System

Abstract

A system according to an embodiment aims to detect early signs of dementia and provide an alert to medical professionals quickly.SOLUTION: A system includes a data collection part, an analysis part, a pattern detection part, and a warning provision part. The data collection unit collects individual patient data. The analysis part analyzes the patient data collected by the data collection part. The pattern detection unit detects a characteristic pattern of dementia from the data analyzed by the analysis unit. The alert provider provides an early alert to a medical professional based on the symptoms of dementia detected by the pattern detector.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to efficiently detect early signs of dementia and provide prompt warnings to medical professionals.

[0005] Systems according to embodiments aim to detect early signs of dementia and provide rapid alerts to medical professionals. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a pattern detection unit, and an alert provision unit. The data collection unit collects individual patient data. The analysis unit analyzes the patient data collected by the data collection unit. The pattern detection unit detects characteristic patterns of dementia from the data analyzed by the analysis unit. The alert provision unit provides an early alert to a medical professional based on the signs of dementia detected by the pattern detection unit. [Effects of the Invention]

[0007] Systems according to embodiments can detect early signs of dementia and provide rapid alerts to medical professionals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The generative AI system according to an embodiment of the present invention analyzes individual patient data and medical information, detects early signs of dementia, and provides early warnings to medical professionals, thereby facilitating early detection and appropriate treatment of dementia.

[0029] The generative AI system according to the embodiment includes a data collection unit, an analysis unit, a pattern detection unit, and a warning provision unit. The data collection unit collects individual patient data, such as the patient's electronic medical record, medical records, test results, and lifestyle data. The data collection unit can also collect information such as the patient's age, gender, medical history, drug use, and daily behavior patterns. The analysis unit analyzes the patient data collected by the data collection unit. For example, the generative AI analyzes the data using statistical analysis or machine learning algorithms. The generative AI can also analyze the data using data mining techniques. The pattern detection unit detects characteristic patterns of dementia from the data analyzed by the analysis unit. For example, the generative AI can detect declines in memory, declines in judgment, changes in language ability, and changes in daily behavior patterns. The generative AI can also analyze changes in the patient's conversation content and behavior patterns to detect an increased risk of dementia. The warning provision unit provides an early warning to medical professionals based on the signs of dementia detected by the pattern detection unit. For example, if the generative AI determines that a patient is at high risk of dementia, it notifies the medical professional of this information. The warning provision unit can also suggest additional tests or revisions to the treatment plan for the patient. This allows the generative AI system according to the embodiment to promote early detection of dementia and appropriate treatment. For example, the generative AI system continuously monitors the patient's condition and provides feedback. Patient data is collected periodically and analyzed by the generative AI to understand the progression of dementia. This allows medical professionals to take appropriate measures according to the patient's condition. For example, the effectiveness of treatment can be evaluated and the treatment plan can be adjusted.

[0030] The data collection unit can collect the patient's voice data and video data. For example, the data collection unit periodically collects the patient's voice data, and the generation AI analyzes changes in the tone and speaking style of the voice. For example, it detects changes in the speed and rhythm of the voice, and changes in word choice. The data collection unit also collects the patient's video data, and the generation AI analyzes changes in facial expressions. For example, it can detect changes in facial expressions and movement patterns. This makes it possible to detect signs of dementia with high accuracy by analyzing changes in voice and facial expressions.

[0031] The data collection unit can collect the patient's social media activities and online behavior. For example, the data collection unit can analyze the content of the patient's social media posts to detect changes in their daily lives. For example, it can analyze changes in the frequency and content of posts, and changes in the words used. The data collection unit also collects the patient's online behavior, and the generation AI analyzes changes in their daily lives. For example, it can analyze website browsing history and online shopping history to detect changes in their daily lives. This makes it possible to detect signs of dementia by analyzing changes in social media activities and online behavior.

[0032] The data collection unit collects data from the patient's home IoT devices, and the analysis unit analyzes changes in lifestyle habits to detect signs of dementia. The data collection unit collects voice command data from, for example, a smart speaker, and the generation AI analyzes changes in lifestyle habits. For example, it detects changes in the frequency and content of voice commands. The data collection unit also collects smart light usage data, and the generation AI analyzes changes in lifestyle habits. For example, it can detect changes in the frequency and time of day that lights are turned on and off. In this way, by analyzing data from home IoT devices, changes in lifestyle habits can be detected and signs of dementia can be identified.

