System

Abstract

An object of a system according to an embodiment is to generate a response close to a human emotion.SOLUTION: A system includes a reception unit, an estimation unit, a generation unit, and a provision unit. The receiving unit receives an input from a user. The estimation unit estimates an emotion based on the information received by the reception unit. The generation unit generates a response on the basis of the emotion estimated by the estimation unit. The providing unit provides the response generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult for machine learning models to properly express emotions, and there is room for improvement in generating responses that are closer to human emotions.

[0005] The system according to the embodiment aims to generate responses that are close to human emotions. [Means for solving the problem]

[0006] A system according to an embodiment includes a receiving unit, an estimating unit, a generating unit, and a providing unit. The receiving unit receives input from a user. The estimating unit estimates an emotion based on the information received by the receiving unit. The generating unit generates a response based on the emotion estimated by the estimating unit. The providing unit provides the response generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate responses that are close to human emotions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention utilizes a generative AI to generate responses that closely resemble human emotions. This system accepts input from a user, analyzes the input, and generates a response that includes an appropriate emotional expression. For example, if a user inputs "I'm tired today," the generative AI analyzes the input and generates a response such as "Thank you for your hard work. Please get plenty of rest." This response is in tune with the user's emotions and includes human-like emotional expressions. This allows the system to generate and provide responses that are in tune with the user's emotions. For example, if a user inputs "I'm happy today," the generative AI generates a response such as "That's great! Did something good happen?" In this way, the system realizes a dialogue that is in tune with the user's emotions. Furthermore, the system can learn from the user's past input history to more accurately estimate emotions and generate responses. This allows the system to provide more appropriate responses to each user's individual emotions.

[0029] An emotion response system according to an embodiment includes a receiving unit, an estimation unit, a generation unit, and a providing unit. The receiving unit receives input from a user. The user input includes, but is not limited to, text and voice. For example, when a user inputs "I'm tired today," the receiving unit receives the information. The estimation unit uses a generation AI to estimate an emotion based on the information received by the receiving unit. For example, the estimation unit estimates that the user is tired in response to the input "I'm tired today." The generation unit uses the generation AI to generate a response based on the emotion estimated by the estimation unit. For example, in response to the input "I'm tired today," the generation unit generates a response such as "Thank you for your hard work. Please take a good rest." The providing unit provides the response generated by the generation unit to the user. For example, the providing unit provides the generated response to the user as a text message or a voice message. This allows the emotion response system according to an embodiment to generate and provide a response that is in line with the user's emotion.

[0030] The estimation unit can analyze the user's input content using a generation AI and estimate the emotion. The generation AI includes, but is not limited to, a text generation AI (e.g., LLM). For example, if a user inputs "I'm tired today," the estimation unit analyzes the content using the generation AI and estimates that the user is tired. The estimation unit can also use an emotion estimation algorithm when analyzing the user's input content. For example, the estimation unit uses an emotion estimation algorithm to estimate the emotion from the user's input content. As a result, using the generation AI improves the accuracy of emotion estimation.

[0031] The generation unit can generate a response based on the emotion estimated using the generation AI. The generation AI includes, but is not limited to, a text generation AI (e.g., LLM). For example, if a user inputs "I'm tired today," the generation unit can analyze the content using the generation AI and generate a response such as "Thank you for your hard work. Please take a good rest." The generation unit can also use a response generation algorithm when generating a response based on the emotion estimated using the generation AI. For example, the generation unit uses a response generation algorithm to generate a response that is in line with the user's emotion. This improves the accuracy of response generation by using the generation AI.

[0032] The providing unit can provide the generated response to the user. The providing unit, for example, provides the generated response to the user as a text message or a voice message. For example, if the user inputs "I'm tired today," the providing unit provides the generated response "Thank you for your hard work. Please get plenty of rest" to the user as a text message. The providing unit can also provide the generated response as a voice message. For example, the providing unit provides the generated response as a voice message using voice synthesis technology. In this way, by providing the generated response to the user, a dialogue that is in tune with the user's emotions can be realized.

[0033] The reception unit can receive text or voice input from the user. For example, when the user provides input in text format, the reception unit receives the text. Furthermore, when the user provides input in voice format, the reception unit can also receive the voice. For example, when the user vocally inputs "I'm tired today," the reception unit receives the voice. Furthermore, the reception unit can use technology to convert voice input into text. For example, the reception unit uses voice recognition technology to convert the user's voice input into text. This improves convenience by accommodating a variety of user input formats.

[0034] The estimation unit can learn the user's past input history and improve the accuracy of emotion estimation. The estimation unit, for example, learns the content the user has input in the past and improves the accuracy of emotion estimation. For example, the estimation unit learns the response when the user previously input "tired" and reflects that in subsequent responses. The estimation unit can also analyze the user's past input history and optimize the emotion estimation algorithm. For example, the estimation unit extracts specific emotion patterns from the user's past input history and improves the accuracy of emotion estimation. In this way, the accuracy of emotion estimation is improved by learning the past input history.

