System

Abstract

An object of a system according to an embodiment is to accurately grasp a desire of a dementia patient and reduce a burden on a caregiver.SOLUTION: A system includes a behavior pattern measurement part, a speech analysis part, a facial expression analysis part, and a desire estimation part. The behavior pattern measurement unit measures a behavior pattern using a smartwatch. The remark analysis unit analyzes the remark using a voice recognition technique. The facial expression analysis unit analyzes facial expressions using a camera or a sensor. A desire estimation part comprehensively analyzes the data of the action pattern measurement part, the utterance analysis part and the expression analysis part and estimates the desire of the person himself / herself.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to accurately grasp the wishes of dementia patients, placing a heavy burden on caregivers.

[0005] The system according to the embodiment aims to accurately understand the wishes of dementia patients and reduce the burden on caregivers. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavior pattern measurement unit, a utterance analysis unit, a facial expression analysis unit, and a wish estimation unit. The behavior pattern measurement unit measures behavior patterns using a smartwatch. The utterance analysis unit analyzes utterances using voice recognition technology. The facial expression analysis unit analyzes facial expressions using a camera or sensor. The wish estimation unit comprehensively analyzes data from the behavior pattern measurement unit, utterance analysis unit, and facial expression analysis unit to estimate the person's wishes. [Effects of the Invention]

[0007] The system according to the embodiment can accurately grasp the wishes of a dementia patient and reduce the burden on caregivers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The support system according to an embodiment of the present invention measures the behavioral patterns, speech, and facial expressions of a dementia patient, and uses a generative AI to predict the patient's wishes. This allows caregivers and family members providing care to accurately understand the wishes of the dementia patient and provide appropriate support.

[0029] The support system according to the embodiment includes a behavioral pattern measurement unit, a speech analysis unit, a facial expression analysis unit, and a desire estimation unit. The behavioral pattern measurement unit measures behavioral patterns using a device such as a smartwatch. For example, it records walking rhythm, meal timing, sleep patterns, and the like. The speech analysis unit analyzes speech using voice recognition technology. For example, if a patient says "I'm hungry," it analyzes the speech and infers that the patient desires a meal. The facial expression analysis unit analyzes facial expressions using a camera or sensor. For example, if the patient smiles, it analyzes the facial expression and infers that the patient feels joy or satisfaction. The desire estimation unit comprehensively analyzes data from the behavioral pattern measurement unit, speech analysis unit, and facial expression analysis unit to infer the patient's desires. For example, it determines from the behavioral pattern that mealtime is approaching, confirms from the speech that the patient is hungry, and analyzes from the facial expression that the patient does not feel satisfied, thereby inferring that the patient desires a meal. As a result, the support system according to the embodiment comprehensively analyzes the behavioral patterns, speech, and facial expressions of a dementia patient to accurately infer the patient's desires.

[0030] The behavior pattern measurement unit can scan handwritten answers and convert them into digital data. For example, the behavior pattern measurement unit scans handwritten answers and saves them as image data. It then converts the image data into text data using OCR technology. The behavior pattern measurement unit can also take a photo of the handwritten answer using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The behavior pattern measurement unit can also write handwritten answers with a dedicated digital pen, which converts the data into digital data in real time. For example, a sensor can detect the movement of the pen and save it as text data. In this way, converting handwritten answers into digital data makes it easier for generative AI to analyze.

[0031] The behavior pattern measurement unit can analyze writing pressure and stroke order to extract the writer's characteristics. The behavior pattern measurement unit, for example, uses a writing pressure sensor to analyze how an answer is written. For example, it collects data on the strength of writing pressure and extracts the writer's characteristics. In addition, to analyze the stroke order, the behavior pattern measurement unit tracks the movement of a digital pen and collects data on the writer's stroke order. For example, it analyzes the stroke order pattern and identifies the writer's characteristics. In addition, the behavior pattern measurement unit combines the data on writing pressure and stroke order to comprehensively analyze the writer's characteristics. For example, it extracts the writer's characteristics based on the degree of agreement between changes in writing pressure and stroke order. In this way, by analyzing writing pressure and stroke order, the writer's characteristics can be grasped in detail.

