System

Abstract

An object of a system according to an embodiment is to appropriately respond to a speech of a care receiver and reduce a burden on a caregiver.SOLUTION: A system according to an embodiment includes a voice recognition unit, a response generation unit, and a voice output unit. The voice recognition component recognizes the speech of the care receiver. The response generation component generates an appropriate response to the speech of the care receiver recognized by the speech recognition component. The voice output unit outputs the response generated by the response generation unit by voice.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 had the problem of making it difficult to respond appropriately to comments made by the care recipient, which can cause significant stress for the caregiver.

[0005] The system according to the embodiment aims to appropriately respond to the comments of the care-requiring person and reduce the burden on the caregiver. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice recognition unit, a response generation unit, and a voice output unit. The voice recognition unit recognizes utterances made by the care recipient. The response generation unit generates an appropriate response to the utterances made by the care recipient recognized by the voice recognition unit. The voice output unit outputs the response generated by the response generation unit by voice. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately respond to the comments of the care-requiring person and reduce the burden on the caregiver. [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) A care support system according to an embodiment of the present invention is a system that automatically recognizes utterances made by a care recipient, generates appropriate responses using a generation AI, and outputs the responses by voice. As a result, the care support system generates appropriate responses to utterances made by the care recipient and outputs the responses by voice, thereby reducing the burden on the caregiver and alleviating stress.

[0029] A care support system according to an embodiment includes a voice recognition unit, a response generation unit, and a voice output unit. The voice recognition unit recognizes the utterances of the care recipient. For example, the voice recognition unit uses deep learning-based voice recognition technology to recognize the utterances of the care recipient with high accuracy. The voice recognition unit can also use a specific voice recognition engine to recognize the utterances of the care recipient in real time. The voice recognition unit can also use noise canceling technology to remove ambient noise and clearly recognize the utterances of the care recipient. For example, the voice recognition unit uses a deep learning model to convert the utterances of the care recipient into text data. The specific voice recognition engine analyzes the voice in real time and recognizes the content of the utterance. The noise canceling technology filters out ambient noise to clearly recognize the utterances of the care recipient. The response generation unit generates an appropriate response to the utterances of the care recipient recognized by the voice recognition unit. For example, the response generation unit uses natural language generation technology to generate an appropriate response to the utterances of the care recipient. The response generation unit can also use template-based response generation technology to generate a response based on a predefined template. The response generation unit can also generate a response to the utterance of the care recipient using generation AI (e.g., text generation AI or multimodal generation AI). For example, natural language generation technology analyzes the content of the utterance of the care recipient and generates an appropriate response. Template-based response generation technology quickly generates a response based on a predefined template. The generation AI understands the content of the utterance of the care recipient and generates an appropriate response. The voice output unit outputs the response generated by the response generation unit as voice. For example, the voice output unit uses text-to-speech technology to output the generated response as voice. The voice output unit can also output the response in a natural voice using a specific voice synthesis engine. The voice output unit can also adjust the tone and speed of the voice to output the response in a voice that is easy for the care recipient to hear. For example, text-to-speech technology converts the generated text response into voice. A specific voice synthesis engine generates and outputs a response in a natural voice. By adjusting the tone and speed of the voice, the response is provided in a voice that is easy for the care recipient to hear.As a result, the care support system according to the embodiment generates an appropriate response to the utterances of the care recipient and outputs the response in audio, thereby reducing the burden on the care recipient and alleviating stress. For example, the care support system improves the care recipient's sense of security by providing a quick and appropriate response to the utterances of the care recipient. Furthermore, the care support system eliminates the need for the care recipient to repeatedly provide the same response to the utterances of the care recipient, thereby reducing the care recipient's stress. Furthermore, the care support system facilitates communication with the care recipient by providing an appropriate response to the utterances of the care recipient.

[0030] The speech recognition unit can refer to past conversation history to understand the background and intent of the care recipient's utterances and generate more appropriate responses. For example, the speech recognition unit allows the generation AI to refer to the care recipient's past conversation history to understand the background and intent of the utterances. For example, if the care recipient repeatedly asks, "What day is it today?", the frequency and timing of the question can be analyzed from the past conversation history to generate a more appropriate response. The speech recognition unit also analyzes the care recipient's utterance history and learns specific question and utterance patterns. For example, if the care recipient frequently asks, "What's for lunch?", the generation AI can understand the underlying intent and generate an appropriate response. The speech recognition unit also uses the care recipient's utterance history to infer the background and intent of the utterances and generate a more personalized response. For example, if the care recipient asks, "Is anyone there?", the generation AI can understand the underlying sense of loneliness and generate a response that provides a sense of security. By referring to past conversation history, the generation AI can understand the background and intent of the care recipient's utterances and generate a more appropriate response.

