System

Abstract

To provide a system for reducing a burden of a caregiver by making communication with a dementia patient smooth.SOLUTION: A system includes a collection section, an analysis section, a reply section, and a determination section. The collection section collects a dementia patient's voice. The analysis section analyzes voice data collected by the collection section, and analyzes a specific pattern. The reply section gives a reply before a dementia patient on the basis of a pattern specified by the analysis section. The determination section determines a progress situation of dementia on the basis of a reply by the reply section.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has made it difficult to communicate with dementia patients, placing a heavy burden on caregivers.

[0005] The system according to the embodiment aims to facilitate communication with dementia patients and reduce the burden on caregivers. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an answering unit, and a determination unit. The collection unit collects voices of a dementia patient. The analysis unit analyzes the voice data collected by the collection unit and analyzes a specific pattern. The answering unit answers before the dementia patient based on the pattern identified by the analysis unit. The determination unit determines the progression of dementia based on the answer provided by the answering unit. Effect of the Invention

[0007] The system according to the embodiment can facilitate communication with dementia patients and reduce the burden on caregivers. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The dementia care system according to the embodiment of the present invention is a system that collects the voices and questions of dementia patients and analyzes sentences and conversations that are easily forgotten. This system makes statements and answers before the dementia patient and makes the next suggestion, thereby reducing the burden on the caregiver. In addition, the collected data is analyzed to determine and analyze whether it is leading to improvement of dementia. In the future, we aim to contribute to the prevention and improvement of dementia. For example, a device is used that collects the voices and questions spoken by dementia patients. This device collects the statements of dementia patients in real time and transmits them to the system. For example, when a dementia patient asks "What day is it today?", the voice is collected by the device. Next, the collected voice data is analyzed by the system. The system identifies and predicts patterns of sentences and conversations that dementia patients tend to forget. For example, if a dementia patient frequently asks "What day is it today?", the question is identified as a pattern that is easily forgotten. The system makes statements and answers before the dementia patient based on the identified pattern. For example, before the dementia patient asks "What day is it today?", the system answers "Today is XX month XX day". This can reduce anxiety for dementia patients and ease the burden on caregivers. Furthermore, the system analyzes the collected data to determine whether it is leading to an improvement in dementia. For example, if a dementia patient asks certain questions less frequently, the system can determine that improvement is occurring based on that data. In the future, the system aims to contribute to the prevention and improvement of dementia. For example, based on the collected data, it can suggest early detection of dementia and preventive measures. As a result, the dementia care system can reduce the burden on caregivers and contribute to the improvement of dementia by collecting the voices and questions of dementia patients and analyzing sentences and conversations that are often forgotten.

[0029] The dementia care system according to the embodiment includes a collection unit, an analysis unit, a response unit, and a determination unit. The collection unit collects the voice of the dementia patient. The voice of the dementia patient includes, for example, daily conversation, questions, and emotional expressions, but is not limited to such examples. The collection unit collects the voice using, for example, a microphone. The collection unit can also collect the voice using a smartphone or a dedicated device. For example, the collection unit collects the voice spoken by the dementia patient in real time and transmits it to the system. The analysis unit analyzes the voice data collected by the collection unit and identifies a pattern of forgetting. The analysis unit analyzes the voice data using, for example, natural language processing technology. The analysis unit can also analyze the voice data using a machine learning algorithm. For example, the analysis unit extracts specific words or phrases from the voice data and identifies a pattern of forgetting. The response unit responds before the dementia patient based on the pattern identified by the analysis unit. The response unit responds using, for example, voice synthesis technology. The response unit can also respond using text display technology. For example, the answering unit causes the system to automatically answer before the dementia patient asks a question. The determining unit determines the progress of dementia based on the answer given by the answering unit. The determining unit, for example, analyzes collected data and determines the improvement status of dementia. The determining unit can also determine the improvement status using a machine learning algorithm. For example, the determining unit determines that improvement has been observed when the frequency of asking a specific question has decreased. As a result, the dementia care system according to the embodiment can reduce the burden on caregivers and contribute to the improvement of dementia by collecting, analyzing, and answering the voice of a dementia patient and determining improvement.

[0030] The collection unit collects the voice of the dementia patient. The voice of the dementia patient includes, for example, daily conversation, questions, emotional expressions, etc., but is not limited to such examples. The collection unit collects the voice using, for example, a microphone. The collection unit can also collect the voice using a smartphone or a dedicated device. Specifically, the collection unit collects the voice generated by the patient in daily life in real time and transmits it to the system. The microphone is installed in the patient's living space and can monitor the voice at all times. The smartphone or dedicated device can be carried by the patient and can collect voice even when out and about. This allows the collection unit to comprehensively collect voice data covering the entire life of the patient. Furthermore, the collection unit can use noise canceling technology and voice filtering technology to improve the quality of the voice data. This makes it easier for the collected voice data to be processed by the analysis unit. In addition, the collection unit can adjust the frequency and timing of collection of voice data, allowing for flexible response according to specific situations and conditions. For example, if a patient tends to ask many questions during a specific time period, the collection frequency can be increased during that time period. This allows the collection unit to collect voice data efficiently and effectively, improving the performance of the entire system.

[0031] The analysis unit analyzes the voice data collected by the collection unit and identifies patterns that tend to be forgotten. For the analysis, natural language processing technology and machine learning algorithms are used. Specifically, the analysis unit extracts specific words and phrases from the voice data and analyzes their frequency of occurrence and context. For example, it identifies words and phrases that the patient frequently forgets and learns the patterns. By using natural language processing technology, it is possible to convert the voice data into text data and perform grammatical analysis and semantic analysis. Machine learning algorithms learn large amounts of voice data and automatically identify patterns that tend to be forgotten. This allows the analysis unit to grasp the progress of the patient's dementia in real time. Furthermore, the analysis unit can also perform long-term trend analysis and risk assessment by utilizing past data and statistical information. For example, based on past voice data, it analyzes fluctuations in the frequency of occurrence of specific words and phrases and predicts the progress of dementia. In addition, the analysis unit can detect unusual patterns and abnormal data using anomaly detection algorithms and issue an early warning. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The answering section answers before the dementia patient based on the pattern identified by the analysis section. The answering section answers using, for example, voice synthesis technology or text display technology. Specifically, the answering section automatically answers before the patient asks a question. By using voice synthesis technology, it is possible to provide an answer in a natural voice and provide a familiar interface to the patient. By using text display technology, it is possible to provide visual information and present the answer in a form that is easy for the patient to understand. For example, before the patient asks "What day is it today?", the system answers "Today is XX month XX day." In addition, the answering section can provide an appropriate answer according to the patient's emotions and situation. For example, if the patient feels anxious, the system provides an answer that reassures him / her. This enables the answering section to respond flexibly according to the patient's needs. Furthermore, the answering section can monitor the patient's reaction and continuously improve the accuracy and effectiveness of the answer. For example, it evaluates whether the patient is satisfied with the answer and reviews the answer content as necessary. In addition, the answering section can reliably transmit information using multiple communication means. For example, important information can be delivered reliably by using not only voice synthesis but also text display and vibration notifications. This allows the answering section to provide quick and reliable answers to patients, slowing the progression of dementia.

[0033] The determination unit determines the progress of dementia based on the answer given by the answering unit. For example, the determination unit analyzes collected data and determines the improvement of dementia. The improvement can also be determined using a machine learning algorithm. Specifically, the determination unit determines that improvement has been observed when the frequency of asking a specific question has decreased. For example, when the frequency of the patient asking "What day is it today?" has decreased, the determination unit determines that the patient's memory has improved. Furthermore, the determination unit can monitor the patient's reactions and behavior to comprehensively evaluate the progress of dementia. For example, the patient analyzes patterns of behavior and speech in daily life to evaluate the degree of dementia progression. The determination unit can also perform long-term trend analysis and risk assessment using past data and statistical information. For example, based on past data, it analyzes fluctuations in specific behavior and speech to predict the progress of dementia. As a result, the determination unit can not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system. Furthermore, the determination unit can report the progress and improvement of dementia to the patient's family and caregivers, and support appropriate responses. For example, by creating regular reports and providing them to family and caregivers, the system can share information about the patient's condition and provide them with information to provide appropriate care. This not only slows down the progression of dementia, but also improves the patient's quality of life.

[0034] The collection unit can collect the speech of the dementia patient in real time. The collection unit, for example, collects the voice spoken by the dementia patient in real time. For example, the collection unit collects the voice using a microphone and transmits it to the system in real time. The collection unit can also collect the voice using a smartphone or a dedicated device. For example, the collection unit collects the voice spoken by the dementia patient in real time and transmits it to the system. This makes it possible to respond immediately by collecting the speech of the dementia patient in real time. A part or all of the above-mentioned 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 collect the speech of the dementia patient in real time and analyze the voice data using an AI model.