[0032] The behavior pattern measurement unit can convert what the student dictates into text using voice input and treat the text as an answer sheet. For example, the behavior pattern measurement unit records what the student dictates with a microphone and converts it into text data using voice recognition technology. For example, voice recognition software automatically analyzes the voice and saves it as text. The behavior pattern measurement unit also creates a system that recognizes what the student dictates in real time and displays it as text data. For example, text is displayed on a screen simultaneously with voice input. The behavior pattern measurement unit also uses voice input to convert what the student dictates into text and saves the text data as an answer sheet. For example, voice recognition technology is used to perform highly accurate text conversion. As a result, even students who have difficulty writing by hand or typing can submit answer sheets by using voice input.

[0033] The behavior pattern measurement unit can analyze images and diagrams and include visual information in the evaluation. For example, the behavior pattern measurement unit analyzes images and diagrams included in an answer sheet using image recognition technology and converts the content into text data. For example, the content of the diagram is automatically analyzed and reflected in the evaluation. The behavior pattern measurement unit also analyzes images and diagrams included in an answer sheet and builds a system that evaluates based on visual information. For example, the content of the image is analyzed and reflected in the evaluation of the answer. The behavior pattern measurement unit also analyzes answer sheets that include images and diagrams and integrates the visual information with text data for evaluation. For example, the visual information is analyzed using image recognition technology and reflected in the evaluation of the answer. In this way, analyzing answer sheets that include images and diagrams enables more detailed evaluation.

[0034] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI creates a summary, the summary generation unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The summary generation unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The summary generation unit also references related background information and topic models when the generation AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0035] The summary generation unit can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, the summary generation unit uses a generation AI to analyze the logical structure of an answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The summary generation unit also analyzes the development of arguments in an answer and builds a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The summary generation unit also develops an algorithm for the generation AI to analyze the logical structure of an answer and the development of arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of arguments in a question.

[0036] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0037] The support system may further include an environmental data collection unit. The environmental data collection unit collects environmental data, such as room temperature, humidity, and lighting brightness, using sensors. For example, if the room temperature is too high, the patient may be feeling uncomfortable, and appropriate measures can be taken based on the environmental data. Also, if the humidity is low, a humidifier can be used to alleviate discomfort caused by dryness. Furthermore, if the lighting brightness is inappropriate, the lighting can be adjusted to improve the patient's visual comfort. In this way, environmental data can be collected and measures can be taken to improve the patient's comfort.

[0038] The support system can further include a location information acquisition unit. The location information acquisition unit acquires the patient's location information using, for example, GPS or a beacon. For example, if a patient stays in a particular location for a long time, there is a possibility that a problem has occurred at that location, and appropriate measures can be taken based on the location information. Also, if a patient gets lost, the patient can be quickly found and protected based on the location information. Furthermore, the patient's range of movement can be understood based on the location information, and their daily life patterns can be analyzed. This makes it possible to acquire location information and take measures to ensure the patient's safety.

[0039] The support system can further include a health data collection unit. The health data collection unit uses sensors to collect biometric data such as heart rate, blood pressure, and body temperature. For example, if the heart rate is abnormally high, it is possible that the patient is feeling stressed, so appropriate measures can be taken based on the health data. Also, if the blood pressure is high, the patient can be prompted to contact a medical institution. Furthermore, if the body temperature is abnormally high, signs of fever can be detected early and appropriate measures can be taken. This makes it possible to collect health data and take measures to monitor the patient's health condition.

[0040] The support system can further include a dietary management unit. The dietary management unit, for example, records the patient's dietary content and calorie intake and manages nutritional balance. For example, if the patient is consuming an excessive amount of a particular nutrient, the dietary content can be adjusted based on that information. Also, if the patient is not eating, the provision of meals can be encouraged based on that information. Furthermore, the patient's food preferences and allergy information can be recorded and appropriate meals can be provided. This makes it possible to manage the patient's diet and take measures to maintain their health.

