System

Abstract

To provide a system capable of presenting a method of responding to caregivers by predicting the next actions and intentions of a dementia patient.SOLUTION: A system in an embodiment includes a monitoring unit, an analysis unit, a prediction unit, and a proposal unit. The monitoring unit monitors patient status. The analysis unit analyzes the data collected by the monitoring unit. The prediction unit predicts the next action or intention of the patient based on the data obtained by the analysis unit. The proposal unit proposes to caregivers how to respond based on the prediction result obtained by the prediction unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] With conventional technology, there was an issue of it being difficult to communicate smoothly when caring for dementia patients, which increased the burden and stress on caregivers.

[0005] The system according to the embodiment aims to predict the next action or intention of a dementia patient and provide a caregiver with a response method. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, an analysis unit, a prediction unit, and a suggestion unit. The monitoring unit monitors the patient's condition. The analysis unit analyzes data collected by the monitoring unit. The prediction unit predicts the patient's next action or intention based on the data obtained by the analysis unit. The suggestion unit suggests a response method to the caregiver based on the prediction result obtained by the prediction unit. Effect of the Invention

[0007] The system according to the embodiment can predict the next action or intention of a dementia patient and provide a caregiver with information on how to respond. [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 care support system according to the embodiment of the present invention is a system that uses AI to reduce the burden on caregivers and provide appropriate care in caring for dementia patients. This care support system is a mechanism that monitors the situation and behavior of dementia patients in real time, analyzes the collected data, predicts the patient's next behavior and intention, and proposes multiple ways of communicating and responding to the caregiver. For example, the care support system uses a camera or a sensor to collect data such as the patient's movements, facial expressions, and voice. Next, the care support system analyzes the collected data and grasps the patient's behavior pattern. Furthermore, the care support system predicts the patient's next behavior and intention, and proposes multiple ways of communicating and responding to the caregiver. For example, if a patient refuses to eat, the care support system can propose multiple ways of communicating, such as "Method A of recommending a meal," "Method B of recommending a meal," and "Method C of recommending a meal." This allows the caregiver to select the optimal method according to the situation and give appropriate instructions to the patient. This allows the care support system to reduce the burden and stress of the caregiver and provide appropriate care to dementia patients. This prevents inappropriate care and enables flexible response according to the patient's situation, improving the quality of care.

[0029] The nursing care support system according to the embodiment includes a monitoring unit, an analysis unit, a prediction unit, and a proposal unit. The monitoring unit monitors the patient's condition. The monitoring unit collects data such as the patient's movements, facial expressions, and voice using, for example, a camera or a sensor. For example, the monitoring unit can capture the patient's movements even at night using an infrared camera. The monitoring unit can also detect the patient's voice and abnormal sounds using a sound sensor. Furthermore, the monitoring unit can install a camera for analyzing the patient's facial expressions and monitor changes in facial expressions in real time. The analysis unit analyzes the collected data and grasps the patient's behavior pattern. The analysis unit analyzes the data using, for example, a machine learning algorithm and extracts the patient's behavior pattern. The analysis unit can also apply different analysis algorithms depending on the type of data. For example, a movement analysis algorithm is applied to the movement data, and an facial expression analysis algorithm is applied to the facial expression data. The prediction unit predicts the patient's next behavior and intention based on the data obtained by the analysis unit. The prediction unit predicts the patient's next behavior using, for example, a machine learning model. The prediction unit can also optimize the prediction algorithm by referring to the patient's past behavior patterns. For example, the probability of a specific behavior occurring is predicted based on behaviors that the patient frequently performed in the past. The suggestion unit suggests a response method to the caregiver based on the prediction result obtained by the prediction unit. The suggestion unit suggests, for example, multiple ways of communicating and responding. For example, if a patient refuses to eat, the suggestion unit can suggest multiple ways of communicating, such as "Method A of encouraging the patient to eat," "Method B of encouraging the patient to eat," and "Method C of encouraging the patient to eat." As a result, the care support system according to the embodiment can reduce the burden on the caregiver and provide appropriate care.

[0030] The monitoring unit monitors the patient's condition. For example, the monitoring unit uses a camera or a sensor to collect data such as the patient's movements, facial expressions, and voice. Specifically, an infrared camera can be used to capture the patient's movements even at night. Since an infrared camera can capture images with high resolution even in the dark, it is effective when nighttime monitoring is required. In addition, a voice sensor can be used to detect the patient's voice or abnormal sounds. The voice sensor detects the patient's voice calling for help and abnormal sounds (for example, the sound of falling or the sound of something falling) in real time, enabling a quick response. Furthermore, the monitoring unit can install a camera to analyze the patient's facial expressions and monitor changes in facial expressions in real time. The facial expression analysis camera is used to capture subtle changes in facial expressions and determine the patient's emotional state and the presence or absence of pain. This allows the monitoring unit to collect multifaceted data such as the patient's movements, voice, and facial expressions, and to comprehensively grasp the patient's condition. Furthermore, the monitoring unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis unit and prediction unit. In addition, by adjusting the frequency and accuracy of data collection, it is possible to respond flexibly to specific situations and conditions. This allows the monitoring unit to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The analysis unit analyzes the collected data and grasps the behavioral patterns of the patient. The analysis unit, for example, uses a machine learning algorithm to analyze the data and extracts the behavioral patterns of the patient. Specifically, a motion analysis algorithm is applied to the motion data, and an expression analysis algorithm is applied to the expression data. The motion analysis algorithm analyzes the patient's walking, standing, sitting, and other movements to detect abnormal movements and the risk of falling. The expression analysis algorithm determines the emotional state and the presence or absence of pain from the patient's facial expressions, and prompts the necessary response. Furthermore, the analysis unit analyzes the voice data and detects the patient's speaking voice and abnormal sounds. The voice analysis algorithm analyzes the tone and content of the patient's voice and detects calls for help and abnormal sounds. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the patient's behavioral patterns and conditions in real time. Furthermore, the analysis unit can also perform long-term risk assessment and trend analysis by utilizing past data and statistical information. For example, based on past behavioral data, risk fluctuations in specific time periods and situations are predicted, and future measures are proposed. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing 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 prediction unit predicts the patient's next behavior and intention based on the data obtained by the analysis unit. The prediction unit predicts the patient's next behavior using, for example, a machine learning model. Specifically, the prediction algorithm is optimized by referring to the patient's past behavior patterns. For example, the probability of occurrence of a specific behavior is predicted based on the behavior that the patient frequently performed in the past. As a result, the prediction unit can predict with high accuracy what the patient will do next and what intentions he or she has. Furthermore, the prediction unit can continuously correct the prediction result based on data updated in real time to respond to the latest situation. For example, when new movement, facial expression, and voice data of the patient are collected, the prediction unit immediately takes in the new data and updates the prediction result. In addition, the prediction unit can dynamically adjust the prediction algorithm taking into account changes in the patient's health condition and environment. As a result, the prediction unit can always provide a highly accurate prediction based on the latest information and support a prompt and appropriate response. Furthermore, the prediction unit can identify the most likely risk by simulating multiple scenarios and propose specific countermeasures to the caregiver. As a result, the prediction unit can predict the patient's next behavior and intention with high accuracy, reduce the burden on the caregiver, and provide appropriate care.