[0035] The analysis unit can analyze the collected voice data and identify sentences and conversation patterns that dementia patients tend to forget. The analysis unit can, for example, analyze the collected voice data and identify sentences and conversation patterns that dementia patients tend to forget. For example, the analysis unit can analyze the voice data using natural language processing technology. The analysis unit can also analyze the voice data using a machine learning algorithm. For example, the analysis unit can extract specific words and phrases from the voice data and identify patterns that tend to be forgotten. By identifying patterns that tend to be forgotten, it becomes easier to predict the behavior of dementia patients. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the analysis unit can input the collected voice data to a generation AI and cause the generation AI to identify patterns that tend to be forgotten.

[0036] The answering unit can make a statement or answer before the dementia patient based on the identified pattern. The answering unit can make a statement or answer before the dementia patient based on the identified pattern, for example. For example, the answering unit can make an answer using a voice synthesis technology. The answering unit can also make an answer using a text display technology. For example, the answering unit can automatically make an answer before the dementia patient asks a question. This makes a statement or answer before the dementia patient, thereby reducing the anxiety of the patient and the burden on the caregiver. A part or all of the above-mentioned processing in the answering unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the answering unit can generate an answer using a generation AI based on the identified pattern and provide it to the dementia patient.

[0037] The judgment unit can analyze the collected data and judge the improvement of dementia. The judgment unit, for example, analyzes the collected data and judges the improvement of dementia. For example, the judgment unit analyzes changes in the behavior and speech of the dementia patient based on the collected data and judges the improvement status. The judgment unit can also judge the improvement status using a machine learning algorithm. For example, the judgment unit judges that improvement is observed when the frequency of asking a specific question decreases. In this way, the improvement status of dementia can be judged by analyzing the collected data. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the judgment unit can input the collected data to the generation AI and cause the generation AI to execute the judgment of the improvement status.

[0038] The determination unit may include a suggestion unit that contributes to the prevention and improvement of dementia. The determination unit may include, for example, a suggestion unit that contributes to the prevention and improvement of dementia. The suggestion unit may, for example, propose early detection and preventive measures for dementia based on collected data. For example, the suggestion unit may analyze patterns of behavior and speech of dementia patients and propose preventive measures. The suggestion unit may also propose preventive measures using a machine learning algorithm. For example, the suggestion unit may propose early detection and preventive measures for dementia based on collected data. As a result, making suggestions that contribute to the prevention and improvement of dementia is useful for the prevention and improvement of dementia in the future. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit may input collected data to the generation AI and cause the generation AI to execute the proposal of preventive measures.

[0039] The collection unit can analyze the past speech history of the dementia patient and select the optimal collection method. The collection unit, for example, analyzes the past speech history of the dementia patient and selects the optimal collection method. For example, the collection unit preferentially collects specific keywords based on the contents of the dementia patient's frequent past speeches. In addition, if the dementia patient's past speech history shows that there are many speeches in a specific time period, the collection unit can also increase the collection frequency in that time period. Furthermore, the collection unit can analyze the dementia patient's past speech patterns and strengthen collection at times when important speech is predicted. In this way, the optimal collection method can be selected by analyzing the past speech history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the dementia patient's past speech history into AI and cause AI to select the optimal collection method.

[0040] The collection unit can filter the voice data based on the current living situation and areas of interest of the dementia patient when collecting the voice data. For example, when collecting the voice data, the collection unit filters the voice data based on the current living situation and areas of interest of the dementia patient. For example, when the dementia patient shows interest in a specific topic, the collection unit preferentially collects voice data related to the topic. The collection unit can also filter and collect voice data related to daily life according to the living situation of the dementia patient. Furthermore, when the dementia patient is performing a specific activity, the collection unit can also collect voice data related to the activity. As a result, highly relevant data can be collected by filtering based on the living situation and areas of interest. Part or all of the above-mentioned 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 current living situation and areas of interest of the dementia patient to AI and cause the AI ​​to perform filtering.

[0041] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the dementia patient when collecting voice data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the dementia patient when collecting voice data. For example, when the dementia patient is in a specific location, the collection unit preferentially collects voice data related to the location. In addition, when the dementia patient is on the move, the collection unit can also collect voice data related to the destination. Furthermore, when the dementia patient is at home, the collection unit can also preferentially collect conversations within the home. In this way, highly relevant data can be collected by considering the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the dementia patient to AI and cause AI to execute collection of highly relevant data.

[0042] The collection unit can analyze the social media activity of the dementia patient when collecting the voice data and collect related data. For example, the collection unit analyzes the social media activity of the dementia patient when collecting the voice data and collects related data. For example, the collection unit collects related voice data based on keywords frequently used by the dementia patient on social media. The collection unit can also collect voice data according to the time period during which the dementia patient is active on social media. Furthermore, the collection unit can also collect voice data related to topics in which the dementia patient is interested on social media. In this way, related data can be collected by analyzing social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the social media activity of the dementia patient to AI and cause AI to collect related data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the voice data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the voice data during analysis. For example, the analysis unit analyzes voice data including important remarks in detail. The analysis unit can also simplify and analyze voice data of daily conversations. Furthermore, the analysis unit can also analyze voice data including specific keywords in detail. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance. A part 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 the importance of the voice data to AI and cause AI to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the voice data during analysis. The analysis unit can apply different analysis algorithms depending on the category of the voice data during analysis. For example, the analysis unit applies a question analysis algorithm to voice data related to a question. The analysis unit can also apply a conversation analysis algorithm to voice data related to daily conversation. Furthermore, the analysis unit can apply an emotion analysis algorithm to voice data related to emotional expression. This enables highly accurate analysis by applying different analysis algorithms depending on the category. Part 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 category of the voice data to AI and cause the AI ​​to apply different analysis algorithms.

[0045] The analysis unit can determine the priority of the analysis based on the collection time of the voice data during the analysis. The analysis unit, for example, determines the priority of the analysis based on the collection time of the voice data during the analysis. For example, the analysis unit preferentially analyzes the voice data collected recently. The analysis unit can also preferentially analyze the voice data collected in a specific time period. Furthermore, the analysis unit can preferentially analyze the voice data in which a significant change is observed compared with the past data. This enables efficient analysis by determining the priority based on the collection time. A part 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 the collection time of the voice data to AI and cause AI to execute the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the voice data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the voice data during analysis. For example, the analysis unit preferentially analyzes voice data containing important keywords. The analysis unit can also preferentially analyze voice data related to a specific topic. Furthermore, the analysis unit can also preferentially analyze voice data that is highly related to past data. This allows efficient analysis by adjusting the order of analysis based on the relevance. A part 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 the relevance of the voice data to AI and cause AI to adjust the order of analysis.

[0047] The answering unit can adjust the level of detail of the answer based on the importance of the identified pattern when answering. The answering unit adjusts the level of detail of the answer based on the importance of the identified pattern when answering, for example. For example, the answering unit provides a detailed answer to an important question. The answering unit can also provide a simplified answer to an everyday question. Furthermore, the answering unit can also provide a detailed answer to a question containing a specific keyword. This allows for efficient answering by adjusting the level of detail of the answer based on the importance. A part or all of the above-mentioned processing in the answering unit may be performed, for example, using AI or may be performed without using AI. For example, the answering unit can input the importance of the identified pattern to AI and cause the AI ​​to adjust the level of detail of the answer.

[0048] The answering unit can apply different answering algorithms depending on the category of the identified pattern when answering. For example, the answering unit applies different answering algorithms depending on the category of the identified pattern when answering. For example, the answering unit applies a question answering algorithm to an answer to a question. The answering unit can also apply a conversation answering algorithm to an answer to a daily conversation. Furthermore, the answering unit can apply an emotion answering algorithm to an answer to an emotion expression. This allows for highly accurate answers by applying different answering algorithms depending on the category. Part or all of the above-mentioned processing in the answering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the answering unit can input the category of the identified pattern to the AI ​​and cause the AI ​​to apply different answering algorithms.

[0049] The answering unit can determine the priority of the answer based on the occurrence time of the identified pattern at the time of answering. The answering unit, for example, determines the priority of the answer based on the occurrence time of the identified pattern at the time of answering. For example, the answering unit provides an answer preferentially to a pattern that has recently occurred. The answering unit can also provide an answer preferentially to a pattern that has occurred in a specific time period. Furthermore, the answering unit can also provide an answer preferentially to a pattern that shows a significant change compared with past data. This enables efficient answering by determining the priority based on the occurrence time. Part or all of the above-mentioned processing in the answering unit may be performed, for example, using AI or may be performed without using AI. For example, the answering unit can input the occurrence time of the identified pattern to AI and cause the AI ​​to execute the determination of the priority of the answer.