[0041] The support system may further include an exercise management unit. The exercise management unit may, for example, record the amount and pattern of exercise performed by the patient and encourage appropriate exercise. For example, if the patient has been sitting for a long period of time, an alert may be issued to encourage exercise based on that information. Also, if the patient is performing a specific exercise, the effect of that exercise may be recorded and the exercise program may be adjusted. Furthermore, the exercise preferences and physical fitness level of the patient may be recorded and an appropriate exercise program may be provided. This allows for exercise management and measures to maintain the patient's health.

[0042] The processing flow of the first embodiment will be briefly explained below.

[0043] Step 1: The behavioral pattern measurement unit uses a device such as a smartwatch to measure behavioral patterns, such as walking rhythm, meal timing, and sleep patterns. Step 2: The speech analysis unit analyzes the speech using speech recognition technology. For example, if the patient says, "I'm hungry," the speech is analyzed and it is inferred that the patient wants to eat. Step 3: The facial expression analysis unit analyzes facial expressions using cameras and sensors. For example, if the patient smiles, the facial expression is analyzed and it is inferred that the patient is feeling joy or satisfaction. Step 4: The desire inference unit infers the person's desires by comprehensively analyzing data from the behavior pattern measurement unit, speech analysis unit, and facial expression analysis unit. For example, by determining from behavior patterns that mealtime is approaching, confirming from speech that the person is hungry, and analyzing facial expressions that indicate a lack of satisfaction, it infers that the person desires to eat.

[0044] (Example 2) The support system according to an embodiment of the present invention measures the behavioral patterns, speech, and facial expressions of a dementia patient, and uses a generative AI to predict the patient's wishes. This allows caregivers and family members providing care to accurately understand the wishes of the dementia patient and provide appropriate support.

[0045] The support system according to the embodiment includes a behavioral pattern measurement unit, a speech analysis unit, a facial expression analysis unit, and a desire estimation unit. The behavioral pattern measurement unit measures behavioral patterns using a device such as a smartwatch. For example, it records walking rhythm, meal timing, sleep patterns, and the like. The speech analysis unit analyzes speech using voice recognition technology. For example, if a patient says "I'm hungry," it analyzes the speech and infers that the patient desires a meal. The facial expression analysis unit analyzes facial expressions using a camera or sensor. For example, if the patient smiles, it analyzes the facial expression and infers that the patient feels joy or satisfaction. The desire estimation unit comprehensively analyzes data from the behavioral pattern measurement unit, speech analysis unit, and facial expression analysis unit to infer the patient's desires. For example, it determines from the behavioral pattern that mealtime is approaching, confirms from the speech that the patient is hungry, and analyzes from the facial expression that the patient does not feel satisfied, thereby inferring that the patient desires a meal. As a result, the support system according to the embodiment comprehensively analyzes the behavioral patterns, speech, and facial expressions of a dementia patient to accurately infer the patient's desires.

[0046] The behavior pattern measurement unit can scan handwritten answers and convert them into digital data. For example, the behavior pattern measurement unit scans handwritten answers and saves them as image data. It then converts the image data into text data using OCR technology. The behavior pattern measurement unit can also take a photo of the handwritten answer using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The behavior pattern measurement unit can also write handwritten answers with a dedicated digital pen, which converts the data into digital data in real time. For example, a sensor can detect the movement of the pen and save it as text data. In this way, converting handwritten answers into digital data makes it easier for generative AI to analyze.

[0047] The behavior pattern measurement unit can analyze writing pressure and stroke order to extract the writer's characteristics. The behavior pattern measurement unit, for example, uses a writing pressure sensor to analyze how an answer is written. For example, it collects data on the strength of writing pressure and extracts the writer's characteristics. In addition, to analyze the stroke order, the behavior pattern measurement unit tracks the movement of a digital pen and collects data on the writer's stroke order. For example, it analyzes the stroke order pattern and identifies the writer's characteristics. In addition, the behavior pattern measurement unit combines the data on writing pressure and stroke order to comprehensively analyze the writer's characteristics. For example, it extracts the writer's characteristics based on the degree of agreement between changes in writing pressure and stroke order. In this way, by analyzing writing pressure and stroke order, the writer's characteristics can be grasped in detail.