[0035] The reception unit can analyze the user's past input history and select an input reception method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. Furthermore, the reception unit can customize the optimal input reception method based on the content the user has entered in the past. In this way, the optimal input reception method can be provided by analyzing the past input history.

[0036] The reception unit can perform filtering based on the user's current situation or areas of interest when receiving input. The reception unit filters appropriate input content based on, for example, the user's current situation (at work, on break, etc.). The reception unit can also preferentially receive related input content based on the user's areas of interest (hobbies, work, etc.). Furthermore, the reception unit can combine the user's current situation and areas of interest to filter optimal input content. In this way, filtering based on the user's situation and areas of interest makes it possible to receive more appropriate input.

[0037] When receiving input, the reception unit can select the optimal reception means depending on the user's input method (voice, text, image, etc.). For example, if the user selects voice input, the reception unit can accept the input using voice recognition technology. Also, if the user selects text input, the reception unit can also accept the input using text analysis technology. Furthermore, if the user selects image input, the reception unit can also accept the input using image recognition technology. In this way, by selecting the optimal reception means depending on the user's input method, the accuracy of the input is improved.

[0038] When receiving input, the reception unit can prioritize receiving highly relevant inputs by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving inputs related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving inputs related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving inputs related to the home. In this way, by taking into account the user's geographical location information, it is possible to prioritize receiving highly relevant inputs.

[0039] The reception unit can analyze the user's social media activity and receive related inputs when receiving inputs. For example, the reception unit can preferentially receive inputs related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's posts on social media and preferentially receive related inputs. Furthermore, the reception unit can also preferentially receive related inputs by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, it is possible to preferentially receive related inputs.

[0040] The reception unit can improve the reception method by reflecting the user's past feedback when receiving input. For example, the reception unit can suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the reception method. In this way, the reception method can be optimized by reflecting past feedback.

[0041] The estimation unit can improve the accuracy of emotion estimation by referring to the user's past input history. For example, the estimation unit improves the accuracy of emotion estimation based on the content previously input by the user. The estimation unit can also improve the accuracy of estimation by extracting a specific emotion pattern from the user's past input history. Furthermore, the estimation unit can analyze the user's past input history and optimize the emotion estimation algorithm. In this way, the accuracy of emotion estimation is improved by referring to the past input history.

[0042] The estimation unit can estimate emotions based on user attribute information. The estimation unit can adjust the emotion estimation algorithm, for example, by taking into account the user's age. The estimation unit can also adjust the emotion estimation algorithm by taking into account the user's gender. Furthermore, the estimation unit can also adjust the emotion estimation algorithm by taking into account the user's occupation. In this way, by taking into account the user's attribute information, the accuracy of emotion estimation is improved.

[0043] The estimation unit can improve the accuracy of emotion estimation by analyzing the context of the input content during emotion estimation. For example, the estimation unit analyzes the context of the user's input content to improve the accuracy of emotion estimation. The estimation unit can also extract a specific emotion pattern from the context of the user's input content to improve the accuracy of estimation. Furthermore, the estimation unit can analyze the context of the user's input content and optimize the emotion estimation algorithm. In this way, the accuracy of emotion estimation is improved by analyzing the context of the input content.

[0044] The estimation unit can estimate emotions by taking into account the user's geographical location information. For example, if the user is in a specific area, the estimation unit estimates an emotion related to that area. If the user is traveling, the estimation unit can also estimate an emotion related to the travel destination. Furthermore, if the user is at home, the estimation unit can also estimate an emotion related to the home. In this way, by taking into account the geographical location information, the accuracy of emotion estimation is improved.

[0045] The estimation unit can improve the accuracy of emotion estimation based on the user's social media activity. For example, the estimation unit estimates emotions related to places where the user has checked in on social media. The estimation unit can also analyze the content of the user's posts on social media to estimate related emotions. Furthermore, the estimation unit can also estimate related emotions by referring to the activities of the user's friends on social media. In this way, the accuracy of emotion estimation can be improved by referring to social media activity.

[0046] The estimation unit can customize the estimation algorithm by reflecting the user's past feedback when estimating emotions. The estimation unit can optimize the emotion estimation algorithm, for example, based on feedback provided by the user in the past. The estimation unit can also extract specific emotion patterns from the user's past feedback to improve estimation accuracy. Furthermore, the estimation unit can analyze the user's past feedback and customize the emotion estimation algorithm. This allows the estimation algorithm to be optimized by reflecting past feedback.

[0047] When generating a response, the generation unit can improve the accuracy of generation by referring to the user's past response history. The generation unit improves the accuracy of response generation, for example, based on content previously input by the user. The generation unit can also extract specific response patterns from the user's past response history to improve the accuracy of generation. Furthermore, the generation unit can analyze the user's past response history and optimize the response generation algorithm. In this way, the accuracy of response generation is improved by referring to the past response history.