[0050] The answering section can adjust the order of answers based on the relevance of the identified pattern when answering. The answering section, for example, adjusts the order of answers based on the relevance of the identified pattern when answering. For example, the answering section provides answers preferentially to patterns including important keywords. The answering section can also provide answers preferentially to patterns related to a specific topic. Furthermore, the answering section can also provide answers preferentially to patterns highly related to past data. This allows for efficient answering by adjusting the order of answers based on the relevance. A part or all of the above-mentioned processing in the answering section may be performed, for example, using AI, or may be performed without using AI. For example, the answering section can input the relevance of the identified pattern to AI and cause AI to adjust the order of answers.

[0051] The judgment unit can optimize the improvement judgment algorithm by referring to past data at the time of judgment. The judgment unit optimizes the improvement judgment algorithm by referring to past data at the time of judgment, for example. For example, the judgment unit optimizes an algorithm for judging an improvement trend of a dementia patient based on past data. The judgment unit can also optimize an algorithm for judging a degree of improvement by comparing past data with current data. Furthermore, the judgment unit can also optimize an algorithm for judging an improvement pattern of a dementia patient by learning from past data. In this way, the judgment algorithm can be optimized by referring to past data. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input past data to AI and cause AI to execute optimization of the improvement judgment algorithm.

[0052] The judgment unit can apply different judgment methods according to the category of the identified pattern at the time of judgment. The judgment unit applies different judgment methods according to the category of the identified pattern at the time of judgment, for example. For example, the judgment unit applies a question improvement judgment method to a pattern related to a question. The judgment unit can also apply a conversation improvement judgment method to a pattern related to daily conversation. Furthermore, the judgment unit can apply an emotion improvement judgment method to a pattern related to emotional expression. Thereby, by applying different judgment methods according to the category, highly accurate judgment is possible. Part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the category of the identified pattern to AI and cause the AI ​​to apply different judgment methods.

[0053] The judgment unit can judge whether or not the pattern has been improved based on the time of occurrence of the identified pattern at the time of judgment. The judgment unit judges whether or not the pattern has been improved based on the time of occurrence of the identified pattern at the time of judgment. For example, the judgment unit preferentially judges whether or not the pattern has been improved for a pattern that has recently occurred. The judgment unit can also preferentially judge whether or not the pattern has been improved for a pattern that has occurred in a specific time period. Furthermore, the judgment unit can also preferentially judge whether or not the pattern has been improved for a pattern that shows a significant change in comparison with past data. This makes it possible to make an efficient improvement judgment by making a judgment based on the time of occurrence. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the time of occurrence of the identified pattern to an AI and cause the AI ​​to execute the improvement judgment.

[0054] The judgment unit can make a judgment of improvement based on the relevance of the identified pattern at the time of judgment. The judgment unit, for example, makes a judgment of improvement based on the relevance of the identified pattern at the time of judgment. For example, the judgment unit makes a judgment of improvement preferentially for a pattern including an important keyword. The judgment unit can also make a judgment of improvement preferentially for a pattern related to a specific topic. Furthermore, the judgment unit can also make a judgment of improvement preferentially for a pattern highly related to past data. As a result, an efficient improvement judgment can be made by making a judgment based on the relevance. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the relevance of the identified pattern to an AI and cause the AI ​​to execute the judgment of improvement.

[0055] The suggestion unit can adjust the level of detail of the proposal based on the importance of the identified pattern at the time of proposal. The suggestion unit adjusts the level of detail of the proposal based on the importance of the identified pattern, for example, at the time of proposal. For example, the suggestion unit provides a detailed proposal for an important pattern. The suggestion unit can also provide a simplified proposal for an everyday pattern. Furthermore, the suggestion unit can also provide a detailed proposal for a pattern including a specific keyword. This allows efficient proposal by adjusting the level of detail of the proposal based on the importance. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the importance of the identified pattern to AI and cause the AI ​​to adjust the level of detail of the proposal.

[0056] The suggestion unit can apply different suggestion algorithms depending on the category of the identified pattern at the time of suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the identified pattern at the time of suggestion. For example, the suggestion unit applies a question suggestion algorithm to a pattern related to a question. The suggestion unit can also apply a conversation suggestion algorithm to a pattern related to daily conversation. Furthermore, the suggestion unit can apply an emotion suggestion algorithm to a pattern related to emotional expression. This enables highly accurate suggestions by applying different suggestion algorithms depending on the category. Part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the category of the identified pattern to the AI ​​and cause the AI ​​to apply different suggestion algorithms.

[0057] The suggestion unit can determine the priority of the proposal based on the occurrence time of the identified pattern at the time of proposal. The suggestion unit, for example, determines the priority of the proposal based on the occurrence time of the identified pattern at the time of proposal. For example, the suggestion unit provides a proposal preferentially for a pattern that has recently occurred. The suggestion unit can also provide a proposal preferentially for a pattern that has occurred in a specific time period. Furthermore, the suggestion unit can also provide a proposal preferentially for a pattern that shows a significant change compared with past data. This enables efficient proposal by determining the priority based on the occurrence time. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the occurrence time of the identified pattern to an AI and cause the AI ​​to execute the determination of the priority of the proposal.

[0058] The suggestion unit can adjust the order of the suggestions based on the relevance of the identified pattern at the time of suggestion. For example, the suggestion unit adjusts the order of the suggestions based on the relevance of the identified pattern at the time of suggestion. For example, the suggestion unit provides a suggestion preferentially for a pattern including an important keyword. The suggestion unit can also provide a suggestion preferentially for a pattern related to a specific topic. Furthermore, the suggestion unit can also provide a suggestion preferentially for a pattern highly related to past data. This allows efficient suggestions to be made by adjusting the order of the suggestions based on the relevance. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the relevance of the identified pattern to AI and cause AI to adjust the order of the suggestions.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The dementia care system may further include an environmental sensor unit. The environmental sensor unit collects environmental data such as temperature, humidity, illuminance, and sound volume. This allows the living environment of the dementia patient to be monitored and appropriate environmental adjustments to be made. For example, if the temperature is too high, the air conditioning is automatically adjusted. Also, if the volume is too loud, it can be suggested to turn down the volume. Furthermore, if the illuminance is low, the lighting can be automatically adjusted. This allows the living environment of the dementia patient to be optimized and supports a comfortable life.

[0061] The dementia care system may further include a health monitoring unit. The health monitoring unit collects biological data such as heart rate, blood pressure, and body temperature. This allows the health condition of the dementia patient to be monitored in real time, and an alert to be issued if an abnormality is detected. For example, if the heart rate is abnormally high, a medical institution will be notified. Also, if the blood pressure is high, appropriate measures can be suggested. Furthermore, if the body temperature is abnormally high, cooling measures can be suggested. This allows the health condition of the dementia patient to be constantly monitored, enabling rapid response.

[0062] The dementia care system can further include a reminder unit. The reminder unit notifies, for example, the time to take medicine or the time to eat. This can support the dementia patient so that they do not forget important schedules. For example, when it is nearly time to take medicine, a notification is given by voice or text. Also, when it is nearly time to eat, a notification can be given to encourage meal preparation. Furthermore, it can notify the time for regular exercise or rehabilitation. This can support the daily life of the dementia patient and promote health management.

[0063] The dementia care system may further include a communication support unit. The communication support unit may, for example, support video calls and sending and receiving messages with family members and caregivers. This may support dementia patients to avoid isolation. For example, the communication support unit may provide an interface that allows the dementia patient to easily start a video call with family members. It may also support message exchange with caregivers. It may also provide reminders to encourage regular communication. This may enable dementia patients to maintain social connections and maintain their mental health.

[0064] The dementia care system may further include a hobby activity support unit. The hobby activity support unit supports hobby activities such as music, painting, and puzzles. This allows the dementia patient to maintain cognitive function while having fun. For example, a playlist for listening to music may be provided. Digital tools for painting may also be provided. Furthermore, cognitive function may be stimulated through puzzles and games. This allows the dementia patient to maintain cognitive function while having fun through hobby activities, improving the quality of life.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The collection unit collects the voice of the dementia patient. The voice of the dementia patient includes everyday conversations, questions, and emotional expressions. The collection unit collects the voice using a microphone, smartphone, or dedicated device, and transmits it to the system in real time. Step 2: The analysis unit analyzes the voice data collected by the collection unit to identify patterns that are easy to forget. The analysis unit uses natural language processing techniques and machine learning algorithms to analyze the voice data and extract specific words and phrases. Step 3: The answering unit responds to the dementia patient's questions based on the patterns identified by the analysis unit. The answering unit uses voice synthesis technology and text display technology to automatically respond to the dementia patient's questions before they are asked. Step 4: The assessment unit assesses the progression of dementia based on the answers given by the answering unit. The assessment unit analyzes the collected data and determines that improvement is occurring when, for example, the frequency of asking specific questions decreases.

