System

Abstract

An object of a system according to an embodiment is to grasp the state of memory and cognitive function of a patient and provide appropriate support.SOLUTION: A system includes a collection unit, an analysis unit, a provision unit, and a communication unit. The collection unit collects data of a behavior history or conversation contents of a patient. The analysis unit analyzes the data collected by the collection unit, and grasps the state of memory and cognitive function of the patient. The providing unit provides advice or guidance for supporting memory on the basis of the analysis result obtained by the analysis unit. The communication unit communicates with the patient on the basis of the advice provided by the providing unit.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 do not adequately assess the state of memory and cognitive function of each patient and provide appropriate support, and there is room for improvement.

[0005] The system according to the embodiment aims to understand the state of a patient's memory and cognitive function and provide appropriate support. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a communication unit. The collection unit collects data on the patient's behavioral history or conversation content. The analysis unit analyzes the data collected by the collection unit and understands the state of the patient's memory and cognitive function. The provision unit provides advice and guidance to support memory based on the analysis results obtained by the analysis unit. The communication unit communicates with the patient based on the advice provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the state of a patient's memory and cognitive function and provide appropriate support. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A memory support system according to an embodiment of the present invention collects patient data, analyzes it using AI, and provides advice and guidance to support memory. The memory support system collects data such as the patient's behavioral history and conversation content, analyzes it using AI, and understands the state of the patient's memory and cognitive function. Based on this data, the AI ​​provides advice and guidance to support memory. Patients can understand their own memory and cognitive status and receive necessary support through dialogue with the AI ​​via their smartphone. The memory support system also provides a sense of security through communication with patients and supports the maintenance and improvement of cognitive function in daily life. For example, the memory support system collects data such as the patient's behavioral history and conversation content. For example, the memory support system collects data such as the patient's behavioral history when visiting specific places and conversations with family members. Next, the memory support system uses AI to analyze the collected data and understand the state of the patient's memory and cognitive function. For example, the AI ​​detects events the patient is about to forget and provides reminders based on the detected events. Next, the memory support system uses AI to provide advice and guidance to support memory. For example, when a patient visits a specific location, the system reminds the patient of past events related to that location. In this way, the memory support system provides a sense of security through communication with the patient and supports the maintenance and improvement of cognitive function in daily life. In this way, the memory support system can support the patient's memory and cognitive function and provide a sense of security. For example, the patient can understand the state of their own memory and cognition and receive the necessary support. In addition, communication with the patient helps maintain and improve cognitive function in daily life.

[0029] A memory support system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a communication unit. The collection unit collects data on a patient's behavioral history or conversation content. The behavioral history includes, but is not limited to, a travel history and an activity log. The collection unit can collect data such as the patient's behavioral history when visiting specific locations and conversation content with family members. For example, the collection unit can record the travel history using a smartphone's GPS function and collect conversation content using a voice recording function. The collection unit can also analyze the patient's behavioral patterns and detect specific behaviors. The analysis unit analyzes the data collected by the collection unit to understand the state of the patient's memory and cognitive function. For example, the analysis unit can analyze the collected data using AI to detect events the patient is forgetting. For example, the analysis unit can analyze the conversation content using natural language processing technology and extract important events. The analysis unit can also analyze the behavioral history using a machine learning algorithm to evaluate the state of memory and cognitive function. The provision unit provides advice and guidance to support memory based on the analysis results obtained by the analysis unit. The providing unit can, for example, use AI to generate a reminder based on the analysis results and notify the patient. For example, when the patient goes to a specific location, the providing unit can remind the patient of past events related to that location. The providing unit can also provide appropriate advice and guidance depending on the state of the patient's memory and cognitive function. The communication unit communicates with the patient based on the advice provided by the providing unit. The communication unit can, for example, interact with the patient via a smartphone to provide reassurance. For example, the communication unit can interact with the patient using a voice call or messaging function and provide necessary support. The communication unit can also collect patient feedback and provide the feedback to the providing unit. As a result, the memory support system according to the embodiment can support the patient's memory and cognitive function and provide reassurance.

[0030] The collection unit can collect data on the patient's behavioral history when he or she visits a specific location or the content of conversations with family members. Specific locations include, but are not limited to, home, hospitals, public facilities, etc. The collection unit, for example, collects the patient's behavioral history when he or she visits a specific location. For example, the collection unit can record the patient's movement history using a smartphone's GPS function. The collection unit can also collect the content of conversations the patient has with family members. For example, the collection unit can collect the content of conversations using an audio recording function. This enables more accurate analysis by collecting the patient's behavioral history and the content of conversations in detail. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the audio recording data into a generation AI and have the generation AI convert the content of the conversations into text.

[0031] The analysis unit can analyze the collected data and detect events that the patient is likely to forget. Examples of events that the patient is likely to forget include, but are not limited to, past events and important dates. The analysis unit can, for example, use AI to analyze the collected data and detect events that the patient is likely to forget. For example, the analysis unit can analyze conversation content using natural language processing technology and extract important events. The analysis unit can also analyze behavioral history using a machine learning algorithm to detect events that the patient is likely to forget. This allows the detection of events that the patient is likely to forget to provide appropriate reminders. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI detect events that the patient is likely to forget.

[0032] The providing unit can remind the patient of past events related to a specific location when the patient goes to that location. Past events include, but are not limited to, for example, photos and memory cards. The providing unit can, for example, use AI to generate a reminder based on the analysis results and notify the patient. For example, the providing unit can remind the patient of past events related to a specific location when the patient goes to that location. This supports memory by reminding the patient of related past events when the patient goes to the specific location. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the analysis results to a generating AI and cause the generating AI to generate a reminder.

[0033] The communication unit can provide a sense of security by interacting with the patient via a smartphone. For example, the content, tone, and frequency of the interaction are important factors in providing a sense of security. The communication unit can provide a sense of security by interacting with the patient via a smartphone. For example, the communication unit can interact with the patient using a voice call or messaging function and provide necessary support. The communication unit can also collect patient feedback and provide it to the providing unit. This provides a sense of security by interacting with the patient via a smartphone. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without AI. For example, the communication unit can input voice call data into a generation AI and have the generation AI analyze the content of the interaction.