[0031] The response generation unit learns the speech patterns of the care recipient, predicts future speech, and can provide preventative responses. For example, the response generation unit uses a generation AI to learn the speech patterns of the care recipient and predict future speech. For example, if the care recipient frequently asks, "What day is it today?", the AI ​​learns that pattern and provides preventative responses. The response generation unit also analyzes the speech history of the care recipient and learns the speech patterns to predict future speech and generate appropriate responses. For example, the AI ​​responds before the care recipient asks, "What's for lunch?" The response generation unit also learns the speech patterns of the care recipient and provides preventative responses to predicted speech. For example, the AI ​​responds, "I'm here," before the care recipient asks, "Is anyone there?" This learning speech patterns, predicting future speech, and providing preventative responses improves the care recipient's sense of security.

[0032] The voice recognition unit can share the utterances of the care recipient with other caregivers or family members in real time. For example, the voice recognition unit constructs a system in which the generation AI shares the utterances of the care recipient with other caregivers and family members in real time. For example, if the care recipient asks, "What day is it today?", the utterance is notified to family members. The voice recognition unit also shares the utterances of the care recipient in real time, allowing caregivers and family members to take appropriate action. For example, if the care recipient asks, "Is anyone there?", the utterance is notified to family members, urging them to take action. The voice recognition unit also develops a system in which the generation AI shares the utterances of the care recipient in real time, improving the quality of care. For example, if the care recipient asks, "What's for lunch?", the utterance is notified to the caregiver, urging them to take action. In this way, the quality of care is improved by sharing the utterances of the care recipient in real time.

[0033] The voice recognition unit can record the statements made by the care recipient so that the caregiver can check them later. For example, the voice recognition unit will build a system in which the generation AI records the statements made by the care recipient so that the caregiver can check them later. For example, if the care recipient repeatedly asks, "What day is it today?", the statements will be recorded so that the caregiver can check them later. The voice recognition unit will also record the statements made by the care recipient so that the caregiver can check them later to find areas for improvement in care. For example, if the care recipient frequently asks, "Is anyone home?", the statements will be recorded so that the caregiver can review their response. The voice recognition unit will also develop a system in which the generation AI will record the statements made by the care recipient so that the caregiver can check them later. For example, if the care recipient repeatedly asks, "What's for lunch?", the statements will be recorded so that the caregiver can review their response. In this way, the statements made by the care recipient can be recorded so that the caregiver can review them later to find areas for improvement in care.

[0034] The response generation unit analyzes the utterances of the care recipient and plays appropriate music or video depending on the content of the utterance, thereby soothing the care recipient's mood. For example, the response generation unit constructs a system in which a generation AI analyzes the utterances of the care recipient and plays appropriate music depending on the content of the utterance. For example, if the care recipient asks, "What day is it today?", relaxing music is played. The response generation unit also develops a system in which the response generation unit analyzes the utterances of the care recipient and plays appropriate video depending on the content of the utterance. For example, if the care recipient asks, "What's for lunch?", a video of delicious-looking food is played. The response generation unit also constructs a system in which a generation AI analyzes the utterances of the care recipient and plays music or video depending on the content of the utterance, thereby soothing the care recipient's mood. For example, if the care recipient asks, "Is anyone there?", a video that gives a sense of security is played. In this way, appropriate music or video is played depending on the utterances of the care recipient, thereby soothing the care recipient's mood.

[0035] The response generation unit analyzes the statements made by the care recipient, monitors their health status based on the frequency and content of the statements, and notifies the caregiver if any abnormalities are detected. For example, the response generation unit uses a generation AI to analyze the statements made by the care recipient and build a system that monitors their health status based on the frequency and content of the statements. For example, if the care recipient frequently asks, "What day is it today?", the frequency of this request is recorded and the health status is monitored. The response generation unit also develops a system that analyzes the statements made by the care recipient and notifies the caregiver if any abnormalities are detected. For example, if the care recipient frequently asks, "Is anyone home?", the response generation unit uses a generation AI to analyze the statements made by the care recipient, monitors their health status based on the frequency and content of the statements, and notifies the caregiver if any abnormalities are detected. For example, if the care recipient frequently asks, "What's for lunch?", the response generation unit records the statement and notifies the caregiver. This allows the caregiver to be notified if any abnormalities are detected by analyzing the statements made by the care recipient and monitoring their health status.