[0033] The suggestion unit suggests a response method to the caregiver based on the prediction result obtained by the prediction unit. The suggestion unit suggests, for example, multiple ways of communicating and responding. Specifically, if the patient refuses to eat, the suggestion unit can suggest multiple ways of communicating, such as "Method A of recommending a meal," "Method B of recommending a meal," and "Method C of recommending a meal." This allows the caregiver to select the optimal response method according to the situation. Furthermore, the suggestion unit can customize the content of the suggestion based on the patient's condition and past reactions. For example, if the patient has accepted a meal in a specific way in the past, that method is preferentially suggested. In addition, the suggestion unit can collect feedback from the caregiver and continuously improve the accuracy and effectiveness of the content of the suggestion. For example, whether the proposed method was effective is evaluated and reflected in the next proposal. This allows the suggestion unit to provide the caregiver with a quick and appropriate response method and improve the quality of care. Furthermore, the suggestion unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. in combination. This allows the suggestion unit to provide the caregiver with a quick and reliable response method and improve the quality of care.

[0034] The monitoring unit can collect data on the patient's movements, facial expressions, and voice using a camera or a sensor. The monitoring unit can capture the patient's movements even at night using, for example, an infrared camera. The monitoring unit can also detect the patient's voice or abnormal sounds using a voice sensor. Furthermore, the monitoring unit can install a camera for analyzing the patient's facial expressions and monitor changes in facial expressions in real time. This makes it possible to grasp the patient's detailed condition in real time by using a camera or a sensor. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input video data acquired by a camera to a generation AI and have the generation AI execute analysis of movements and facial expressions from the video data.

[0035] The analysis unit can analyze the collected data and grasp the behavioral patterns of the patient. The analysis unit can, for example, use a machine learning algorithm to analyze the data and extract the behavioral patterns of the patient. The analysis unit can also apply different analysis algorithms depending on the type of data. For example, a motion analysis algorithm is applied to the motion data, and an expression analysis algorithm is applied to the expression data. This makes it possible to analyze the data to grasp the behavioral patterns of the patient and provide a basis for predicting the next action. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input the collected data to the generation AI and have the generation AI execute the analysis of the behavioral patterns.

[0036] The prediction unit can predict the patient's next behavior and intention. The prediction unit predicts the patient's next behavior using, for example, a machine learning model. The prediction unit can also optimize the prediction algorithm by referring to the patient's past behavior patterns. For example, the prediction unit predicts the occurrence probability of a specific behavior based on the behavior that the patient frequently performed in the past. This allows the caregiver to prepare an appropriate response in advance by predicting the patient's next behavior and intention. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input collected data to a generation AI and cause the generation AI to predict the next behavior and intention.

[0037] The suggestion unit can suggest multiple ways of communicating or responding to the caregiver based on the prediction result. The suggestion unit, for example, suggests multiple ways of communicating or responding. For example, if a patient refuses to eat, the suggestion unit can suggest multiple ways of communicating, such as "Method A of encouraging a patient to eat," "Method B of encouraging a patient to eat," and "Method C of encouraging a patient to eat." By suggesting multiple ways of communicating or responding, the caregiver can select the optimal method according to the situation. 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 prediction result to a generation AI and cause the generation AI to execute the proposal of multiple ways of communicating or responding.

[0038] The suggestion unit can present multiple methods of recommending meals when a patient refuses to eat. The suggestion unit presents multiple methods of recommending meals when a patient refuses to eat, for example. For example, the suggestion unit can present multiple methods such as "Method A of recommending meals," "Method B of recommending meals," and "Method C of recommending meals." This allows meals to be recommended to a patient who refuses to eat in multiple ways. Some 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 prediction result to the generation AI and cause the generation AI to execute the suggestion of methods of recommending multiple meals.

[0039] The monitoring unit can analyze the patient's past behavior history and select the optimal monitoring method. For example, the monitoring unit can focus on monitoring a specific behavior based on the patient's frequent behavior in the past. The monitoring unit can also focus on monitoring a specific time period based on the patient's past behavior patterns. Furthermore, the monitoring unit can analyze the patient's past behavior history, identify situations in which abnormal behavior is likely to occur, and strengthen monitoring. This enables efficient monitoring by selecting the optimal monitoring method based on the past behavior history. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past behavior history data to the generation AI and cause the generation AI to select the optimal monitoring method.

[0040] The monitoring unit can perform filtering based on the patient's current health condition and environment during monitoring. For example, when the patient's health condition is deteriorating, the monitoring unit filters the monitoring data in real time and prioritizes displaying important information. In addition, when the patient's environment changes, the monitoring unit can also select a monitoring method adapted to the new environment. Furthermore, the monitoring unit can remove noise from the monitoring data based on the patient's health condition and environment and provide accurate information. As a result, important information can be provided preferentially by filtering based on the health condition and environment. Part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input health condition and environmental data to the generation AI and leave the execution of filtering to the generation AI.

[0041] During monitoring, the monitoring unit can preferentially collect highly relevant data in consideration of the geographical location information of the patient. For example, when the patient is in a specific location, the monitoring unit preferentially collects data related to the location. In addition, when the patient is moving, the monitoring unit can also preferentially collect data related to the moving route. Furthermore, when the patient is in a specific facility, the monitoring unit can also preferentially collect environmental data of the facility. In this way, by considering the geographical location information, highly relevant data can be preferentially collected. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0042] The monitoring unit can analyze the patient's social media activity during monitoring and collect related data. For example, the monitoring unit analyzes the content posted by the patient on social media and monitors changes in emotions and behavior. The monitoring unit can also collect related data based on location information shared by the patient on social media. Furthermore, the monitoring unit can grasp the patient's interest in a specific topic from the patient's social media activity and collect related data. In this way, changes in the patient's emotions and behavior can be grasped by analyzing the social media activity. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input social media data to the generation AI and cause the generation AI to collect related data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance to obtain a highly accurate result. The analysis unit can also perform a simple analysis on data with low importance to save resources. Furthermore, the analysis unit can set an analysis priority order according to the importance of the data and perform efficient analysis. This allows efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a motion analysis algorithm to motion data to grasp detailed motion patterns. The analysis unit can also apply a facial expression analysis algorithm to facial expression data to grasp changes in emotions. Furthermore, the analysis unit can apply a voice analysis algorithm to voice data to analyze the content and tone of speech. This allows for highly accurate analysis by applying an analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0045] The analysis unit can adjust the order of analysis based on the collection time of the data during analysis. For example, the analysis unit prioritizes analysis of the latest data and grasps the situation in real time. The analysis unit can also refer to past data and grasp long-term behavioral patterns. Furthermore, the analysis unit can prioritize analysis of data collected in a specific time period and grasp behavioral patterns for each time period. This allows the situation to be grasped in real time by adjusting the order of analysis based on the collection time of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the collection time of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data to obtain highly accurate results. The analysis unit can also perform efficient analysis by deferring data with low relevance. Furthermore, the analysis unit can set analysis priorities according to the relevance of data and quickly grasp important information. As a result, important information can be quickly grasped by adjusting the order of analysis based on the relevance of data. 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 data to the generation AI and cause the generation AI to adjust the order of analysis.