[0034] The collection unit can analyze the patient's past behavioral history and select the optimal data collection method. The optimal data collection method includes, but is not limited to, the type of sensor and the frequency of data collection. For example, the collection unit can analyze the patient's past behavioral history and select the optimal data collection method. For example, the collection unit can select the data collection method based on the patient's behavioral history at places frequently visited in the past. The collection unit can also analyze the patient's past behavioral patterns and suggest the most efficient data collection method. The collection unit can also adjust the timing of data collection based on specific behaviors the patient has performed in the past. In this way, the optimal data collection method can be selected by analyzing the patient's past behavioral history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input past behavioral history data into a generation AI and have the generation AI select the optimal data collection method.

[0035] The collection unit can filter data based on the patient's current health condition and lifestyle when collecting data. Examples of health condition and lifestyle data include, but are not limited to, medical records and lifestyle data. The collection unit, for example, filters the data to be collected, taking into account the patient's current health condition. For example, the collection unit selects the type of data to collect based on the patient's lifestyle (work, family, etc.). The collection unit can also temporarily suspend data collection if the patient's health condition is deteriorating. This enables appropriate data collection by filtering data according to the patient's health condition and lifestyle. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input health condition data to a generation AI and have the generation AI filter the data.

[0036] When collecting data, the collection unit can select the optimal collection means depending on the patient's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. The collection unit selects the optimal collection means depending on the patient's input method. For example, if the patient prefers voice input, the collection unit can prioritize collecting voice data. Also, if the patient prefers text input, the collection unit can prioritize collecting text data. Also, if the patient prefers image input, the collection unit can prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the patient's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input input data to a generation AI and have the generation AI select the optimal collection means.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the patient's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location history. For example, when the patient is in a specific location, the collection unit prioritizes collecting data related to that location. The collection unit can also collect highly relevant data based on the patient's current location. When the patient is traveling, the collection unit can also collect data related to the patient's destination. This enables appropriate data collection by collecting highly relevant data based on the patient's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0038] During data collection, the collection unit can analyze the patient's social media activity and collect relevant data. Social media activity includes, but is not limited to, the content of posts and the number of likes. For example, the collection unit collects data regarding the locations where the patient checked in on social media. The collection unit can also analyze the content of the patient's social media posts and collect relevant data. The collection unit can also collect relevant data by referring to the activities of the patient's friends on social media. In this way, relevant data can be efficiently collected by analyzing the patient's social media activity. Some or all of the above-described processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input social media data into the generation AI and cause the generation AI to collect relevant data.

[0039] The collection unit can customize the data collection method by reflecting the patient's past feedback when collecting data. Past feedback includes, but is not limited to, questionnaire results and user reviews. The collection unit can adjust the data collection method, for example, based on feedback provided by the patient in the past. The collection unit can also select the type of data to collect by reflecting the patient's past feedback. The collection unit can also adjust the timing of data collection based on the patient's feedback. In this way, the collection method can be optimized by reflecting the patient's past feedback. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input feedback data to a generation AI and have the generation AI customize the collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The importance of the data includes, but is not limited to, the reliability and relevance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a concise analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. Data categories include, but are not limited to, text data and image data. For example, the analysis unit can apply a behavioral analysis algorithm to behavioral history data. The analysis unit can also apply a natural language processing algorithm to conversation content data. The analysis unit can also apply a health analysis algorithm to health condition data. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results. Past analysis results include, but are not limited to, database references and historical analysis. For example, the analysis unit corrects the current analysis result based on the patient's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the past analysis results. The analysis unit can also improve the accuracy of the analysis by using the past analysis results. In this way, the accuracy of the analysis can be improved by referring to the patient's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The time when the data was collected includes, but is not limited to, for example, the collection date and time period. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also postpone analyzing older data. The analysis unit can also prioritize analyzing data from a specific period. This enables efficient analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input collection time data into the generation AI and have the generation AI determine the analysis priority.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. Examples of data relevance include, but are not limited to, correlation and causal relationships. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the patient's level of expertise. Examples of technical terminology include, but are not limited to, educational background and professional experience. For example, if the patient has technical expertise, the analysis unit uses a lot of technical terminology. Furthermore, if the patient does not have technical expertise, the analysis unit can explain the analysis results in simple terms. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the patient's level of expertise. This allows for appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the patient's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input technical terminology level data into the generation AI and have the generation AI use technical terminology.

[0046] When providing advice or guidance, the providing unit can adjust the level of detail based on the importance of the patient's memory or cognitive function. Examples of the importance of memory or cognitive function include, but are not limited to, cognitive test results and doctor's diagnoses. For example, the providing unit can provide detailed advice for highly important memory or cognitive functions. The providing unit can also provide concise advice for less important memory or cognitive functions. The providing unit can also provide advice with an appropriate level of detail for medium-importance memory or cognitive functions. This allows appropriate support to be provided by adjusting the level of detail according to the importance of the patient's memory or cognitive function. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input importance data of memory or cognitive function into the generating AI and cause the generating AI to adjust the level of detail.

[0047] When providing advice or guidance, the providing unit can apply different providing algorithms depending on the patient category. Patient categories include, but are not limited to, age and medical history. For example, the providing unit can provide concise and easy-to-understand advice to elderly people. The providing unit can also provide detailed and specific advice to young people. The providing unit can also provide advice specialized for a patient with a specific disease. This allows effective support to be provided by applying an appropriate providing algorithm depending on the patient category. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input patient category data into the generating AI and cause the generating AI to apply the providing algorithm.

[0048] When providing advice or guidance, the providing unit can improve accuracy by referring to past provision results for the patient. Past provision results include, but are not limited to, feedback data and success stories. For example, the providing unit corrects current advice based on past advice provision results for the patient. The providing unit can also adjust the provision algorithm by referring to past provision results. The providing unit can also improve the accuracy of advice by using past provision results. In this way, the accuracy of advice or guidance can be improved by referring to past provision results for the patient. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past provision result data into the generating AI and cause the generating AI to improve accuracy.

[0049] The providing unit can determine priorities based on the patient's behavioral history when providing advice or guidance. The behavioral history includes, but is not limited to, for example, a movement history and an activity log. The providing unit can, for example, prioritize advice based on the patient's frequent past behaviors. The providing unit can also analyze the patient's behavioral history and prioritize the most important advice. The providing unit can also provide advice at appropriate times based on the patient's behavioral patterns. Thus, by determining priorities based on the patient's behavioral history, support can be provided at appropriate times. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input behavioral history data to a generating AI and have the generating AI determine the priorities.