[0033] The data collection unit collects the patient's exercise data from the wearable device, and the analysis unit analyzes changes in physical activity to detect signs of dementia. The data collection unit, for example, collects step count data from the wearable device, and the generation AI analyzes changes in daily walking patterns. For example, it detects a decrease in the number of steps or changes in walking speed. The data collection unit also collects heart rate data from the wearable device, and the generation AI analyzes changes in physical activity. For example, it can detect fluctuations in heart rate and changes in exercise intensity. As a result, by analyzing the exercise data from the wearable device, changes in physical activity can be detected and signs of dementia can be identified.

[0034] The pattern detection unit analyzes the patient's sleep patterns and can detect whether changes in the quality or quantity of sleep appear as signs of dementia. For example, the pattern detection unit collects the patient's sleep data, and the generation AI analyzes changes in the quality and quantity of sleep. For example, it detects a decrease in sleep time and the frequency of waking up during the night. The pattern detection unit also analyzes the depth of the patient's sleep, and the generation AI detects changes in sleep quality. For example, it can detect a decrease in the time spent in deep sleep. This makes it possible to identify signs of dementia by analyzing changes in sleep patterns.

[0035] The pattern detection unit analyzes the patient's eating patterns and can detect whether changes in nutritional intake appear as signs of dementia. For example, the pattern detection unit collects the patient's dietary data, and the generation AI analyzes changes in nutritional intake. For example, it detects changes in meal frequency and content. The pattern detection unit also analyzes the patient's calorie intake, and the generation AI detects changes in nutritional balance. For example, it can detect a decrease in the intake of specific nutrients. This makes it possible to identify signs of dementia by analyzing changes in eating patterns.

[0036] The pattern detection unit can analyze a patient's financial transaction data and detect whether abnormal spending patterns indicate signs of dementia. For example, the pattern detection unit collects a patient's financial transaction data, and the generation AI analyzes abnormal spending patterns. For example, it detects sudden increases in spending or unnatural transactions. The pattern detection unit can also analyze a patient's credit card usage history, and the generation AI detects abnormal spending patterns. For example, it can detect excessive spending in specific categories. This makes it possible to identify signs of dementia by analyzing changes in financial transaction data.

[0037] The pattern detection unit analyzes changes in the patient's hobbies and interests and can detect whether these changes appear as signs of dementia. For example, the pattern detection unit collects data on the patient's hobbies and interests, and the generation AI analyzes those changes. For example, it detects changes in the frequency of hobbies and the subjects of interest. The pattern detection unit also analyzes the amount of time the patient spends on hobbies, and the generation AI detects changes in hobbies. For example, it can detect a decrease in the amount of time spent on hobbies. This makes it possible to identify signs of dementia by analyzing changes in hobbies and interests.

[0038] The warning provision unit can notify medical professionals of a patient's dementia risk in real time and encourage them to take immediate action. For example, the warning provision unit builds a system that notifies medical professionals in real time when the generation AI detects a dementia risk. For example, it sends a notification to a smartphone or tablet. The warning provision unit can also monitor a patient's dementia risk in real time and encourage immediate action when an abnormality is detected. For example, it can notify emergency response procedures so that medical professionals can respond quickly. In this way, by notifying the patient of the dementia risk in real time, medical professionals can respond quickly.

[0039] The warning provision unit can provide medical professionals with specific countermeasures based on the patient's dementia risk. For example, the warning provision unit builds a system that provides medical professionals with specific countermeasures when the generation AI detects a dementia risk. For example, it may notify the medical professional of suggestions for additional testing or a review of the treatment plan. The warning provision unit can also provide medical professionals with details of the treatment plan based on the patient's dementia risk. For example, it may suggest specific treatments or drug therapies. In this way, by providing specific countermeasures based on the dementia risk, medical professionals can take appropriate action quickly.