[0036] The response generation unit can analyze the utterances of the care recipient and suggest appropriate exercises or rehabilitation depending on the content of the utterance. For example, the response generation unit builds a system in which a generation AI analyzes the utterances of the care recipient and suggests appropriate exercises depending on the content of the utterance. For example, if the care recipient asks, "What day is it today?", light stretching would be suggested. The response generation unit also develops a system in which the response generation unit analyzes the utterances of the care recipient and suggests appropriate rehabilitation depending on the content of the utterance. For example, if the care recipient asks, "What's for lunch?", hand exercises before the meal would be suggested. The response generation unit also builds a system in which a generation AI analyzes the utterances of the care recipient and suggests exercises or rehabilitation depending on the content of the utterance. For example, if the care recipient asks, "Is anyone home?", relaxing breathing techniques would be suggested. This makes it possible to support the health of the care recipient by suggesting appropriate exercises or rehabilitation depending on their utterances.

[0037] The response generation unit records the statements of the care recipient and automatically generates a diary based on the content of the statements, thereby supporting the memory of the care recipient. For example, the response generation unit builds a system in which a generation AI records the statements of the care recipient and automatically generates a diary based on the content of the statements. For example, when the care recipient asks, "What day is it today?", the events of that day are recorded in the diary. The response generation unit also develops a system in which the response generation unit records the statements of the care recipient and automatically generates a diary based on the content of the statements, thereby supporting memory. For example, when the care recipient asks, "What's for lunch?", the meal contents of that day are recorded in the diary. The response generation unit also builds a system in which a generation AI records the statements of the care recipient and automatically generates a diary based on the content of the statements, thereby supporting the memory of the care recipient. For example, when the care recipient asks, "Is anyone out?", the visitors of that day are recorded in the diary. In this way, the memory of the care recipient can be supported by recording the statements of the care recipient and automatically generating a diary.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The care support system can also analyze the comments of the care recipient and suggest appropriate exercises or rehabilitation based on the content of the comments. For example, if the care recipient asks, "What day is it today?", it can suggest light stretching. If the care recipient asks, "What's for lunch?", it can also suggest hand exercises to do before the meal. Furthermore, if the care recipient asks, "Is anyone home?" it can also suggest relaxing breathing techniques. In this way, the health of the care recipient can be supported by suggesting appropriate exercises or rehabilitation based on their comments.

[0040] The care support system can also record the comments of the care recipient and automatically generate a diary based on the content of the comments, thereby supporting the memory of the care recipient. For example, if the care recipient asks, "What day is it today?", the events of that day can be recorded in the diary. Also, if the care recipient asks, "What's for lunch?", the contents of the meals that day can be recorded in the diary. Furthermore, if the care recipient asks, "Is anyone home?", the visitors that day can be recorded in the diary. In this way, by recording the comments of the care recipient and automatically generating a diary, the memory of the care recipient can be supported.

[0041] The care support system can also analyze the comments of the care recipient and play appropriate music or images according to the content of the comments, thereby easing the care recipient's mood. For example, if the care recipient asks, "What day is it today?", relaxing music can be played. If the care recipient asks, "What's for lunch?", a video of delicious-looking food can be played. Furthermore, if the care recipient asks, "Is anyone home?", a video that gives a sense of security can be played. In this way, appropriate music or images can be played according to the care recipient's comments, thereby easing the care recipient's mood.

[0042] The care support system can also analyze the comments of the care recipient, monitor their health condition based on the frequency and content of their comments, and notify the caregiver if there are any abnormalities. For example, if the care recipient frequently asks, "What day is it today?", the frequency of this request can be recorded and their health condition can be monitored. Also, if the care recipient frequently asks, "Is anyone home?", these comments can be recorded and notified to the caregiver. Furthermore, if the care recipient frequently asks, "What's for lunch?", these comments can be recorded and notified to the caregiver. In this way, by analyzing the comments of the care recipient and monitoring their health condition, it is possible to notify the caregiver if there are any abnormalities.