[0050] When providing advice or guidance, the providing unit can adjust the order based on the relevance of the advice or guidance to the patient. Relevance includes, but is not limited to, correlation and causal relationships, for example. The providing unit, for example, prioritizes providing advice that is most relevant to the patient's current situation. The providing unit can also prioritize providing highly relevant advice based on the patient's past behavior. The providing unit can also prioritize providing highly relevant advice based on the patient's current health condition. In this way, appropriate support can be provided by adjusting the order based on the relevance of the patient. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without using AI. For example, the providing unit can input the relevance data to a generating AI and cause the generating AI to adjust the order.

[0051] When providing advice or guidance, the providing unit can adjust the use of technical terminology according to the patient's level of expertise. Examples of technical terminology include, but are not limited to, educational background and professional experience. For example, if the patient has technical expertise, the providing unit uses a lot of technical terminology. Furthermore, if the patient does not have technical expertise, the providing unit can provide advice in simple language. Furthermore, the providing unit can adjust the way the advice is expressed according to the patient's level of expertise. This allows appropriate assistance to be provided by adjusting the use of technical terminology according to the patient's level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input technical terminology level data into a generating AI and cause the generating AI to use technical terminology.

[0052] During a dialogue, the communication unit can select an optimal dialogue method by referring to the patient's past communication history. The communication history includes, for example, past dialogue content and message history, but is not limited to these examples. The communication unit selects the optimal dialogue method, for example, based on the dialogue method preferred by the patient in the past. The communication unit can also analyze the patient's past communication history and suggest the most effective dialogue method. The communication unit can also adjust the dialogue progress method by referring to the patient's past dialogue content. In this way, the optimal dialogue method can be selected by referring to the patient's past communication history. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input communication history data to a generation AI and cause the generation AI to select an optimal dialogue method.

[0053] During the dialogue, the communication unit can customize the dialogue content according to the patient's current task. Examples of the current task include, but are not limited to, a schedule or a task management app. The communication unit, for example, provides dialogue content related to the task the patient is currently performing. The communication unit can also adjust the way the dialogue proceeds based on the patient's current task. The communication unit can also customize the dialogue content according to the progress of the patient's task. This allows for appropriate dialogue to be provided by customizing the dialogue content according to the patient's current task. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input task data to a generation AI and cause the generation AI to customize the dialogue content.

[0054] During dialogue, the communication unit can select the optimal dialogue method by taking into account the patient's device information. Device information includes, but is not limited to, the device type and OS version. For example, if the patient is using a smartphone, the communication unit can provide a dialogue method optimized for the smartphone. Furthermore, if the patient is using a tablet, the communication unit can provide a dialogue method optimized for the tablet. Furthermore, if the patient is using a smartwatch, the communication unit can provide a dialogue method optimized for the smartwatch. This allows for appropriate dialogue by selecting the optimal dialogue method based on the patient's device information. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input device information data into the generation AI and cause the generation AI to select the optimal dialogue method.

[0055] During dialogue, the communication unit can make the dialogue content multilingual according to the patient's language setting. Language settings include, but are not limited to, for example, the device's language setting and the user's language selection. For example, the communication unit automatically sets the dialogue language based on the patient's device's language setting. The communication unit can also provide a language switching function when the patient speaks multiple languages. If the patient selects a specific language, the communication unit can also provide dialogue in that language. This makes it possible to provide appropriate dialogue by making the dialogue content multilingual according to the patient's language setting. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without AI. For example, the communication unit can input language setting data to a generation AI and cause the generation AI to execute multilingual dialogue content.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The collection unit can analyze the patient's past behavioral history and select the optimal data collection method. For example, the collection unit can select the data collection method based on the patient's behavioral history of places that the patient frequently visited in the past. The collection unit can also analyze the patient's past behavioral patterns and suggest the most efficient data collection method. Furthermore, the collection unit can adjust the timing of data collection based on specific behaviors that the patient has performed in the past. In this way, the optimal data collection method can be selected by analyzing the patient's past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using AI or without AI. For example, the collection unit can input past behavioral history data into the generation AI and have the generation AI select the optimal data collection method.

[0058] When collecting data, the collection unit can filter the data based on the patient's current health condition and living situation. For example, the data to be collected can be filtered taking into account the patient's current health condition. The collection unit can also select the type of data to collect based on the patient's living situation (work, family, etc.). Furthermore, the collection unit can temporarily suspend data collection if the patient's health condition is deteriorating. This enables appropriate data collection by filtering data according to the patient's health condition and living situation. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input health condition data into the generation AI and have the generation AI perform data filtering.

[0059] When collecting data, the collection unit can select the optimal collection means depending on the patient's input method. For example, if the patient prefers voice input, the collection unit can prioritize collecting voice data. Also, if the patient prefers text input, the collection unit can prioritize collecting text data. Furthermore, if the patient prefers image input, the collection unit can prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the patient's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input input data to a generation AI and have the generation AI select the optimal collection means.

[0060] During data collection, the collection unit can analyze the patient's social media activity and collect relevant data. For example, it can collect data on where the patient checked in on social media. The collection unit can also analyze the patient's social media posts and collect relevant data. Furthermore, the collection unit can collect relevant data by referring to the activities of the patient's friends on social media. In this way, relevant data can be efficiently collected by analyzing the patient's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI or without AI. For example, the collection unit can input social media data into the generation AI and cause the generation AI to collect relevant data.