[0067] (Example 2) The dementia care system according to the embodiment of the present invention is a system that collects the voices and questions of dementia patients and analyzes sentences and conversations that are easily forgotten. This system makes statements and answers before the dementia patient and makes the next suggestion, thereby reducing the burden on the caregiver. In addition, the collected data is analyzed to determine and analyze whether it is leading to improvement of dementia. In the future, we aim to contribute to the prevention and improvement of dementia. For example, a device is used that collects the voices and questions spoken by dementia patients. This device collects the statements of dementia patients in real time and transmits them to the system. For example, when a dementia patient asks "What day is it today?", the voice is collected by the device. Next, the collected voice data is analyzed by the system. The system identifies and predicts patterns of sentences and conversations that dementia patients tend to forget. For example, if a dementia patient frequently asks "What day is it today?", the question is identified as a pattern that is easily forgotten. The system makes statements and answers before the dementia patient based on the identified pattern. For example, before the dementia patient asks "What day is it today?", the system answers "Today is XX month XX day". This can reduce anxiety for dementia patients and ease the burden on caregivers. Furthermore, the system analyzes the collected data to determine whether it is leading to an improvement in dementia. For example, if a dementia patient asks certain questions less frequently, the system can determine that improvement is occurring based on that data. In the future, the system aims to contribute to the prevention and improvement of dementia. For example, based on the collected data, it can suggest early detection of dementia and preventive measures. As a result, the dementia care system can reduce the burden on caregivers and contribute to the improvement of dementia by collecting the voices and questions of dementia patients and analyzing sentences and conversations that are often forgotten.

[0068] The dementia care system according to the embodiment includes a collection unit, an analysis unit, a response unit, and a determination unit. The collection unit collects the voice of the dementia patient. The voice of the dementia patient includes, for example, daily conversation, questions, and emotional expressions, but is not limited to such examples. The collection unit collects the voice using, for example, a microphone. The collection unit can also collect the voice using a smartphone or a dedicated device. For example, the collection unit collects the voice spoken by the dementia patient in real time and transmits it to the system. The analysis unit analyzes the voice data collected by the collection unit and identifies a pattern of forgetting. The analysis unit analyzes the voice data using, for example, natural language processing technology. The analysis unit can also analyze the voice data using a machine learning algorithm. For example, the analysis unit extracts specific words or phrases from the voice data and identifies a pattern of forgetting. The response unit responds before the dementia patient based on the pattern identified by the analysis unit. The response unit responds using, for example, voice synthesis technology. The response unit can also respond using text display technology. For example, the answering unit causes the system to automatically answer before the dementia patient asks a question. The determining unit determines the progress of dementia based on the answer given by the answering unit. The determining unit, for example, analyzes collected data and determines the improvement status of dementia. The determining unit can also determine the improvement status using a machine learning algorithm. For example, the determining unit determines that improvement has been observed when the frequency of asking a specific question has decreased. As a result, the dementia care system according to the embodiment can reduce the burden on caregivers and contribute to the improvement of dementia by collecting, analyzing, and answering the voice of a dementia patient and determining improvement.

[0069] The collection unit collects the voice of the dementia patient. The voice of the dementia patient includes, for example, daily conversation, questions, emotional expressions, etc., but is not limited to such examples. The collection unit collects the voice using, for example, a microphone. The collection unit can also collect the voice using a smartphone or a dedicated device. Specifically, the collection unit collects the voice generated by the patient in daily life in real time and transmits it to the system. The microphone is installed in the patient's living space and can monitor the voice at all times. The smartphone or dedicated device can be carried by the patient and can collect voice even when out and about. This allows the collection unit to comprehensively collect voice data covering the entire life of the patient. Furthermore, the collection unit can use noise canceling technology and voice filtering technology to improve the quality of the voice data. This makes it easier for the collected voice data to be processed by the analysis unit. In addition, the collection unit can adjust the frequency and timing of collection of voice data, allowing for flexible response according to specific situations and conditions. For example, if a patient tends to ask many questions during a specific time period, the collection frequency can be increased during that time period. This allows the collection unit to collect voice data efficiently and effectively, improving the performance of the entire system.

[0070] The analysis unit analyzes the voice data collected by the collection unit and identifies patterns that tend to be forgotten. For the analysis, natural language processing technology and machine learning algorithms are used. Specifically, the analysis unit extracts specific words and phrases from the voice data and analyzes their frequency of occurrence and context. For example, it identifies words and phrases that the patient frequently forgets and learns the patterns. By using natural language processing technology, it is possible to convert the voice data into text data and perform grammatical analysis and semantic analysis. Machine learning algorithms learn large amounts of voice data and automatically identify patterns that tend to be forgotten. This allows the analysis unit to grasp the progress of the patient's dementia in real time. Furthermore, the analysis unit can also perform long-term trend analysis and risk assessment by utilizing past data and statistical information. For example, based on past voice data, it analyzes fluctuations in the frequency of occurrence of specific words and phrases and predicts the progress of dementia. In addition, the analysis unit can detect unusual patterns and abnormal data using anomaly detection algorithms and issue an early warning. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0071] The answering section answers before the dementia patient based on the pattern identified by the analysis section. The answering section answers using, for example, voice synthesis technology or text display technology. Specifically, the answering section automatically answers before the patient asks a question. By using voice synthesis technology, it is possible to provide an answer in a natural voice and provide a familiar interface to the patient. By using text display technology, it is possible to provide visual information and present the answer in a form that is easy for the patient to understand. For example, before the patient asks "What day is it today?", the system answers "Today is XX month XX day." In addition, the answering section can provide an appropriate answer according to the patient's emotions and situation. For example, if the patient feels anxious, the system provides an answer that reassures him / her. This enables the answering section to respond flexibly according to the patient's needs. Furthermore, the answering section can monitor the patient's reaction and continuously improve the accuracy and effectiveness of the answer. For example, it evaluates whether the patient is satisfied with the answer and reviews the answer content as necessary. In addition, the answering section can reliably transmit information using multiple communication means. For example, important information can be delivered reliably by using not only voice synthesis but also text display and vibration notifications. This allows the answering section to provide quick and reliable answers to patients, slowing the progression of dementia.

[0072] The determination unit determines the progress of dementia based on the answer given by the answering unit. For example, the determination unit analyzes collected data and determines the improvement of dementia. The improvement can also be determined using a machine learning algorithm. Specifically, the determination unit determines that improvement has been observed when the frequency of asking a specific question has decreased. For example, when the frequency of the patient asking "What day is it today?" has decreased, the determination unit determines that the patient's memory has improved. Furthermore, the determination unit can monitor the patient's reactions and behavior to comprehensively evaluate the progress of dementia. For example, the patient analyzes patterns of behavior and speech in daily life to evaluate the degree of dementia progression. The determination unit can also perform long-term trend analysis and risk assessment using past data and statistical information. For example, based on past data, it analyzes fluctuations in specific behavior and speech to predict the progress of dementia. As a result, the determination unit can not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system. Furthermore, the determination unit can report the progress and improvement of dementia to the patient's family and caregivers, and support appropriate responses. For example, by creating regular reports and providing them to family and caregivers, the system can share information about the patient's condition and provide them with information to provide appropriate care. This not only slows down the progression of dementia, but also improves the patient's quality of life.

[0073] The collection unit can collect the speech of the dementia patient in real time. The collection unit, for example, collects the voice spoken by the dementia patient in real time. For example, the collection unit collects the voice using a microphone and transmits it to the system in real time. The collection unit can also collect the voice using a smartphone or a dedicated device. For example, the collection unit collects the voice spoken by the dementia patient in real time and transmits it to the system. This makes it possible to respond immediately by collecting the speech of the dementia patient in real time. A part or all of the above-mentioned 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 collect the speech of the dementia patient in real time and analyze the voice data using an AI model.

[0074] The analysis unit can analyze the collected voice data and identify sentences and conversation patterns that dementia patients tend to forget. The analysis unit can, for example, analyze the collected voice data and identify sentences and conversation patterns that dementia patients tend to forget. For example, the analysis unit can analyze the voice data using natural language processing technology. The analysis unit can also analyze the voice data using a machine learning algorithm. For example, the analysis unit can extract specific words and phrases from the voice data and identify patterns that tend to be forgotten. By identifying patterns that tend to be forgotten, it becomes easier to predict the behavior of dementia patients. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the analysis unit can input the collected voice data to a generation AI and cause the generation AI to identify patterns that tend to be forgotten.