[0043] The care support system can also share the comments of the care recipient with other caregivers or family members in real time. For example, if the care recipient asks, "What day is it today?", the comment will be notified to family members. Also, if the care recipient asks, "Is anyone home?", the comment can be notified to family members to prompt them to respond. Furthermore, if the care recipient asks, "What's for lunch?", the comment can be notified to the caregiver to prompt them to respond. In this way, the quality of care can be improved by sharing the comments of the care recipient in real time.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The voice recognition unit recognizes the speech of the person requiring care. For example, the voice recognition unit uses voice recognition technology that uses deep learning to recognize the speech of the person requiring care with high accuracy. The voice recognition unit can also use a specific voice recognition engine to recognize the speech of the person requiring care in real time. Furthermore, the voice recognition unit can also use noise canceling technology to remove ambient noise and clearly recognize the speech of the person requiring care. Step 2: The response generation unit generates an appropriate response to the utterance of the care recipient recognized by the speech recognition unit. For example, the response generation unit uses natural language generation technology to generate an appropriate response to the utterance of the care recipient. The response generation unit can also use template-based response generation technology to generate a response based on a predefined template. Furthermore, the response generation unit can also use generation AI (e.g., text generation AI or multimodal generation AI) to generate a response to the utterance of the care recipient. Step 3: The audio output unit outputs the response generated by the response generation unit by voice. For example, the audio output unit uses text-to-speech technology to output the generated response by voice. The audio output unit can also use a specific voice synthesis engine to output the response in a natural voice. Furthermore, the audio output unit can adjust the tone and speed of the voice to output the response in a voice that is easy for the person requiring care to hear.

[0046] (Example 2) A care support system according to an embodiment of the present invention is a system that automatically recognizes utterances made by a care recipient, generates appropriate responses using a generation AI, and outputs the responses by voice. As a result, the care support system generates appropriate responses to utterances made by the care recipient and outputs the responses by voice, thereby reducing the burden on the caregiver and alleviating stress.

[0047] A care support system according to an embodiment includes a voice recognition unit, a response generation unit, and a voice output unit. The voice recognition unit recognizes the utterances of the care recipient. For example, the voice recognition unit uses deep learning-based voice recognition technology to recognize the utterances of the care recipient with high accuracy. The voice recognition unit can also use a specific voice recognition engine to recognize the utterances of the care recipient in real time. The voice recognition unit can also use noise canceling technology to remove ambient noise and clearly recognize the utterances of the care recipient. For example, the voice recognition unit uses a deep learning model to convert the utterances of the care recipient into text data. The specific voice recognition engine analyzes the voice in real time and recognizes the content of the utterance. The noise canceling technology filters out ambient noise to clearly recognize the utterances of the care recipient. The response generation unit generates an appropriate response to the utterances of the care recipient recognized by the voice recognition unit. For example, the response generation unit uses natural language generation technology to generate an appropriate response to the utterances of the care recipient. The response generation unit can also use template-based response generation technology to generate a response based on a predefined template. The response generation unit can also generate a response to the utterance of the care recipient using generation AI (e.g., text generation AI or multimodal generation AI). For example, natural language generation technology analyzes the content of the utterance of the care recipient and generates an appropriate response. Template-based response generation technology quickly generates a response based on a predefined template. The generation AI understands the content of the utterance of the care recipient and generates an appropriate response. The voice output unit outputs the response generated by the response generation unit as voice. For example, the voice output unit uses text-to-speech technology to output the generated response as voice. The voice output unit can also output the response in a natural voice using a specific voice synthesis engine. The voice output unit can also adjust the tone and speed of the voice to output the response in a voice that is easy for the care recipient to hear. For example, text-to-speech technology converts the generated text response into voice. A specific voice synthesis engine generates and outputs a response in a natural voice. By adjusting the tone and speed of the voice, the response is provided in a voice that is easy for the care recipient to hear.As a result, the care support system according to the embodiment generates an appropriate response to the utterances of the care recipient and outputs the response in audio, thereby reducing the burden on the care recipient and alleviating stress. For example, the care support system improves the care recipient's sense of security by providing a quick and appropriate response to the utterances of the care recipient. Furthermore, the care support system eliminates the need for the care recipient to repeatedly provide the same response to the utterances of the care recipient, thereby reducing the care recipient's stress. Furthermore, the care support system facilitates communication with the care recipient by providing an appropriate response to the utterances of the care recipient.

