support system to facilitate the early detection of dementia and provide patients and their families with an opportunity to seek professional consultation and treatment.

[0029] The dementia early detection support system according to this embodiment comprises a data collection unit, an analysis unit, a pattern detection unit, and a reporting unit. The data collection unit collects the subject's health status or conversation patterns and medical information. For example, the data collection unit can collect data such as blood pressure, heart rate, and body temperature as the subject's health status. The data collection unit can also collect data such as speaking speed, word choice, and grammatical accuracy as the subject's conversation patterns. Furthermore, the data collection unit can collect data such as diagnosis results, prescriptions, and medical history as the subject's medical information. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze vocabulary richness, grammatical accuracy, and pronunciation clarity to evaluate the subject's language ability. Furthermore, the analysis unit can analyze short-term memory, long-term memory, and episodic memory to evaluate the subject's memory ability. Furthermore, the analysis unit can analyze attention, problem-solving ability, and judgment to evaluate the subject's cognitive ability. The pattern detection unit captures characteristic patterns or changes of dementia based on the analysis results obtained by the analysis unit. The pattern detection unit can, for example, detect signs of dementia such as a decrease in the frequency of use of certain words or changes in sentence structure in the subject's conversations. The reporting unit reports to medical professionals based on the patterns or changes detected by the pattern detection unit. The reporting unit can, for example, integrate the subject's data with medical information to determine the progression and type of dementia and report the patient's condition, risk factors, and treatment methods to medical professionals. As a result, the dementia early detection support system according to this embodiment can support the early detection of dementia and provide patients and their families with an opportunity to seek professional consultation and treatment.

[0030] The data collection unit collects data on the subject's health status, conversation patterns, and medical information. Specifically, it can collect data such as blood pressure, heart rate, and body temperature as part of the subject's health status. This data is collected in real time through wearable devices and home medical devices and transmitted to a cloud server. For example, smartwatches and fitness trackers can continuously monitor heart rate and body temperature and issue alerts if abnormalities are detected. The data collection unit can also collect data on the subject's conversation patterns, such as speaking speed, word choice, and grammatical accuracy. This involves converting conversations into text using speech recognition technology and analyzing language patterns using natural language processing (NLP) technology. For example, smart speakers or voice assistants that the subject uses daily record conversations and transmit that data to the data collection unit. The data collection unit can also collect data such as diagnostic results, prescriptions, and medical history as part of the subject's medical information. This includes the ability to automatically acquire necessary information by linking with electronic health record (EHR) systems and medical institution databases. For example, the latest diagnostic results and prescription information can be obtained from medical institutions that the subject regularly visits and integrated into the data collection unit. This allows the data collection unit to gather a wide range of information from diverse data sources and comprehensively monitor changes in the subjects' health status and cognitive function. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis unit and pattern detection unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit. Specifically, it can analyze vocabulary richness, grammatical accuracy, and pronunciation clarity to evaluate the subject's language ability. This involves using natural language processing (NLP) techniques to analyze the subject's conversational data in detail and detect changes or anomalies in language patterns. For example, it evaluates the types and frequency of vocabulary used by the subject, the number of grammatical errors, and pronunciation clarity, and monitors changes in language ability based on this data. The analysis unit can also analyze short-term memory, long-term memory, and episodic memory to evaluate the subject's memory. This involves recording the subject's responses to tasks and questions performed daily and evaluating changes in memory. For example, it evaluates how accurately the subject remembers specific information and how accurately they can recall past events, enabling early detection of memory decline. Furthermore, the analysis unit can analyze attention, problem-solving ability, and judgment to evaluate the subject's cognitive ability. This involves recording the subject's performance in daily activities and tasks and evaluating changes in cognitive ability. For example, the system evaluates how efficiently individuals can perform complex tasks and how quickly and accurately they can make decisions when solving problems, enabling early detection of cognitive decline. This allows the analysis unit to quickly and accurately analyze the collected data and comprehensively evaluate changes in the individuals' health status and cognitive function. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For instance, it can detect specific patterns and trends based on past health and cognitive function data to predict future risks. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The pattern detection unit captures characteristic patterns or changes in dementia based on the analysis results obtained by the analysis unit. Specifically, it can identify signs of dementia such as a decrease in the frequency of use of certain words in the subject's conversations or changes in sentence structure. This is done using machine learning algorithms to detect abnormal patterns by comparing them with past data. For example, if a subject suddenly stops using words they previously used frequently, or if the structure of their sentences becomes simpler, this can be identified as an early sign of dementia. The pattern detection unit can also evaluate the progression of dementia and risk factors based on the subject's health data and medical information. For example, it can assess the risk of dementia based on fluctuations in the subject's blood pressure and heart rate, past medical history, and diagnostic results, allowing for early intervention. Furthermore, the pattern detection unit can monitor changes in the subject's behavioral patterns and daily life to detect signs of dementia early. For example, if a subject suddenly stops an activity they regularly performed, or if they become more confused or forgetful in their daily life, this can be identified as a sign of dementia. As a result, the pattern detection unit can quickly and accurately capture characteristic patterns and changes in dementia based on the data obtained by the analysis unit. Furthermore, the pattern detection unit can continuously monitor changes in the subject's health and cognitive function based on the detected patterns and changes, enabling early intervention. For example, based on the detected patterns and changes, it can provide appropriate advice and support to the subject and implement measures to slow the progression of dementia. In this way, the pattern detection unit can support the early detection and intervention of dementia, improving the subject's quality of life.

[0033] The reporting unit reports to medical professionals based on patterns or changes detected by the pattern detection unit. Specifically, it integrates the subject's data with medical information to determine the progression and type of dementia, and can report the patient's condition, risk factors, and treatment options to medical professionals. This includes a function to automatically acquire necessary information by linking with electronic health record (EHR) systems and medical institution databases. For example, it can evaluate the progression of dementia based on the subject's latest diagnosis and prescription information and provide a detailed report to medical professionals. Furthermore, the reporting unit can provide appropriate advice and support to the subject based on the detected patterns and changes. For example, it can suggest lifestyle improvements to slow the progression of dementia and propose appropriate treatment options. In addition, the reporting unit can strengthen collaboration with medical professionals and continuously monitor changes in the subject's health status and cognitive function. For example, it can evaluate the subject's condition based on the results of regular examinations and tests, and revise the treatment plan as needed. As a result, the reporting unit can provide medical professionals with rapid and accurate information, supporting the early detection and management of dementia. Furthermore, the reporting department can provide appropriate information to the subjects and their families, deepening their understanding of dementia. For example, it can provide information on the progression of dementia, risk factors, and treatment methods, supporting the subjects and their families in taking appropriate measures. In this way, the reporting department can support the early detection and management of dementia, and provide subjects and their families with opportunities to seek professional consultation and treatment.

[0034] The data collection unit can collect data on the subject's everyday speech, writing, and drawings. For example, the data collection unit can collect the subject's everyday speech. For example, it can collect everyday conversations, telephone conversations, and interview conversations. The data collection unit can also collect the subject's writing. For example, it can collect the subject's diaries, emails, and memos. Furthermore, the data collection unit can also collect the subject's drawings. For example, it can collect the subject's sketches, illustrations, and diagrams. By collecting data on the subject's everyday speech, writing, and drawings, more detailed information can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can record the subject's everyday conversations, input the audio data into a generating AI, and convert it into text data using the generating AI.

[0035] The analysis unit can evaluate the subject's language ability, memory, cognitive abilities, etc. For example, to evaluate the subject's language ability, the analysis unit can analyze the richness of their vocabulary, grammatical accuracy, and clarity of pronunciation. For example, to evaluate the richness of the subject's vocabulary, the analysis unit can analyze the types and frequency of words the subject uses. To evaluate the grammatical accuracy of the subject, the analysis unit can analyze the structure of the subject's sentences and grammatical errors. Furthermore, to evaluate the clarity of the subject's pronunciation, the analysis unit can analyze the accuracy and clarity of the subject's pronunciation. In addition, the analysis unit can analyze the subject's memory, including short-term memory, long-term memory, and episodic memory. For example, to evaluate the subject's short-term memory, the analysis unit can analyze how accurately the subject can recall information learned in a short period of time. Furthermore, to evaluate the subject's long-term memory, the analysis unit can analyze how accurately the subject can recall information learned over a long period of time. Furthermore, to evaluate the subject's episodic memory, the analysis unit can analyze how accurately the subject can recall specific events or experiences. Furthermore, the analysis unit can analyze attention, problem-solving ability, judgment, etc., to evaluate the subject's cognitive abilities. For example, to evaluate the subject's attention, the analysis unit can analyze how well the subject can concentrate on a specific task. Also, to evaluate the subject's problem-solving ability, the analysis unit can analyze how effectively the subject can solve a specific problem. Furthermore, to evaluate the subject's judgment, the analysis unit can analyze how well the subject can make appropriate judgments in specific situations. This allows for a more accurate detection of signs of dementia by evaluating the subject's language ability, memory, cognitive abilities, etc. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subject's language data into a generating AI, and the generating AI can evaluate language ability.