[0048] The behavior pattern measurement unit can estimate the student's emotions using an emotion estimation function and reflect the emotion data in the evaluation of the answer sheet. For example, the behavior pattern measurement unit uses a camera to capture the student's facial expression while writing the answer sheet and analyzes the emotion using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expression. The behavior pattern measurement unit also records the student's voice while writing the answer sheet and estimates the emotion using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates the emotion score. The behavior pattern measurement unit also uses a sensor to collect the student's biometric data (heart rate and electrodermal activity) while writing the answer sheet and analyzes the emotion using an emotion estimation algorithm. For example, it calculates an emotion score based on fluctuations in heart rate. This allows the student's emotions to be reflected in the evaluation, making it possible to perform a more comprehensive evaluation.

[0049] The behavior pattern measurement unit can convert what the student dictates into text using voice input and treat the text as an answer sheet. For example, the behavior pattern measurement unit records what the student dictates with a microphone and converts it into text data using voice recognition technology. For example, voice recognition software automatically analyzes the voice and saves it as text. The behavior pattern measurement unit also creates a system that recognizes what the student dictates in real time and displays it as text data. For example, text is displayed on a screen simultaneously with voice input. The behavior pattern measurement unit also uses voice input to convert what the student dictates into text and saves the text data as an answer sheet. For example, voice recognition technology is used to perform highly accurate text conversion. As a result, even students who have difficulty writing by hand or typing can submit answer sheets by using voice input.

[0050] The behavior pattern measurement unit can analyze images and diagrams and include visual information in the evaluation. For example, the behavior pattern measurement unit analyzes images and diagrams included in an answer sheet using image recognition technology and converts the content into text data. For example, the content of the diagram is automatically analyzed and reflected in the evaluation. The behavior pattern measurement unit also analyzes images and diagrams included in an answer sheet and builds a system that evaluates based on visual information. For example, the content of the image is analyzed and reflected in the evaluation of the answer. The behavior pattern measurement unit also analyzes answer sheets that include images and diagrams and integrates the visual information with text data for evaluation. For example, the visual information is analyzed using image recognition technology and reflected in the evaluation of the answer. In this way, analyzing answer sheets that include images and diagrams enables more detailed evaluation.

[0051] The behavior pattern measurement unit can monitor students' emotions in real time using an emotion estimation function and provide feedback according to their emotions. For example, the behavior pattern measurement unit captures the student's facial expression while reading an answer sheet with a camera and analyzes their emotions in real time using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expression and provides feedback. The behavior pattern measurement unit also records the student's voice while reading an answer sheet and estimates their emotions in real time using voice analysis technology. For example, it analyzes the tone and speed of the voice, calculates an emotion score, and provides feedback. The behavior pattern measurement unit also collects the student's biometric data (heart rate and electrodermal activity) using a sensor while reading an answer sheet and analyzes their emotions in real time using an emotion estimation algorithm. For example, it calculates an emotion score based on fluctuations in heart rate and provides feedback. In this way, the student's emotions can be monitored in real time and appropriate feedback can be provided, thereby improving learning effectiveness.

[0052] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI creates a summary, the summary generation unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The summary generation unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The summary generation unit also references related background information and topic models when the generation AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0053] The summary generation unit can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, the summary generation unit uses a generation AI to analyze the logical structure of an answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The summary generation unit also analyzes the development of arguments in an answer and builds a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The summary generation unit also develops an algorithm for the generation AI to analyze the logical structure of an answer and the development of arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of arguments in a question.

[0054] The summary generation unit uses the emotion estimation function to generate a summary that captures the emotional nuances of the answer, allowing the emotional elements to be reflected in the evaluation. For example, when the generation AI summarizes, the summary generation unit uses the emotion estimation function to capture the emotional nuances of the answer. For example, it generates a summary based on an emotion score. The summary generation unit also uses the emotion estimation function to build a system in which the generation AI reflects the emotional elements of the answer in the evaluation. For example, it performs the evaluation based on the emotion score. The summary generation unit also develops an algorithm for the generation AI to use the emotion estimation function to generate a summary that captures the emotional nuances of the answer. For example, it generates a summary based on the emotion score and reflects that in the evaluation. In this way, by generating a summary that captures the emotional nuances, the emotional elements can also be reflected in the evaluation.