[0047] The prediction unit can optimize the prediction algorithm by referring to the patient's past behavioral patterns at the time of prediction. The prediction unit, for example, predicts the probability of occurrence of a specific behavior based on the patient's past behavioral patterns. The prediction unit can also predict the timing of occurrence of abnormal behavior from the patient's past behavioral patterns. Furthermore, the prediction unit can also optimize the prediction algorithm by referring to the patient's past behavioral patterns to improve accuracy. As a result, the prediction algorithm can be optimized and accuracy can be improved by referring to the past behavioral patterns. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI. For example, the prediction unit can input past behavioral pattern data to the generation AI and cause the generation AI to execute optimization of the prediction algorithm.

[0048] The prediction unit can customize the prediction means based on the patient's current health condition at the time of prediction. For example, when the patient's health condition is deteriorating, the prediction unit sets the probability of abnormal behavior to be high. In addition, when the patient's health condition is stable, the prediction unit can also make predictions based on daily behavior patterns. Furthermore, the prediction unit can adjust parameters of the prediction algorithm based on the patient's health condition to improve accuracy. This enables highly accurate predictions by customizing the prediction means based on the patient's health condition. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI. For example, the prediction unit can input health condition data to the generation AI and cause the generation AI to customize the prediction means.

[0049] The prediction unit can select the optimal prediction method in consideration of the geographical location information of the patient at the time of prediction. For example, when the patient is in a specific location, the prediction unit predicts behavior related to the location. In addition, when the patient is moving, the prediction unit can also predict behavior related to the moving route. Furthermore, when the patient is in a specific facility, the prediction unit can also predict behavior based on the environment of the facility. As a result, by considering the geographical location information, the optimal prediction method can be selected, and highly accurate prediction is possible. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input geographical location information to the generation AI and cause the generation AI to select the optimal prediction method.

[0050] At the time of prediction, the prediction unit can analyze the social media activity of the patient and propose a means of prediction. The prediction unit can predict changes in emotions and behavior based on, for example, the content posted by the patient on social media. The prediction unit can also predict related behavior based on location information shared by the patient on social media. Furthermore, the prediction unit can grasp the interest in a specific topic from the patient's social media activity and predict related behavior. In this way, by analyzing the social media activity, it is possible to predict changes in the patient's emotions and behavior and propose appropriate means. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI. For example, the prediction unit can input social media data to the generation AI and cause the generation AI to execute the proposal of a means of prediction.

[0051] The suggestion unit can adjust the level of detail of the proposal based on the importance of the response method at the time of proposal. For example, the suggestion unit makes a detailed proposal for a response method with high importance. The suggestion unit can also make a concise proposal for a response method with low importance. Furthermore, the suggestion unit can set a priority order for the proposal according to the importance of the response method and make an efficient proposal. This allows for an efficient proposal by adjusting the level of detail of the proposal based on the importance of the response method. 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 importance of the response method to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0052] The suggestion unit can apply different suggestion algorithms depending on the category of the response method when making a suggestion. For example, the suggestion unit applies a meal suggestion algorithm to a response method related to meals to make a detailed suggestion. The suggestion unit can also apply a movement suggestion algorithm to a response method related to movement to make an efficient suggestion. Furthermore, the suggestion unit can apply a communication suggestion algorithm to a response method related to communication to make an appropriate suggestion. Thereby, by applying a suggestion algorithm depending on the category of the response method, an appropriate suggestion becomes possible. 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 a category of the response method to the generation AI and cause the generation AI to apply an appropriate suggestion algorithm.

[0053] The suggestion unit can adjust the order of the proposals based on the submission timing of the response methods at the time of proposal. For example, the suggestion unit preferentially suggests a response method with high urgency. The suggestion unit can also propose a response method with low urgency later. Furthermore, the suggestion unit can set the priority of the proposals according to the submission timing and make efficient proposals. This enables efficient proposals by adjusting the order of the proposals based on the submission timing of the response methods. 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 submission timing data to the generation AI and cause the generation AI to adjust the order of the proposals.

[0054] The suggestion unit can adjust the order of suggestions based on the relevance of the response methods at the time of suggestion. For example, the suggestion unit preferentially suggests highly relevant response methods. The suggestion unit can also suggest less relevant response methods later. Furthermore, the suggestion unit can set the priority of the suggestions according to the relevance of the response methods and provide important information quickly. As a result, important information can be provided quickly by adjusting the order of suggestions based on the relevance of the response methods. 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 relevance data to the generation AI and cause the generation AI to adjust the order of suggestions.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The nursing care support system can further include a vital sign monitoring unit that monitors the patient's vital signs. The vital sign monitoring unit collects data such as heart rate, blood pressure, and body temperature, and provides it to the analysis unit. For example, if the heart rate suddenly increases, the analysis unit can detect the possibility of abnormal behavior based on the data and notify the prediction unit. Also, if there is a large fluctuation in blood pressure, the prediction unit can predict the next behavior based on the data and have the suggestion unit suggest an appropriate response method. This makes it possible to grasp the patient's health condition in real time and respond quickly.

[0057] The monitoring unit can monitor the patient's sleep patterns and provide them to the analysis unit. For example, if the patient wakes up frequently during the night, the analysis unit can detect the possibility of a sleep disorder based on that data and notify the prediction unit. Also, if the quality of sleep is declining, the prediction unit can predict the behavior pattern for the next day based on that data and have the suggestion unit suggest an appropriate response method. This makes it possible to grasp the patient's sleep state and provide appropriate care.

[0058] The analysis unit can analyze the patient's dietary intake data and grasp the nutritional state. For example, if the patient is lacking a certain nutrient, the analysis unit can detect the possibility of nutritional deficiency based on the data and notify the prediction unit. Also, if the patient's food intake is decreasing, the prediction unit can predict the patient's next action based on the data and have the suggestion unit suggest an appropriate response method. This makes it possible to grasp the patient's nutritional state and provide appropriate dietary advice.

[0059] The prediction unit can predict the next behavior based on the patient's social interaction data. For example, if the patient is isolated, the prediction unit can detect the possibility of social isolation based on the data and notify the suggestion unit. Also, if the patient frequently interacts with others, the prediction unit can predict the next behavior based on the data and have the suggestion unit suggest an appropriate response method. This makes it possible to grasp the patient's social interaction state and provide appropriate care.