[0040] The warning providing unit can provide medical professionals with a dashboard that visualizes the patient's dementia risk, allowing them to intuitively understand the risk. The warning providing unit, for example, develops a dashboard that visualizes the dementia risk detected by the generation AI. For example, it displays risk scores and risk factors in graphs and charts. The warning providing unit can also update the patient's dementia risk in real time, allowing medical professionals to intuitively understand the risk. For example, it can provide an interactive dashboard so that medical professionals can check detailed information. By visualizing the dementia risk, medical professionals can intuitively understand the risk.

[0041] The warning provision unit can provide medical professionals with preventive measures based on the patient's dementia risk. The warning provision unit builds a system that provides preventive measures to medical professionals based on the dementia risk detected by the generation AI, for example. For example, it can suggest lifestyle improvements and nutritional guidance. The warning provision unit can also provide details of specific preventive measures to medical professionals based on the patient's dementia risk. For example, it can suggest specific exercise programs and meal plans. In this way, by providing preventive measures based on the dementia risk, medical professionals can suggest appropriate preventive measures.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The generative AI system can also collect the patient's exercise data, and the analysis unit can analyze changes in physical activity to detect signs of dementia. For example, step count data can be collected from a wearable device to analyze changes in daily walking patterns. This can detect a decrease in the number of steps or changes in walking speed. It can also collect heart rate data and analyze heart rate fluctuations and changes in exercise intensity. This makes it possible to identify signs of dementia by analyzing changes in physical activity.

[0044] The generative AI system can also collect a patient's financial transaction data, and the analysis unit can analyze abnormal spending patterns to detect signs of dementia. For example, it can detect sudden increases in spending or unusual transactions. It can also analyze credit card usage history to detect excessive spending in specific categories. This makes it possible to identify signs of dementia by analyzing changes in financial transaction data.

[0045] The generative AI system also collects data on the patient's hobbies and interests, and the analysis unit analyzes changes in these to detect signs of dementia. For example, it can detect changes in the frequency of hobbies and the subjects of interest. It can also detect a decrease in the amount of time spent on hobbies. By analyzing changes in hobbies and interests, it is possible to identify signs of dementia.

[0046] The generative AI system can also collect the patient's sleep data, and the analysis unit can detect signs of dementia by analyzing changes in the quality and quantity of sleep. For example, it can detect a decrease in sleep time and the frequency of waking up during the night. It can also analyze the depth of sleep and detect a decrease in the time spent in deep sleep. This makes it possible to identify signs of dementia by analyzing changes in sleep patterns.

[0047] The generative AI system can also collect dietary data from patients, and the analysis unit can analyze changes in nutritional intake to detect signs of dementia. For example, it can detect changes in meal frequency and content. It can also analyze calorie intake and detect decreases in the intake of specific nutrients. This makes it possible to identify signs of dementia by analyzing changes in dietary patterns.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The data collection department collects individual patient data, such as the patient's electronic medical record, medical records, test results, lifestyle data, age, gender, medical history, drug use status, and daily behavior patterns. Step 2: The analysis unit analyzes the patient data collected by the data collection unit, for example, by using statistical analysis, machine learning algorithms, or data mining techniques. Step 3: The pattern detection unit detects characteristic patterns of dementia from the data analyzed by the analysis unit, such as decline in memory, decline in judgment, changes in language ability, changes in daily behavior patterns, and changes in conversation content and behavior patterns, to detect an increased risk of dementia. Step 4: The warning provider provides early warnings to medical professionals based on the signs of dementia detected by the pattern detector. For example, if the risk of dementia is determined to be high, the warning provider will be notified of this information and suggest additional testing or a revision of the treatment plan.

[0050] (Example 2) The generative AI system according to an embodiment of the present invention analyzes individual patient data and medical information, detects early signs of dementia, and provides early warnings to medical professionals, thereby facilitating early detection and appropriate treatment of dementia.