[0036] The pattern detection unit can detect signs of dementia when the frequency of use of specific words or changes in sentence structure occur in a subject's conversation. For example, the pattern detection unit can detect a decrease in the frequency of use of specific words in a subject's conversation as a sign of dementia. For example, if a subject stops using words they previously used frequently, the pattern detection unit can detect this change as a sign of dementia. The pattern detection unit can also detect changes in the structure of a subject's sentences as a sign of dementia. For example, if a subject's sentences become shorter than before or contain more grammatical errors, the pattern detection unit can detect this change as a sign of dementia. Furthermore, the pattern detection unit can comprehensively evaluate the frequency of use of specific words and changes in sentence structure in a subject's conversation to detect signs of dementia. This allows for early detection of signs of dementia by detecting the frequency of use of specific words and changes in sentence structure in a subject's conversation as signs of dementia. Some or all of the above processing in the pattern detection unit may be performed using AI, for example, or without AI. For example, the pattern detection unit inputs the subject's conversation data into a generating AI, which can then analyze the frequency of use of specific words and changes in sentence structure.

[0037] The reporting unit can integrate the subject's data and medical information to determine the progression and type of dementia and report the patient's condition, risk factors, and treatment methods to medical professionals. For example, the reporting unit can integrate the subject's health data and medical information to determine the progression and type of dementia. For example, the reporting unit can collect data such as blood pressure, heart rate, and body temperature as the subject's health data and integrate this data with medical information. The reporting unit can also collect data such as diagnosis results, prescriptions, and medical history as the subject's medical information and integrate this data with health data. Furthermore, the reporting unit can use algorithms to integrate the subject's data and medical information to determine the progression and type of dementia. For example, the reporting unit can use an AI model that takes the subject's data and medical information as input and outputs the progression and type of dementia. This allows the reporting unit to determine the progression and type of dementia and report the patient's condition, risk factors, and treatment methods to medical professionals. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without using AI. For example, the reporting department can input data and medical information of the subject into a generating AI, which can then determine the progression and type of dementia.

[0038] The data collection unit can analyze the subject's past health data and select the optimal data collection method. For example, the data collection unit can periodically monitor the subject's health status based on the subject's past health checkup results. For example, the data collection unit can analyze the subject's past health checkup results and periodically monitor health conditions such as blood pressure and heart rate. The data collection unit can also collect data focusing on specific symptoms, referencing the subject's past medical history. For example, the data collection unit can analyze the subject's past medical history and collect data related to specific symptoms. Furthermore, the data collection unit can analyze the subject's past medical records and determine the optimal data collection frequency. For example, the data collection unit can analyze the subject's past medical records and determine the optimal data collection frequency. This allows for efficient data collection by analyzing the subject's past health data and selecting the optimal data collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's past health data into a generating AI, which can then select the optimal data collection method.

[0039] The data collection unit can filter data based on the subject's living environment and daily activities during data collection. For example, when the subject is at home, the data collection unit can collect data based on their living environment. For example, when the subject is at home, the data collection unit can collect data based on their activities within the home and the condition of their residence. Furthermore, when the subject is out, the data collection unit can collect data based on their daily activities. For example, when the subject is out, the data collection unit can collect data related to their activities and travel while out. In addition, when the subject is performing a specific activity, the data collection unit can prioritize the collection of data related to that activity. For example, when the subject is exercising, the data collection unit can prioritize the collection of data related to exercise. By filtering the data based on the subject's living environment and daily activities, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's living environment data into a generating AI, and the generating AI can filter the data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the subject during data collection. For example, if the subject is in a hospital, the data collection unit can prioritize the collection of medical data such as diagnosis results and prescriptions. Furthermore, if the subject is at home, the data collection unit can prioritize the collection of data related to their living environment. For example, if the subject is at home, the data collection unit can prioritize the collection of data related to household activities and housing conditions. In addition, if the subject is traveling, the data collection unit can prioritize the collection of data related to travel. For example, if the subject is traveling, the data collection unit can prioritize the collection of data related to activities and travel at their destination. By considering the geographical location information of the subject during data collection, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze the subject's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of the subject's social media posts and collect psychological data. For example, the data collection unit can analyze the content of the subject's social media posts and collect data related to the subject's psychological state. The data collection unit can also collect behavioral data based on the subject's frequency of social media activity. For example, the data collection unit can analyze the subject's frequency of social media activity and collect data related to the subject's behavior. Furthermore, the data collection unit can analyze the subject's social relationships on social media and collect social data. For example, the data collection unit can analyze the subject's social relationships on social media and collect data related to the subject's social relationships. This allows for the collection of more multifaceted data by analyzing the subject's social media activity and collecting data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's social media data into a generating AI, and the generating AI can collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the subject's health data during the analysis. For example, the analysis unit can perform a detailed analysis on important health data. For example, the analysis unit can perform a detailed analysis on the subject's heart-related data. The analysis unit can also perform a simplified analysis on general health data. For example, the analysis unit can perform a simplified analysis on the subject's general health checkup results. Furthermore, the analysis unit can perform a rapid analysis on data with high urgency. For example, the analysis unit can rapidly analyze the subject's urgent symptoms. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the subject's health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subject's health data into a generating AI, and the generating AI can adjust the level of detail of the analysis based on importance.

[0043] The analysis unit can apply different analysis algorithms depending on the subject's health category during analysis. For example, the analysis unit can apply a specialized cardiac analysis algorithm to cardiac-related data. For example, the analysis unit can apply a specialized cardiac machine learning algorithm to cardiac-related data. The analysis unit can also apply a specialized brain analysis algorithm to brain-related data. For example, the analysis unit can apply a specialized brain statistical analysis algorithm to brain-related data. Furthermore, the analysis unit can apply a specialized respiratory analysis algorithm to respiratory-related data. For example, the analysis unit can apply a specialized respiratory clustering algorithm to respiratory-related data. By applying different analysis algorithms depending on the subject's health category, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subject's health data into a generating AI, and the generating AI can apply an analysis algorithm according to the health category.

[0044] The analysis unit can determine the priority of analysis based on the timing of data submission from the subjects. For example, the analysis unit can prioritize the analysis of data with high urgency. For example, the analysis unit can prioritize the analysis of data related to the subjects' urgent symptoms. The analysis unit can also prioritize the analysis of data submitted regularly. For example, the analysis unit can prioritize the analysis of the subjects' regular health checkup results. Furthermore, the analysis unit can prioritize the analysis of data that shows abnormalities when compared to past data. For example, the analysis unit can prioritize the analysis of data that shows abnormalities when compared to the subjects' past health data. In this way, by determining the priority of analysis based on the timing of data submission from the subjects, data with high urgency can be prioritized. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subjects' data into a generating AI, and the generating AI can determine the priority of analysis based on the submission timing.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the subjects during the analysis. For example, the analysis unit can prioritize the analysis of data related to the subjects' health status. For example, the analysis unit can prioritize the analysis of data related to the subjects' past medical history. For example, the analysis unit can prioritize the analysis of data related to the subjects' past medical history. Furthermore, the analysis unit can prioritize the analysis of data related to the subjects' current symptoms. For example, the analysis unit can prioritize the analysis of data related to the subjects' current symptoms. By adjusting the order of analysis based on the relevance of the subjects, more important data can be prioritized for analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subjects' data into a generating AI, and the generating AI can adjust the order of analysis based on relevance.

[0046] The pattern detection unit can improve the accuracy of pattern detection by considering the interrelationships of the subject's data during pattern detection. For example, the pattern detection unit can detect patterns by analyzing the interrelationships between the subject's health data and conversation data. For example, the pattern detection unit can analyze the interrelationships between the subject's health data and conversation data and detect patterns based on the correlations between these data. The pattern detection unit can also detect patterns by analyzing the interrelationships between the subject's medical information and living environment data. For example, the pattern detection unit can analyze the interrelationships between the subject's medical information and living environment data and detect patterns based on the causal relationships between these data. Furthermore, the pattern detection unit can detect patterns by analyzing the interrelationships between the subject's past data and current data. For example, the pattern detection unit can analyze the interrelationships between the subject's past data and current data and detect patterns based on the co-occurrence relationships between these data. By considering the interrelationships of the subject's data, more accurate pattern detection becomes possible. Some or all of the above-described processes in the pattern detection unit may be performed using AI, for example, or without using AI. For example, the pattern detection unit inputs data of the subject into a generating AI, which then detects patterns by considering the interrelationships between the data.