[0048] The speech recognition unit can refer to past conversation history to understand the background and intent of the care recipient's utterances and generate more appropriate responses. For example, the speech recognition unit allows the generation AI to refer to the care recipient's past conversation history to understand the background and intent of the utterances. For example, if the care recipient repeatedly asks, "What day is it today?", the frequency and timing of the question can be analyzed from the past conversation history to generate a more appropriate response. The speech recognition unit also analyzes the care recipient's utterance history and learns specific question and utterance patterns. For example, if the care recipient frequently asks, "What's for lunch?", the generation AI can understand the underlying intent and generate an appropriate response. The speech recognition unit also uses the care recipient's utterance history to infer the background and intent of the utterances and generate a more personalized response. For example, if the care recipient asks, "Is anyone there?", the generation AI can understand the underlying sense of loneliness and generate a response that provides a sense of security. By referring to past conversation history, the generation AI can understand the background and intent of the care recipient's utterances and generate a more appropriate response.

[0049] The response generation unit can use the emotion estimation function to estimate the emotion of the care recipient's utterance and generate a response according to that emotion. For example, the response generation unit uses a generation AI to analyze the care recipient's utterance and estimate the emotion using the emotion estimation function. For example, when the care recipient asks, "What day is it today?", the response generation unit analyzes the tone of voice and facial expression to estimate the emotion and generate a response. The response generation unit also estimates the emotion of the care recipient's utterance and generates a response according to that emotion. For example, if the care recipient asks in an anxious voice, "Is anyone there?", the generation AI provides a response that gives a sense of security. The response generation unit also uses the emotion estimation function to analyze the emotion of the care recipient's utterance in real time and generate an appropriate response according to the emotion. For example, if the care recipient says, "I just said that," with anger, the generation AI provides a calm response. In this way, by using the emotion estimation function, a response according to the care recipient's emotion can be generated.

[0050] The response generation unit learns the speech patterns of the care recipient, predicts future speech, and can provide preventative responses. For example, the response generation unit uses a generation AI to learn the speech patterns of the care recipient and predict future speech. For example, if the care recipient frequently asks, "What day is it today?", the AI ​​learns that pattern and provides preventative responses. The response generation unit also analyzes the speech history of the care recipient and learns the speech patterns to predict future speech and generate appropriate responses. For example, the AI ​​responds before the care recipient asks, "What's for lunch?" The response generation unit also learns the speech patterns of the care recipient and provides preventative responses to predicted speech. For example, the AI ​​responds, "I'm here," before the care recipient asks, "Is anyone there?" This learning speech patterns, predicting future speech, and providing preventative responses improves the care recipient's sense of security.

[0051] The voice recognition unit can share the utterances of the care recipient with other caregivers or family members in real time. For example, the voice recognition unit constructs a system in which the generation AI shares the utterances of the care recipient with other caregivers and family members in real time. For example, if the care recipient asks, "What day is it today?", the utterance is notified to family members. The voice recognition unit also shares the utterances of the care recipient in real time, allowing caregivers and family members to take appropriate action. For example, if the care recipient asks, "Is anyone there?", the utterance is notified to family members, urging them to take action. The voice recognition unit also develops a system in which the generation AI shares the utterances of the care recipient in real time, improving the quality of care. For example, if the care recipient asks, "What's for lunch?", the utterance is notified to the caregiver, urging them to take action. In this way, the quality of care is improved by sharing the utterances of the care recipient in real time.

[0052] The voice recognition unit can record the statements made by the care recipient so that the caregiver can check them later. For example, the voice recognition unit will build a system in which the generation AI records the statements made by the care recipient so that the caregiver can check them later. For example, if the care recipient repeatedly asks, "What day is it today?", the statements will be recorded so that the caregiver can check them later. The voice recognition unit will also record the statements made by the care recipient so that the caregiver can check them later to find areas for improvement in care. For example, if the care recipient frequently asks, "Is anyone home?", the statements will be recorded so that the caregiver can review their response. The voice recognition unit will also develop a system in which the generation AI will record the statements made by the care recipient so that the caregiver can check them later. For example, if the care recipient repeatedly asks, "What's for lunch?", the statements will be recorded so that the caregiver can review their response. In this way, the statements made by the care recipient can be recorded so that the caregiver can review them later to find areas for improvement in care.