[0051] The generative AI system according to the embodiment includes a data collection unit, an analysis unit, a pattern detection unit, and a warning provision unit. The data collection unit collects individual patient data, such as the patient's electronic medical record, medical records, test results, and lifestyle data. The data collection unit can also collect information such as the patient's age, gender, medical history, drug use, and daily behavior patterns. The analysis unit analyzes the patient data collected by the data collection unit. For example, the generative AI analyzes the data using statistical analysis or machine learning algorithms. The generative AI can also analyze the data using data mining techniques. The pattern detection unit detects characteristic patterns of dementia from the data analyzed by the analysis unit. For example, the generative AI can detect declines in memory, declines in judgment, changes in language ability, and changes in daily behavior patterns. The generative AI can also analyze changes in the patient's conversation content and behavior patterns to detect an increased risk of dementia. The warning provision unit provides an early warning to medical professionals based on the signs of dementia detected by the pattern detection unit. For example, if the generative AI determines that a patient is at high risk of dementia, it notifies the medical professional of this information. The warning provision unit can also suggest additional tests or revisions to the treatment plan for the patient. This allows the generative AI system according to the embodiment to promote early detection of dementia and appropriate treatment. For example, the generative AI system continuously monitors the patient's condition and provides feedback. Patient data is collected periodically and analyzed by the generative AI to understand the progression of dementia. This allows medical professionals to take appropriate measures according to the patient's condition. For example, the effectiveness of treatment can be evaluated and the treatment plan can be adjusted.

[0052] The data collection unit can collect the patient's voice data and video data. For example, the data collection unit periodically collects the patient's voice data, and the generation AI analyzes changes in the tone and speaking style of the voice. For example, it detects changes in the speed and rhythm of the voice, and changes in word choice. The data collection unit also collects the patient's video data, and the generation AI analyzes changes in facial expressions. For example, it can detect changes in facial expressions and movement patterns. This makes it possible to detect signs of dementia with high accuracy by analyzing changes in voice and facial expressions.

[0053] The data collection unit can collect the patient's social media activities and online behavior. For example, the data collection unit can analyze the content of the patient's social media posts to detect changes in their daily lives. For example, it can analyze changes in the frequency and content of posts, and changes in the words used. The data collection unit also collects the patient's online behavior, and the generation AI analyzes changes in their daily lives. For example, it can analyze website browsing history and online shopping history to detect changes in their daily lives. This makes it possible to detect signs of dementia by analyzing changes in social media activities and online behavior.

[0054] The data collection unit can use the emotion estimation function to analyze changes in the patient's emotions and detect whether emotional instability is a sign of dementia. The data collection unit, for example, analyzes the patient's voice data and detects changes in emotions using the emotion estimation function. For example, it analyzes emotional instability from changes in voice tone or speaking style. The data collection unit also analyzes the patient's facial expression data and detects changes in emotions using the emotion estimation function. For example, it can analyze emotional instability from changes in facial expressions or movement patterns. In this way, signs of dementia can be detected by analyzing changes in emotions.

[0055] The data collection unit collects data from the patient's home IoT devices, and the analysis unit analyzes changes in lifestyle habits to detect signs of dementia. The data collection unit collects voice command data from, for example, a smart speaker, and the generation AI analyzes changes in lifestyle habits. For example, it detects changes in the frequency and content of voice commands. The data collection unit also collects smart light usage data, and the generation AI analyzes changes in lifestyle habits. For example, it can detect changes in the frequency and time of day that lights are turned on and off. In this way, by analyzing data from home IoT devices, changes in lifestyle habits can be detected and signs of dementia can be identified.

[0056] The data collection unit collects the patient's exercise data from the wearable device, and the analysis unit analyzes changes in physical activity to detect signs of dementia. The data collection unit, for example, collects step count data from the wearable device, and the generation AI analyzes changes in daily walking patterns. For example, it detects a decrease in the number of steps or changes in walking speed. The data collection unit also collects heart rate data from the wearable device, and the generation AI analyzes changes in physical activity. For example, it can detect fluctuations in heart rate and changes in exercise intensity. As a result, by analyzing the exercise data from the wearable device, changes in physical activity can be detected and signs of dementia can be identified.

[0057] The data collection unit can use the emotion estimation function to analyze the levels of stress and anxiety felt by the patient in daily life and detect whether these are related to signs of dementia. The data collection unit, for example, analyzes the patient's voice data and detects the level of stress or anxiety using the emotion estimation function. For example, the level of stress or anxiety can be analyzed from changes in voice tone or speaking style. The data collection unit can also analyze the patient's facial expression data and detect the level of stress or anxiety using the emotion estimation function. For example, the level of stress or anxiety can be analyzed from changes in facial expressions or movement patterns. In this way, by analyzing the level of stress and anxiety, signs of dementia can be identified.