[0047] The pattern detection unit can perform pattern detection while considering the attribute information of the subject. For example, the pattern detection unit can detect patterns based on the subject's age. For example, the pattern detection unit can detect age-specific patterns based on the subject's age. The pattern detection unit can also detect patterns based on the subject's gender. For example, the pattern detection unit can detect gender-specific patterns based on the subject's gender. Furthermore, the pattern detection unit can detect patterns based on the subject's occupation. For example, the pattern detection unit can detect occupation-specific patterns based on the subject's occupation. This makes it possible to perform more appropriate pattern detection by considering the subject's attribute information. Some or all of the above processing in the pattern detection unit may be performed using AI, for example, or without using AI. For example, the pattern detection unit can input the subject's attribute information into a generating AI, and the generating AI can detect patterns while considering the attribute information.

[0048] The pattern detection unit can perform pattern detection while considering the geographical distribution of the subject. For example, the pattern detection unit can detect patterns while considering the characteristics of the area where the subject lives. For example, the pattern detection unit can analyze the characteristics of the area where the subject lives and detect patterns specific to that area. The pattern detection unit can also detect patterns while considering the characteristics of places the subject frequently visits. For example, the pattern detection unit can analyze the characteristics of places the subject frequently visits and detect patterns specific to those places. Furthermore, the pattern detection unit can detect patterns while considering the subject's travel history. For example, the pattern detection unit can analyze the subject's travel history and detect patterns related to activities at destinations. By detecting patterns while considering the geographical distribution of the subject, more appropriate pattern detection becomes possible. Some or all of the above processing in the pattern detection unit may be performed using AI, for example, or without AI. For example, the pattern detection unit can input the subject's geographical distribution data into a generating AI, and the generating AI can detect patterns while considering the geographical distribution.

[0049] The pattern detection unit can improve the accuracy of pattern detection by referring to relevant literature on the subject during pattern detection. For example, the pattern detection unit can detect patterns by referring to medical literature related to the subject's symptoms. For example, the pattern detection unit can refer to medical literature related to the subject's symptoms and detect patterns based on that literature. The pattern detection unit can also detect patterns by referring to research papers related to the subject's medical history. For example, the pattern detection unit can refer to research papers related to the subject's medical history and detect patterns based on those papers. Furthermore, the pattern detection unit can detect patterns by referring to statistical data related to the subject's lifestyle. For example, the pattern detection unit can refer to statistical data related to the subject's lifestyle and detect patterns based on that data. By doing so, more accurate pattern detection becomes possible by referring to relevant literature on the subject. Some or all of the above processing in the pattern detection unit may be performed using AI, for example, or without AI. For example, the pattern detection unit can input relevant literature data on the subject into a generating AI, and the generating AI can refer to the literature and detect patterns.

[0050] The reporting unit can adjust the level of detail in its reports based on the importance of the data of the subject. For example, the reporting unit can provide detailed reports for important data. For example, it can provide detailed reports for the subject's cardiac-related data. The reporting unit can also provide simplified reports for general data. For example, it can provide simplified reports for the subject's general health checkup results. Furthermore, the reporting unit can provide rapid reports for urgent data. For example, it can provide rapid reports for urgent symptoms of the subject. This allows for efficient reporting by adjusting the level of detail based on the importance of the subject's data. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input the subject's data into a generating AI, which can then adjust the level of detail in the report based on its importance.

[0051] The reporting unit can apply different reporting algorithms depending on the health category of the subject when reporting. For example, the reporting unit can apply a specialized cardiac reporting algorithm to cardiac-related data. Similarly, the reporting unit can apply a specialized brain reporting algorithm to brain-related data. Furthermore, the reporting unit can apply a specialized respiratory reporting algorithm to respiratory-related data. This allows for more accurate reporting by applying different reporting algorithms depending on the health category of the subject. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the subject's health data into a generating AI, which can then apply a reporting algorithm appropriate to the health category.

[0052] The reporting department can prioritize reporting based on the timing of data submission from the subjects. For example, the reporting department can prioritize reporting data of high urgency. For example, it can prioritize reporting data related to a subject's urgent symptoms. The reporting department can also prioritize reporting data submitted regularly. For example, it can prioritize reporting the results of a subject's regular health checkups. Furthermore, the reporting department can prioritize reporting data that shows abnormalities when compared to past data. For example, it can prioritize reporting data that shows abnormalities when compared to a subject's past health data. This allows for prioritizing reporting of high-urgency data by determining the timing of data submission from the subjects. Some or all of the above processing in the reporting department may be performed using AI, for example, or not. For example, the reporting department can input the subject's data into a generating AI, which can then determine the reporting priority based on the submission timing.

[0053] The reporting unit can adjust the order of reporting based on the relevance of the subject. For example, the reporting unit can prioritize reporting data related to the subject's health status. For example, the reporting unit can prioritize reporting data related to the subject's past medical history. For example, the reporting unit can prioritize reporting data related to the subject's past medical history. Furthermore, the reporting unit can prioritize reporting data related to the subject's current symptoms. For example, the reporting unit can prioritize reporting data related to the subject's current symptoms. This allows for prioritizing the reporting of more important data by adjusting the order of reporting based on the subject's relevance. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input subject data into a generating AI, which can then adjust the order of reporting based on relevance.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The dementia early detection support system can further collect and analyze the sleep patterns of the subjects. For example, the collection unit can collect data such as the subject's sleep duration, sleep quality, and number of nighttime awakenings. The analysis unit can analyze this data and evaluate changes in the subject's sleep patterns. The pattern detection unit can determine whether the changes in sleep patterns are signs of dementia. The reporting unit can report to medical professionals based on the changes in sleep patterns. In this way, monitoring the subject's sleep patterns can further support the early detection of dementia.

[0056] The dementia early detection support system can further collect and analyze the dietary patterns of the subjects. For example, the collection unit can collect data such as the content of the subjects' meals, the frequency of meals, and the amount of food they eat. The analysis unit can analyze this data and evaluate changes in the subjects' dietary patterns. The pattern detection unit can determine whether the changes in dietary patterns are signs of dementia. The reporting unit can report to medical professionals based on the changes in dietary patterns. In this way, monitoring the subjects' dietary patterns can further support the early detection of dementia.

[0057] The dementia early detection support system can further collect and analyze the exercise patterns of the subject. For example, the collection unit can collect data such as the amount of exercise, type of exercise, and frequency of exercise. The analysis unit can analyze this data and evaluate changes in the subject's exercise patterns. The pattern detection unit can determine whether the changes in exercise patterns are signs of dementia. The reporting unit can report to medical professionals based on the changes in exercise patterns. In this way, monitoring the subject's exercise patterns can further support the early detection of dementia.

[0058] The dementia early detection support system can further collect and analyze the social interaction patterns of the target individual. For example, the collection unit can collect data such as the frequency, content, and quality of interactions with the target individual's friends and family. The analysis unit can analyze this data and evaluate changes in the target individual's social interaction patterns. The pattern detection unit can determine whether the changes in social interaction patterns are signs of dementia. The reporting unit can report to medical professionals based on the changes in social interaction patterns. In this way, monitoring the target individual's social interaction patterns can further support the early detection of dementia.

[0059] The dementia early detection support system can further collect and analyze the stress levels of the subjects. For example, the data collection unit can collect data such as the subject's heart rate, blood pressure, and stress hormone levels. The analysis unit can analyze this data and evaluate changes in the subject's stress levels. The pattern detection unit can determine whether the changes in stress levels are signs of dementia. The reporting unit can report to medical professionals based on the changes in stress levels. In this way, monitoring the subject's stress levels can further support the early detection of dementia.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects the subject's health status, conversation patterns, and medical information. For example, the data collection unit collects data such as blood pressure, heart rate, and body temperature as part of the subject's health status, and data such as speaking speed, word choice, and grammatical accuracy as part of conversation patterns. The data collection unit also collects data such as diagnosis results, prescriptions, and medical history as part of the subject's medical information. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes vocabulary richness, grammatical accuracy, and pronunciation clarity to evaluate the subject's language ability, and analyzes short-term memory, long-term memory, and episodic memory to evaluate memory ability. Furthermore, the analysis unit analyzes attention, problem-solving ability, and judgment to evaluate the subject's cognitive ability. Step 3: The pattern detection unit identifies characteristic patterns or changes in dementia based on the analysis results obtained by the analysis unit. For example, the pattern detection unit can identify signs of dementia such as a decrease in the frequency of use of certain words in the subject's conversation or a change in sentence structure. Step 4: The reporting unit reports to medical professionals based on the patterns or changes detected by the pattern detection unit. For example, the reporting unit can integrate the subject's data and medical information to determine the progression and type of dementia and report the patient's condition, risk factors, and treatment options to medical professionals.