[0048] The generation unit can generate a response based on the user's attribute information. The generation unit can adjust the response generation algorithm, for example, taking into account the user's age. The generation unit can also adjust the response generation algorithm, taking into account the user's gender. Furthermore, the generation unit can also adjust the response generation algorithm, taking into account the user's occupation. In this way, the accuracy of response generation is improved by taking into account the user's attribute information.

[0049] The generation unit can analyze the context of the input content when generating a response to improve the accuracy of the generation. For example, the generation unit analyzes the context of the user's input content to improve the accuracy of the response generation. The generation unit can also extract a specific response pattern from the context of the user's input content to improve the accuracy of the generation. Furthermore, the generation unit can analyze the context of the user's input content to optimize the response generation algorithm. In this way, the accuracy of the response generation is improved by analyzing the context of the input content.

[0050] The generation unit can generate a response taking into account the user's geographical location information. For example, if the user is in a specific area, the generation unit generates a response related to that area. If the user is traveling, the generation unit can also generate a response related to the user's travel destination. Furthermore, if the user is at home, the generation unit can also generate a response related to the user's home. In this way, taking into account the geographical location information improves the accuracy of response generation.

[0051] When generating a response, the generation unit can improve the accuracy of generation by referring to the user's social media activity. For example, the generation unit generates a response related to a place where the user has checked in on social media. The generation unit can also analyze the content of the user's social media posts to generate a related response. Furthermore, the generation unit can also generate a related response by referring to the activity of the user's friends on social media. In this way, by referring to social media activity, the accuracy of response generation is improved.

[0052] When generating a response, the generation unit can improve the generation algorithm by reflecting the user's past feedback. For example, the generation unit optimizes the response generation algorithm based on feedback provided by the user in the past. The generation unit can also extract specific response patterns from the user's past feedback to improve the accuracy of generation. Furthermore, the generation unit can analyze the user's past feedback and customize the response generation algorithm. In this way, the generation algorithm can be optimized by reflecting past feedback.

[0053] When providing a response, the providing unit can improve the accuracy of the response by referring to the user's past response history. The providing unit improves the accuracy of the response provided, for example, based on content previously input by the user. The providing unit can also extract specific response patterns from the user's past response history to improve the accuracy of the response provided. Furthermore, the providing unit can analyze the user's past response history and optimize the response providing algorithm. In this way, the accuracy of the response provided is improved by referring to the past response history.

[0054] When providing a response, the providing unit can provide the response based on the user's attribute information. The providing unit can adjust the response providing algorithm, for example, taking into account the user's age. The providing unit can also adjust the response providing algorithm, taking into account the user's gender. Furthermore, the providing unit can also adjust the response providing algorithm, taking into account the user's occupation. In this way, by taking into account the user's attribute information, the accuracy of providing a response is improved.

[0055] When providing a response, the providing unit can analyze the context of the input content to improve the accuracy of the response provided. For example, the providing unit analyzes the context of the user's input content to improve the accuracy of the response provided. The providing unit can also extract a specific response pattern from the context of the user's input content to improve the accuracy of the response provided. Furthermore, the providing unit can analyze the context of the user's input content to optimize the response providing algorithm. In this way, the accuracy of the response provided is improved by analyzing the context of the input content.

[0056] The providing unit can provide a response taking into consideration the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide a response related to that area. Furthermore, if the user is traveling, the providing unit can also provide a response related to the user's travel destination. Furthermore, if the user is at home, the providing unit can also provide a response related to the user's home. In this way, by taking into consideration the geographical location information, the accuracy of providing a response is improved.

[0057] When providing a response, the providing unit can improve the accuracy of the response by referring to the user's social media activity. For example, the providing unit can provide a response related to a location where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide a related response. Furthermore, the providing unit can also provide a related response by referring to the activity of the user's friends on social media. In this way, by referring to social media activity, the accuracy of the response provided can be improved.

[0058] When providing a response, the providing unit can customize the response providing method by reflecting the user's past feedback. The providing unit can, for example, optimize the response providing algorithm based on feedback provided by the user in the past. The providing unit can also extract specific response patterns from the user's past feedback to improve the accuracy of the response provided. Furthermore, the providing unit can analyze the user's past feedback and customize the response providing algorithm. In this way, the response providing method can be optimized by reflecting the past feedback.

[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 reception unit can also estimate the user's current health condition based on the user's input. For example, if the user inputs "I have a headache," the reception unit analyzes the information and estimates that the user is experiencing a headache. The reception unit can also estimate the user's stress level from the user's input and provide appropriate advice. Furthermore, the reception unit can provide the user with medical information they require based on the user's input. This allows for appropriate responses according to the user's health condition.