[0075] The answering unit can make a statement or answer before the dementia patient based on the identified pattern. The answering unit can make a statement or answer before the dementia patient based on the identified pattern, for example. For example, the answering unit can make an answer using a voice synthesis technology. The answering unit can also make an answer using a text display technology. For example, the answering unit can automatically make an answer before the dementia patient asks a question. This makes a statement or answer before the dementia patient, thereby reducing the anxiety of the patient and the burden on the caregiver. A part or all of the above-mentioned processing in the answering unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the answering unit can generate an answer using a generation AI based on the identified pattern and provide it to the dementia patient.

[0076] The judgment unit can analyze the collected data and judge the improvement of dementia. The judgment unit, for example, analyzes the collected data and judges the improvement of dementia. For example, the judgment unit analyzes changes in the behavior and speech of the dementia patient based on the collected data and judges the improvement status. The judgment unit can also judge the improvement status using a machine learning algorithm. For example, the judgment unit judges that improvement is observed when the frequency of asking a specific question decreases. In this way, the improvement status of dementia can be judged by analyzing the collected data. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the judgment unit can input the collected data to the generation AI and cause the generation AI to execute the judgment of the improvement status.

[0077] The determination unit may include a suggestion unit that contributes to the prevention and improvement of dementia. The determination unit may include, for example, a suggestion unit that contributes to the prevention and improvement of dementia. The suggestion unit may, for example, propose early detection and preventive measures for dementia based on collected data. For example, the suggestion unit may analyze patterns of behavior and speech of dementia patients and propose preventive measures. The suggestion unit may also propose preventive measures using a machine learning algorithm. For example, the suggestion unit may propose early detection and preventive measures for dementia based on collected data. As a result, making suggestions that contribute to the prevention and improvement of dementia is useful for the prevention and improvement of dementia in the future. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit may input collected data to the generation AI and cause the generation AI to execute the proposal of preventive measures.

[0078] The collection unit can estimate the emotion of the dementia patient and adjust the collection timing of the voice data based on the estimated emotion. The collection unit, for example, estimates the emotion of the dementia patient and adjusts the collection timing of the voice data based on the estimated emotion. For example, the collection unit increases the collection frequency of the voice data when the dementia patient feels anxious. Also, the collection unit can reduce the collection frequency of the voice data when the dementia patient is relaxed. Furthermore, when the dementia patient is excited, the collection unit adjusts the collection timing of the voice data so as not to miss important remarks. In this way, by adjusting the collection timing according to the emotion of the dementia patient, important remarks can be collected without missing them. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can collect the voice data using an AI model that estimates the emotion of the dementia patient and adjusts the collection timing of the voice data based on the estimated emotion.

[0079] The collection unit can analyze the past speech history of the dementia patient and select the optimal collection method. The collection unit, for example, analyzes the past speech history of the dementia patient and selects the optimal collection method. For example, the collection unit preferentially collects specific keywords based on the contents of the dementia patient's frequent past speeches. In addition, if the dementia patient's past speech history shows that there are many speeches in a specific time period, the collection unit can also increase the collection frequency in that time period. Furthermore, the collection unit can analyze the dementia patient's past speech patterns and strengthen collection at times when important speech is predicted. In this way, the optimal collection method can be selected by analyzing the past speech history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the dementia patient's past speech history into AI and cause AI to select the optimal collection method.

[0080] The collection unit can filter the voice data based on the current living situation and areas of interest of the dementia patient when collecting the voice data. For example, when collecting the voice data, the collection unit filters the voice data based on the current living situation and areas of interest of the dementia patient. For example, when the dementia patient shows interest in a specific topic, the collection unit preferentially collects voice data related to the topic. The collection unit can also filter and collect voice data related to daily life according to the living situation of the dementia patient. Furthermore, when the dementia patient is performing a specific activity, the collection unit can also collect voice data related to the activity. As a result, highly relevant data can be collected by filtering based on the living situation and areas of interest. Part or all of the above-mentioned 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 current living situation and areas of interest of the dementia patient to AI and cause the AI ​​to perform filtering.

[0081] The collection unit can estimate the emotion of the dementia patient and determine the priority of the voice data to be collected based on the estimated emotion. The collection unit, for example, estimates the emotion of the dementia patient and determines the priority of the voice data to be collected based on the estimated emotion. For example, when the dementia patient feels anxious, the collection unit preferentially collects voice data related to the anxiety. In addition, when the dementia patient is relaxed, the collection unit can also preferentially collect voice data of daily conversation. Furthermore, when the dementia patient is excited, the collection unit can also preferentially collect voice data related to the excitement. In this way, important data can be preferentially collected by determining the priority based on the emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can collect voice data using an AI model that estimates the emotion of the dementia patient and determines the priority of voice data based on the estimated emotion.

[0082] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the dementia patient when collecting voice data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the dementia patient when collecting voice data. For example, when the dementia patient is in a specific location, the collection unit preferentially collects voice data related to the location. In addition, when the dementia patient is on the move, the collection unit can also collect voice data related to the destination. Furthermore, when the dementia patient is at home, the collection unit can also preferentially collect conversations within the home. In this way, highly relevant data can be collected by considering the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the dementia patient to AI and cause AI to execute collection of highly relevant data.

[0083] The collection unit can analyze the social media activity of the dementia patient when collecting the voice data and collect related data. For example, the collection unit analyzes the social media activity of the dementia patient when collecting the voice data and collects related data. For example, the collection unit collects related voice data based on keywords frequently used by the dementia patient on social media. The collection unit can also collect voice data according to the time period during which the dementia patient is active on social media. Furthermore, the collection unit can also collect voice data related to topics in which the dementia patient is interested on social media. In this way, related data can be collected by analyzing social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the social media activity of the dementia patient to AI and cause AI to collect related data.

[0084] The analysis unit can estimate the emotion of the dementia patient and adjust the expression method of the analysis based on the estimated emotion. The analysis unit, for example, estimates the emotion of the dementia patient and adjusts the expression method of the analysis based on the estimated emotion. For example, the analysis unit uses a simple and easy-to-understand expression method when the dementia patient feels anxious. In addition, the analysis unit can also provide detailed analysis results when the dementia patient is relaxed. Furthermore, the analysis unit can also use a visually stimulating expression method when the dementia patient is excited. In this way, by adjusting the expression method of the analysis based on the emotion, it is possible to provide an analysis result suitable for the dementia patient. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can provide an analysis result using an AI model that estimates the emotion of the dementia patient and adjusts the expression method of the analysis based on the estimated emotion.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the voice data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the voice data during analysis. For example, the analysis unit analyzes voice data including important remarks in detail. The analysis unit can also simplify and analyze voice data of daily conversations. Furthermore, the analysis unit can also analyze voice data including specific keywords in detail. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance. A part 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 the importance of the voice data to AI and cause AI to adjust the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the category of the voice data during analysis. The analysis unit can apply different analysis algorithms depending on the category of the voice data during analysis. For example, the analysis unit applies a question analysis algorithm to voice data related to a question. The analysis unit can also apply a conversation analysis algorithm to voice data related to daily conversation. Furthermore, the analysis unit can apply an emotion analysis algorithm to voice data related to emotional expression. This enables highly accurate analysis by applying different analysis algorithms depending on the category. Part 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 category of the voice data to AI and cause the AI ​​to apply different analysis algorithms.

[0087] The analysis unit can estimate the emotion of the dementia patient and adjust the length of the analysis based on the estimated emotion. The analysis unit, for example, estimates the emotion of the dementia patient and adjusts the length of the analysis based on the estimated emotion. For example, the analysis unit provides a short and to the point analysis result when the dementia patient feels anxious. The analysis unit can also provide a detailed analysis result when the dementia patient is relaxed. Furthermore, the analysis unit can also provide a visually stimulating analysis result when the dementia patient is excited. This allows the analysis result suitable for the dementia patient to be provided by adjusting the length of the analysis based on the emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can provide an analysis result using an AI model that estimates the emotion of the dementia patient and adjusts the length of the analysis based on the estimated emotion.

[0088] The analysis unit can determine the priority of the analysis based on the collection time of the voice data during the analysis. The analysis unit, for example, determines the priority of the analysis based on the collection time of the voice data during the analysis. For example, the analysis unit preferentially analyzes the voice data collected recently. The analysis unit can also preferentially analyze the voice data collected in a specific time period. Furthermore, the analysis unit can preferentially analyze the voice data in which a significant change is observed compared with the past data. This enables efficient analysis by determining the priority based on the collection time. A part 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 the collection time of the voice data to AI and cause AI to execute the determination of the analysis priority.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the voice data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the voice data during analysis. For example, the analysis unit preferentially analyzes voice data containing important keywords. The analysis unit can also preferentially analyze voice data related to a specific topic. Furthermore, the analysis unit can also preferentially analyze voice data that is highly related to past data. This allows efficient analysis by adjusting the order of analysis based on the relevance. A part 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 the relevance of the voice data to AI and cause AI to adjust the order of analysis.