[0058] The pattern detection unit analyzes the patient's sleep patterns and can detect whether changes in the quality or quantity of sleep appear as signs of dementia. For example, the pattern detection unit collects the patient's sleep data, and the generation AI analyzes changes in the quality and quantity of sleep. For example, it detects a decrease in sleep time and the frequency of waking up during the night. The pattern detection unit also analyzes the depth of the patient's sleep, and the generation AI detects changes in sleep quality. For example, it can detect a decrease in the time spent in deep sleep. This makes it possible to identify signs of dementia by analyzing changes in sleep patterns.

[0059] The pattern detection unit analyzes the patient's eating patterns and can detect whether changes in nutritional intake appear as signs of dementia. For example, the pattern detection unit collects the patient's dietary data, and the generation AI analyzes changes in nutritional intake. For example, it detects changes in meal frequency and content. The pattern detection unit also analyzes the patient's calorie intake, and the generation AI detects changes in nutritional balance. For example, it can detect a decrease in the intake of specific nutrients. This makes it possible to identify signs of dementia by analyzing changes in eating patterns.

[0060] The pattern detection unit uses the emotion estimation function to analyze emotional changes from the patient's conversation content and can detect whether emotional instability is a sign of dementia. For example, the pattern detection unit collects the patient's conversation data and the generation AI analyzes emotional changes. For example, emotional instability can be detected from changes in the content and tone of the conversation. The pattern detection unit also analyzes the patient's text data and the generation AI detects emotional changes. For example, emotional instability can be detected from changes in the words used and changes in sentence structure. This makes it possible to identify signs of dementia by analyzing emotional changes from the content of the conversation.

[0061] The pattern detection unit can analyze a patient's financial transaction data and detect whether abnormal spending patterns indicate signs of dementia. For example, the pattern detection unit collects a patient's financial transaction data, and the generation AI analyzes abnormal spending patterns. For example, it detects sudden increases in spending or unnatural transactions. The pattern detection unit can also analyze a patient's credit card usage history, and the generation AI detects abnormal spending patterns. For example, it can detect excessive spending in specific categories. This makes it possible to identify signs of dementia by analyzing changes in financial transaction data.

[0062] The pattern detection unit analyzes changes in the patient's hobbies and interests and can detect whether these changes appear as signs of dementia. For example, the pattern detection unit collects data on the patient's hobbies and interests, and the generation AI analyzes those changes. For example, it detects changes in the frequency of hobbies and the subjects of interest. The pattern detection unit also analyzes the amount of time the patient spends on hobbies, and the generation AI detects changes in hobbies. For example, it can detect a decrease in the amount of time spent on hobbies. This makes it possible to identify signs of dementia by analyzing changes in hobbies and interests.

[0063] The pattern detection unit uses the emotion estimation function to analyze changes in the patient's emotions regarding their hobbies and interests, and can detect whether these changes appear as signs of dementia. For example, the pattern detection unit collects data regarding the patient's hobbies and interests, and the generation AI analyzes the changes in emotions. For example, it detects fluctuations in emotions regarding hobbies. The pattern detection unit also analyzes the intensity of the patient's emotions regarding their hobbies, and the generation AI detects changes in emotions. For example, it can detect a decrease in interest in hobbies or changes in the intensity of emotions. This makes it possible to identify signs of dementia by analyzing changes in emotions regarding hobbies and interests.

[0064] The warning provision unit can notify medical professionals of a patient's dementia risk in real time and encourage them to take immediate action. For example, the warning provision unit builds a system that notifies medical professionals in real time when the generation AI detects a dementia risk. For example, it sends a notification to a smartphone or tablet. The warning provision unit can also monitor a patient's dementia risk in real time and encourage immediate action when an abnormality is detected. For example, it can notify emergency response procedures so that medical professionals can respond quickly. In this way, by notifying the patient of the dementia risk in real time, medical professionals can respond quickly.

[0065] The warning provision unit can provide medical professionals with specific countermeasures based on the patient's dementia risk. For example, the warning provision unit builds a system that provides medical professionals with specific countermeasures when the generation AI detects a dementia risk. For example, it may notify the medical professional of suggestions for additional testing or a review of the treatment plan. The warning provision unit can also provide medical professionals with details of the treatment plan based on the patient's dementia risk. For example, it may suggest specific treatments or drug therapies. In this way, by providing specific countermeasures based on the dementia risk, medical professionals can take appropriate action quickly.