[0061] The collection unit can customize the collection method by reflecting the patient's past feedback when collecting data. For example, the collection unit can adjust the data collection method based on feedback provided by the patient in the past. The collection unit can also select the type of data to collect by reflecting the patient's past feedback. Furthermore, the collection unit can adjust the timing of data collection based on the patient's feedback. In this way, the collection method can be optimized by reflecting the patient's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI or without AI. For example, the collection unit can input feedback data to the generation AI and cause the generation AI to customize the collection method.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The collection unit collects data on the patient's behavioral history or conversation content. The behavioral history includes, for example, movement history and activity logs. The collection unit can collect data such as the patient's behavioral history when they go to specific places and the content of conversations with family members. For example, the collection unit can record the movement history using the smartphone's GPS function and collect the content of conversations using the voice recording function. The collection unit can also analyze the patient's behavioral patterns and detect specific behaviors. Step 2: The analysis unit analyzes the data collected by the collection unit and understands the state of the patient's memory and cognitive function. The analysis unit can, for example, use AI to analyze the collected data and detect events that the patient is forgetting. For example, the analysis unit can analyze conversation content using natural language processing technology and extract important events. The analysis unit can also analyze behavioral history using machine learning algorithms to evaluate the state of memory and cognitive function. Step 3: The provision unit provides advice and guidance to support memory based on the analysis results obtained by the analysis unit. The provision unit can, for example, use AI to generate reminders based on the analysis results and notify the patient. For example, when the patient goes to a specific location, the provision unit can remind the patient of past events related to that location. The provision unit can also provide appropriate advice and guidance depending on the state of the patient's memory and cognitive function. Step 4: The communication unit communicates with the patient based on the advice provided by the provision unit. The communication unit can, for example, interact with the patient via a smartphone to provide reassurance. For example, the communication unit can interact with the patient using voice calls or messaging functions to provide necessary support. The communication unit can also collect patient feedback and provide the feedback to the provision unit.

[0064] (Example 2) A memory support system according to an embodiment of the present invention collects patient data, analyzes it using AI, and provides advice and guidance to support memory. The memory support system collects data such as the patient's behavioral history and conversation content, analyzes it using AI, and understands the state of the patient's memory and cognitive function. Based on this data, the AI ​​provides advice and guidance to support memory. Patients can understand their own memory and cognitive status and receive necessary support through dialogue with the AI ​​via their smartphone. The memory support system also provides a sense of security through communication with patients and supports the maintenance and improvement of cognitive function in daily life. For example, the memory support system collects data such as the patient's behavioral history and conversation content. For example, the memory support system collects data such as the patient's behavioral history when visiting specific places and conversations with family members. Next, the memory support system uses AI to analyze the collected data and understand the state of the patient's memory and cognitive function. For example, the AI ​​detects events the patient is about to forget and provides reminders based on the detected events. Next, the memory support system uses AI to provide advice and guidance to support memory. For example, when a patient visits a specific location, the system reminds the patient of past events related to that location. In this way, the memory support system provides a sense of security through communication with the patient and supports the maintenance and improvement of cognitive function in daily life. In this way, the memory support system can support the patient's memory and cognitive function and provide a sense of security. For example, the patient can understand the state of their own memory and cognition and receive the necessary support. In addition, communication with the patient helps maintain and improve cognitive function in daily life.

[0065] A memory support system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a communication unit. The collection unit collects data on a patient's behavioral history or conversation content. The behavioral history includes, but is not limited to, a travel history and an activity log. The collection unit can collect data such as the patient's behavioral history when visiting specific locations and conversation content with family members. For example, the collection unit can record the travel history using a smartphone's GPS function and collect conversation content using a voice recording function. The collection unit can also analyze the patient's behavioral patterns and detect specific behaviors. The analysis unit analyzes the data collected by the collection unit to understand the state of the patient's memory and cognitive function. For example, the analysis unit can analyze the collected data using AI to detect events the patient is forgetting. For example, the analysis unit can analyze the conversation content using natural language processing technology and extract important events. The analysis unit can also analyze the behavioral history using a machine learning algorithm to evaluate the state of memory and cognitive function. The provision unit provides advice and guidance to support memory based on the analysis results obtained by the analysis unit. The providing unit can, for example, use AI to generate a reminder based on the analysis results and notify the patient. For example, when the patient goes to a specific location, the providing unit can remind the patient of past events related to that location. The providing unit can also provide appropriate advice and guidance depending on the state of the patient's memory and cognitive function. The communication unit communicates with the patient based on the advice provided by the providing unit. The communication unit can, for example, interact with the patient via a smartphone to provide reassurance. For example, the communication unit can interact with the patient using a voice call or messaging function and provide necessary support. The communication unit can also collect patient feedback and provide the feedback to the providing unit. As a result, the memory support system according to the embodiment can support the patient's memory and cognitive function and provide reassurance.

[0066] The collection unit can collect data on the patient's behavioral history when he or she visits a specific location or the content of conversations with family members. Specific locations include, but are not limited to, home, hospitals, public facilities, etc. The collection unit, for example, collects the patient's behavioral history when he or she visits a specific location. For example, the collection unit can record the patient's movement history using a smartphone's GPS function. The collection unit can also collect the content of conversations the patient has with family members. For example, the collection unit can collect the content of conversations using an audio recording function. This enables more accurate analysis by collecting the patient's behavioral history and the content of conversations in detail. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the audio recording data into a generation AI and have the generation AI convert the content of the conversations into text.

[0067] The analysis unit can analyze the collected data and detect events that the patient is likely to forget. Examples of events that the patient is likely to forget include, but are not limited to, past events and important dates. The analysis unit can, for example, use AI to analyze the collected data and detect events that the patient is likely to forget. For example, the analysis unit can analyze conversation content using natural language processing technology and extract important events. The analysis unit can also analyze behavioral history using a machine learning algorithm to detect events that the patient is likely to forget. This allows the detection of events that the patient is likely to forget to provide appropriate reminders. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI detect events that the patient is likely to forget.

[0068] The providing unit can remind the patient of past events related to a specific location when the patient goes to that location. Past events include, but are not limited to, for example, photos and memory cards. The providing unit can, for example, use AI to generate a reminder based on the analysis results and notify the patient. For example, the providing unit can remind the patient of past events related to a specific location when the patient goes to that location. This supports memory by reminding the patient of related past events when the patient goes to the specific location. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the analysis results to a generating AI and cause the generating AI to generate a reminder.

[0069] The communication unit can provide a sense of security by interacting with the patient via a smartphone. For example, the content, tone, and frequency of the interaction are important factors in providing a sense of security. The communication unit can provide a sense of security by interacting with the patient via a smartphone. For example, the communication unit can interact with the patient using a voice call or messaging function and provide necessary support. The communication unit can also collect patient feedback and provide it to the providing unit. This provides a sense of security by interacting with the patient via a smartphone. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without AI. For example, the communication unit can input voice call data into a generation AI and have the generation AI analyze the content of the interaction.