[0060] The suggestion unit can customize the suggestion content based on the patient's hobbies and interests. For example, if the patient likes music, the suggestion unit can suggest a relaxation method using music. If the patient likes painting, the suggestion unit can suggest a rehabilitation method using painting. If the patient likes gardening, the suggestion unit can suggest an activity using gardening. In this way, more effective care can be provided by making suggestions according to the patient's hobbies and interests.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The monitoring unit monitors the patient's condition. The monitoring unit uses cameras and sensors to collect data such as the patient's movements, facial expressions, and voice. For example, an infrared camera can be used to capture the patient's movements even at night, and a sound sensor can be used to detect the patient's voice or abnormal sounds. In addition, a camera can be installed to analyze the patient's facial expressions, making it possible to monitor changes in facial expressions in real time. Step 2: The analysis unit analyzes the collected data and understands the patient's behavioral patterns. The analysis unit uses machine learning algorithms to analyze the data and extract the patient's behavioral patterns. Different analysis algorithms can also be applied depending on the type of data. For example, a motion analysis algorithm is applied to motion data, and an expression analysis algorithm is applied to expression data. Step 3: The prediction unit predicts the patient's next behavior or intention based on the data obtained by the analysis unit. The prediction unit predicts the patient's next behavior using a machine learning model. It can also optimize the prediction algorithm by referring to the patient's past behavior patterns. For example, it predicts the probability of a specific behavior occurring based on the behavior the patient frequently performed in the past. Step 4: The suggestion unit suggests a response method to the caregiver based on the prediction results obtained by the prediction unit. The suggestion unit suggests multiple ways of communicating and responding. For example, if a patient refuses to eat, the suggestion unit can suggest multiple ways of communicating, such as "Method A of encouraging the patient to eat," "Method B of encouraging the patient to eat," and "Method C of encouraging the patient to eat."

[0063] (Example 2) The care support system according to the embodiment of the present invention is a system that uses AI to reduce the burden on caregivers and provide appropriate care in caring for dementia patients. This care support system is a mechanism that monitors the situation and behavior of dementia patients in real time, analyzes the collected data, predicts the patient's next behavior and intention, and proposes multiple ways of communicating and responding to the caregiver. For example, the care support system uses a camera or a sensor to collect data such as the patient's movements, facial expressions, and voice. Next, the care support system analyzes the collected data and grasps the patient's behavior pattern. Furthermore, the care support system predicts the patient's next behavior and intention, and proposes multiple ways of communicating and responding to the caregiver. For example, if a patient refuses to eat, the care support system can propose multiple ways of communicating, such as "Method A of recommending a meal," "Method B of recommending a meal," and "Method C of recommending a meal." This allows the caregiver to select the optimal method according to the situation and give appropriate instructions to the patient. This allows the care support system to reduce the burden and stress of the caregiver and provide appropriate care to dementia patients. This prevents inappropriate care and enables flexible response according to the patient's situation, improving the quality of care.

[0064] The nursing care support system according to the embodiment includes a monitoring unit, an analysis unit, a prediction unit, and a proposal unit. The monitoring unit monitors the patient's condition. The monitoring unit collects data such as the patient's movements, facial expressions, and voice using, for example, a camera or a sensor. For example, the monitoring unit can capture the patient's movements even at night using an infrared camera. The monitoring unit can also detect the patient's voice and abnormal sounds using a sound sensor. Furthermore, the monitoring unit can install a camera for analyzing the patient's facial expressions and monitor changes in facial expressions in real time. The analysis unit analyzes the collected data and grasps the patient's behavior pattern. The analysis unit analyzes the data using, for example, a machine learning algorithm and extracts the patient's behavior pattern. The analysis unit can also apply different analysis algorithms depending on the type of data. For example, a movement analysis algorithm is applied to the movement data, and an facial expression analysis algorithm is applied to the facial expression data. The prediction unit predicts the patient's next behavior and intention based on the data obtained by the analysis unit. The prediction unit predicts the patient's next behavior using, for example, a machine learning model. The prediction unit can also optimize the prediction algorithm by referring to the patient's past behavior patterns. For example, the probability of a specific behavior occurring is predicted based on behaviors that the patient frequently performed in the past. The suggestion unit suggests a response method to the caregiver based on the prediction result obtained by the prediction unit. The suggestion unit suggests, for example, multiple ways of communicating and responding. For example, if a patient refuses to eat, the suggestion unit can suggest multiple ways of communicating, such as "Method A of encouraging the patient to eat," "Method B of encouraging the patient to eat," and "Method C of encouraging the patient to eat." As a result, the care support system according to the embodiment can reduce the burden on the caregiver and provide appropriate care.

[0065] The monitoring unit monitors the patient's condition. For example, the monitoring unit uses a camera or a sensor to collect data such as the patient's movements, facial expressions, and voice. Specifically, an infrared camera can be used to capture the patient's movements even at night. Since an infrared camera can capture images with high resolution even in the dark, it is effective when nighttime monitoring is required. In addition, a voice sensor can be used to detect the patient's voice or abnormal sounds. The voice sensor detects the patient's voice calling for help and abnormal sounds (for example, the sound of falling or the sound of something falling) in real time, enabling a quick response. Furthermore, the monitoring unit can install a camera to analyze the patient's facial expressions and monitor changes in facial expressions in real time. The facial expression analysis camera is used to capture subtle changes in facial expressions and determine the patient's emotional state and the presence or absence of pain. This allows the monitoring unit to collect multifaceted data such as the patient's movements, voice, and facial expressions, and to comprehensively grasp the patient's condition. Furthermore, the monitoring unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis unit and prediction unit. In addition, by adjusting the frequency and accuracy of data collection, it is possible to respond flexibly to specific situations and conditions. This allows the monitoring unit to collect data efficiently and effectively, improving the performance of the entire system.

[0066] The analysis unit analyzes the collected data and grasps the behavioral patterns of the patient. The analysis unit, for example, uses a machine learning algorithm to analyze the data and extracts the behavioral patterns of the patient. Specifically, a motion analysis algorithm is applied to the motion data, and an expression analysis algorithm is applied to the expression data. The motion analysis algorithm analyzes the patient's walking, standing, sitting, and other movements to detect abnormal movements and the risk of falling. The expression analysis algorithm determines the emotional state and the presence or absence of pain from the patient's facial expressions, and prompts the necessary response. Furthermore, the analysis unit analyzes the voice data and detects the patient's speaking voice and abnormal sounds. The voice analysis algorithm analyzes the tone and content of the patient's voice and detects calls for help and abnormal sounds. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the patient's behavioral patterns and conditions in real time. Furthermore, the analysis unit can also perform long-term risk assessment and trend analysis by utilizing past data and statistical information. For example, based on past behavioral data, risk fluctuations in specific time periods and situations are predicted, and future measures are proposed. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing 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.

[0067] The prediction unit predicts the patient's next behavior and intention based on the data obtained by the analysis unit. The prediction unit predicts the patient's next behavior using, for example, a machine learning model. Specifically, the prediction algorithm is optimized by referring to the patient's past behavior patterns. For example, the probability of occurrence of a specific behavior is predicted based on the behavior that the patient frequently performed in the past. As a result, the prediction unit can predict with high accuracy what the patient will do next and what intentions he or she has. Furthermore, the prediction unit can continuously correct the prediction result based on data updated in real time to respond to the latest situation. For example, when new movement, facial expression, and voice data of the patient are collected, the prediction unit immediately takes in the new data and updates the prediction result. In addition, the prediction unit can dynamically adjust the prediction algorithm taking into account changes in the patient's health condition and environment. As a result, the prediction unit can always provide a highly accurate prediction based on the latest information and support a prompt and appropriate response. Furthermore, the prediction unit can identify the most likely risk by simulating multiple scenarios and propose specific countermeasures to the caregiver. As a result, the prediction unit can predict the patient's next behavior and intention with high accuracy, reduce the burden on the caregiver, and provide appropriate care.