[0062] (Example of form 2) The dementia early detection support system according to an embodiment of the present invention is a system that collects the subject's health status, conversation patterns, and medical information, and uses a generating AI to analyze this data to identify characteristic patterns and changes of dementia and provide early warnings to medical professionals. The dementia early detection support system collects the subject's health status, conversation patterns, and medical information, and the generating AI analyzes this data to evaluate the subject's language ability, memory, cognitive ability, etc. Furthermore, based on the analysis results, the generating AI identifies characteristic patterns and changes of dementia and provides early warnings to medical professionals. This mechanism enables the early detection of dementia and provides patients and their families with an opportunity to seek professional consultation and treatment. For example, the dementia early detection support system collects data such as the words the subject speaks, the sentences they write, and the pictures they draw on a daily basis. The generating AI analyzes this data to evaluate the subject's language ability, memory, cognitive ability, etc. The generating AI recognizes a decrease in the frequency of use of certain words in the subject's conversations or a change in the structure of sentences as signs of dementia. The generating AI integrates data and medical information of the subject to determine the progression and type of dementia, and reports the patient's condition, risk factors, and treatment options to medical professionals. This allows the dementia early detection support system to facilitate the early detection of dementia and provide patients and their families with an opportunity to seek professional consultation and treatment.

[0063] The dementia early detection support system according to this embodiment comprises a data collection unit, an analysis unit, a pattern detection unit, and a reporting unit. The data collection unit collects the subject's health status or conversation patterns and medical information. For example, the data collection unit can collect data such as blood pressure, heart rate, and body temperature as the subject's health status. The data collection unit can also collect data such as speaking speed, word choice, and grammatical accuracy as the subject's conversation patterns. Furthermore, the data collection unit can collect data such as diagnosis results, prescriptions, and medical history as the subject's medical information. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze vocabulary richness, grammatical accuracy, and pronunciation clarity to evaluate the subject's language ability. Furthermore, the analysis unit can analyze short-term memory, long-term memory, and episodic memory to evaluate the subject's memory ability. Furthermore, the analysis unit can analyze attention, problem-solving ability, and judgment to evaluate the subject's cognitive ability. The pattern detection unit captures characteristic patterns or changes of dementia based on the analysis results obtained by the analysis unit. The pattern detection unit can, for example, detect signs of dementia such as a decrease in the frequency of use of certain words or changes in sentence structure in the subject's conversations. The reporting unit reports to medical professionals based on the patterns or changes detected by the pattern detection unit. The reporting unit can, for example, integrate the subject's data with medical information to determine the progression and type of dementia and report the patient's condition, risk factors, and treatment methods to medical professionals. As a result, the dementia early detection support system according to this embodiment can support the early detection of dementia and provide patients and their families with an opportunity to seek professional consultation and treatment.

[0064] The data collection unit collects data on the subject's health status, conversation patterns, and medical information. Specifically, it can collect data such as blood pressure, heart rate, and body temperature as part of the subject's health status. This data is collected in real time through wearable devices and home medical devices and transmitted to a cloud server. For example, smartwatches and fitness trackers can continuously monitor heart rate and body temperature and issue alerts if abnormalities are detected. The data collection unit can also collect data on the subject's conversation patterns, such as speaking speed, word choice, and grammatical accuracy. This involves converting conversations into text using speech recognition technology and analyzing language patterns using natural language processing (NLP) technology. For example, smart speakers or voice assistants that the subject uses daily record conversations and transmit that data to the data collection unit. The data collection unit can also collect data such as diagnostic results, prescriptions, and medical history as part of the subject's medical information. This includes the ability to automatically acquire necessary information by linking with electronic health record (EHR) systems and medical institution databases. For example, the latest diagnostic results and prescription information can be obtained from medical institutions that the subject regularly visits and integrated into the data collection unit. This allows the data collection unit to gather a wide range of information from diverse data sources and comprehensively monitor changes in the subjects' health status and cognitive function. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis unit and pattern detection unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0065] The analysis unit analyzes the data collected by the data collection unit. Specifically, it can analyze vocabulary richness, grammatical accuracy, and pronunciation clarity to evaluate the subject's language ability. This involves using natural language processing (NLP) techniques to analyze the subject's conversational data in detail and detect changes or anomalies in language patterns. For example, it evaluates the types and frequency of vocabulary used by the subject, the number of grammatical errors, and pronunciation clarity, and monitors changes in language ability based on this data. The analysis unit can also analyze short-term memory, long-term memory, and episodic memory to evaluate the subject's memory. This involves recording the subject's responses to tasks and questions performed daily and evaluating changes in memory. For example, it evaluates how accurately the subject remembers specific information and how accurately they can recall past events, enabling early detection of memory decline. Furthermore, the analysis unit can analyze attention, problem-solving ability, and judgment to evaluate the subject's cognitive ability. This involves recording the subject's performance in daily activities and tasks and evaluating changes in cognitive ability. For example, the system evaluates how efficiently individuals can perform complex tasks and how quickly and accurately they can make decisions when solving problems, enabling early detection of cognitive decline. This allows the analysis unit to quickly and accurately analyze the collected data and comprehensively evaluate changes in the individuals' health status and cognitive function. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For instance, it can detect specific patterns and trends based on past health and cognitive function data to predict future risks. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0066] The pattern detection unit captures characteristic patterns or changes in dementia based on the analysis results obtained by the analysis unit. Specifically, it can identify signs of dementia such as a decrease in the frequency of use of certain words in the subject's conversations or changes in sentence structure. This is done using machine learning algorithms to detect abnormal patterns by comparing them with past data. For example, if a subject suddenly stops using words they previously used frequently, or if the structure of their sentences becomes simpler, this can be identified as an early sign of dementia. The pattern detection unit can also evaluate the progression of dementia and risk factors based on the subject's health data and medical information. For example, it can assess the risk of dementia based on fluctuations in the subject's blood pressure and heart rate, past medical history, and diagnostic results, allowing for early intervention. Furthermore, the pattern detection unit can monitor changes in the subject's behavioral patterns and daily life to detect signs of dementia early. For example, if a subject suddenly stops an activity they regularly performed, or if they become more confused or forgetful in their daily life, this can be identified as a sign of dementia. As a result, the pattern detection unit can quickly and accurately capture characteristic patterns and changes in dementia based on the data obtained by the analysis unit. Furthermore, the pattern detection unit can continuously monitor changes in the subject's health and cognitive function based on the detected patterns and changes, enabling early intervention. For example, based on the detected patterns and changes, it can provide appropriate advice and support to the subject and implement measures to slow the progression of dementia. In this way, the pattern detection unit can support the early detection and intervention of dementia, improving the subject's quality of life.

[0067] The reporting unit reports to medical professionals based on patterns or changes detected by the pattern detection unit. Specifically, it integrates the subject's data with medical information to determine the progression and type of dementia, and can report the patient's condition, risk factors, and treatment options to medical professionals. This includes a function to automatically acquire necessary information by linking with electronic health record (EHR) systems and medical institution databases. For example, it can evaluate the progression of dementia based on the subject's latest diagnosis and prescription information and provide a detailed report to medical professionals. Furthermore, the reporting unit can provide appropriate advice and support to the subject based on the detected patterns and changes. For example, it can suggest lifestyle improvements to slow the progression of dementia and propose appropriate treatment options. In addition, the reporting unit can strengthen collaboration with medical professionals and continuously monitor changes in the subject's health status and cognitive function. For example, it can evaluate the subject's condition based on the results of regular examinations and tests, and revise the treatment plan as needed. As a result, the reporting unit can provide medical professionals with rapid and accurate information, supporting the early detection and management of dementia. Furthermore, the reporting department can provide appropriate information to the subjects and their families, deepening their understanding of dementia. For example, it can provide information on the progression of dementia, risk factors, and treatment methods, supporting the subjects and their families in taking appropriate measures. In this way, the reporting department can support the early detection and management of dementia, and provide subjects and their families with opportunities to seek professional consultation and treatment.