[0053] The response generation unit can use the emotion estimation function to analyze the caregiver's emotional reaction to the statement made by the care recipient and provide advice to reduce the caregiver's stress. The response generation unit, for example, uses the emotion estimation function to analyze the caregiver's emotional reaction to the statement made by the care recipient in real time. For example, if the caregiver gets angry and says, "I just said that," the response generation unit analyzes the caregiver's emotion and provides advice. The response generation unit also builds a system that analyzes the caregiver's emotional reaction and provides advice to reduce stress. For example, if the caregiver feels stressed, the response generation unit analyzes the emotion and suggests a relaxation method. The response generation unit also uses the emotion estimation function to analyze the caregiver's emotional reaction and provide advice to reduce stress. For example, if the caregiver feels anger, the response generation unit analyzes the emotion and suggests an appropriate way to respond. In this way, the caregiver's emotional reaction is analyzed and advice to reduce stress is provided, thereby reducing the burden on the caregiver.

[0054] The response generation unit analyzes the utterances of the care recipient and plays appropriate music or video depending on the content of the utterance, thereby soothing the care recipient's mood. For example, the response generation unit constructs a system in which a generation AI analyzes the utterances of the care recipient and plays appropriate music depending on the content of the utterance. For example, if the care recipient asks, "What day is it today?", relaxing music is played. The response generation unit also develops a system in which the response generation unit analyzes the utterances of the care recipient and plays appropriate video depending on the content of the utterance. For example, if the care recipient asks, "What's for lunch?", a video of delicious-looking food is played. The response generation unit also constructs a system in which a generation AI analyzes the utterances of the care recipient and plays music or video depending on the content of the utterance, thereby soothing the care recipient's mood. For example, if the care recipient asks, "Is anyone there?", a video that gives a sense of security is played. In this way, appropriate music or video is played depending on the utterances of the care recipient, thereby soothing the care recipient's mood.

[0055] The response generation unit analyzes the statements made by the care recipient, monitors their health status based on the frequency and content of the statements, and notifies the caregiver if any abnormalities are detected. For example, the response generation unit uses a generation AI to analyze the statements made by the care recipient and build a system that monitors their health status based on the frequency and content of the statements. For example, if the care recipient frequently asks, "What day is it today?", the frequency of this request is recorded and the health status is monitored. The response generation unit also develops a system that analyzes the statements made by the care recipient and notifies the caregiver if any abnormalities are detected. For example, if the care recipient frequently asks, "Is anyone home?", the response generation unit uses a generation AI to analyze the statements made by the care recipient, monitors their health status based on the frequency and content of the statements, and notifies the caregiver if any abnormalities are detected. For example, if the care recipient frequently asks, "What's for lunch?", the response generation unit records the statement and notifies the caregiver. This allows the caregiver to be notified if any abnormalities are detected by analyzing the statements made by the care recipient and monitoring their health status.

[0056] The response generation unit can analyze the utterances of the care recipient and suggest appropriate exercises or rehabilitation depending on the content of the utterance. For example, the response generation unit builds a system in which a generation AI analyzes the utterances of the care recipient and suggests appropriate exercises depending on the content of the utterance. For example, if the care recipient asks, "What day is it today?", light stretching would be suggested. The response generation unit also develops a system in which the response generation unit analyzes the utterances of the care recipient and suggests appropriate rehabilitation depending on the content of the utterance. For example, if the care recipient asks, "What's for lunch?", hand exercises before the meal would be suggested. The response generation unit also builds a system in which a generation AI analyzes the utterances of the care recipient and suggests exercises or rehabilitation depending on the content of the utterance. For example, if the care recipient asks, "Is anyone home?", relaxing breathing techniques would be suggested. This makes it possible to support the health of the care recipient by suggesting appropriate exercises or rehabilitation depending on their utterances.