[0070] The collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated patient emotions. To estimate emotions, technologies such as facial expression analysis and voice analysis are used. The collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated patient emotions. For example, the collection unit can select the timing to collect data when the patient is relaxed to avoid stress. The collection unit can also temporarily suspend data collection when the patient is feeling stressed and resume it later. The collection unit can also collect detailed data at the timing when the patient is concentrating. This can avoid stress by adjusting the timing of data collection according to the patient's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0071] The collection unit can analyze the patient's past behavioral history and select the optimal data collection method. The optimal data collection method includes, but is not limited to, the type of sensor and the frequency of data collection. For example, the collection unit can analyze the patient's past behavioral history and select the optimal data collection method. For example, the collection unit can select the data collection method based on the patient's behavioral history at places frequently visited in the past. The collection unit can also analyze the patient's past behavioral patterns and suggest the most efficient data collection method. The collection unit can also adjust the timing of data collection based on specific behaviors the patient has performed in the past. In this way, the optimal data collection method can be selected by analyzing the patient's past behavioral history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input past behavioral history data into a generation AI and have the generation AI select the optimal data collection method.

[0072] The collection unit can filter data based on the patient's current health condition and lifestyle when collecting data. Examples of health condition and lifestyle data include, but are not limited to, medical records and lifestyle data. The collection unit, for example, filters the data to be collected, taking into account the patient's current health condition. For example, the collection unit selects the type of data to collect based on the patient's lifestyle (work, family, etc.). The collection unit can also temporarily suspend data collection if the patient's health condition is deteriorating. This enables appropriate data collection by filtering data according to the patient's health condition and lifestyle. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input health condition data to a generation AI and have the generation AI filter the data.

[0073] When collecting data, the collection unit can select the optimal collection means depending on the patient's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. The collection unit selects the optimal collection means depending on the patient's input method. For example, if the patient prefers voice input, the collection unit can prioritize collecting voice data. Also, if the patient prefers text input, the collection unit can prioritize collecting text data. Also, if the patient prefers image input, the collection unit can prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the patient's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input input data to a generation AI and have the generation AI select the optimal collection means.

[0074] The collection unit can estimate the patient's emotions and determine the priority of data to be collected based on the estimated patient's emotions. Examples of data priorities include, but are not limited to, importance and urgency. The collection unit can estimate the patient's emotions and determine the priority of data to be collected based on the estimated patient's emotions. For example, the collection unit can prioritize collecting detailed data when the patient is relaxed. Alternatively, the collection unit can collect only basic data when the patient is stressed. Alternatively, the collection unit can prioritize collecting specific data when the patient is concentrating. This enables efficient data collection by determining the priority of data to be collected based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input emotion data to a generation AI and have the generation AI determine the data priority.

[0075] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the patient's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location history. For example, when the patient is in a specific location, the collection unit prioritizes collecting data related to that location. The collection unit can also collect highly relevant data based on the patient's current location. When the patient is traveling, the collection unit can also collect data related to the patient's destination. This enables appropriate data collection by collecting highly relevant data based on the patient's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0076] During data collection, the collection unit can analyze the patient's social media activity and collect relevant data. Social media activity includes, but is not limited to, the content of posts and the number of likes. For example, the collection unit collects data regarding the locations where the patient checked in on social media. The collection unit can also analyze the content of the patient's social media posts and collect relevant data. The collection unit can also collect relevant data by referring to the activities of the patient's friends on social media. In this way, relevant data can be efficiently collected by analyzing the patient's social media activity. Some or all of the above-described processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input social media data into the generation AI and cause the generation AI to collect relevant data.

[0077] The collection unit can customize the data collection method by reflecting the patient's past feedback when collecting data. Past feedback includes, but is not limited to, questionnaire results and user reviews. The collection unit can adjust the data collection method, for example, based on feedback provided by the patient in the past. The collection unit can also select the type of data to collect by reflecting the patient's past feedback. The collection unit can also adjust the timing of data collection based on the patient's feedback. In this way, the collection method can be optimized by reflecting the patient's past feedback. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input feedback data to a generation AI and have the generation AI customize the collection method.

[0078] The analysis unit can estimate the patient's emotions and adjust the presentation method of the analysis based on the estimated patient's emotions. Examples of the presentation method of the analysis include, but are not limited to, graph display and text report. For example, the analysis unit can estimate the patient's emotions and adjust the presentation method of the analysis based on the estimated patient's emotions. For example, the analysis unit can provide detailed analysis results when the patient is relaxed. For example, the analysis unit can provide concise analysis results when the patient is stressed. For example, the analysis unit can emphasize specific analysis results when the patient is concentrating. This allows the presentation method of the analysis to be adjusted according to the patient's emotions, thereby providing appropriate analysis results. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI. For example, the analysis unit can input emotion data to the generation AI and have the generation AI adjust the presentation method of the analysis.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The importance of the data includes, but is not limited to, the reliability and relevance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a concise analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. Data categories include, but are not limited to, text data and image data. For example, the analysis unit can apply a behavioral analysis algorithm to behavioral history data. The analysis unit can also apply a natural language processing algorithm to conversation content data. The analysis unit can also apply a health analysis algorithm to health condition data. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results. Past analysis results include, but are not limited to, database references and historical analysis. For example, the analysis unit corrects the current analysis result based on the patient's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the past analysis results. The analysis unit can also improve the accuracy of the analysis by using the past analysis results. In this way, the accuracy of the analysis can be improved by referring to the patient's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0082] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated patient's emotions. Examples of the length of the analysis include, but are not limited to, the analysis time and the amount of data. The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated patient's emotions. For example, the analysis unit can perform a detailed analysis when the patient is relaxed. The analysis unit can also perform a concise analysis when the patient is stressed. The analysis unit can also emphasize specific analysis results when the patient is concentrating. This allows the length of the analysis to be adjusted according to the patient's emotions, thereby providing appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input emotion data to the generation AI and have the generation AI adjust the length of the analysis.

[0083] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The time when the data was collected includes, but is not limited to, for example, the collection date and time period. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also postpone analyzing older data. The analysis unit can also prioritize analyzing data from a specific period. This enables efficient analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input collection time data into the generation AI and have the generation AI determine the analysis priority.