[0068] The data collection unit can collect data on the subject's everyday speech, writing, and drawings. For example, the data collection unit can collect the subject's everyday speech. For example, it can collect everyday conversations, telephone conversations, and interview conversations. The data collection unit can also collect the subject's writing. For example, it can collect the subject's diaries, emails, and memos. Furthermore, the data collection unit can also collect the subject's drawings. For example, it can collect the subject's sketches, illustrations, and diagrams. By collecting data on the subject's everyday speech, writing, and drawings, more detailed information can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can record the subject's everyday conversations, input the audio data into a generating AI, and convert it into text data using the generating AI.

[0069] The analysis unit can evaluate the subject's language ability, memory, cognitive abilities, etc. For example, to evaluate the subject's language ability, the analysis unit can analyze the richness of their vocabulary, grammatical accuracy, and clarity of pronunciation. For example, to evaluate the richness of the subject's vocabulary, the analysis unit can analyze the types and frequency of words the subject uses. To evaluate the grammatical accuracy of the subject, the analysis unit can analyze the structure of the subject's sentences and grammatical errors. Furthermore, to evaluate the clarity of the subject's pronunciation, the analysis unit can analyze the accuracy and clarity of the subject's pronunciation. In addition, the analysis unit can analyze the subject's memory, including short-term memory, long-term memory, and episodic memory. For example, to evaluate the subject's short-term memory, the analysis unit can analyze how accurately the subject can recall information learned in a short period of time. Furthermore, to evaluate the subject's long-term memory, the analysis unit can analyze how accurately the subject can recall information learned over a long period of time. Furthermore, to evaluate the subject's episodic memory, the analysis unit can analyze how accurately the subject can recall specific events or experiences. Furthermore, the analysis unit can analyze attention, problem-solving ability, judgment, etc., to evaluate the subject's cognitive abilities. For example, to evaluate the subject's attention, the analysis unit can analyze how well the subject can concentrate on a specific task. Also, to evaluate the subject's problem-solving ability, the analysis unit can analyze how effectively the subject can solve a specific problem. Furthermore, to evaluate the subject's judgment, the analysis unit can analyze how well the subject can make appropriate judgments in specific situations. This allows for a more accurate detection of signs of dementia by evaluating the subject's language ability, memory, cognitive abilities, etc. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subject's language data into a generating AI, and the generating AI can evaluate language ability.

[0070] The pattern detection unit can detect signs of dementia when the frequency of use of specific words or changes in sentence structure occur in a subject's conversation. For example, the pattern detection unit can detect a decrease in the frequency of use of specific words in a subject's conversation as a sign of dementia. For example, if a subject stops using words they previously used frequently, the pattern detection unit can detect this change as a sign of dementia. The pattern detection unit can also detect changes in the structure of a subject's sentences as a sign of dementia. For example, if a subject's sentences become shorter than before or contain more grammatical errors, the pattern detection unit can detect this change as a sign of dementia. Furthermore, the pattern detection unit can comprehensively evaluate the frequency of use of specific words and changes in sentence structure in a subject's conversation to detect signs of dementia. This allows for early detection of signs of dementia by detecting the frequency of use of specific words and changes in sentence structure in a subject's conversation as signs of dementia. Some or all of the above processing in the pattern detection unit may be performed using AI, for example, or without AI. For example, the pattern detection unit inputs the subject's conversation data into a generating AI, which can then analyze the frequency of use of specific words and changes in sentence structure.

[0071] The reporting unit can integrate the subject's data and medical information to determine the progression and type of dementia and report the patient's condition, risk factors, and treatment methods to medical professionals. For example, the reporting unit can integrate the subject's health data and medical information to determine the progression and type of dementia. For example, the reporting unit can collect data such as blood pressure, heart rate, and body temperature as the subject's health data and integrate this data with medical information. The reporting unit can also collect data such as diagnosis results, prescriptions, and medical history as the subject's medical information and integrate this data with health data. Furthermore, the reporting unit can use algorithms to integrate the subject's data and medical information to determine the progression and type of dementia. For example, the reporting unit can use an AI model that takes the subject's data and medical information as input and outputs the progression and type of dementia. This allows the reporting unit to determine the progression and type of dementia and report the patient's condition, risk factors, and treatment methods to medical professionals. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without using AI. For example, the reporting department can input data and medical information of the subject into a generating AI, which can then determine the progression and type of dementia.

[0072] The data collection unit can estimate the subject's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can collect conversation patterns when the subject is relaxed. For example, the data collection unit can collect the subject's everyday conversations when the subject is relaxed. The data collection unit can also refrain from collecting health data when the subject is stressed. For example, the data collection unit can refrain from collecting data such as blood pressure and heart rate when the subject is stressed. Furthermore, the data collection unit can collect medical information when the subject is focused. For example, the data collection unit can collect medical information such as diagnostic results and prescriptions when the subject is focused. By adjusting the timing of data collection based on the subject's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's facial expression data into a generating AI, have the generating AI estimate the emotion, and adjust the timing of data collection based on that emotion.

[0073] The data collection unit can analyze the subject's past health data and select the optimal data collection method. For example, the data collection unit can periodically monitor the subject's health status based on the subject's past health checkup results. For example, the data collection unit can analyze the subject's past health checkup results and periodically monitor health conditions such as blood pressure and heart rate. The data collection unit can also collect data focusing on specific symptoms, referencing the subject's past medical history. For example, the data collection unit can analyze the subject's past medical history and collect data related to specific symptoms. Furthermore, the data collection unit can analyze the subject's past medical records and determine the optimal data collection frequency. For example, the data collection unit can analyze the subject's past medical records and determine the optimal data collection frequency. This allows for efficient data collection by analyzing the subject's past health data and selecting the optimal data collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's past health data into a generating AI, which can then select the optimal data collection method.

[0074] The data collection unit can filter data based on the subject's living environment and daily activities during data collection. For example, when the subject is at home, the data collection unit can collect data based on their living environment. For example, when the subject is at home, the data collection unit can collect data based on their activities within the home and the condition of their residence. Furthermore, when the subject is out, the data collection unit can collect data based on their daily activities. For example, when the subject is out, the data collection unit can collect data related to their activities and travel while out. In addition, when the subject is performing a specific activity, the data collection unit can prioritize the collection of data related to that activity. For example, when the subject is exercising, the data collection unit can prioritize the collection of data related to exercise. By filtering the data based on the subject's living environment and daily activities, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's living environment data into a generating AI, and the generating AI can filter the data.

[0075] The data collection unit can estimate the subject's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the subject is feeling anxious, the data collection unit can prioritize collecting psychological data. For example, if the subject is feeling anxious, the data collection unit can prioritize collecting data related to the subject's psychological state. Also, if the subject is relaxed, the data collection unit can prioritize collecting physical data. For example, if the subject is relaxed, the data collection unit can prioritize collecting data related to the subject's physical state. Furthermore, if the subject is excited, the data collection unit can prioritize collecting behavioral data. For example, if the subject is excited, the data collection unit can prioritize collecting data related to the subject's behavior. This allows for the priority of collecting more important data by determining the priority of data to collect based on the subject's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's facial expression data into a generating AI, which then estimates the emotion, and based on that emotion, it can determine the priority of the data to be collected.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the subject during data collection. For example, if the subject is in a hospital, the data collection unit can prioritize the collection of medical data such as diagnosis results and prescriptions. Furthermore, if the subject is at home, the data collection unit can prioritize the collection of data related to their living environment. For example, if the subject is at home, the data collection unit can prioritize the collection of data related to household activities and housing conditions. In addition, if the subject is traveling, the data collection unit can prioritize the collection of data related to travel. For example, if the subject is traveling, the data collection unit can prioritize the collection of data related to activities and travel at their destination. By considering the geographical location information of the subject during data collection, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0077] The data collection unit can analyze the subject's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of the subject's social media posts and collect psychological data. For example, the data collection unit can analyze the content of the subject's social media posts and collect data related to the subject's psychological state. The data collection unit can also collect behavioral data based on the subject's frequency of social media activity. For example, the data collection unit can analyze the subject's frequency of social media activity and collect data related to the subject's behavior. Furthermore, the data collection unit can analyze the subject's social relationships on social media and collect social data. For example, the data collection unit can analyze the subject's social relationships on social media and collect data related to the subject's social relationships. This allows for the collection of more multifaceted data by analyzing the subject's social media activity and collecting data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the subject's social media data into a generating AI, and the generating AI can collect relevant data.