[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 support system may further include an environmental data collection unit. The environmental data collection unit collects environmental data, such as room temperature, humidity, and lighting brightness, using sensors. For example, if the room temperature is too high, the patient may be feeling uncomfortable, and appropriate measures can be taken based on the environmental data. Also, if the humidity is low, a humidifier can be used to alleviate discomfort caused by dryness. Furthermore, if the lighting brightness is inappropriate, the lighting can be adjusted to improve the patient's visual comfort. In this way, environmental data can be collected and measures can be taken to improve the patient's comfort.

[0057] The support system can further include a location information acquisition unit. The location information acquisition unit acquires the patient's location information using, for example, GPS or a beacon. For example, if a patient stays in a particular location for a long time, there is a possibility that a problem has occurred at that location, and appropriate measures can be taken based on the location information. Also, if a patient gets lost, the patient can be quickly found and protected based on the location information. Furthermore, the patient's range of movement can be understood based on the location information, and their daily life patterns can be analyzed. This makes it possible to acquire location information and take measures to ensure the patient's safety.

[0058] The support system can further include a health data collection unit. The health data collection unit uses sensors to collect biometric data such as heart rate, blood pressure, and body temperature. For example, if the heart rate is abnormally high, it is possible that the patient is feeling stressed, so appropriate measures can be taken based on the health data. Also, if the blood pressure is high, the patient can be prompted to contact a medical institution. Furthermore, if the body temperature is abnormally high, signs of fever can be detected early and appropriate measures can be taken. This makes it possible to collect health data and take measures to monitor the patient's health condition.

[0059] The support system can further include a dietary management unit. The dietary management unit, for example, records the patient's dietary content and calorie intake and manages nutritional balance. For example, if the patient is consuming an excessive amount of a particular nutrient, the dietary content can be adjusted based on that information. Also, if the patient is not eating, the provision of meals can be encouraged based on that information. Furthermore, the patient's food preferences and allergy information can be recorded and appropriate meals can be provided. This makes it possible to manage the patient's diet and take measures to maintain their health.

[0060] The support system may further include an exercise management unit. The exercise management unit may, for example, record the amount and pattern of exercise performed by the patient and encourage appropriate exercise. For example, if the patient has been sitting for a long period of time, an alert may be issued to encourage exercise based on that information. Also, if the patient is performing a specific exercise, the effect of that exercise may be recorded and the exercise program may be adjusted. Furthermore, the exercise preferences and physical fitness level of the patient may be recorded and an appropriate exercise program may be provided. This allows for exercise management and measures to maintain the patient's health.

[0061] The support system can further use the emotion estimation function to provide music based on the patient's emotions. For example, if the patient is feeling anxious, music with a relaxing effect can be provided. If the patient is feeling happy, music to further enhance that emotion can be provided. Furthermore, if the patient is feeling sad, music to soothe that emotion can be provided. In this way, by using the emotion estimation function to provide music that corresponds to the patient's emotions, emotional stability can be achieved.

[0062] The support system can further use the emotion estimation function to adjust lighting based on the patient's emotions. For example, if the patient needs to relax, soft warm-colored lighting can be provided. If the patient needs to concentrate, bright white lighting can be provided. Furthermore, if the patient feels drowsy, the lighting can be dimmed to encourage sleep. In this way, a comfortable environment can be provided by adjusting lighting according to the patient's emotions using the emotion estimation function.

[0063] The support system can further use the emotion estimation function to provide aromatherapy based on the patient's emotions. For example, if the patient is feeling stressed, an aroma with a relaxing effect can be provided. If the patient needs vitality, an aroma that increases energy can be provided. Furthermore, if the patient is feeling anxious, an aroma that soothes the patient's emotions can be provided. In this way, by using the emotion estimation function to provide aromatherapy that corresponds to the patient's emotions, emotional stability can be achieved.