[0068] The suggestion unit suggests a response method to the caregiver based on the prediction result obtained by the prediction unit. The suggestion unit suggests, for example, multiple ways of communicating and responding. Specifically, if the patient refuses to eat, the suggestion unit can suggest multiple ways of communicating, such as "Method A of recommending a meal," "Method B of recommending a meal," and "Method C of recommending a meal." This allows the caregiver to select the optimal response method according to the situation. Furthermore, the suggestion unit can customize the content of the suggestion based on the patient's condition and past reactions. For example, if the patient has accepted a meal in a specific way in the past, that method is preferentially suggested. In addition, the suggestion unit can collect feedback from the caregiver and continuously improve the accuracy and effectiveness of the content of the suggestion. For example, whether the proposed method was effective is evaluated and reflected in the next proposal. This allows the suggestion unit to provide the caregiver with a quick and appropriate response method and improve the quality of care. Furthermore, the suggestion unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. in combination. This allows the suggestion unit to provide the caregiver with a quick and reliable response method and improve the quality of care.

[0069] The monitoring unit can collect data on the patient's movements, facial expressions, and voice using a camera or a sensor. The monitoring unit can capture the patient's movements even at night using, for example, an infrared camera. The monitoring unit can also detect the patient's voice or abnormal sounds using a voice sensor. Furthermore, the monitoring unit can install a camera for analyzing the patient's facial expressions and monitor changes in facial expressions in real time. This makes it possible to grasp the patient's detailed condition in real time by using a camera or a sensor. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input video data acquired by a camera to a generation AI and have the generation AI execute analysis of movements and facial expressions from the video data.

[0070] The analysis unit can analyze the collected data and grasp the behavioral patterns of the patient. The analysis unit can, for example, use a machine learning algorithm to analyze the data and extract the behavioral patterns of the patient. The analysis unit can also apply different analysis algorithms depending on the type of data. For example, a motion analysis algorithm is applied to the motion data, and an expression analysis algorithm is applied to the expression data. This makes it possible to analyze the data to grasp the behavioral patterns of the patient and provide a basis for predicting the next action. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input the collected data to the generation AI and have the generation AI execute the analysis of the behavioral patterns.

[0071] The prediction unit can predict the patient's next behavior and intention. The prediction unit predicts the patient's next behavior using, for example, a machine learning model. The prediction unit can also optimize the prediction algorithm by referring to the patient's past behavior patterns. For example, the prediction unit predicts the occurrence probability of a specific behavior based on the behavior that the patient frequently performed in the past. This allows the caregiver to prepare an appropriate response in advance by predicting the patient's next behavior and intention. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input collected data to a generation AI and cause the generation AI to predict the next behavior and intention.

[0072] The suggestion unit can suggest multiple ways of communicating or responding to the caregiver based on the prediction result. The suggestion unit, for example, suggests multiple ways of communicating or responding. For example, if a patient refuses to eat, the suggestion unit can suggest multiple ways of communicating, such as "Method A of encouraging a patient to eat," "Method B of encouraging a patient to eat," and "Method C of encouraging a patient to eat." By suggesting multiple ways of communicating or responding, the caregiver can select the optimal method according to the situation. 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 prediction result to a generation AI and cause the generation AI to execute the proposal of multiple ways of communicating or responding.

[0073] The suggestion unit can present multiple methods of recommending meals when a patient refuses to eat. The suggestion unit presents multiple methods of recommending meals when a patient refuses to eat, for example. For example, the suggestion unit can present multiple methods such as "Method A of recommending meals," "Method B of recommending meals," and "Method C of recommending meals." This allows meals to be recommended to a patient who refuses to eat in multiple ways. Some 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 prediction result to the generation AI and cause the generation AI to execute the suggestion of methods of recommending multiple meals.

[0074] The monitoring unit can estimate the patient's emotions and adjust the monitoring frequency based on the estimated patient's emotions. For example, when the patient feels anxious, the monitoring unit increases the monitoring frequency to grasp the situation in real time. Also, when the patient is relaxed, the monitoring unit can also lower the monitoring frequency to respect the patient's privacy. Furthermore, when the patient is excited, the monitoring unit can set the monitoring frequency to a medium level and appropriately track changes in the situation. This enables appropriate monitoring by adjusting the monitoring frequency according to the patient's emotions. The estimation of emotions 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 monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input image data of students taken by a camera to the generation AI and cause the generation AI to execute an estimation of the student's emotions.

[0075] The monitoring unit can analyze the patient's past behavior history and select the optimal monitoring method. For example, the monitoring unit can focus on monitoring a specific behavior based on the patient's frequent behavior in the past. The monitoring unit can also focus on monitoring a specific time period based on the patient's past behavior patterns. Furthermore, the monitoring unit can analyze the patient's past behavior history, identify situations in which abnormal behavior is likely to occur, and strengthen monitoring. This enables efficient monitoring by selecting the optimal monitoring method based on the past behavior history. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past behavior history data to the generation AI and cause the generation AI to select the optimal monitoring method.

[0076] The monitoring unit can perform filtering based on the patient's current health condition and environment during monitoring. For example, when the patient's health condition is deteriorating, the monitoring unit filters the monitoring data in real time and prioritizes displaying important information. In addition, when the patient's environment changes, the monitoring unit can also select a monitoring method adapted to the new environment. Furthermore, the monitoring unit can remove noise from the monitoring data based on the patient's health condition and environment and provide accurate information. As a result, important information can be provided preferentially by filtering based on the health condition and environment. Part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input health condition and environmental data to the generation AI and leave the execution of filtering to the generation AI.

[0077] The monitoring unit can estimate the patient's emotion and determine the priority of data to be monitored based on the estimated emotion of the patient. For example, when the patient feels anxious, the monitoring unit preferentially monitors heart rate and facial expression data. Also, when the patient is relaxed, the monitoring unit can preferentially monitor motion data. Furthermore, when the patient is excited, the monitoring unit can preferentially monitor voice data. In this way, important data can be preferentially monitored by determining the priority of data based on the emotion of the 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 monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input emotion data to the generation AI and cause the generation AI to execute the determination of the priority of data.

[0078] During monitoring, the monitoring unit can preferentially collect highly relevant data in consideration of the geographical location information of the patient. For example, when the patient is in a specific location, the monitoring unit preferentially collects data related to the location. In addition, when the patient is moving, the monitoring unit can also preferentially collect data related to the moving route. Furthermore, when the patient is in a specific facility, the monitoring unit can also preferentially collect environmental data of the facility. In this way, by considering the geographical location information, highly relevant data can be preferentially collected. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0079] The monitoring unit can analyze the patient's social media activity during monitoring and collect related data. For example, the monitoring unit analyzes the content posted by the patient on social media and monitors changes in emotions and behavior. The monitoring unit can also collect related data based on location information shared by the patient on social media. Furthermore, the monitoring unit can grasp the patient's interest in a specific topic from the patient's social media activity and collect related data. In this way, changes in the patient's emotions and behavior can be grasped by analyzing the social media activity. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input social media data to the generation AI and cause the generation AI to collect related data.