[0090] The answering unit can estimate the emotion of the dementia patient and adjust the way of expressing the answer based on the estimated emotion. The answering unit, for example, estimates the emotion of the dementia patient and adjusts the way of expressing the answer based on the estimated emotion. For example, if the dementia patient feels anxious, the answering unit answers in a calm way. Also, if the dementia patient is relaxed, the answering unit can also answer in an expression way including detailed information. Furthermore, if the dementia patient is excited, the answering unit can also answer in a visually stimulating way. In this way, by adjusting the way of expressing the answer based on the emotion, an answer suitable for the dementia patient can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the answering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the answering unit can provide an answer using an AI model that estimates the emotion of the dementia patient and adjusts the way of expressing the answer based on the estimated emotion.

[0091] The answering unit can adjust the level of detail of the answer based on the importance of the identified pattern when answering. The answering unit adjusts the level of detail of the answer based on the importance of the identified pattern when answering, for example. For example, the answering unit provides a detailed answer to an important question. The answering unit can also provide a simplified answer to an everyday question. Furthermore, the answering unit can also provide a detailed answer to a question containing a specific keyword. This allows for efficient answering by adjusting the level of detail of the answer based on the importance. A part or all of the above-mentioned processing in the answering unit may be performed, for example, using AI or may be performed without using AI. For example, the answering unit can input the importance of the identified pattern to AI and cause the AI ​​to adjust the level of detail of the answer.

[0092] The answering unit can apply different answering algorithms depending on the category of the identified pattern when answering. For example, the answering unit applies different answering algorithms depending on the category of the identified pattern when answering. For example, the answering unit applies a question answering algorithm to an answer to a question. The answering unit can also apply a conversation answering algorithm to an answer to a daily conversation. Furthermore, the answering unit can apply an emotion answering algorithm to an answer to an emotion expression. This allows for highly accurate answers by applying different answering algorithms depending on the category. Part or all of the above-mentioned processing in the answering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the answering unit can input the category of the identified pattern to the AI ​​and cause the AI ​​to apply different answering algorithms.

[0093] The answering unit can estimate the emotion of the dementia patient and adjust the length of the answer based on the estimated emotion. The answering unit, for example, estimates the emotion of the dementia patient and adjusts the length of the answer based on the estimated emotion. For example, the answering unit provides a short and to the point answer when the dementia patient feels anxious. The answering unit can also provide a detailed answer when the dementia patient is relaxed. Furthermore, the answering unit can also provide a visually stimulating answer when the dementia patient is excited. This allows an answer suitable for the dementia patient to be provided by adjusting the length of the answer based on the emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the answering unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the answering unit can provide an answer using an AI model that estimates the emotion of the dementia patient and adjusts the length of the answer based on the estimated emotion.

[0094] The answering unit can determine the priority of the answer based on the occurrence time of the identified pattern at the time of answering. The answering unit, for example, determines the priority of the answer based on the occurrence time of the identified pattern at the time of answering. For example, the answering unit provides an answer preferentially to a pattern that has recently occurred. The answering unit can also provide an answer preferentially to a pattern that has occurred in a specific time period. Furthermore, the answering unit can also provide an answer preferentially to a pattern that shows a significant change compared with past data. This enables efficient answering by determining the priority based on the occurrence time. Part or all of the above-mentioned processing in the answering unit may be performed, for example, using AI or may be performed without using AI. For example, the answering unit can input the occurrence time of the identified pattern to AI and cause the AI ​​to execute the determination of the priority of the answer.

[0095] The answering section can adjust the order of answers based on the relevance of the identified pattern when answering. The answering section, for example, adjusts the order of answers based on the relevance of the identified pattern when answering. For example, the answering section provides answers preferentially to patterns including important keywords. The answering section can also provide answers preferentially to patterns related to a specific topic. Furthermore, the answering section can also provide answers preferentially to patterns highly related to past data. This allows for efficient answering by adjusting the order of answers based on the relevance. A part or all of the above-mentioned processing in the answering section may be performed, for example, using AI, or may be performed without using AI. For example, the answering section can input the relevance of the identified pattern to AI and cause AI to adjust the order of answers.

[0096] The judgment unit can estimate the emotion of the dementia patient and adjust the improvement judgment method based on the estimated emotion. The judgment unit, for example, estimates the emotion of the dementia patient and adjusts the improvement judgment method based on the estimated emotion. For example, the judgment unit uses a simple and easy-to-understand judgment method when the dementia patient feels anxious. In addition, the judgment unit can also use a detailed judgment method when the dementia patient is relaxed. Furthermore, the judgment unit can also use a visually stimulating judgment method when the dementia patient is excited. In this way, by adjusting the judgment method based on the emotion, it is possible to provide a judgment result suitable for the dementia patient. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can provide a judgment result using an AI model that estimates the emotion of the dementia patient and adjusts the improvement judgment method based on the estimated emotion.

[0097] The judgment unit can optimize the improvement judgment algorithm by referring to past data at the time of judgment. The judgment unit optimizes the improvement judgment algorithm by referring to past data at the time of judgment, for example. For example, the judgment unit optimizes an algorithm for judging an improvement trend of a dementia patient based on past data. The judgment unit can also optimize an algorithm for judging a degree of improvement by comparing past data with current data. Furthermore, the judgment unit can also optimize an algorithm for judging an improvement pattern of a dementia patient by learning from past data. In this way, the judgment algorithm can be optimized by referring to past data. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input past data to AI and cause AI to execute optimization of the improvement judgment algorithm.

[0098] The judgment unit can apply different judgment methods according to the category of the identified pattern at the time of judgment. The judgment unit applies different judgment methods according to the category of the identified pattern at the time of judgment, for example. For example, the judgment unit applies a question improvement judgment method to a pattern related to a question. The judgment unit can also apply a conversation improvement judgment method to a pattern related to daily conversation. Furthermore, the judgment unit can apply an emotion improvement judgment method to a pattern related to emotional expression. Thereby, by applying different judgment methods according to the category, highly accurate judgment is possible. Part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the category of the identified pattern to AI and cause the AI ​​to apply different judgment methods.

[0099] The determination unit can estimate the emotion of the dementia patient and determine the priority order of improvement based on the estimated emotion. The determination unit, for example, estimates the emotion of the dementia patient and determines the priority order of improvement based on the estimated emotion. For example, when the dementia patient feels anxious, the determination unit determines preferentially an improvement measure that reduces the anxiety. In addition, when the dementia patient is relaxed, the determination unit can also determine preferentially an improvement measure that improves the quality of daily life. Furthermore, when the dementia patient is excited, the determination unit can also determine preferentially an improvement measure that suppresses the excitement. In this way, by determining the priority order based on the emotion, it is possible to provide an improvement measure suitable for the dementia patient. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can provide an improvement measure using an AI model that estimates the emotion of the dementia patient and determines the priority order of improvement based on the estimated emotion.

[0100] The judgment unit can judge whether or not the pattern has been improved based on the time of occurrence of the identified pattern at the time of judgment. The judgment unit judges whether or not the pattern has been improved based on the time of occurrence of the identified pattern at the time of judgment. For example, the judgment unit preferentially judges whether or not the pattern has been improved for a pattern that has recently occurred. The judgment unit can also preferentially judge whether or not the pattern has been improved for a pattern that has occurred in a specific time period. Furthermore, the judgment unit can also preferentially judge whether or not the pattern has been improved for a pattern that shows a significant change in comparison with past data. This makes it possible to make an efficient improvement judgment by making a judgment based on the time of occurrence. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the time of occurrence of the identified pattern to an AI and cause the AI ​​to execute the improvement judgment.

[0101] The judgment unit can make a judgment of improvement based on the relevance of the identified pattern at the time of judgment. The judgment unit, for example, makes a judgment of improvement based on the relevance of the identified pattern at the time of judgment. For example, the judgment unit makes a judgment of improvement preferentially for a pattern including an important keyword. The judgment unit can also make a judgment of improvement preferentially for a pattern related to a specific topic. Furthermore, the judgment unit can also make a judgment of improvement preferentially for a pattern highly related to past data. As a result, an efficient improvement judgment can be made by making a judgment based on the relevance. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the relevance of the identified pattern to an AI and cause the AI ​​to execute the judgment of improvement.

[0102] The suggestion unit can estimate the emotion of the dementia patient and adjust the expression method of the proposal based on the estimated emotion. The suggestion unit, for example, estimates the emotion of the dementia patient and adjusts the expression method of the proposal based on the estimated emotion. For example, when the dementia patient feels anxious, the suggestion unit makes a suggestion in a calm expression method. In addition, when the dementia patient is relaxed, the suggestion unit can also make a suggestion in an expression method including detailed information. Furthermore, when the dementia patient is excited, the suggestion unit can also make a suggestion in a visually stimulating expression method. In this way, by adjusting the expression method of the proposal based on the emotion, a suggestion suitable for the dementia patient can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can provide a suggestion using an AI model that estimates the emotion of the dementia patient and adjusts the expression method of the proposal based on the estimated emotion.