[0066] The warning provision unit uses the emotion estimation function to suggest countermeasures to medical professionals based on the patient's emotional state, thereby reducing the patient's psychological burden. For example, the warning provision unit analyzes the patient's emotional state, and the generation AI suggests countermeasures to medical professionals based on the emotions. For example, it suggests counseling to reduce the patient's anxiety. The warning provision unit can also provide medical professionals with specific methods for reducing the psychological burden based on the patient's emotional state. For example, it suggests stress management and relaxation techniques. In this way, by suggesting countermeasures based on the patient's emotional state, it is possible to reduce the patient's psychological burden.

[0067] The warning providing unit can provide medical professionals with a dashboard that visualizes the patient's dementia risk, allowing them to intuitively understand the risk. The warning providing unit, for example, develops a dashboard that visualizes the dementia risk detected by the generation AI. For example, it displays risk scores and risk factors in graphs and charts. The warning providing unit can also update the patient's dementia risk in real time, allowing medical professionals to intuitively understand the risk. For example, it can provide an interactive dashboard so that medical professionals can check detailed information. By visualizing the dementia risk, medical professionals can intuitively understand the risk.

[0068] The warning provision unit can provide medical professionals with preventive measures based on the patient's dementia risk. The warning provision unit builds a system that provides preventive measures to medical professionals based on the dementia risk detected by the generation AI, for example. For example, it can suggest lifestyle improvements and nutritional guidance. The warning provision unit can also provide details of specific preventive measures to medical professionals based on the patient's dementia risk. For example, it can suggest specific exercise programs and meal plans. In this way, by providing preventive measures based on the dementia risk, medical professionals can suggest appropriate preventive measures.

[0069] The warning provision unit can use the emotion estimation function to suggest preventive measures to medical professionals based on the patient's emotional state, thereby reducing the patient's psychological burden. For example, the warning provision unit analyzes the patient's emotional state, and the generation AI suggests emotion-based preventive measures to medical professionals. For example, it suggests stress management or relaxation techniques. The warning provision unit can also provide specific preventive measures to medical professionals to reduce the psychological burden based on the patient's emotional state. For example, it can provide counseling or suggest relaxation techniques. In this way, the psychological burden of patients can be reduced by suggesting preventive measures based on the patient's emotional state.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The generative AI system can also collect the patient's exercise data, and the analysis unit can analyze changes in physical activity to detect signs of dementia. For example, step count data can be collected from a wearable device to analyze changes in daily walking patterns. This can detect a decrease in the number of steps or changes in walking speed. It can also collect heart rate data and analyze heart rate fluctuations and changes in exercise intensity. This makes it possible to identify signs of dementia by analyzing changes in physical activity.

[0072] The generative AI system can also collect a patient's financial transaction data, and the analysis unit can analyze abnormal spending patterns to detect signs of dementia. For example, it can detect sudden increases in spending or unusual transactions. It can also analyze credit card usage history to detect excessive spending in specific categories. This makes it possible to identify signs of dementia by analyzing changes in financial transaction data.

[0073] The generative AI system also collects data on the patient's hobbies and interests, and the analysis unit analyzes changes in these to detect signs of dementia. For example, it can detect changes in the frequency of hobbies and the subjects of interest. It can also detect a decrease in the amount of time spent on hobbies. By analyzing changes in hobbies and interests, it is possible to identify signs of dementia.

[0074] The generative AI system can also collect the patient's sleep data, and the analysis unit can detect signs of dementia by analyzing changes in the quality and quantity of sleep. For example, it can detect a decrease in sleep time and the frequency of waking up during the night. It can also analyze the depth of sleep and detect a decrease in the time spent in deep sleep. This makes it possible to identify signs of dementia by analyzing changes in sleep patterns.

[0075] The generative AI system can also collect dietary data from patients, and the analysis unit can analyze changes in nutritional intake to detect signs of dementia. For example, it can detect changes in meal frequency and content. It can also analyze calorie intake and detect decreases in the intake of specific nutrients. This makes it possible to identify signs of dementia by analyzing changes in dietary patterns.