[0080] The analysis unit can estimate the patient's emotions and adjust the analysis method based on the estimated patient's emotions. For example, when the patient feels anxious, the analysis unit performs a detailed analysis to aim for early detection of abnormal behavior. In addition, when the patient is relaxed, the analysis unit can also perform a simple analysis to grasp the patient's daily behavior pattern. Furthermore, when the patient is excited, the analysis unit can also perform an analysis focusing on a specific behavior. This allows appropriate analysis by adjusting the analysis method based on the patient's emotions. The estimation of emotions 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input emotion data to the generation AI and cause the generation AI to adjust the analysis method.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance to obtain a highly accurate result. The analysis unit can also perform a simple analysis on data with low importance to save resources. Furthermore, the analysis unit can set an analysis priority order according to the importance of the data and perform efficient analysis. This allows efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a motion analysis algorithm to motion data to grasp detailed motion patterns. The analysis unit can also apply a facial expression analysis algorithm to facial expression data to grasp changes in emotions. Furthermore, the analysis unit can apply a voice analysis algorithm to voice data to analyze the content and tone of speech. This allows for highly accurate analysis by applying an analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0083] The analysis unit can estimate the patient's emotion and determine the priority of analysis based on the estimated emotion of the patient. For example, if the patient feels anxious, the analysis unit prioritizes the analysis of heart rate and facial expression data. Also, if the patient is relaxed, the analysis unit can prioritize the analysis of motion data. Furthermore, if the patient is excited, the analysis unit can prioritize the analysis of voice data. In this way, important data can be analyzed preferentially by determining the priority of analysis based on the emotion of the patient. 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input emotion data to the generation AI and cause the generation AI to execute the determination of the priority of analysis.

[0084] The analysis unit can adjust the order of analysis based on the collection time of the data during analysis. For example, the analysis unit prioritizes analysis of the latest data and grasps the situation in real time. The analysis unit can also refer to past data and grasp long-term behavioral patterns. Furthermore, the analysis unit can prioritize analysis of data collected in a specific time period and grasp behavioral patterns for each time period. This allows the situation to be grasped in real time by adjusting the order of analysis based on the collection time of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the collection time of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data to obtain highly accurate results. The analysis unit can also perform efficient analysis by deferring data with low relevance. Furthermore, the analysis unit can set analysis priorities according to the relevance of data and quickly grasp important information. As a result, important information can be quickly grasped by adjusting the order of analysis based on the relevance of data. 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 data to the generation AI and cause the generation AI to adjust the order of analysis.

[0086] The prediction unit can estimate the patient's emotions and adjust the accuracy of the prediction based on the estimated patient's emotions. For example, when the patient feels anxious, the prediction unit aims to detect abnormal behavior early by increasing the accuracy of the prediction. In addition, when the patient is relaxed, the prediction unit can set the accuracy of the prediction to a medium level and grasp the daily behavior pattern. Furthermore, when the patient is excited, the prediction unit can make a prediction focusing on a specific behavior. As a result, appropriate prediction is possible by adjusting the accuracy of the prediction based on the patient's emotions. The estimation of emotions 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 prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input emotion data to the generation AI and cause the generation AI to adjust the accuracy of the prediction.

[0087] The prediction unit can optimize the prediction algorithm by referring to the patient's past behavioral patterns at the time of prediction. The prediction unit, for example, predicts the probability of occurrence of a specific behavior based on the patient's past behavioral patterns. The prediction unit can also predict the timing of occurrence of abnormal behavior from the patient's past behavioral patterns. Furthermore, the prediction unit can also optimize the prediction algorithm by referring to the patient's past behavioral patterns to improve accuracy. As a result, the prediction algorithm can be optimized and accuracy can be improved by referring to the past behavioral patterns. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI. For example, the prediction unit can input past behavioral pattern data to the generation AI and cause the generation AI to execute optimization of the prediction algorithm.

[0088] The prediction unit can customize the prediction means based on the patient's current health condition at the time of prediction. For example, when the patient's health condition is deteriorating, the prediction unit sets the probability of abnormal behavior to be high. In addition, when the patient's health condition is stable, the prediction unit can also make predictions based on daily behavior patterns. Furthermore, the prediction unit can adjust parameters of the prediction algorithm based on the patient's health condition to improve accuracy. This enables highly accurate predictions by customizing the prediction means based on the patient's health condition. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI. For example, the prediction unit can input health condition data to the generation AI and cause the generation AI to customize the prediction means.

[0089] The prediction unit can estimate the patient's emotion and determine the priority of prediction based on the estimated emotion of the patient. For example, when the patient feels anxious, the prediction unit prioritizes prediction of abnormal behavior. In addition, when the patient is relaxed, the prediction unit can also prioritize prediction of daily behavior patterns. Furthermore, when the patient is excited, the prediction unit can also prioritize prediction of specific behaviors. In this way, important predictions can be prioritized by determining the priority of predictions based on the emotion of the patient. 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 prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input emotion data to the generation AI and cause the generation AI to execute the determination of the priority of predictions.

[0090] The prediction unit can select the optimal prediction method in consideration of the geographical location information of the patient at the time of prediction. For example, when the patient is in a specific location, the prediction unit predicts behavior related to the location. In addition, when the patient is moving, the prediction unit can also predict behavior related to the moving route. Furthermore, when the patient is in a specific facility, the prediction unit can also predict behavior based on the environment of the facility. As a result, by considering the geographical location information, the optimal prediction method can be selected, and highly accurate prediction is possible. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input geographical location information to the generation AI and cause the generation AI to select the optimal prediction method.

[0091] At the time of prediction, the prediction unit can analyze the social media activity of the patient and propose a means of prediction. The prediction unit can predict changes in emotions and behavior based on, for example, the content posted by the patient on social media. The prediction unit can also predict related behavior based on location information shared by the patient on social media. Furthermore, the prediction unit can grasp the interest in a specific topic from the patient's social media activity and predict related behavior. In this way, by analyzing the social media activity, it is possible to predict changes in the patient's emotions and behavior and propose appropriate means. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI. For example, the prediction unit can input social media data to the generation AI and cause the generation AI to execute the proposal of a means of prediction.

[0092] The suggestion unit can estimate the patient's emotion and adjust the way of expressing the suggestion based on the estimated emotion of the patient. For example, when the patient feels anxious, the suggestion unit makes the suggestion in a gentle tone. Also, when the patient is relaxed, the suggestion unit can make the suggestion in a friendly tone. Furthermore, when the patient is excited, the suggestion unit can make the suggestion in a calm tone. This allows for more effective suggestions by adjusting the way of expressing the suggestion based on the emotion of the 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 input emotion data to the generation AI and cause the generation AI to adjust the way of expressing the suggestion.

[0093] The suggestion unit can adjust the level of detail of the proposal based on the importance of the response method at the time of proposal. For example, the suggestion unit makes a detailed proposal for a response method with high importance. The suggestion unit can also make a concise proposal for a response method with low importance. Furthermore, the suggestion unit can set a priority order for the proposal according to the importance of the response method and make an efficient proposal. This allows for an efficient proposal by adjusting the level of detail of the proposal based on the importance of the response method. 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 importance of the response method to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0094] The suggestion unit can apply different suggestion algorithms depending on the category of the response method when making a suggestion. For example, the suggestion unit applies a meal suggestion algorithm to a response method related to meals to make a detailed suggestion. The suggestion unit can also apply a movement suggestion algorithm to a response method related to movement to make an efficient suggestion. Furthermore, the suggestion unit can apply a communication suggestion algorithm to a response method related to communication to make an appropriate suggestion. Thereby, by applying a suggestion algorithm depending on the category of the response method, an appropriate suggestion becomes possible. 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 a category of the response method to the generation AI and cause the generation AI to apply an appropriate suggestion algorithm.