[0078] The analysis unit can estimate the subject's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the subject is feeling anxious, the analysis unit can present the analysis results simply. For example, if the subject is feeling anxious, the analysis unit can summarize the analysis results concisely and provide a reassuring presentation. Also, if the subject is relaxed, the analysis unit can provide detailed analysis results. For example, if the subject is relaxed, the analysis unit can provide analysis results that include detailed data and graphs. Furthermore, if the subject is excited, the analysis unit can provide visually stimulating analysis results. For example, if the subject is excited, the analysis unit can provide analysis results that include colorful graphs and animations. In this way, by adjusting the presentation of the analysis based on the subject's emotions, it is possible to provide analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subject's facial expression data into a generating AI, which will then estimate the emotion, and adjust the method of expression in the analysis based on that emotion.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the subject's health data during the analysis. For example, the analysis unit can perform a detailed analysis on important health data. For example, the analysis unit can perform a detailed analysis on the subject's heart-related data. The analysis unit can also perform a simplified analysis on general health data. For example, the analysis unit can perform a simplified analysis on the subject's general health checkup results. Furthermore, the analysis unit can perform a rapid analysis on data with high urgency. For example, the analysis unit can rapidly analyze the subject's urgent symptoms. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the subject's health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subject's health data into a generating AI, and the generating AI can adjust the level of detail of the analysis based on importance.

[0080] The analysis unit can apply different analysis algorithms depending on the subject's health category during analysis. For example, the analysis unit can apply a specialized cardiac analysis algorithm to cardiac-related data. For example, the analysis unit can apply a specialized cardiac machine learning algorithm to cardiac-related data. The analysis unit can also apply a specialized brain analysis algorithm to brain-related data. For example, the analysis unit can apply a specialized brain statistical analysis algorithm to brain-related data. Furthermore, the analysis unit can apply a specialized respiratory analysis algorithm to respiratory-related data. For example, the analysis unit can apply a specialized respiratory clustering algorithm to respiratory-related data. By applying different analysis algorithms depending on the subject's health category, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subject's health data into a generating AI, and the generating AI can apply an analysis algorithm according to the health category.

[0081] The analysis unit can estimate the subject's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the subject is in a hurry, the analysis unit can provide a short analysis result. For example, if the subject is in a hurry, the analysis unit can provide a concise analysis result that gets straight to the point. Also, if the subject is relaxed, the analysis unit can provide a detailed analysis result. For example, if the subject is relaxed, the analysis unit can provide a longer analysis result that includes detailed data and graphs. Furthermore, if the subject is excited, the analysis unit can provide a visually stimulating analysis result. For example, if the subject is excited, the analysis unit can provide an analysis result that includes colorful graphs and animations. By adjusting the length of the analysis based on the subject's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subject's facial expression data into a generating AI, which then estimates the emotion, and adjust the length of the analysis based on that emotion.

[0082] The analysis unit can determine the priority of analysis based on the timing of data submission from the subjects. For example, the analysis unit can prioritize the analysis of data with high urgency. For example, the analysis unit can prioritize the analysis of data related to the subjects' urgent symptoms. The analysis unit can also prioritize the analysis of data submitted regularly. For example, the analysis unit can prioritize the analysis of the subjects' regular health checkup results. Furthermore, the analysis unit can prioritize the analysis of data that shows abnormalities when compared to past data. For example, the analysis unit can prioritize the analysis of data that shows abnormalities when compared to the subjects' past health data. In this way, by determining the priority of analysis based on the timing of data submission from the subjects, data with high urgency can be prioritized. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subjects' data into a generating AI, and the generating AI can determine the priority of analysis based on the submission timing.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the subjects during the analysis. For example, the analysis unit can prioritize the analysis of data related to the subjects' health status. For example, the analysis unit can prioritize the analysis of data related to the subjects' past medical history. For example, the analysis unit can prioritize the analysis of data related to the subjects' past medical history. Furthermore, the analysis unit can prioritize the analysis of data related to the subjects' current symptoms. For example, the analysis unit can prioritize the analysis of data related to the subjects' current symptoms. By adjusting the order of analysis based on the relevance of the subjects, more important data can be prioritized for analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the subjects' data into a generating AI, and the generating AI can adjust the order of analysis based on relevance.

[0084] The pattern detection unit can estimate the subject's emotions and adjust the pattern detection criteria based on the estimated emotions. For example, if the subject is feeling anxious, the pattern detection unit can prioritize the detection of emotional patterns. For example, if the subject is feeling anxious, the pattern detection unit can prioritize the detection of patterns related to the subject's emotional changes. Also, if the subject is relaxed, the pattern detection unit can prioritize the detection of behavioral patterns. For example, if the subject is relaxed, the pattern detection unit can prioritize the detection of patterns related to the subject's behavior. Furthermore, if the subject is excited, the pattern detection unit can prioritize the detection of linguistic patterns. For example, if the subject is excited, the pattern detection unit can prioritize the detection of patterns related to the subject's language. By adjusting the pattern detection criteria based on the subject's emotions, more appropriate patterns can be detected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the pattern detection unit may be performed using AI, for example, or without AI. For example, the pattern detection unit can input the subject's facial expression data into a generating AI, have the generating AI estimate emotions, and adjust the pattern detection criteria based on those emotions.

[0085] The pattern detection unit can improve the accuracy of pattern detection by considering the interrelationships of the subject's data during pattern detection. For example, the pattern detection unit can detect patterns by analyzing the interrelationships between the subject's health data and conversation data. For example, the pattern detection unit can analyze the interrelationships between the subject's health data and conversation data and detect patterns based on the correlations between these data. The pattern detection unit can also detect patterns by analyzing the interrelationships between the subject's medical information and living environment data. For example, the pattern detection unit can analyze the interrelationships between the subject's medical information and living environment data and detect patterns based on the causal relationships between these data. Furthermore, the pattern detection unit can detect patterns by analyzing the interrelationships between the subject's past data and current data. For example, the pattern detection unit can analyze the interrelationships between the subject's past data and current data and detect patterns based on the co-occurrence relationships between these data. By considering the interrelationships of the subject's data, more accurate pattern detection becomes possible. Some or all of the above-described processes in the pattern detection unit may be performed using AI, for example, or without using AI. For example, the pattern detection unit inputs data of the subject into a generating AI, which then detects patterns by considering the interrelationships between the data.

[0086] The pattern detection unit can perform pattern detection while considering the attribute information of the subject. For example, the pattern detection unit can detect patterns based on the subject's age. For example, the pattern detection unit can detect age-specific patterns based on the subject's age. The pattern detection unit can also detect patterns based on the subject's gender. For example, the pattern detection unit can detect gender-specific patterns based on the subject's gender. Furthermore, the pattern detection unit can detect patterns based on the subject's occupation. For example, the pattern detection unit can detect occupation-specific patterns based on the subject's occupation. This makes it possible to perform more appropriate pattern detection by considering the subject's attribute information. Some or all of the above processing in the pattern detection unit may be performed using AI, for example, or without using AI. For example, the pattern detection unit can input the subject's attribute information into a generating AI, and the generating AI can detect patterns while considering the attribute information.

[0087] The pattern detection unit can estimate the subject's emotions and adjust the order in which the pattern detection results are displayed based on the estimated emotions. For example, if the subject is feeling anxious, the pattern detection unit can display important patterns first. For example, if the subject is feeling anxious, the pattern detection unit can prioritize the display of patterns of high importance. Also, if the subject is relaxed, the pattern detection unit can display detailed patterns. For example, if the subject is relaxed, the pattern detection unit can display patterns that include detailed data and graphs. Furthermore, if the subject is excited, the pattern detection unit can display visually stimulating patterns. For example, if the subject is excited, the pattern detection unit can display patterns that include colorful graphs and animations. By adjusting the order in which the pattern detection results are displayed based on the subject's emotions, it is possible to provide results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the pattern detection unit may be performed using AI, for example, or without AI. For example, the pattern detection unit can input the subject's facial expression data into a generating AI, which then estimates the emotion, and adjust the order in which the pattern detection results are displayed based on that emotion.

[0088] The pattern detection unit can perform pattern detection while considering the geographical distribution of the subject. For example, the pattern detection unit can detect patterns while considering the characteristics of the area where the subject lives. For example, the pattern detection unit can analyze the characteristics of the area where the subject lives and detect patterns specific to that area. The pattern detection unit can also detect patterns while considering the characteristics of places the subject frequently visits. For example, the pattern detection unit can analyze the characteristics of places the subject frequently visits and detect patterns specific to those places. Furthermore, the pattern detection unit can detect patterns while considering the subject's travel history. For example, the pattern detection unit can analyze the subject's travel history and detect patterns related to activities at destinations. By detecting patterns while considering the geographical distribution of the subject, more appropriate pattern detection becomes possible. Some or all of the above processing in the pattern detection unit may be performed using AI, for example, or without AI. For example, the pattern detection unit can input the subject's geographical distribution data into a generating AI, and the generating AI can detect patterns while considering the geographical distribution.