[0076] The generative AI system can also analyze the patient's emotional state and detect whether emotional changes are signs of dementia. For example, by analyzing voice data, it can detect emotional instability from changes in voice tone and speaking style. It can also analyze facial expression data and detect emotional instability from changes in facial expressions and movement patterns. This makes it possible to identify signs of dementia by analyzing emotional changes.

[0077] The generative AI system can also analyze the patient's conversation content to detect whether emotional changes are signs of dementia. For example, it can detect emotional instability from changes in the content and tone of the conversation. It can also analyze text data to detect emotional instability from changes in the words used and sentence structure. This makes it possible to identify signs of dementia by analyzing emotional changes from the content of the conversation.

[0078] The generative AI system can also analyze the levels of stress and anxiety experienced by patients in their daily lives and detect whether these are related to signs of dementia. For example, it can analyze voice data and detect levels of stress and anxiety from changes in voice tone and speaking style. It can also analyze facial expression data and detect levels of stress and anxiety from changes in facial expressions and movement patterns. In this way, analyzing levels of stress and anxiety can identify signs of dementia.

[0079] The generative AI system can also analyze changes in a patient's emotions toward hobbies and interests and detect whether these changes are signs of dementia. For example, it can detect fluctuations in emotions toward hobbies. It can also analyze the intensity of emotions toward hobbies to detect a decrease in interest or changes in the intensity of emotions. This makes it possible to identify signs of dementia by analyzing changes in emotions toward hobbies and interests.

[0080] The generative AI system can also suggest countermeasures to medical professionals based on the patient's emotional state to reduce the patient's psychological burden. For example, it can suggest counseling to reduce the patient's anxiety. It can also provide medical professionals with specific methods to reduce the psychological burden based on the patient's emotional state. For example, it can suggest stress management and relaxation techniques. This reduces the patient's psychological burden by suggesting countermeasures based on the patient's emotional state.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The data collection department collects individual patient data, such as the patient's electronic medical record, medical records, test results, lifestyle data, age, gender, medical history, drug use status, and daily behavior patterns. Step 2: The analysis unit analyzes the patient data collected by the data collection unit, for example, by using statistical analysis, machine learning algorithms, or data mining techniques. Step 3: The pattern detection unit detects characteristic patterns of dementia from the data analyzed by the analysis unit, such as decline in memory, decline in judgment, changes in language ability, changes in daily behavior patterns, and changes in conversation content and behavior patterns, to detect an increased risk of dementia. Step 4: The warning provider provides early warnings to medical professionals based on the signs of dementia detected by the pattern detector. For example, if the risk of dementia is determined to be high, the warning provider will be notified of this information and suggest additional testing or a revision of the treatment plan.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] 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.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects individual patient data; an analysis unit that analyzes the patient data collected by the data collection unit; a pattern detection unit that detects a characteristic pattern of dementia from the data analyzed by the analysis unit; and a warning providing unit that provides an early warning to a medical professional based on the signs of dementia detected by the pattern detecting unit. A system characterized by:

2. The data collection unit Collect patient audio and video data, The analysis unit The signs of dementia are detected by analyzing changes in voice and facial expressions from the voice data and video data.

2. The system of claim 1.

3. The data collection unit Collect data from patients' home IoT devices The analysis unit The signs of dementia are detected by analyzing changes in lifestyle habits based on data collected from the home IoT devices.

2. The system of claim 1.

4. The pattern detection unit Analyzing the patient's sleep patterns Detect whether changes in sleep quality or quantity appear as signs of dementia 2. The system of claim 1.

5. The warning providing unit Notifying healthcare professionals of dementia risk in real time, Encourage immediate response 2. The system of claim 1.

6. The data collection unit Analyzing changes in the patient's emotions, The analysis unit Detecting whether the emotional instability is a symptom of the dementia.

2. The system of claim 1.

7. The pattern detection unit Analyzing changes in emotions from the content of the patient's conversation, Detecting whether the emotional instability is a symptom of the dementia.

2. The system of claim 1.

8. The warning providing unit Recommending solutions to healthcare professionals based on the patient's emotional state, Reduce the psychological burden on patients 2. The system of claim 1.

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