[0064] The support system can also use an emotion estimation function to support communication based on the patient's emotions. For example, if the patient feels lonely, it can provide topics to encourage communication. If the patient feels angry, it can provide dialogue to ease those emotions. If the patient feels happy, it can provide dialogue to share those emotions. In this way, the emotion estimation function can be used to support communication based on the patient's emotions, thereby stabilizing their emotions.

[0065] The support system can further use the emotion estimation function to provide meals based on the patient's emotions. For example, if the patient is feeling stressed, it can provide meals that have a relaxing effect. If the patient is feeling happy, it can provide meals that will further enhance that emotion. Furthermore, if the patient is feeling sad, it can provide meals that will ease that emotion. In this way, by using the emotion estimation function to provide meals that correspond to the patient's emotions, it is possible to stabilize emotions.

[0066] The processing flow of the second embodiment will be briefly explained below.

[0067] Step 1: The behavioral pattern measurement unit uses a device such as a smartwatch to measure behavioral patterns, such as walking rhythm, meal timing, and sleep patterns. Step 2: The speech analysis unit analyzes the speech using speech recognition technology. For example, if the patient says, "I'm hungry," the speech is analyzed and it is inferred that the patient wants to eat. Step 3: The facial expression analysis unit analyzes facial expressions using cameras and sensors. For example, if the patient smiles, the facial expression is analyzed and it is inferred that the patient is feeling joy or satisfaction. Step 4: The desire inference unit infers the person's desires by comprehensively analyzing data from the behavior pattern measurement unit, speech analysis unit, and facial expression analysis unit. For example, by determining from behavior patterns that mealtime is approaching, confirming from speech that the person is hungry, and analyzing facial expressions that indicate a lack of satisfaction, it infers that the person desires to eat.

[0068] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0069] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0070] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0071] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0072] 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.

[0073] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0074] 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.

[0075] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0076] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0077] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0078] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0079] 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.

[0080] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0081] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0082] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0085] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0086] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0087] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0089] The 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.

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

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

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

[0093] Fig. 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.

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

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

[0096] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0098] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0100] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0102] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0104] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

[0108] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0118] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0119] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0120] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0121] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0122] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0123] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0124] 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.

[0125] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0126] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0127] 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.

[0128] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0129] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0130] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0131] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0132] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0133] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0134] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0135] 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 behavior pattern measurement unit that measures behavior patterns using a smart watch; a speech analysis unit that analyzes speech using speech recognition technology; An expression analysis unit that analyzes facial expressions using a camera or a sensor; a desire inference unit that comprehensively analyzes data from the behavior pattern measurement unit, the utterance analysis unit, and the facial expression analysis unit to infer the desires of the person. A system characterized by:

2. The behavior pattern measurement unit In addition to smartwatches, smart shoes and smart belts will be used to collect more detailed behavioral data, which will then be analyzed using generative AI. The system of claim 1 .

3. The utterance analysis unit Adding parameters such as tone, speed, and pauses to speech analysis for more detailed emotional analysis The system of claim 1 .

4. The facial expression analysis unit Adding parameters such as subtle facial movements, eye movements, and pupil dilation to facial expression analysis for more detailed emotion analysis The system of claim 1 .

5. The desired estimation unit Integrates behavioral patterns, speech, and facial expression data, as well as past medical records or lifestyle history, to make more accurate predictions about desires. The system of claim 1 .

6. The behavior pattern measurement unit Using emotion estimation functionality, we analyze emotional fluctuations that accompany changes in behavioral patterns and predict the impact of emotional changes on behavior. The system of claim 1 .

7. The utterance analysis unit Using emotion estimation, the degree of agreement between the content of a statement and its emotion is analyzed, and the intention of the statement is inferred based on the emotion. The system of claim 1 .

8. The facial expression analysis unit Using emotion estimation, we analyze emotional fluctuations accompanying changes in facial expressions and predict how these changes affect behavior or speech. The system of claim 1 .

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