[0061] The estimation unit can also estimate the user's interests and concerns based on the user's input. For example, if the user inputs "I saw a movie recently," the estimation unit estimates that the user is interested in movies. The estimation unit can also estimate the user's hobbies and preferences from the user's input and provide related information. Furthermore, the estimation unit can suggest events and activities that the user is interested in based on the user's input. This makes it possible to provide information that matches the user's interests and concerns.

[0062] The generation unit can also estimate the user's learning style based on the user's input and provide appropriate learning resources. For example, if the user inputs "I'm not good at studying math," the generation unit can estimate that the user has a low level of confidence in math and provide appropriate learning resources. The generation unit can also estimate the user's learning progress from the user's input and suggest what content to study next. Furthermore, the generation unit can provide advice to increase the user's motivation to study based on the user's input. This makes it possible to provide appropriate support according to the user's learning style.

[0063] The providing unit can also provide advice to improve the user's lifestyle habits based on the user's input. For example, if the user inputs "I haven't been getting enough exercise lately," the providing unit analyzes the information and provides advice to resolve the lack of exercise. The providing unit can also suggest improvements to the user's diet based on the user's input. Furthermore, the providing unit can also suggest stress management methods based on the user's input. This makes it possible to provide appropriate advice tailored to the user's lifestyle habits.

[0064] The reception unit can also support the user's travel plans based on the user's input. For example, if the user inputs "I want to go on a trip during my next holiday," the reception unit analyzes the information and suggests travel destinations. The reception unit can also estimate the travel budget and schedule from the user's input and suggest an optimal travel plan. Furthermore, the reception unit can provide points to note during the trip and recommended tourist spots based on the user's input. This allows for appropriate support tailored to the user's travel plans.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives input from the user. The user input includes text and voice. For example, if the user inputs "I'm tired today," the information is received. Step 2: The estimation unit estimates the emotion based on the information received by the reception unit. For example, the estimation unit estimates that the user is tired in response to an input of "I'm tired today." Step 3: The generator generates a response based on the emotion estimated by the estimator. For example, in response to an input such as "I'm tired today," the generator generates a response such as "Thank you for your hard work. Please take a good rest." Step 4: The providing unit provides the response generated by the generating unit to the user, for example, as a text message or a voice message.

[0067] (Example 2) A system according to an embodiment of the present invention utilizes a generative AI to generate responses that closely resemble human emotions. This system accepts input from a user, analyzes the input, and generates a response that includes an appropriate emotional expression. For example, if a user inputs "I'm tired today," the generative AI analyzes the input and generates a response such as "Thank you for your hard work. Please get plenty of rest." This response is in tune with the user's emotions and includes human-like emotional expressions. This allows the system to generate and provide responses that are in tune with the user's emotions. For example, if a user inputs "I'm happy today," the generative AI generates a response such as "That's great! Did something good happen?" In this way, the system realizes a dialogue that is in tune with the user's emotions. Furthermore, the system can learn from the user's past input history to more accurately estimate emotions and generate responses. This allows the system to provide more appropriate responses to each user's individual emotions.

[0068] An emotion response system according to an embodiment includes a receiving unit, an estimation unit, a generation unit, and a providing unit. The receiving unit receives input from a user. The user input includes, but is not limited to, text and voice. For example, when a user inputs "I'm tired today," the receiving unit receives the information. The estimation unit uses a generation AI to estimate an emotion based on the information received by the receiving unit. For example, the estimation unit estimates that the user is tired in response to the input "I'm tired today." The generation unit uses the generation AI to generate a response based on the emotion estimated by the estimation unit. For example, in response to the input "I'm tired today," the generation unit generates a response such as "Thank you for your hard work. Please take a good rest." The providing unit provides the response generated by the generation unit to the user. For example, the providing unit provides the generated response to the user as a text message or a voice message. This allows the emotion response system according to an embodiment to generate and provide a response that is in line with the user's emotion.

[0069] The estimation unit can analyze the user's input content using a generation AI and estimate the emotion. The generation AI includes, but is not limited to, a text generation AI (e.g., LLM). For example, if a user inputs "I'm tired today," the estimation unit analyzes the content using the generation AI and estimates that the user is tired. The estimation unit can also use an emotion estimation algorithm when analyzing the user's input content. For example, the estimation unit uses an emotion estimation algorithm to estimate the emotion from the user's input content. As a result, using the generation AI improves the accuracy of emotion estimation.

[0070] The generation unit can generate a response based on the emotion estimated using the generation AI. The generation AI includes, but is not limited to, a text generation AI (e.g., LLM). For example, if a user inputs "I'm tired today," the generation unit can analyze the content using the generation AI and generate a response such as "Thank you for your hard work. Please take a good rest." The generation unit can also use a response generation algorithm when generating a response based on the emotion estimated using the generation AI. For example, the generation unit uses a response generation algorithm to generate a response that is in line with the user's emotion. This improves the accuracy of response generation by using the generation AI.