[0057] The response generation unit records the statements of the care recipient and automatically generates a diary based on the content of the statements, thereby supporting the memory of the care recipient. For example, the response generation unit builds a system in which a generation AI records the statements of the care recipient and automatically generates a diary based on the content of the statements. For example, when the care recipient asks, "What day is it today?", the events of that day are recorded in the diary. The response generation unit also develops a system in which the response generation unit records the statements of the care recipient and automatically generates a diary based on the content of the statements, thereby supporting memory. For example, when the care recipient asks, "What's for lunch?", the meal contents of that day are recorded in the diary. The response generation unit also builds a system in which a generation AI records the statements of the care recipient and automatically generates a diary based on the content of the statements, thereby supporting the memory of the care recipient. For example, when the care recipient asks, "Is anyone out?", the visitors of that day are recorded in the diary. In this way, the memory of the care recipient can be supported by recording the statements of the care recipient and automatically generating a diary.

[0058] The response generation unit can use the emotion estimation function to analyze the emotion in response to the utterance of the care recipient and suggest a relaxation method according to the emotion. For example, the response generation unit uses the emotion estimation function to analyze the emotion in response to the utterance of the care recipient in real time and build a system that suggests a relaxation method according to the emotion. For example, if the care recipient asks, "What day is it today?" in an anxious voice, relaxing music is suggested. The response generation unit also develops a system that analyzes the emotion in response to the utterance of the care recipient and suggests a relaxation method according to the emotion. For example, if the care recipient asks, "Is anyone there?" with an angry feeling, deep breathing is suggested. The response generation unit also uses the emotion estimation function to build a system that analyzes the emotion in response to the utterance of the care recipient and suggests a relaxation method according to the emotion. For example, if the care recipient asks, "What's for lunch?" with a sad feeling, a relaxing video is suggested. In this way, the emotion in response to the utterance of the care recipient is analyzed and a relaxation method according to the emotion is suggested, thereby alleviating the care recipient's mood.

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

[0060] The care support system can also analyze the comments of the care recipient and suggest appropriate exercises or rehabilitation based on the content of the comments. For example, if the care recipient asks, "What day is it today?", it can suggest light stretching. If the care recipient asks, "What's for lunch?", it can also suggest hand exercises to do before the meal. Furthermore, if the care recipient asks, "Is anyone home?" it can also suggest relaxing breathing techniques. In this way, the health of the care recipient can be supported by suggesting appropriate exercises or rehabilitation based on their comments.

[0061] The care support system can also record the comments of the care recipient and automatically generate a diary based on the content of the comments, thereby supporting the memory of the care recipient. For example, if the care recipient asks, "What day is it today?", the events of that day can be recorded in the diary. Also, if the care recipient asks, "What's for lunch?", the contents of the meals that day can be recorded in the diary. Furthermore, if the care recipient asks, "Is anyone home?", the visitors that day can be recorded in the diary. In this way, by recording the comments of the care recipient and automatically generating a diary, the memory of the care recipient can be supported.

[0062] The care support system can also analyze the comments of the care recipient and play appropriate music or images according to the content of the comments, thereby easing the care recipient's mood. For example, if the care recipient asks, "What day is it today?", relaxing music can be played. If the care recipient asks, "What's for lunch?", a video of delicious-looking food can be played. Furthermore, if the care recipient asks, "Is anyone home?", a video that gives a sense of security can be played. In this way, appropriate music or images can be played according to the care recipient's comments, thereby easing the care recipient's mood.

[0063] The care support system can also analyze the comments of the care recipient, monitor their health condition based on the frequency and content of their comments, and notify the caregiver if there are any abnormalities. For example, if the care recipient frequently asks, "What day is it today?", the frequency of this request can be recorded and their health condition can be monitored. Also, if the care recipient frequently asks, "Is anyone home?", these comments can be recorded and notified to the caregiver. Furthermore, if the care recipient frequently asks, "What's for lunch?", these comments can be recorded and notified to the caregiver. In this way, by analyzing the comments of the care recipient and monitoring their health condition, it is possible to notify the caregiver if there are any abnormalities.

[0064] The care support system can also share the comments of the care recipient with other caregivers or family members in real time. For example, if the care recipient asks, "What day is it today?", the comment will be notified to family members. Also, if the care recipient asks, "Is anyone home?", the comment can be notified to family members to prompt them to respond. Furthermore, if the care recipient asks, "What's for lunch?", the comment can be notified to the caregiver to prompt them to respond. In this way, the quality of care can be improved by sharing the comments of the care recipient in real time.