[0089] The pattern detection unit can improve the accuracy of pattern detection by referring to relevant literature on the subject during pattern detection. For example, the pattern detection unit can detect patterns by referring to medical literature related to the subject's symptoms. For example, the pattern detection unit can refer to medical literature related to the subject's symptoms and detect patterns based on that literature. The pattern detection unit can also detect patterns by referring to research papers related to the subject's medical history. For example, the pattern detection unit can refer to research papers related to the subject's medical history and detect patterns based on those papers. Furthermore, the pattern detection unit can detect patterns by referring to statistical data related to the subject's lifestyle. For example, the pattern detection unit can refer to statistical data related to the subject's lifestyle and detect patterns based on that data. By doing so, more accurate pattern detection becomes possible by referring to relevant literature on the subject. Some or all of the above processing in the pattern detection unit may be performed using AI, for example, or without AI. For example, the pattern detection unit can input relevant literature data on the subject into a generating AI, and the generating AI can refer to the literature and detect patterns.

[0090] The reporting unit can estimate the subject's emotions and adjust the presentation of the report based on the estimated emotions. For example, if the subject is feeling anxious, the reporting unit can provide a simple and reassuring presentation. For example, if the subject is feeling anxious, the reporting unit can provide a concise and reassuring presentation. Also, if the subject is relaxed, the reporting unit can provide a presentation that includes detailed information. For example, if the subject is relaxed, the reporting unit can provide a presentation that includes detailed data and graphs. Furthermore, if the subject is excited, the reporting unit can provide a visually stimulating presentation. For example, if the subject is excited, the reporting unit can provide a presentation that includes colorful graphs and animations. By adjusting the presentation of the report based on the subject's emotions, a more easily understandable report can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting department can input the subject's facial expression data into a generating AI, which will estimate the emotion, and then adjust the way the report is expressed based on that emotion.

[0091] The reporting unit can adjust the level of detail in its reports based on the importance of the data of the subject. For example, the reporting unit can provide detailed reports for important data. For example, it can provide detailed reports for the subject's cardiac-related data. The reporting unit can also provide simplified reports for general data. For example, it can provide simplified reports for the subject's general health checkup results. Furthermore, the reporting unit can provide rapid reports for urgent data. For example, it can provide rapid reports for urgent symptoms of the subject. This allows for efficient reporting by adjusting the level of detail based on the importance of the subject's data. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input the subject's data into a generating AI, which can then adjust the level of detail in the report based on its importance.

[0092] The reporting unit can apply different reporting algorithms depending on the health category of the subject when reporting. For example, the reporting unit can apply a specialized cardiac reporting algorithm to cardiac-related data. Similarly, the reporting unit can apply a specialized brain reporting algorithm to brain-related data. Furthermore, the reporting unit can apply a specialized respiratory reporting algorithm to respiratory-related data. This allows for more accurate reporting by applying different reporting algorithms depending on the health category of the subject. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the subject's health data into a generating AI, which can then apply a reporting algorithm appropriate to the health category.

[0093] The reporting unit can estimate the subject's emotions and adjust the length of the report based on the estimated emotions. For example, if the subject is in a hurry, the reporting unit can provide a short, to-the-point report. For example, if the subject is in a hurry, the reporting unit can provide a short, to-the-point report. For example, if the subject is relaxed, the reporting unit can provide a longer report that includes detailed explanations. For example, if the subject is relaxed, the reporting unit can provide a longer report that includes detailed data and graphs. Furthermore, if the subject is excited, the reporting unit can provide a report with visually stimulating effects. For example, if the subject is excited, the reporting unit can provide a report that includes colorful graphs and animations. This allows for the provision of more appropriate reports by adjusting the length of the report based on the subject's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting department can input the subject's facial expression data into a generating AI, which will then estimate the emotion, and adjust the length of the report based on that emotion.

[0094] The reporting department can prioritize reporting based on the timing of data submission from the subjects. For example, the reporting department can prioritize reporting data of high urgency. For example, it can prioritize reporting data related to a subject's urgent symptoms. The reporting department can also prioritize reporting data submitted regularly. For example, it can prioritize reporting the results of a subject's regular health checkups. Furthermore, the reporting department can prioritize reporting data that shows abnormalities when compared to past data. For example, it can prioritize reporting data that shows abnormalities when compared to a subject's past health data. This allows for prioritizing reporting of high-urgency data by determining the timing of data submission from the subjects. Some or all of the above processing in the reporting department may be performed using AI, for example, or not. For example, the reporting department can input the subject's data into a generating AI, which can then determine the reporting priority based on the submission timing.

[0095] The reporting unit can adjust the order of reporting based on the relevance of the subject. For example, the reporting unit can prioritize reporting data related to the subject's health status. For example, the reporting unit can prioritize reporting data related to the subject's past medical history. For example, the reporting unit can prioritize reporting data related to the subject's past medical history. Furthermore, the reporting unit can prioritize reporting data related to the subject's current symptoms. For example, the reporting unit can prioritize reporting data related to the subject's current symptoms. This allows for prioritizing the reporting of more important data by adjusting the order of reporting based on the subject's relevance. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input subject data into a generating AI, which can then adjust the order of reporting based on relevance.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The dementia early detection support system can further collect and analyze the sleep patterns of the subjects. For example, the collection unit can collect data such as the subject's sleep duration, sleep quality, and number of nighttime awakenings. The analysis unit can analyze this data and evaluate changes in the subject's sleep patterns. The pattern detection unit can determine whether the changes in sleep patterns are signs of dementia. The reporting unit can report to medical professionals based on the changes in sleep patterns. In this way, monitoring the subject's sleep patterns can further support the early detection of dementia.

[0098] The dementia early detection support system can further collect and analyze the dietary patterns of the subjects. For example, the collection unit can collect data such as the content of the subjects' meals, the frequency of meals, and the amount of food they eat. The analysis unit can analyze this data and evaluate changes in the subjects' dietary patterns. The pattern detection unit can determine whether the changes in dietary patterns are signs of dementia. The reporting unit can report to medical professionals based on the changes in dietary patterns. In this way, monitoring the subjects' dietary patterns can further support the early detection of dementia.

[0099] The dementia early detection support system can further collect and analyze the exercise patterns of the subject. For example, the collection unit can collect data such as the amount of exercise, type of exercise, and frequency of exercise. The analysis unit can analyze this data and evaluate changes in the subject's exercise patterns. The pattern detection unit can determine whether the changes in exercise patterns are signs of dementia. The reporting unit can report to medical professionals based on the changes in exercise patterns. In this way, monitoring the subject's exercise patterns can further support the early detection of dementia.

[0100] The dementia early detection support system can further collect and analyze the social interaction patterns of the target individual. For example, the collection unit can collect data such as the frequency, content, and quality of interactions with the target individual's friends and family. The analysis unit can analyze this data and evaluate changes in the target individual's social interaction patterns. The pattern detection unit can determine whether the changes in social interaction patterns are signs of dementia. The reporting unit can report to medical professionals based on the changes in social interaction patterns. In this way, monitoring the target individual's social interaction patterns can further support the early detection of dementia.

[0101] The dementia early detection support system can further collect and analyze the stress levels of the subjects. For example, the data collection unit can collect data such as the subject's heart rate, blood pressure, and stress hormone levels. The analysis unit can analyze this data and evaluate changes in the subject's stress levels. The pattern detection unit can determine whether the changes in stress levels are signs of dementia. The reporting unit can report to medical professionals based on the changes in stress levels. In this way, monitoring the subject's stress levels can further support the early detection of dementia.

[0102] The dementia early detection support system can further estimate the emotions of the individual and provide appropriate reminders based on those emotions. For example, the data collection unit can estimate the individual's emotions and, if the individual is feeling anxious, provide a reminder to help them relax. If the individual is relaxed, it can provide a reminder to help them remember their daily tasks. Furthermore, if the individual is agitated, it can provide a reminder to help them calm down. By providing appropriate reminders based on the individual's emotions, the system can improve the individual's quality of life.