[0103] The suggestion unit can adjust the level of detail of the proposal based on the importance of the identified pattern at the time of proposal. The suggestion unit adjusts the level of detail of the proposal based on the importance of the identified pattern, for example, at the time of proposal. For example, the suggestion unit provides a detailed proposal for an important pattern. The suggestion unit can also provide a simplified proposal for an everyday pattern. Furthermore, the suggestion unit can also provide a detailed proposal for a pattern including a specific keyword. This allows efficient proposal by adjusting the level of detail of the proposal based on the importance. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the importance of the identified pattern to AI and cause the AI ​​to adjust the level of detail of the proposal.

[0104] The suggestion unit can apply different suggestion algorithms depending on the category of the identified pattern at the time of suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the identified pattern at the time of suggestion. For example, the suggestion unit applies a question suggestion algorithm to a pattern related to a question. The suggestion unit can also apply a conversation suggestion algorithm to a pattern related to daily conversation. Furthermore, the suggestion unit can apply an emotion suggestion algorithm to a pattern related to emotional expression. This enables highly accurate suggestions by applying different suggestion algorithms depending on the category. Part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the category of the identified pattern to the AI ​​and cause the AI ​​to apply different suggestion algorithms.

[0105] The suggestion unit can estimate the emotion of the dementia patient and adjust the length of the proposal based on the estimated emotion. The suggestion unit, for example, estimates the emotion of the dementia patient and adjusts the length of the proposal based on the estimated emotion. For example, the suggestion unit provides a short and to the point proposal when the dementia patient is anxious. The suggestion unit can also provide a detailed proposal when the dementia patient is relaxed. Furthermore, the suggestion unit can also provide a visually stimulating proposal when the dementia patient is excited. In this way, the length of the proposal can be adjusted based on the emotion, thereby providing a proposal suitable for the dementia patient. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can provide a proposal using an AI model that estimates the emotion of the dementia patient and adjusts the length of the proposal based on the estimated emotion.

[0106] The suggestion unit can determine the priority of the proposal based on the occurrence time of the identified pattern at the time of proposal. The suggestion unit, for example, determines the priority of the proposal based on the occurrence time of the identified pattern at the time of proposal. For example, the suggestion unit provides a proposal preferentially for a pattern that has recently occurred. The suggestion unit can also provide a proposal preferentially for a pattern that has occurred in a specific time period. Furthermore, the suggestion unit can also provide a proposal preferentially for a pattern that shows a significant change compared with past data. This enables efficient proposal by determining the priority based on the occurrence time. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the occurrence time of the identified pattern to an AI and cause the AI ​​to execute the determination of the priority of the proposal.

[0107] The suggestion unit can adjust the order of the suggestions based on the relevance of the identified pattern at the time of suggestion. For example, the suggestion unit adjusts the order of the suggestions based on the relevance of the identified pattern at the time of suggestion. For example, the suggestion unit provides a suggestion preferentially for a pattern including an important keyword. The suggestion unit can also provide a suggestion preferentially for a pattern related to a specific topic. Furthermore, the suggestion unit can also provide a suggestion preferentially for a pattern highly related to past data. This allows efficient suggestions to be made by adjusting the order of the suggestions based on the relevance. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the relevance of the identified pattern to AI and cause AI to adjust the order of the suggestions.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The dementia care system may further include an environmental sensor unit. The environmental sensor unit collects environmental data such as temperature, humidity, illuminance, and sound volume. This allows the living environment of the dementia patient to be monitored and appropriate environmental adjustments to be made. For example, if the temperature is too high, the air conditioning is automatically adjusted. Also, if the volume is too loud, it can be suggested to turn down the volume. Furthermore, if the illuminance is low, the lighting can be automatically adjusted. This allows the living environment of the dementia patient to be optimized and supports a comfortable life.

[0110] The dementia care system may further include a health monitoring unit. The health monitoring unit collects biological data such as heart rate, blood pressure, and body temperature. This allows the health condition of the dementia patient to be monitored in real time, and an alert to be issued if an abnormality is detected. For example, if the heart rate is abnormally high, a medical institution will be notified. Also, if the blood pressure is high, appropriate measures can be suggested. Furthermore, if the body temperature is abnormally high, cooling measures can be suggested. This allows the health condition of the dementia patient to be constantly monitored, enabling rapid response.

[0111] The dementia care system can further include a reminder unit. The reminder unit notifies, for example, the time to take medicine or the time to eat. This can support the dementia patient so that they do not forget important schedules. For example, when it is nearly time to take medicine, a notification is given by voice or text. Also, when it is nearly time to eat, a notification can be given to encourage meal preparation. Furthermore, it can notify the time for regular exercise or rehabilitation. This can support the daily life of the dementia patient and promote health management.

[0112] The dementia care system may further include a communication support unit. The communication support unit may, for example, support video calls and sending and receiving messages with family members and caregivers. This may support dementia patients to avoid isolation. For example, the communication support unit may provide an interface that allows the dementia patient to easily start a video call with family members. It may also support message exchange with caregivers. It may also provide reminders to encourage regular communication. This may enable dementia patients to maintain social connections and maintain their mental health.

[0113] The dementia care system may further include a hobby activity support unit. The hobby activity support unit supports hobby activities such as music, painting, and puzzles. This allows the dementia patient to maintain cognitive function while having fun. For example, a playlist for listening to music may be provided. Digital tools for painting may also be provided. Furthermore, cognitive function may be stimulated through puzzles and games. This allows the dementia patient to maintain cognitive function while having fun through hobby activities, improving the quality of life.

[0114] The dementia care system may further include an emotion diary unit. The emotion diary unit records the emotions of the dementia patient and tracks daily changes in emotions. This makes it easier to understand the mental health of the dementia patient. For example, an interface is provided that allows the patient to easily record daily emotions. Changes in emotions can also be visualized in graphs and charts. Furthermore, appropriate measures and advice can be provided based on changes in emotions. This makes it possible to support the mental health of the dementia patient and detect problems early.

[0115] The dementia care system may further include an emotion feedback unit. The emotion feedback unit feeds back the emotion of the dementia patient in real time and responds according to the change in emotion. This makes it possible to stabilize the emotion of the dementia patient. For example, if the dementia patient feels anxious, music or images for relaxation can be provided. Also, if the dementia patient is excited, a guide for calming down can be provided. Furthermore, appropriate activities can be suggested according to the change in emotion. This makes it possible to stabilize the emotion of the dementia patient and support their mental health.

[0116] The dementia care system may further include an emotion prediction unit. The emotion prediction unit predicts the change in the emotion of the dementia patient based on past data and responds based on the predicted emotion. This allows for proactive responses to the change in the emotion of the dementia patient. For example, if it is known from past data that the dementia patient is prone to feeling anxious at a certain time of day, an activity for relaxing at that time of day may be suggested. Also, if it is known that the dementia patient is prone to becoming excited in a certain situation, it may be suggested that the dementia patient avoid that situation. Furthermore, it is also possible to predict the change in emotion and prepare appropriate measures in advance. This allows for proactive responses to the change in emotion of the dementia patient and support mental health.

[0117] The dementia care system may further include an emotion sharing unit. The emotion sharing unit shares the emotions of the dementia patient with family members and caregivers to promote empathy. This allows the dementia patient to deepen the bond with family members and caregivers without feeling isolated. For example, the emotion of the dementia patient may be notified to family members in real time. In addition, the emotion change may be visualized in a graph or chart and shared with family members and caregivers. Furthermore, messages and activities based on the emotion may be suggested. This allows the emotion of the dementia patient to be shared with family members and caregivers to promote empathy and support mental health.

[0118] The dementia care system may further include an emotion training unit. The emotion training unit trains the emotions of the dementia patient and supports emotion control. This allows the dementia patient to better understand and control his / her emotions. For example, it provides guides and activities for emotion training. It is also possible to record changes in emotions and evaluate the effectiveness of the training. Furthermore, it is also possible to visualize the progress of the emotion training in graphs and charts. This allows the dementia patient to improve his / her ability to understand and control his / her emotions and support his / her mental health.

[0119] The process flow of the second embodiment will be briefly described below.