[0071] The providing unit can provide the generated response to the user. The providing unit, for example, provides the generated response to the user as a text message or a voice message. For example, if the user inputs "I'm tired today," the providing unit provides the generated response "Thank you for your hard work. Please get plenty of rest" to the user as a text message. The providing unit can also provide the generated response as a voice message. For example, the providing unit provides the generated response as a voice message using voice synthesis technology. In this way, by providing the generated response to the user, a dialogue that is in tune with the user's emotions can be realized.

[0072] The reception unit can receive text or voice input from the user. For example, when the user provides input in text format, the reception unit receives the text. Furthermore, when the user provides input in voice format, the reception unit can also receive the voice. For example, when the user vocally inputs "I'm tired today," the reception unit receives the voice. Furthermore, the reception unit can use technology to convert voice input into text. For example, the reception unit uses voice recognition technology to convert the user's voice input into text. This improves convenience by accommodating a variety of user input formats.

[0073] The estimation unit can learn the user's past input history and improve the accuracy of emotion estimation. The estimation unit, for example, learns the content the user has input in the past and improves the accuracy of emotion estimation. For example, the estimation unit learns the response when the user previously input "tired" and reflects that in subsequent responses. The estimation unit can also analyze the user's past input history and optimize the emotion estimation algorithm. For example, the estimation unit extracts specific emotion patterns from the user's past input history and improves the accuracy of emotion estimation. In this way, the accuracy of emotion estimation is improved by learning the past input history.

[0074] The reception unit can estimate the user's emotion and adjust the timing of input reception based on the estimated user emotion. For example, if the user is feeling stressed, the reception unit can delay the timing of input reception to provide the user with time to relax. Furthermore, if the user is excited, the reception unit can quickly accept input and respond while the user's emotion is heightened. Furthermore, if the user is tired, the reception unit can adjust the timing of input reception to allow the user to rest. This allows for more appropriate responses by adjusting the timing of input reception according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The reception unit can analyze the user's past input history and select an input reception method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. Furthermore, the reception unit can customize the optimal input reception method based on the content the user has entered in the past. In this way, the optimal input reception method can be provided by analyzing the past input history.

[0076] The reception unit can perform filtering based on the user's current situation or areas of interest when receiving input. The reception unit filters appropriate input content based on, for example, the user's current situation (at work, on break, etc.). The reception unit can also preferentially receive related input content based on the user's areas of interest (hobbies, work, etc.). Furthermore, the reception unit can combine the user's current situation and areas of interest to filter optimal input content. In this way, filtering based on the user's situation and areas of interest makes it possible to receive more appropriate input.

[0077] When receiving input, the reception unit can select the optimal reception means depending on the user's input method (voice, text, image, etc.). For example, if the user selects voice input, the reception unit can accept the input using voice recognition technology. Also, if the user selects text input, the reception unit can also accept the input using text analysis technology. Furthermore, if the user selects image input, the reception unit can also accept the input using image recognition technology. In this way, by selecting the optimal reception means depending on the user's input method, the accuracy of the input is improved.

[0078] The reception unit can estimate the user's emotion and determine the priority of inputs to be received based on the estimated user's emotion. For example, when the user is nervous, the reception unit can prioritize receiving important inputs. Furthermore, when the user is relaxed, the reception unit can also prioritize receiving detailed inputs. Furthermore, when the user is in a hurry, the reception unit can also prioritize receiving inputs that require a quick response. In this way, by determining the priority of inputs based on the user's emotion, important inputs can be processed preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] When receiving input, the reception unit can prioritize receiving highly relevant inputs by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving inputs related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving inputs related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving inputs related to the home. In this way, by taking into account the user's geographical location information, it is possible to prioritize receiving highly relevant inputs.

[0080] The reception unit can analyze the user's social media activity and receive related inputs when receiving inputs. For example, the reception unit can preferentially receive inputs related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's posts on social media and preferentially receive related inputs. Furthermore, the reception unit can also preferentially receive related inputs by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, it is possible to preferentially receive related inputs.

[0081] The reception unit can improve the reception method by reflecting the user's past feedback when receiving input. For example, the reception unit can suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the reception method. In this way, the reception method can be optimized by reflecting past feedback.

[0082] The estimation unit can estimate the user's emotion and adjust the emotion estimation algorithm based on the estimated user emotion. For example, if the user is relaxed, the estimation unit can gently adjust the emotion estimation algorithm. Furthermore, if the user is nervous, the estimation unit can also sensitively adjust the emotion estimation algorithm. Furthermore, if the user is excited, the estimation unit can quickly adjust the emotion estimation algorithm. This improves the accuracy of emotion estimation by adjusting the algorithm based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0083] The estimation unit can improve the accuracy of emotion estimation by referring to the user's past input history. For example, the estimation unit improves the accuracy of emotion estimation based on the content previously input by the user. The estimation unit can also improve the accuracy of estimation by extracting a specific emotion pattern from the user's past input history. Furthermore, the estimation unit can analyze the user's past input history and optimize the emotion estimation algorithm. In this way, the accuracy of emotion estimation is improved by referring to the past input history.