[0095] The suggestion unit can estimate the patient's emotion and determine the priority of suggestions based on the estimated emotion of the patient. For example, if the patient feels anxious, the suggestion unit prioritizes suggestions that give a sense of security. In addition, if the patient is relaxed, the suggestion unit can also prioritize daily suggestions. Furthermore, if the patient is excited, the suggestion unit can also prioritize suggestions that calm the patient. In this way, important suggestions can be prioritized by determining the priority of suggestions based on the patient's emotion. The estimation of emotions 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 input emotion data to the generation AI and cause the generation AI to execute the determination of the priority of suggestions.

[0096] The suggestion unit can adjust the order of the proposals based on the submission timing of the response methods at the time of proposal. For example, the suggestion unit preferentially suggests a response method with high urgency. The suggestion unit can also propose a response method with low urgency later. Furthermore, the suggestion unit can set the priority of the proposals according to the submission timing and make efficient proposals. This enables efficient proposals by adjusting the order of the proposals based on the submission timing of the response methods. 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 submission timing data to the generation AI and cause the generation AI to adjust the order of the proposals.

[0097] The suggestion unit can adjust the order of suggestions based on the relevance of the response methods at the time of suggestion. For example, the suggestion unit preferentially suggests highly relevant response methods. The suggestion unit can also suggest less relevant response methods later. Furthermore, the suggestion unit can set the priority of the suggestions according to the relevance of the response methods and provide important information quickly. As a result, important information can be provided quickly by adjusting the order of suggestions based on the relevance of the response methods. 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 relevance data to the generation AI and cause the generation AI to adjust the order of suggestions.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The nursing care support system can further include a vital sign monitoring unit that monitors the patient's vital signs. The vital sign monitoring unit collects data such as heart rate, blood pressure, and body temperature, and provides it to the analysis unit. For example, if the heart rate suddenly increases, the analysis unit can detect the possibility of abnormal behavior based on the data and notify the prediction unit. Also, if there is a large fluctuation in blood pressure, the prediction unit can predict the next behavior based on the data and have the suggestion unit suggest an appropriate response method. This makes it possible to grasp the patient's health condition in real time and respond quickly.

[0100] The monitoring unit can monitor the patient's sleep patterns and provide them to the analysis unit. For example, if the patient wakes up frequently during the night, the analysis unit can detect the possibility of a sleep disorder based on that data and notify the prediction unit. Also, if the quality of sleep is declining, the prediction unit can predict the behavior pattern for the next day based on that data and have the suggestion unit suggest an appropriate response method. This makes it possible to grasp the patient's sleep state and provide appropriate care.

[0101] The analysis unit can analyze the patient's dietary intake data and grasp the nutritional state. For example, if the patient is lacking a certain nutrient, the analysis unit can detect the possibility of nutritional deficiency based on the data and notify the prediction unit. Also, if the patient's food intake is decreasing, the prediction unit can predict the patient's next action based on the data and have the suggestion unit suggest an appropriate response method. This makes it possible to grasp the patient's nutritional state and provide appropriate dietary advice.

[0102] The prediction unit can predict the next behavior based on the patient's social interaction data. For example, if the patient is isolated, the prediction unit can detect the possibility of social isolation based on the data and notify the suggestion unit. Also, if the patient frequently interacts with others, the prediction unit can predict the next behavior based on the data and have the suggestion unit suggest an appropriate response method. This makes it possible to grasp the patient's social interaction state and provide appropriate care.

[0103] The suggestion unit can customize the suggestion content based on the patient's hobbies and interests. For example, if the patient likes music, the suggestion unit can suggest a relaxation method using music. If the patient likes painting, the suggestion unit can suggest a rehabilitation method using painting. If the patient likes gardening, the suggestion unit can suggest an activity using gardening. In this way, more effective care can be provided by making suggestions according to the patient's hobbies and interests.

[0104] The monitoring unit can estimate the patient's emotions and adjust the monitoring range based on the estimated patient's emotions. For example, if the patient feels anxious, the monitoring range can be expanded to collect detailed data. If the patient feels relaxed, the monitoring range can be narrowed to respect the patient's privacy. Furthermore, if the patient is excited, the monitoring range can be set to a medium level to appropriately track changes in the situation. This allows appropriate monitoring by adjusting the monitoring range according to the patient's emotions.

[0105] The analysis unit can estimate the patient's emotions and adjust the accuracy of the analysis based on the estimated patient's emotions. For example, if the patient is feeling anxious, a detailed analysis can be performed to detect abnormal behavior early. If the patient is relaxed, a simple analysis can be performed to understand the patient's daily behavioral patterns. Furthermore, if the patient is excited, an analysis focusing on a specific behavior can be performed. This allows for appropriate analysis by adjusting the accuracy of the analysis based on the patient's emotions.

[0106] The prediction unit can estimate the patient's emotions and adjust the timing of prediction based on the estimated patient's emotions. For example, if the patient is feeling anxious, the timing of prediction can be accelerated to detect abnormal behavior early. Also, if the patient is relaxed, the timing of prediction can be delayed to grasp the patient's daily behavioral patterns. Furthermore, if the patient is excited, the timing of prediction can be set to a medium level to perform prediction focusing on specific behavior. In this way, appropriate prediction can be made by adjusting the timing of prediction based on the patient's emotions.

[0107] The suggestion unit can estimate the patient's emotions and adjust the content of the suggestions based on the estimated patient's emotions. For example, if the patient feels anxious, the suggestion unit can make a suggestion that gives the patient a sense of security. If the patient feels relaxed, the suggestion unit can make daily suggestions. Furthermore, if the patient feels excited, the suggestion unit can make a suggestion to calm the patient. This allows more effective suggestions to be made by adjusting the content of the suggestions based on the patient's emotions.

[0108] The suggestion unit can estimate the patient's emotions and adjust the timing of suggestions based on the estimated patient's emotions. For example, if the patient is feeling anxious, the timing of suggestions can be accelerated to provide a sense of security. Also, if the patient is relaxed, the timing of suggestions can be delayed to provide daily suggestions. Furthermore, if the patient is excited, the timing of suggestions can be set to a medium level to provide calming suggestions. In this way, more effective suggestions can be made by adjusting the timing of suggestions based on the patient's emotions.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The monitoring unit monitors the patient's condition. The monitoring unit uses cameras and sensors to collect data such as the patient's movements, facial expressions, and voice. For example, an infrared camera can be used to capture the patient's movements even at night, and a sound sensor can be used to detect the patient's voice or abnormal sounds. In addition, a camera can be installed to analyze the patient's facial expressions, making it possible to monitor changes in facial expressions in real time. Step 2: The analysis unit analyzes the collected data and understands the patient's behavioral patterns. The analysis unit uses machine learning algorithms to analyze the data and extract the patient's behavioral patterns. Different analysis algorithms can also be applied depending on the type of data. For example, a motion analysis algorithm is applied to motion data, and an expression analysis algorithm is applied to expression data. Step 3: The prediction unit predicts the patient's next behavior or intention based on the data obtained by the analysis unit. The prediction unit predicts the patient's next behavior using a machine learning model. It can also optimize the prediction algorithm by referring to the patient's past behavior patterns. For example, it predicts the probability of a specific behavior occurring based on the behavior the patient frequently performed in the past. Step 4: The suggestion unit suggests a response method to the caregiver based on the prediction results obtained by the prediction unit. The suggestion unit suggests multiple ways of communicating and responding. For example, if a patient refuses to eat, the suggestion unit can suggest multiple ways of communicating, such as "Method A of encouraging the patient to eat," "Method B of encouraging the patient to eat," and "Method C of encouraging the patient to eat."