[0084] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. Examples of data relevance include, but are not limited to, correlation and causal relationships. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0085] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the patient's level of expertise. Examples of technical terminology include, but are not limited to, educational background and professional experience. For example, if the patient has technical expertise, the analysis unit uses a lot of technical terminology. Furthermore, if the patient does not have technical expertise, the analysis unit can explain the analysis results in simple terms. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the patient's level of expertise. This allows for appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the patient's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input technical terminology level data into the generation AI and have the generation AI use technical terminology.

[0086] The providing unit can estimate the patient's emotions and adjust the way in which advice and guidance are presented based on the estimated patient's emotions. Examples of how advice and guidance are presented include, but are not limited to, the use of language and the level of detail in explanations. The providing unit can estimate the patient's emotions and adjust the way in which advice and guidance are presented based on the estimated patient's emotions. For example, the providing unit can provide detailed advice when the patient is relaxed. The providing unit can also provide concise advice when the patient is stressed. The providing unit can also emphasize specific advice when the patient is concentrating. This allows appropriate support to be provided by adjusting the way in which advice and guidance are presented based on the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input emotional data into the generating AI and have the generating AI adjust the way advice or guidance is expressed.

[0087] When providing advice or guidance, the providing unit can adjust the level of detail based on the importance of the patient's memory or cognitive function. Examples of the importance of memory or cognitive function include, but are not limited to, cognitive test results and doctor's diagnoses. For example, the providing unit can provide detailed advice for highly important memory or cognitive functions. The providing unit can also provide concise advice for less important memory or cognitive functions. The providing unit can also provide advice with an appropriate level of detail for medium-importance memory or cognitive functions. This allows appropriate support to be provided by adjusting the level of detail according to the importance of the patient's memory or cognitive function. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input importance data of memory or cognitive function into the generating AI and cause the generating AI to adjust the level of detail.

[0088] When providing advice or guidance, the providing unit can apply different providing algorithms depending on the patient category. Patient categories include, but are not limited to, age and medical history. For example, the providing unit can provide concise and easy-to-understand advice to elderly people. The providing unit can also provide detailed and specific advice to young people. The providing unit can also provide advice specialized for a patient with a specific disease. This allows effective support to be provided by applying an appropriate providing algorithm depending on the patient category. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input patient category data into the generating AI and cause the generating AI to apply the providing algorithm.

[0089] When providing advice or guidance, the providing unit can improve accuracy by referring to past provision results for the patient. Past provision results include, but are not limited to, feedback data and success stories. For example, the providing unit corrects current advice based on past advice provision results for the patient. The providing unit can also adjust the provision algorithm by referring to past provision results. The providing unit can also improve the accuracy of advice by using past provision results. In this way, the accuracy of advice or guidance can be improved by referring to past provision results for the patient. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past provision result data into the generating AI and cause the generating AI to improve accuracy.

[0090] The providing unit can estimate the patient's emotions and adjust the length of the advice or guidance based on the estimated patient's emotions. Examples of the length of the advice or guidance include, but are not limited to, a time limit and an amount of information. The providing unit can estimate the patient's emotions and adjust the length of the advice or guidance based on the estimated patient's emotions. For example, the providing unit can provide detailed advice when the patient is relaxed. The providing unit can also provide concise advice when the patient is stressed. The providing unit can also emphasize specific advice when the patient is concentrating. This allows appropriate support to be provided by adjusting the length of the advice or guidance according to the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI. For example, the providing unit can input emotion data into the generation AI and cause the generation AI to adjust the length of the advice or guidance.

[0091] The providing unit can determine priorities based on the patient's behavioral history when providing advice or guidance. The behavioral history includes, but is not limited to, for example, a movement history and an activity log. The providing unit can, for example, prioritize advice based on the patient's frequent past behaviors. The providing unit can also analyze the patient's behavioral history and prioritize the most important advice. The providing unit can also provide advice at appropriate times based on the patient's behavioral patterns. Thus, by determining priorities based on the patient's behavioral history, support can be provided at appropriate times. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input behavioral history data to a generating AI and have the generating AI determine the priorities.

[0092] When providing advice or guidance, the providing unit can adjust the order based on the relevance of the advice or guidance to the patient. Relevance includes, but is not limited to, correlation and causal relationships, for example. The providing unit, for example, prioritizes providing advice that is most relevant to the patient's current situation. The providing unit can also prioritize providing highly relevant advice based on the patient's past behavior. The providing unit can also prioritize providing highly relevant advice based on the patient's current health condition. In this way, appropriate support can be provided by adjusting the order based on the relevance of the patient. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without using AI. For example, the providing unit can input the relevance data to a generating AI and cause the generating AI to adjust the order.

[0093] When providing advice or guidance, the providing unit can adjust the use of technical terminology according to the patient's level of expertise. Examples of technical terminology include, but are not limited to, educational background and professional experience. For example, if the patient has technical expertise, the providing unit uses a lot of technical terminology. Furthermore, if the patient does not have technical expertise, the providing unit can provide advice in simple language. Furthermore, the providing unit can adjust the way the advice is expressed according to the patient's level of expertise. This allows appropriate assistance to be provided by adjusting the use of technical terminology according to the patient's level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input technical terminology level data into a generating AI and cause the generating AI to use technical terminology.

[0094] The communication unit can estimate the patient's emotions and adjust the dialogue expression method based on the estimated patient's emotions. Examples of dialogue expression methods include, but are not limited to, wording and level of detail in explanations. The communication unit can estimate the patient's emotions and adjust the dialogue expression method based on the estimated patient's emotions. For example, the communication unit can provide detailed dialogue when the patient is relaxed. Alternatively, the communication unit can provide concise dialogue when the patient is stressed. Alternatively, the communication unit can emphasize specific dialogue content when the patient is concentrating. This allows for appropriate dialogue by adjusting the dialogue expression method according to the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the communication unit may be performed using, for example, an AI. For example, the communication unit can input emotion data to the generation AI and cause the generation AI to adjust the dialogue expression method.