[0084] The estimation unit can estimate emotions based on user attribute information. The estimation unit can adjust the emotion estimation algorithm, for example, by taking into account the user's age. The estimation unit can also adjust the emotion estimation algorithm by taking into account the user's gender. Furthermore, the estimation unit can also adjust the emotion estimation algorithm by taking into account the user's occupation. In this way, by taking into account the user's attribute information, the accuracy of emotion estimation is improved.

[0085] The estimation unit can improve the accuracy of emotion estimation by analyzing the context of the input content during emotion estimation. For example, the estimation unit analyzes the context of the user's input content to improve the accuracy of emotion estimation. The estimation unit can also extract a specific emotion pattern from the context of the user's input content to improve the accuracy of estimation. Furthermore, the estimation unit can analyze the context of the user's input content and optimize the emotion estimation algorithm. In this way, the accuracy of emotion estimation is improved by analyzing the context of the input content.

[0086] The estimation unit can estimate the user's emotion and adjust the display method of the estimation result based on the estimated user emotion. For example, if the user is nervous, the estimation unit provides a simple, highly visible display method. If the user is relaxed, the estimation unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the estimation unit can also provide a display method that focuses on the main points. This improves visibility by adjusting the display method based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] The estimation unit can estimate emotions by taking into account the user's geographical location information. For example, if the user is in a specific area, the estimation unit estimates an emotion related to that area. If the user is traveling, the estimation unit can also estimate an emotion related to the travel destination. Furthermore, if the user is at home, the estimation unit can also estimate an emotion related to the home. In this way, by taking into account the geographical location information, the accuracy of emotion estimation is improved.

[0088] The estimation unit can improve the accuracy of emotion estimation based on the user's social media activity. For example, the estimation unit estimates emotions related to places where the user has checked in on social media. The estimation unit can also analyze the content of the user's posts on social media to estimate related emotions. Furthermore, the estimation unit can also estimate related emotions by referring to the activities of the user's friends on social media. In this way, the accuracy of emotion estimation can be improved by referring to social media activity.

[0089] The estimation unit can customize the estimation algorithm by reflecting the user's past feedback when estimating emotions. The estimation unit can optimize the emotion estimation algorithm, for example, based on feedback provided by the user in the past. The estimation unit can also extract specific emotion patterns from the user's past feedback to improve estimation accuracy. Furthermore, the estimation unit can analyze the user's past feedback and customize the emotion estimation algorithm. This allows the estimation algorithm to be optimized by reflecting past feedback.

[0090] The generation unit can estimate the user's emotions and adjust the response generation algorithm based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a response that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a quick and concise response. Furthermore, if the user is excited, the generation unit can also generate a response that adds a visually stimulating effect. This improves the accuracy of the response by adjusting the response generation algorithm based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] When generating a response, the generation unit can improve the accuracy of generation by referring to the user's past response history. The generation unit improves the accuracy of response generation, for example, based on content previously input by the user. The generation unit can also extract specific response patterns from the user's past response history to improve the accuracy of generation. Furthermore, the generation unit can analyze the user's past response history and optimize the response generation algorithm. In this way, the accuracy of response generation is improved by referring to the past response history.

[0092] The generation unit can generate a response based on the user's attribute information. The generation unit can adjust the response generation algorithm, for example, taking into account the user's age. The generation unit can also adjust the response generation algorithm, taking into account the user's gender. Furthermore, the generation unit can also adjust the response generation algorithm, taking into account the user's occupation. In this way, the accuracy of response generation is improved by taking into account the user's attribute information.

[0093] The generation unit can analyze the context of the input content when generating a response to improve the accuracy of the generation. For example, the generation unit analyzes the context of the user's input content to improve the accuracy of the response generation. The generation unit can also extract a specific response pattern from the context of the user's input content to improve the accuracy of the generation. Furthermore, the generation unit can analyze the context of the user's input content to optimize the response generation algorithm. In this way, the accuracy of the response generation is improved by analyzing the context of the input content.

[0094] The generation unit can estimate the user's emotions and adjust the response display method based on the estimated user emotions. For example, if the user is nervous, the generation unit provides a simple, highly visible display method. If the user is relaxed, the generation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can also provide a display method that focuses on the main points. This improves visibility by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0095] The generation unit can generate a response taking into account the user's geographical location information. For example, if the user is in a specific area, the generation unit generates a response related to that area. If the user is traveling, the generation unit can also generate a response related to the user's travel destination. Furthermore, if the user is at home, the generation unit can also generate a response related to the user's home. In this way, taking into account the geographical location information improves the accuracy of response generation.

[0096] When generating a response, the generation unit can improve the accuracy of generation by referring to the user's social media activity. For example, the generation unit generates a response related to a place where the user has checked in on social media. The generation unit can also analyze the content of the user's social media posts to generate a related response. Furthermore, the generation unit can also generate a related response by referring to the activity of the user's friends on social media. In this way, by referring to social media activity, the accuracy of response generation is improved.