[0103] The dementia early detection support system can further estimate the emotions of the individual and suggest appropriate activities based on those emotions. For example, the data collection unit can estimate the individual's emotions and, if the individual is feeling anxious, suggest activities to help them relax. If the individual is relaxed, it can suggest intellectual activities. Furthermore, if the individual is agitated, it can suggest physical activities. By suggesting appropriate activities based on the individual's emotions, the system can improve the individual's quality of life.

[0104] The dementia early detection support system can further estimate the emotions of the subject and provide appropriate information based on those emotions. For example, the data collection unit can estimate the subject's emotions and, if the subject is feeling anxious, provide information that provides a sense of security. If the subject is relaxed, it can provide interesting information. Furthermore, if the subject is agitated, it can provide information to help them calm down. In this way, by providing appropriate information based on the subject's emotions, the quality of life of the subject can be improved.

[0105] The dementia early detection support system can further estimate the emotions of the individual and provide appropriate support based on those emotions. For example, the data collection unit can estimate the individual's emotions and provide psychological support if the individual is feeling anxious. If the individual is relaxed, it can provide social support. Furthermore, if the individual is agitated, it can provide support to help them calm down. In this way, by providing appropriate support based on the individual's emotions, the quality of life of the individual can be improved.

[0106] The dementia early detection support system can further estimate the emotions of the subject and provide appropriate feedback based on those emotions. For example, the data collection unit can estimate the subject's emotions and, if the subject is feeling anxious, provide reassuring feedback. It can also provide positive feedback if the subject is relaxed. Furthermore, if the subject is agitated, it can provide feedback to help them calm down. By providing appropriate feedback based on the subject's emotions, the system can improve the subject's quality of life.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects the subject's health status, conversation patterns, and medical information. For example, the data collection unit collects data such as blood pressure, heart rate, and body temperature as part of the subject's health status, and data such as speaking speed, word choice, and grammatical accuracy as part of conversation patterns. The data collection unit also collects data such as diagnosis results, prescriptions, and medical history as part of the subject's medical information. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes vocabulary richness, grammatical accuracy, and pronunciation clarity to evaluate the subject's language ability, and analyzes short-term memory, long-term memory, and episodic memory to evaluate memory ability. Furthermore, the analysis unit analyzes attention, problem-solving ability, and judgment to evaluate the subject's cognitive ability. Step 3: The pattern detection unit identifies characteristic patterns or changes in dementia based on the analysis results obtained by the analysis unit. For example, the pattern detection unit can identify signs of dementia such as a decrease in the frequency of use of certain words in the subject's conversation or a change in sentence structure. Step 4: The reporting unit reports to medical professionals based on the patterns or changes detected by the pattern detection unit. For example, the reporting unit can integrate the subject's data and medical information to determine the progression and type of dementia and report the patient's condition, risk factors, and treatment options to medical professionals.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] For example, the data collection unit can collect the subject's health status and conversation patterns using the camera 42 and microphone 38B of the smart device 14. The data collection unit can also collect medical information via the communication I / F 26 of the data processing unit 12. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The pattern detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and captures characteristic patterns and changes of dementia based on the analysis results. The reporting unit is implemented by the identification processing unit 290 of the data processing unit 12 and reports to medical professionals. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] For example, the data collection unit can collect the subject's health status and conversation patterns using the camera 42 and microphone 238 of the smart glasses 214. The data collection unit can also collect medical information via the communication I / F 26 of the data processing unit 12. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The pattern detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and captures characteristic patterns and changes of dementia based on the analysis results. The reporting unit is implemented by the identification processing unit 290 of the data processing unit 12 and reports to medical professionals. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] For example, the data collection unit can collect the subject's health status and conversation patterns using the camera 42 and microphone 238 of the headset terminal 314. The data collection unit can also collect medical information via the communication I / F 26 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The pattern detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and captures characteristic patterns and changes of dementia based on the analysis results. The reporting unit is implemented by the specific processing unit 290 of the data processing unit 12 and reports to medical professionals. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] For example, the data collection unit can collect the subject's health status and conversation patterns using the camera 42 and microphone 238 of the robot 414. The data collection unit can also collect medical information via the communication I / F 26 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The pattern detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and captures characteristic patterns and changes of dementia based on the analysis results. The reporting unit is implemented by the specific processing unit 290 of the data processing unit 12 and reports to medical professionals. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A collection unit that collects the health status or conversation patterns of the subjects, and medical information, An analysis unit analyzes the data collected by the aforementioned collection unit, A pattern detection unit that captures characteristic patterns or changes of dementia based on the analysis results obtained by the aforementioned analysis unit, The system includes a reporting unit that reports to a medical professional based on the pattern or change detected by the pattern detection unit. A system characterized by the following features. (Note 2) The system described in Appendix 1 is characterized in that the collection unit collects data of words spoken or written by the subject on a daily basis, or of pictures drawn. (Note 3) The aforementioned analysis unit, The subject's language ability, memory, cognitive ability, etc. will be evaluated. The system described in Appendix 1, characterized by the features described herein. (Note 4) The system according to Appendix 1, characterized in that the pattern detection unit recognizes a decrease in the frequency of use of a particular word or a change in the structure of a sentence in the subject's conversation as an indication of dementia. (Note 5) The aforementioned reporting department, The system integrates data and medical information from the subjects to determine the progression and type of dementia, and reports the patient's condition, risk factors, and treatment options to medical professionals. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the emotions of the subjects and adjusts the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the subject's past health 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 data, filtering is performed based on the subjects' living environment and daily activities. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the emotions of the subjects and prioritizes the data to be collected based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the geographical location information of the subjects is taken into consideration to prioritize the collection of highly relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the social media activity of the subjects is analyzed, and relevant data is collected. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the emotions of the subjects and adjusts the representation of the analysis based on the estimated emotions of the subjects. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the subjects' health 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 health category of the subjects. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the emotions of the subjects and adjusts the length of the analysis based on the estimated 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 participants submitted their data. 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 subjects. The system described in Appendix 1, characterized by the features described herein. (Note 18) The pattern detection unit, The system estimates the emotions of the subjects and adjusts the pattern detection criteria based on the estimated emotions of the subjects. The system described in Appendix 1, characterized by the features described herein. (Note 19) The pattern detection unit, When detecting patterns, we improve the accuracy of pattern detection by considering the interrelationships between the data of the subjects. The system described in Appendix 1, characterized by the features described herein. (Note 20) The pattern detection unit, When detecting patterns, the attribute information of the subject is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The pattern detection unit, The system estimates the subject's emotions and adjusts the order in which the pattern detection results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The pattern detection unit, When performing pattern detection, the geographical distribution of the subjects is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The pattern detection unit, When detecting patterns, we improve the accuracy of pattern detection by referring to relevant literature for the subjects. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reporting department, We estimate the emotions of the subjects and adjust the way the report is presented based on the estimated emotions of the subjects. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reporting department, When reporting, adjust the level of detail in the report based on the importance of the data for each participant. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reporting department, When reporting, different reporting algorithms are applied depending on the health category of the subject. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reporting department, The report length is adjusted based on the estimated emotions of the subjects, and the emotions of the subjects are estimated. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reporting department, When reporting, prioritize reports based on when the data from each participant was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reporting department, When reporting, adjust the order of reporting based on the relevance of the subjects. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 collection unit that collects the health status or conversation patterns of the subjects, and medical information, An analysis unit analyzes the data collected by the aforementioned collection unit, A pattern detection unit that captures characteristic patterns or changes of dementia based on the analysis results obtained by the aforementioned analysis unit, The system includes a reporting unit that reports to a medical professional based on the pattern or change detected by the pattern detection unit. A system characterized by the following features.

2. The aforementioned collection unit collects data on the words the subject speaks or writes, or the pictures they draw on a daily basis. The system according to feature 1.

3. The aforementioned analysis unit, The subject's language ability, memory, cognitive ability, etc. will be evaluated. The system according to feature 1.

4. The pattern detection unit detects when the frequency of use of a particular word decreases or the structure of a sentence changes in the subject's conversation. The system according to claim 1, characterized in that it recognizes this as a sign of dementia.

5. The aforementioned reporting department, The system integrates data and medical information from the subjects to determine the progression and type of dementia, and reports the patient's condition, risk factors, and treatment options to medical professionals. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the emotions of the subjects and adjusts the timing of data collection based on the estimated emotions. The system according to feature 1.

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

8. The aforementioned collection unit is When collecting data, filtering is performed based on the subjects' living environment and daily activities. The system according to feature 1.

9. The aforementioned collection unit is The system estimates the emotions of the subjects and prioritizes the data to be collected based on the estimated emotions. The system according to feature 1.

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