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements including the monitoring unit, the analysis unit, the prediction unit, and the 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 monitoring unit collects data such as the patient's movements, facial expressions, and voice using the camera 42 and sensors of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to grasp the patient's behavior pattern. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the patient's next behavior and intention based on the analysis result. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14, and proposes a response method to the caregiver based on the prediction result. The correspondence relationship between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements including the monitoring unit, the analysis unit, the prediction unit, and the suggestion unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit collects data such as the patient's movements, facial expressions, and voice using the camera 42 and sensors of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to grasp the patient's behavior pattern. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the patient's next behavior and intention based on the analysis result. The suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214, and proposes a response method to the caregiver based on the prediction result. The correspondence relationship between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements including the monitoring unit, the analysis unit, the prediction unit, and the suggestion unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the monitoring unit collects data such as the patient's movements, facial expressions, and voice using the camera 42 and sensors of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to grasp the patient's behavior pattern. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the patient's next behavior and intention based on the analysis result. The suggestion unit is realized, for example, by the control unit 46A of the headset type terminal 314, and proposes a response method to the caregiver based on the prediction result. The correspondence relationship between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements including the monitoring unit, the analysis unit, the prediction unit, and the suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit collects data such as the patient's movements, facial expressions, and voice using the camera 42 and sensors of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to grasp the patient's behavior pattern. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the patient's next behavior and intention based on the analysis result. The suggestion unit is realized, for example, by the control unit 46A of the robot 414, and proposes a response method to the caregiver based on the prediction result. The correspondence relationship between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Appendix 1) A monitoring unit that monitors the condition of a patient; an analysis unit that analyzes the data collected by the monitoring unit; a prediction unit that predicts a next action or intention of a patient based on the data obtained by the analysis unit; a suggestion unit that suggests a response method to a caregiver based on the prediction result obtained by the prediction unit; Equipped A system characterized by: (Appendix 2) The monitoring unit is Use a camera or sensor to collect data on the patient's movements, facial expressions, and voice. 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the collected data to understand patient behavior patterns 2. The system of claim 1, (Appendix 4) The prediction unit is Predicting a patient's next actions and intentions 2. The system of claim 1, (Appendix 5) The suggestion unit, Suggest multiple ways to communicate or respond to the caregiver based on the prediction results 2. The system of claim 1, (Appendix 6) The suggestion unit, Offer multiple meals if the patient refuses to eat 2. The system of claim 1, (Appendix 7) The monitoring unit is Estimating a patient's emotions and adjusting the frequency of monitoring based on the estimated patient's emotions 2. The system of claim 1, (Appendix 8) The monitoring unit is Analyze the patient's past behavioral history and select the most appropriate monitoring method 2. The system of claim 1, (Appendix 9) The monitoring unit is Filtering based on the patient's current health status and environment during monitoring 2. The system of claim 1, (Appendix 10) The monitoring unit is Estimating patient emotions and prioritizing data to be monitored based on the estimated patient emotions 2. The system of claim 1, (Appendix 11) The monitoring unit is During monitoring, prioritize collection of relevant data by taking into account the geographic location of the patient 2. The system of claim 1, (Appendix 12) The monitoring unit is During monitoring, analyze the patient's social media activity and collect relevant data 2. The system of claim 1, (Appendix 13) The analysis unit is Infer the patient's emotions and adjust the analysis method based on the inferred patient emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 16) The analysis unit is Estimate the patient's emotions and prioritize analysis based on the estimated patient's emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, adjust the order of analysis based on when the data was collected 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 19) The prediction unit is Estimate the patient's emotions and adjust the accuracy of the prediction based on the estimated patient emotions 2. The system of claim 1, (Appendix 20) The prediction unit is When making predictions, the prediction algorithm is optimized by referencing the patient's past behavioral patterns. 2. The system of claim 1, (Appendix 21) The prediction unit is At the time of prediction, customize the prediction measures based on the patient's current health status 2. The system of claim 1, (Appendix 22) The prediction unit is Estimate the patient's emotions and prioritize predictions based on the estimated patient emotions 2. The system of claim 1, (Appendix 23) The prediction unit is When making predictions, the geographic location of the patient is taken into account to select the most appropriate prediction method. 2. The system of claim 1, (Appendix 24) The prediction unit is At the time of prediction, the social media activity of the patient is analyzed to suggest a means of prediction. 2. The system of claim 1, (Appendix 25) The suggestion unit, Inferring the patient's emotions and adjusting the way the suggestions are presented based on the inferred patient emotions 2. The system of claim 1, (Appendix 26) The suggestion unit, When making a proposal, adjust the level of detail in the proposal based on the importance of the action 2. The system of claim 1, (Appendix 27) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of the response method. 2. The system of claim 1, (Appendix 28) The suggestion unit, Estimate patient sentiment and prioritize suggestions based on the estimated patient sentiment 2. The system of claim 1, (Appendix 29) The suggestion unit, When making proposals, adjust the order of proposals based on when the solutions were submitted 2. The system of claim 1, (Appendix 30) The suggestion unit, When making suggestions, adjust the order of suggestions based on the relevance of the response 2. The system of claim 1, [Explanation of symbols]

[0183] 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 monitoring unit that monitors the condition of a patient; an analysis unit that analyzes the data collected by the monitoring unit; a prediction unit that predicts a next action or intention of the patient based on the data obtained by the analysis unit; A suggestion unit that suggests a response method to a caregiver based on the prediction result obtained by the prediction unit. A system characterized by:

2. The monitoring unit is Collecting motion, facial expression, and voice data of said patient 2. The system of claim 1 .

3. The analysis unit is Analyzing the data collected by the monitoring unit to understand the behavioral patterns of the patient 2. The system of claim 1 .

4. The suggestion unit, A plurality of ways of communicating or responding to a caregiver are proposed based on the prediction result obtained by the prediction unit.

2. The system of claim 1 .

5. The suggestion unit, If the patient refuses to eat, provide multiple ways to encourage them to eat 2. The system of claim 1 .

6. The monitoring unit is Estimating the patient's emotion and adjusting the frequency of monitoring based on the estimated patient's emotion.

2. The system of claim 1 .

7. The monitoring unit is Analyze the patient's past behavioral history and select the optimal monitoring method.

2. The system of claim 1 .

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