[0097] When generating a response, the generation unit can improve the generation algorithm by reflecting the user's past feedback. For example, the generation unit optimizes the response generation algorithm based on feedback provided by the user in the past. The generation unit can also extract specific response patterns from the user's past feedback to improve the accuracy of generation. Furthermore, the generation unit can analyze the user's past feedback and customize the response generation algorithm. In this way, the generation algorithm can be optimized by reflecting past feedback.

[0098] The providing unit can estimate the user's emotions and adjust the timing of providing a response based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide a response at a leisurely pace. Also, if the user is in a hurry, the providing unit can provide a response quickly. Furthermore, if the user is excited, the providing unit can provide a response at the appropriate time. This allows for a more appropriate response by adjusting the timing of providing a response based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0099] When providing a response, the providing unit can improve the accuracy of the response by referring to the user's past response history. The providing unit improves the accuracy of the response provided, for example, based on content previously input by the user. The providing unit can also extract specific response patterns from the user's past response history to improve the accuracy of the response provided. Furthermore, the providing unit can analyze the user's past response history and optimize the response providing algorithm. In this way, the accuracy of the response provided is improved by referring to the past response history.

[0100] When providing a response, the providing unit can provide the response based on the user's attribute information. The providing unit can adjust the response providing algorithm, for example, taking into account the user's age. The providing unit can also adjust the response providing algorithm, taking into account the user's gender. Furthermore, the providing unit can also adjust the response providing algorithm, taking into account the user's occupation. In this way, by taking into account the user's attribute information, the accuracy of providing a response is improved.

[0101] When providing a response, the providing unit can analyze the context of the input content to improve the accuracy of the response provided. For example, the providing unit analyzes the context of the user's input content to improve the accuracy of the response provided. The providing unit can also extract a specific response pattern from the context of the user's input content to improve the accuracy of the response provided. Furthermore, the providing unit can analyze the context of the user's input content to optimize the response providing algorithm. In this way, the accuracy of the response provided is improved by analyzing the context of the input content.

[0102] The providing unit can estimate the user's emotions and adjust the response display method based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This improves visibility by adjusting the display method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0103] The providing unit can provide a response taking into consideration the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide a response related to that area. Furthermore, if the user is traveling, the providing unit can also provide a response related to the user's travel destination. Furthermore, if the user is at home, the providing unit can also provide a response related to the user's home. In this way, by taking into consideration the geographical location information, the accuracy of providing a response is improved.

[0104] When providing a response, the providing unit can improve the accuracy of the response by referring to the user's social media activity. For example, the providing unit can provide a response related to a location where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide a related response. Furthermore, the providing unit can also provide a related response by referring to the activity of the user's friends on social media. In this way, by referring to social media activity, the accuracy of the response provided can be improved.

[0105] When providing a response, the providing unit can customize the response providing method by reflecting the user's past feedback. The providing unit can, for example, optimize the response providing algorithm based on feedback provided by the user in the past. The providing unit can also extract specific response patterns from the user's past feedback to improve the accuracy of the response provided. Furthermore, the providing unit can analyze the user's past feedback and customize the response providing algorithm. In this way, the response providing method can be optimized by reflecting the past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, estimation unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive input from a user using the reception device 38 or microphone 38B of the smart device 14. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 and estimates an emotion using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a response based on the estimated emotion. The provision unit provides the generated response to the user using the output device 40 or speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, estimation unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive input from a user using the microphone 238 of the smart glasses 214. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 and estimates an emotion using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a response based on the estimated emotion. The provision unit provides the generated response to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, estimation unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit can receive input from a user using the microphone 238 of the headset type terminal 314. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 and estimates an emotion using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a response based on the estimated emotion. The provision unit provides the generated response to the user using the speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, estimation unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive input from a user using the microphone 238 of the robot 414. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 and estimates an emotion using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a response based on the estimated emotion. The provision unit provides the generated response to the user using the speaker 240 of the robot 414.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can also estimate the user's current health condition based on the user's input. For example, if the user inputs "I have a headache," the reception unit analyzes the information and estimates that the user is experiencing a headache. The reception unit can also estimate the user's stress level from the user's input and provide appropriate advice. Furthermore, the reception unit can provide the user with medical information they require based on the user's input. This allows for appropriate responses according to the user's health condition.

[0108] The estimation unit can also estimate the user's interests and concerns based on the user's input. For example, if the user inputs "I saw a movie recently," the estimation unit estimates that the user is interested in movies. The estimation unit can also estimate the user's hobbies and preferences from the user's input and provide related information. Furthermore, the estimation unit can suggest events and activities that the user is interested in based on the user's input. This makes it possible to provide information that matches the user's interests and concerns.