[0065] The care support system can also analyze the comments of the care recipient and suggest appropriate exercises or rehabilitation depending on the content of the comments. For example, if the care recipient asks, "What day is it today?", it can suggest light stretching. If the care recipient asks, "What's for lunch?", it can also suggest hand exercises to do before the meal. Furthermore, if the care recipient asks, "Is anyone home?" it can also suggest relaxing breathing techniques. In this way, the health of the care recipient can be supported by suggesting appropriate exercises or rehabilitation depending on their comments.

[0066] The care support system can also record the comments of the care recipient and automatically generate a diary based on the content of the comments, thereby supporting the memory of the care recipient. For example, if the care recipient asks, "What day is it today?", the events of that day can be recorded in the diary. Also, if the care recipient asks, "What's for lunch?", the contents of the meals that day can be recorded in the diary. Furthermore, if the care recipient asks, "Is anyone home?", the visitors that day can be recorded in the diary. In this way, by recording the comments of the care recipient and automatically generating a diary, the memory of the care recipient can be supported.

[0067] The care support system can also analyze the comments of the care recipient and play appropriate music or images according to the content of the comments, thereby easing the care recipient's mood. For example, if the care recipient asks, "What day is it today?", relaxing music can be played. If the care recipient asks, "What's for lunch?", a video of delicious-looking food can be played. Furthermore, if the care recipient asks, "Is anyone home?", a video that gives a sense of security can be played. In this way, appropriate music or images can be played according to the care recipient's comments, thereby easing the care recipient's mood.

[0068] The care support system can also analyze the comments of the care recipient, monitor their health condition based on the frequency and content of their comments, and notify the caregiver if there are any abnormalities. For example, if the care recipient frequently asks, "What day is it today?", the frequency of this request can be recorded and their health condition can be monitored. Also, if the care recipient frequently asks, "Is anyone home?", these comments can be recorded and notified to the caregiver. Furthermore, if the care recipient frequently asks, "What's for lunch?", these comments can be recorded and notified to the caregiver. In this way, by analyzing the comments of the care recipient and monitoring their health condition, it is possible to notify the caregiver if there are any abnormalities.

[0069] The care support system can also share the comments of the care recipient with other caregivers or family members in real time. For example, if the care recipient asks, "What day is it today?", the comment will be notified to family members. Also, if the care recipient asks, "Is anyone home?", the comment can be notified to family members to prompt them to respond. Furthermore, if the care recipient asks, "What's for lunch?", the comment can be notified to the caregiver to prompt them to respond. In this way, the quality of care can be improved by sharing the comments of the care recipient in real time.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The voice recognition unit recognizes the speech of the person requiring care. For example, the voice recognition unit uses voice recognition technology that uses deep learning to recognize the speech of the person requiring care with high accuracy. The voice recognition unit can also use a specific voice recognition engine to recognize the speech of the person requiring care in real time. Furthermore, the voice recognition unit can also use noise canceling technology to remove ambient noise and clearly recognize the speech of the person requiring care. Step 2: The response generation unit generates an appropriate response to the utterance of the care recipient recognized by the speech recognition unit. For example, the response generation unit uses natural language generation technology to generate an appropriate response to the utterance of the care recipient. The response generation unit can also use template-based response generation technology to generate a response based on a predefined template. Furthermore, the response generation unit can also use generation AI (e.g., text generation AI or multimodal generation AI) to generate a response to the utterance of the care recipient. Step 3: The audio output unit outputs the response generated by the response generation unit by voice. For example, the audio output unit uses text-to-speech technology to output the generated response by voice. The audio output unit can also use a specific voice synthesis engine to output the response in a natural voice. Furthermore, the audio output unit can adjust the tone and speed of the voice to output the response in a voice that is easy for the person requiring care to hear.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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]

[0139] 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 voice recognition unit that recognizes speech from the person requiring care; a response generation unit that generates an appropriate response to the utterance of the care-requiring person recognized by the voice recognition unit; an audio output unit that outputs the response generated by the response generation unit by voice; A system characterized by:

2. The voice recognition unit Refer to past conversation history to understand the background and intention of the care recipient's comments and generate a more appropriate response 2. The system of claim 1.

3. The voice recognition unit Sharing the statements of the care recipient with other caregivers or family members in real time 2. The system of claim 1.

4. The response generation unit The remarks of the care recipient are translated in real time to provide multilingual care.

2. The system of claim 1.

5. The response generation unit Estimate the emotion of the person requiring care in response to the statement and generate a response according to the emotion.

2. The system of claim 1.

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