[0095] During a dialogue, the communication unit can select an optimal dialogue method by referring to the patient's past communication history. The communication history includes, for example, past dialogue content and message history, but is not limited to these examples. The communication unit selects the optimal dialogue method, for example, based on the dialogue method preferred by the patient in the past. The communication unit can also analyze the patient's past communication history and suggest the most effective dialogue method. The communication unit can also adjust the dialogue progress method by referring to the patient's past dialogue content. In this way, the optimal dialogue method can be selected by referring to the patient's past communication history. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input communication history data to a generation AI and cause the generation AI to select an optimal dialogue method.

[0096] During the dialogue, the communication unit can customize the dialogue content according to the patient's current task. Examples of the current task include, but are not limited to, a schedule or a task management app. The communication unit, for example, provides dialogue content related to the task the patient is currently performing. The communication unit can also adjust the way the dialogue proceeds based on the patient's current task. The communication unit can also customize the dialogue content according to the progress of the patient's task. This allows for appropriate dialogue to be provided by customizing the dialogue content according to the patient's current task. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input task data to a generation AI and cause the generation AI to customize the dialogue content.

[0097] The communication unit can estimate the patient's emotions and adjust the dialogue operation procedure based on the estimated patient's emotions. Examples of dialogue operation procedures include, but are not limited to, step-by-step instructions and interactive guides. The communication unit can estimate the patient's emotions and adjust the dialogue operation procedure based on the estimated patient's emotions. For example, the communication unit can provide detailed operation procedures when the patient is relaxed. The communication unit can also provide concise operation procedures when the patient is stressed. The communication unit can also emphasize specific operation procedures when the patient is concentrating. This allows for appropriate dialogue by adjusting the dialogue operation procedure according to the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the communication unit can be performed using, for example, an AI. For example, the communication unit can input emotion data to the generation AI and have the generation AI adjust the operation procedure.

[0098] During dialogue, the communication unit can select the optimal dialogue method by taking into account the patient's device information. Device information includes, but is not limited to, the device type and OS version. For example, if the patient is using a smartphone, the communication unit can provide a dialogue method optimized for the smartphone. Furthermore, if the patient is using a tablet, the communication unit can provide a dialogue method optimized for the tablet. Furthermore, if the patient is using a smartwatch, the communication unit can provide a dialogue method optimized for the smartwatch. This allows for appropriate dialogue by selecting the optimal dialogue method based on the patient's device information. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input device information data into the generation AI and cause the generation AI to select the optimal dialogue method.

[0099] During dialogue, the communication unit can make the dialogue content multilingual according to the patient's language setting. Language settings include, but are not limited to, for example, the device's language setting and the user's language selection. For example, the communication unit automatically sets the dialogue language based on the patient's device's language setting. The communication unit can also provide a language switching function when the patient speaks multiple languages. If the patient selects a specific language, the communication unit can also provide dialogue in that language. This makes it possible to provide appropriate dialogue by making the dialogue content multilingual according to the patient's language setting. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without AI. For example, the communication unit can input language setting data to a generation AI and cause the generation AI to execute multilingual dialogue content. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and communication unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect the patient's behavioral history and conversation content using the camera 42 and microphone 38B of the smart device 14. For example, the collection unit can also be implemented by the specific processing unit 290 of the data processing device 12 and can analyze the patient's behavioral patterns and detect specific behaviors. For example, the analysis unit can be implemented by the specific processing unit 290 of the data processing device 12 and can analyze the collected data using AI to detect events that the patient is likely to forget. For example, the provision unit can be implemented by the specific processing unit 290 of the data processing device 12 and can generate reminders based on the analysis results and notify the patient. For example, the communication unit can interact with the patient using the control unit 46A of the smart device 14 to provide reassurance. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and communication unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect the patient's behavioral history and conversation content using the camera 42 and microphone 238 of the smart glasses 214. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can analyze the patient's behavioral patterns and detect specific actions. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and can analyze the collected data using AI to detect events that the patient is likely to forget. For example, the provision unit can be realized by the specific processing unit 290 of the data processing device 12 and can generate a reminder based on the analysis result and notify the patient. For example, the communication unit can communicate with the patient using the control unit 46A of the smart glasses 214 to provide reassurance. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and communication unit, described above, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect the patient's behavioral history and conversation content using the camera 42 and microphone 238 of the headset terminal 314. For example, the collection unit can also be implemented by the specific processing unit 290 of the data processing device 12 and can analyze the patient's behavioral patterns and detect specific behaviors. For example, the analysis unit can be implemented by the specific processing unit 290 of the data processing device 12 and can analyze the collected data using AI to detect events that the patient is likely to forget. For example, the provision unit can be implemented by the specific processing unit 290 of the data processing device 12 and can generate a reminder based on the analysis result and notify the patient. For example, the communication unit can communicate with the patient using the control unit 46A of the headset terminal 314 to provide reassurance. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and communication unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect the patient's behavioral history and conversation content using the camera 42 and microphone 238 of the robot 414. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can analyze the patient's behavioral patterns and detect specific behaviors. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and can analyze the collected data using AI to detect events that the patient is likely to forget. For example, the provision unit can be realized by the specific processing unit 290 of the data processing device 12 and can generate a reminder based on the analysis result and notify the patient. For example, the communication unit can communicate with the patient using the control unit 46A of the robot 414 to provide reassurance.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The analysis unit can estimate the patient's emotions and determine the analysis priority based on the estimated patient's emotions. For example, if the patient is feeling stressed, the analysis unit can prioritize analyzing data to reduce stress. Also, if the patient is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the patient is concentrating, the analysis unit can prioritize analyzing specific important data. This allows the analysis priority to be adjusted according to the patient's emotions, providing appropriate analysis results. The emotion estimation is achieved using an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input emotion data into a generative AI and have the generative AI determine the analysis priority.

[0102] The providing unit can estimate the patient's emotions and adjust the way advice and guidance are presented based on the estimated patient's emotions. For example, if the patient is relaxed, detailed advice can be provided. If the patient is stressed, concise advice can be provided. Furthermore, if the patient is concentrating, specific advice can be emphasized. This allows appropriate support to be provided by adjusting the way advice and guidance are presented according to the patient's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI or without AI. For example, the providing unit can input emotion data into a generation AI and have the generation AI adjust the way advice and guidance are presented.