[0120] Step 1: The collection unit collects the voice of the dementia patient. The voice of the dementia patient includes everyday conversations, questions, and emotional expressions. The collection unit collects the voice using a microphone, smartphone, or dedicated device, and transmits it to the system in real time. Step 2: The analysis unit analyzes the voice data collected by the collection unit to identify patterns that are easy to forget. The analysis unit uses natural language processing techniques and machine learning algorithms to analyze the voice data and extract specific words and phrases. Step 3: The answering unit responds to the dementia patient's questions based on the patterns identified by the analysis unit. The answering unit uses voice synthesis technology and text display technology to automatically respond to the dementia patient's questions before they are asked. Step 4: The assessment unit assesses the progression of dementia based on the answers given by the answering unit. The assessment unit analyzes the collected data and determines that improvement is occurring when, for example, the frequency of asking specific questions decreases.

[0121] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0122] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0123] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements including the collection unit, analysis unit, answering unit, determination unit, and suggestion unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the voice of the dementia patient using the microphone of the smart device 14 and transmits it to the system by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected voice data and identifies patterns that tend to forget. The answering unit is realized, for example, by the control unit 46A of the smart device 14, and uses voice synthesis technology to provide an answer before the dementia patient. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to determine the progress of dementia. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes suggestions that contribute to the prevention and improvement of dementia based on the collected data. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0128] 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.

[0129] 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 the voice according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0131] 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 performed in a secure state.

[0132] Fig. 4 shows an example of 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0136] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0137] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0138] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0139] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements including the above-mentioned collection unit, analysis unit, answering unit, determination unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the voice of the dementia patient using the microphone of the smart glasses 214 and transmits it to the system by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected voice data and identifies patterns that tend to forget. The answering unit is realized, for example, by the control unit 46A of the smart glasses 214, and uses voice synthesis technology to provide an answer before the dementia patient. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to determine the progress of dementia. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes suggestions that contribute to the prevention and improvement of dementia based on the collected data. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0144] 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.

[0145] 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 the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] 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 performed in a secure state.

[0148] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0152] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] 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 voice indicating a user input for 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.

[0154] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements including the above-mentioned collection unit, analysis unit, answering unit, determination unit, and suggestion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects the voice of the dementia patient using the microphone of the headset type terminal 314 and transmits it to the system by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected voice data and identifies patterns that tend to forget. The answering unit is realized, for example, by the control unit 46A of the headset type terminal 314, and uses voice synthesis technology to provide an answer before the dementia patient. The judgment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to determine the progress of dementia. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes suggestions that contribute to the prevention and improvement of dementia based on the collected data. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0160] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0161] 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 the voice according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0163] 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 performed in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0165] 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.

[0166] 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.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0168] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0169] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0170] 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 a voice indicating a user input for 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.

[0171] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0172] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0173] Each of the multiple elements including the above-mentioned collection unit, analysis unit, answering unit, judgment unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the voice of the dementia patient using a microphone of the robot 414 and transmits it to the system by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected voice data and identifies patterns that tend to forget. The answering unit is realized, for example, by the control unit 46A of the robot 414, and uses voice synthesis technology to provide an answer before the dementia patient. The judgment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to judge the progress of dementia. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes suggestions that contribute to the prevention and improvement of dementia based on the collected data. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0175] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0177] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0179] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0181] 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.

[0182] 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 Universal Serial Bus (USB) 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.

[0183] In addition, 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 upon request from the data processing device 12.

[0184] 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.

[0185] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0186] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0187] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0188] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0190] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0192] (Appendix 1) A collection unit that collects voices of dementia patients; an analysis unit that analyzes the voice data collected by the collection unit to analyze a specific pattern; A response unit that provides a response prior to a dementia patient based on the pattern identified by the analysis unit; A determination unit that determines a progression state of dementia based on the answer provided by the answering unit. A system characterized by: (Appendix 2) The collecting unit includes: Collecting comments from dementia patients in real time 2. The system of claim 1, (Appendix 3) The analysis unit is Analyzing the collected voice data will identify sentence and conversation patterns that dementia patients tend to forget. 2. The system of claim 1, (Appendix 4) The answering section: Based on identified patterns, they can proactively make statements or respond to people with dementia 2. The system of claim 1, (Appendix 5) The determination unit is Analyze the collected data and assess the improvement of dementia 2. The system of claim 1, (Appendix 6) The determination unit is Equipping a proposal department that contributes to the prevention and improvement of dementia 2. The system of claim 1, (Appendix 7) The collecting unit includes: Estimating the emotions of dementia patients and adjusting the timing of voice data collection based on the estimated emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyzing the past speech history of dementia patients and selecting the optimal collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting voice data, filtering is performed based on the current living situation and areas of interest of the dementia patient. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Estimate the emotions of dementia patients and prioritize the voice data to be collected based on the estimated emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting voice data, the geographic location information of dementia patients is taken into consideration to prioritize the collection of highly relevant data. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting voice data, analyze the social media activity of dementia patients to collect relevant data. 2. The system of claim 1, (Appendix 13) The analysis unit is Estimate the emotions of dementia patients and adjust the way the analysis is presented based on the estimated emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of analysis detail based on the importance of the audio data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of audio data. 2. The system of claim 1, (Appendix 16) The analysis unit is Estimate the emotions of dementia patients and adjust the length of analysis based on the estimated emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the audio data was collected 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on the relevance of the audio data 2. The system of claim 1, (Appendix 19) The answering section: Estimate the emotions of dementia patients and adjust the way they respond based on the estimated emotions 2. The system of claim 1, (Appendix 20) The answering section: When answering, adjust the detail of your answer based on the importance of the patterns identified 2. The system of claim 1, (Appendix 21) The answering section: When answering, apply different answering algorithms depending on the category of the pattern identified 2. The system of claim 1, (Appendix 22) The answering section: Estimate the emotions of dementia patients and adjust the length of responses based on the estimated emotions 2. The system of claim 1, (Appendix 23) The answering section: When answering, prioritize answers based on when identified patterns occur 2. The system of claim 1, (Appendix 24) The answering section: As you answer, adjust the order of answers based on the relevance of the identified patterns 2. The system of claim 1, (Appendix 25) The determination unit is Estimating the emotions of dementia patients and adjusting the method of assessing their improvement based on the estimated emotions 2. The system of claim 1, (Appendix 26) The determination unit is When making a judgment, the improvement judgment algorithm is optimized by referring to past data. 2. The system of claim 1, (Appendix 27) The determination unit is During the assessment, different assessment methods are applied depending on the category of the identified pattern. 2. The system of claim 1, (Appendix 28) The determination unit is Estimate the emotions of dementia patients and determine the priority of improvements based on the estimated emotions 2. The system of claim 1, (Appendix 29) The determination unit is At the time of evaluation, improvement is evaluated based on the time of occurrence of the identified patterns. 2. The system of claim 1, (Appendix 30) The determination unit is At the time of assessment, improvement decisions are made based on the relevance of identified patterns. 2. The system of claim 1, (Appendix 31) The suggestion unit, Estimate the emotions of dementia patients and adjust the way suggestions are presented based on the estimated emotions 3. The system of claim 2, (Appendix 32) The suggestion unit, During suggestions, adjust the granularity of the suggestions based on the importance of the identified patterns 3. The system of claim 2, (Appendix 33) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of the identified pattern. 3. The system of claim 2, (Appendix 34) The suggestion unit, Estimate the emotions of dementia patients and adjust the length of suggestions based on the estimated emotions 3. The system of claim 2, (Appendix 35) The suggestion unit, During proposal creation, proposals are prioritized based on when identified patterns occur 3. The system of claim 2, (Appendix 36) The suggestion unit, During suggestions, adjust the order of suggestions based on the relevance of identified patterns 3. The system of claim 2, [Explanation of symbols]

[0193] 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 voices of dementia patients; an analysis unit that analyzes the voice data collected by the collection unit to analyze a specific pattern; A response unit that provides a response prior to a dementia patient based on the pattern identified by the analysis unit; A determination unit that determines a progression state of dementia based on the answer provided by the answering unit. A system characterized by:

2. The collecting unit includes: Collecting comments from dementia patients in real time 2. The system of claim 1 .

3. The analysis unit is The voice data collected by the collection unit is analyzed to identify sentence and conversation patterns that dementia patients tend to forget.

2. The system of claim 1 .

4. The determination unit is The data collected by the collecting unit is analyzed to determine improvement in dementia.

2. The system of claim 1 .

5. The determination unit is Equipping a proposal department that contributes to the prevention and improvement of dementia 2. The system of claim 1 .

6. The collecting unit includes: Estimating the emotions of dementia patients and adjusting the timing of voice data collection based on the estimated emotions 2. The system of claim 1 .

7. The collecting unit includes: Analyzing the past speech history of dementia patients and selecting the optimal collection method 2. The system of claim 1 .

8. The collecting unit includes: When collecting voice data, filtering is performed based on the current living situation and areas of interest of the dementia patient.

2. The system of claim 1 .

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