[0109] The generation unit can also estimate the user's learning style based on the user's input and provide appropriate learning resources. For example, if the user inputs "I'm not good at studying math," the generation unit can estimate that the user has a low level of confidence in math and provide appropriate learning resources. The generation unit can also estimate the user's learning progress from the user's input and suggest what content to study next. Furthermore, the generation unit can provide advice to increase the user's motivation to study based on the user's input. This makes it possible to provide appropriate support according to the user's learning style.

[0110] The providing unit can also provide advice to improve the user's lifestyle habits based on the user's input. For example, if the user inputs "I haven't been getting enough exercise lately," the providing unit analyzes the information and provides advice to resolve the lack of exercise. The providing unit can also suggest improvements to the user's diet based on the user's input. Furthermore, the providing unit can also suggest stress management methods based on the user's input. This makes it possible to provide appropriate advice tailored to the user's lifestyle habits.

[0111] The reception unit can also support the user's travel plans based on the user's input. For example, if the user inputs "I want to go on a trip during my next holiday," the reception unit analyzes the information and suggests travel destinations. The reception unit can also estimate the travel budget and schedule from the user's input and suggest an optimal travel plan. Furthermore, the reception unit can provide points to note during the trip and recommended tourist spots based on the user's input. This allows for appropriate support tailored to the user's travel plans.

[0112] The estimation unit can also estimate the user's emotions and evaluate the user's stress level based on the estimated user emotions. For example, if the user inputs "I'm busy at work," the estimation unit can estimate that the user is feeling stressed and evaluate the user's stress level. The estimation unit can also identify the cause of stress from the user's input and suggest appropriate countermeasures. Furthermore, the estimation unit can provide advice on relaxation methods and stress management based on the user's emotions. This allows for appropriate responses according to the user's stress level.

[0113] The reception unit can also estimate the user's emotions and suggest activities to stabilize the user's emotions based on the estimated user's emotions. For example, if the user inputs "I'm irritated," the reception unit can estimate that the user is irritated and suggest activities for relaxation. The reception unit can also provide music or videos to stabilize emotions based on the user's emotions. Furthermore, the reception unit can suggest techniques for controlling emotions according to the user's emotions. This makes it possible to suggest appropriate activities according to the user's emotions.

[0114] The estimation unit can estimate the user's emotions, record the user's emotions based on the estimated user emotions, and track changes in the emotions. For example, if the user inputs "I'm having fun today," the estimation unit estimates that the user is feeling happy and records the emotion. The estimation unit can also display changes in the user's emotions in graphs or charts to visualize emotional trends. Furthermore, the estimation unit can suggest methods for managing emotions based on changes in the user's emotions. This makes it possible to track changes in the user's emotions and respond appropriately.

[0115] The generation unit can also estimate the user's emotions and generate a response that is in tune with the user's emotions based on the estimated user emotions. For example, if the user inputs "I'm sad today," the generation unit can estimate that the user is feeling sad and generate a response that includes words of comfort. The generation unit can also generate words of encouragement or an uplifting message based on the user's emotions. Furthermore, the generation unit can provide advice to ease the user's emotions according to the user's emotions. This enables an appropriate response that is in tune with the user's emotions.

[0116] The providing unit can also estimate the user's emotions and provide content that corresponds to the emotions based on the estimated user emotions. For example, if the user inputs "I'm having fun today," the providing unit can estimate that the user is feeling happy and provide content that will further enhance the happy mood. The providing unit can also provide relaxing music or video based on the user's emotions. Furthermore, the providing unit can also provide content that will stabilize the user's emotions based on the user's emotions. This makes it possible to provide appropriate content that corresponds to the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives input from the user. The user input includes text and voice. For example, if the user inputs "I'm tired today," the information is received. Step 2: The estimation unit estimates the emotion based on the information received by the reception unit. For example, the estimation unit estimates that the user is tired in response to an input of "I'm tired today." Step 3: The generator generates a response based on the emotion estimated by the estimator. For example, in response to an input such as "I'm tired today," the generator generates a response such as "Thank you for your hard work. Please take a good rest." Step 4: The providing unit provides the response generated by the generating unit to the user, for example, as a text message or a voice message.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 reception unit that receives input from a user; an estimation unit that estimates an emotion based on the information received by the reception unit; a generation unit that generates a response based on the emotion estimated by the estimation unit; a providing unit that provides the response generated by the generating unit; Equipped with A system characterized by:

2. The estimation unit Generative AI is used to analyze user input and estimate emotions.

2. The system of claim 1.

3. The generation unit Generate responses based on estimated emotions using generative AI 2. The system of claim 1.

4. The providing unit Providing the generated response to the user 2. The system of claim 1.

5. The reception unit Accepts user text or voice input 2. The system of claim 1.

6. The estimation unit Learns the user's past input history to improve the accuracy of emotion estimation 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of input acceptance based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past input history and select the input reception method 2. The system of claim 1.

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