[0103] The communication unit can estimate the patient's emotions and adjust the way the dialogue is expressed based on the estimated patient's emotions. For example, if the patient is relaxed, the communication unit can provide detailed dialogue. If the patient is stressed, the communication unit can provide concise dialogue. Furthermore, if the patient is concentrating, the communication unit can emphasize specific dialogue content. This allows the dialogue to be adjusted according to the patient's emotions, providing an appropriate dialogue. Emotion estimation is achieved using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI or a multimodal generation AI. Some or all of the above-described processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input emotion data into the generation AI and have the generation AI adjust the way the dialogue is expressed.

[0104] The collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated patient emotions. For example, if the patient is relaxed, the timing of data collection can be selected to avoid stress. Also, if the patient is feeling stressed, data collection can be temporarily suspended and resumed later. Furthermore, if the patient is concentrating, detailed data can be collected at that time. This allows stress to be avoided by adjusting the timing of data collection according to the patient's emotions. The emotion estimation is achieved using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using AI, or without AI. For example, the collection unit can input facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0105] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated patient emotions. For example, if the patient is relaxed, a detailed analysis can be performed. Alternatively, if the patient is stressed, a brief analysis can be performed. Furthermore, if the patient is concentrating, specific analysis results can be emphasized. This allows the length of the analysis to be adjusted according to the patient's emotions, thereby providing appropriate analysis results. Emotion estimation is achieved using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using AI or without AI. For example, the analysis unit can input emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0106] The collection unit can analyze the patient's past behavioral history and select the optimal data collection method. For example, the collection unit can select the data collection method based on the patient's behavioral history of places that the patient frequently visited in the past. The collection unit can also analyze the patient's past behavioral patterns and suggest the most efficient data collection method. Furthermore, the collection unit can adjust the timing of data collection based on specific behaviors that the patient has performed in the past. In this way, the optimal data collection method can be selected by analyzing the patient's past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using AI or without AI. For example, the collection unit can input past behavioral history data into the generation AI and have the generation AI select the optimal data collection method.

[0107] When collecting data, the collection unit can filter the data based on the patient's current health condition and living situation. For example, the data to be collected can be filtered taking into account the patient's current health condition. The collection unit can also select the type of data to collect based on the patient's living situation (work, family, etc.). Furthermore, the collection unit can temporarily suspend data collection if the patient's health condition is deteriorating. This enables appropriate data collection by filtering data according to the patient's health condition and living situation. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input health condition data into the generation AI and have the generation AI perform data filtering.

[0108] When collecting data, the collection unit can select the optimal collection means depending on the patient's input method. For example, if the patient prefers voice input, the collection unit can prioritize collecting voice data. Also, if the patient prefers text input, the collection unit can prioritize collecting text data. Furthermore, if the patient prefers image input, the collection unit can prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the patient's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input input data to a generation AI and have the generation AI select the optimal collection means.

[0109] During data collection, the collection unit can analyze the patient's social media activity and collect relevant data. For example, it can collect data on where the patient checked in on social media. The collection unit can also analyze the patient's social media posts and collect relevant data. Furthermore, the collection unit can collect relevant data by referring to the activities of the patient's friends on social media. In this way, relevant data can be efficiently collected by analyzing the patient's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI or without AI. For example, the collection unit can input social media data into the generation AI and cause the generation AI to collect relevant data.

[0110] The collection unit can customize the collection method by reflecting the patient's past feedback when collecting data. For example, the collection unit can adjust the data collection method based on feedback provided by the patient in the past. The collection unit can also select the type of data to collect by reflecting the patient's past feedback. Furthermore, the collection unit can adjust the timing of data collection based on the patient's feedback. In this way, the collection method can be optimized by reflecting the patient's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI or without AI. For example, the collection unit can input feedback data to the generation AI and cause the generation AI to customize the collection method.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The collection unit collects data on the patient's behavioral history or conversation content. The behavioral history includes, for example, movement history and activity logs. The collection unit can collect data such as the patient's behavioral history when they go to specific places and the content of conversations with family members. For example, the collection unit can record the movement history using the smartphone's GPS function and collect the content of conversations using the voice recording function. The collection unit can also analyze the patient's behavioral patterns and detect specific behaviors. Step 2: The analysis unit analyzes the data collected by the collection unit and understands the state of the patient's memory and cognitive function. The analysis unit can, for example, use AI to analyze the collected data and detect events that the patient is forgetting. For example, the analysis unit can analyze conversation content using natural language processing technology and extract important events. The analysis unit can also analyze behavioral history using machine learning algorithms to evaluate the state of memory and cognitive function. Step 3: The provision unit provides advice and guidance to support memory based on the analysis results obtained by the analysis unit. The provision unit can, for example, use AI to generate reminders based on the analysis results and notify the patient. For example, when the patient goes to a specific location, the provision unit can remind the patient of past events related to that location. The provision unit can also provide appropriate advice and guidance depending on the state of the patient's memory and cognitive function. Step 4: The communication unit communicates with the patient based on the advice provided by the provision unit. The communication unit can, for example, interact with the patient via a smartphone to provide reassurance. For example, the communication unit can interact with the patient using voice calls or messaging functions to provide necessary support. The communication unit can also collect patient feedback and provide the feedback to the provision unit.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 AI 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 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.

[0132] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] 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 AI 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.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] 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.

[0162] 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.

[0163] 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 AI 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.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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, in order to avoid confusion and to 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data on the patient's behavior history or conversation content; an analysis unit that analyzes the data collected by the collection unit and grasps the state of memory and cognitive function of the patient; a providing unit that provides advice and guidance to support memory based on the analysis results obtained by the analyzing unit; a communication unit that communicates with the patient based on the advice provided by the provision unit. A system characterized by:

2. The collecting unit Collect data on the patient's behavioral history when they visit specific places or conversations with family members 2. The system of claim 1.

3. The analysis unit Analyze the collected data to detect events that patients may have forgotten 2. The system of claim 1.

4. The providing unit Reminding patients of past events related to a specific location when they visit that location 2. The system of claim 1.

5. The communication unit Communicating with patients via smartphone provides reassurance 2. The system of claim 1.

6. The collecting unit Estimate the patient's emotions and adjust the timing of data collection based on the estimated patient emotions 2. The system of claim 1.

7. The collecting unit Analyze the patient's past behavioral history and select the optimal data collection method 2. The system of claim 1.

8. The collecting unit Filtering data based on the patient's current health and living situation 2. The system of claim 1.

Images