System

Abstract

The system according to the embodiment aims to allow a user to obtain a response reflecting his / her own opinion or idea.SOLUTION: A system according to an embodiment includes an input unit, an analysis unit, a response generation unit, and a provision unit. The input unit inputs a user's opinion or idea. The analysis unit analyzes the information input by the input unit. The response generation unit generates a response based on the information analyzed by the analysis unit. The providing unit provides the response generated by the response 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] Conventional techniques have had the problem that it is difficult for users to receive responses that reflect their own opinions and thoughts.

[0005] The system according to the embodiment aims to enable the user to obtain a response that reflects his or her own opinions and thoughts. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a response generation unit, and a provision unit. The input unit inputs a user's opinions and thoughts. The analysis unit analyzes the information input by the input unit. The response generation unit generates a response based on the information analyzed by the analysis unit. The provision unit provides the response generated by the response generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to obtain a response that reflects his or her own opinions and thoughts. [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 allows people to listen to their own opinions without feeling lonely. In this system, a user inputs their own opinions and thoughts, and the system analyzes the input and generates an appropriate response based on the user's opinions and thoughts. This allows the user to converse with themselves, avoiding loneliness and gaining confidence in their own judgment. The system also allows the user to converse with themselves, allowing them to treat themselves as their best partner. For example, when making important life decisions, listening to one's own opinions helps one make decisions without regrets. Furthermore, talking to oneself can help one find solutions to minor everyday worries. Thus, the present invention provides a system that allows users to converse with themselves, thereby eliminating loneliness and gaining confidence in their own judgment.

[0029] A dialogue support system according to an embodiment includes an input unit, an analysis unit, a response generation unit, and a provision unit. The input unit receives input from a user, and the input unit receives input from the user. The input unit receives input from the user about work-related worries, life choices, and the like, but is not limited to these examples. The input unit can receive input from the user using, for example, a keyboard or a voice input device. The input unit can also receive handwritten input using a touchscreen. The analysis unit analyzes the information input by the input unit. The analysis unit understands the user's opinions and thoughts using, for example, natural language processing technology. The analysis unit segments the input text using, for example, morphological analysis and performs grammatical analysis. The analysis unit can also understand the meaning of the input opinions and thoughts using semantic analysis. The analysis unit can also estimate the user's emotions using sentiment analysis. The response generation unit generates a response based on the information analyzed by the analysis unit. The response generation unit generates an appropriate response using, for example, machine learning. The response generation unit generates a response based on a model previously trained using, for example, supervised learning. The response generation unit can also discover new patterns using unsupervised learning and generate a response. Furthermore, the response generation unit can also generate an optimal response using reinforcement learning. The providing unit provides the response generated by the response generation unit to the user. The providing unit displays the response as, for example, a text message. The providing unit can also play the response as an audio response. Furthermore, the providing unit can also provide the response using an image or video. This allows the dialogue support system according to the embodiment to enable the user to have a dialogue with themselves, avoid feeling lonely, and gain confidence in their own judgment. For example, if a user inputs a problem about work, the system generates advice for that problem and provides it to the user. This allows the user to organize their thoughts and make appropriate decisions.

[0030] The input unit allows the user to input opinions and thoughts. For example, the input unit allows the user to input opinions and thoughts using a keyboard. The input unit can also allow the user to input opinions and thoughts using a voice input device. Furthermore, the input unit can also accept handwritten input using a touch screen. This allows the user to freely input opinions and thoughts, making the system easier to use. For example, the user can input work-related worries using a keyboard. The user can also input opinions about life choices using a voice input device. Furthermore, the user can input minor everyday worries by handwriting using a touch screen.

[0031] The analysis unit can analyze a user's opinions and thoughts using natural language processing technology. For example, the analysis unit divides input text using morphological analysis and performs grammatical analysis. For example, the analysis unit divides a sentence input by the user into words and identifies the part of speech of each word. The analysis unit can also understand the meaning of the input opinions and thoughts using semantic analysis. For example, the analysis unit understands the context of the sentence input by the user and extracts the appropriate meaning. Furthermore, the analysis unit can estimate the user's emotions using sentiment analysis. For example, the analysis unit estimates emotions from the sentence input by the user and adjusts the analysis results based on the emotions. In this way, the use of natural language processing technology allows the user's opinions and thoughts to be accurately understood. For example, the analysis unit uses morphological analysis to analyze work-related worries input by the user. The analysis unit can also use semantic analysis to understand opinions about life choices input by the user. Furthermore, the analysis unit can estimate emotions about minor everyday worries input by the user using sentiment analysis.

[0032] The response generation unit can generate a response using machine learning. The response generation unit generates a response based on a model previously trained using, for example, supervised learning. For example, the response generation unit generates advice for a work-related concern input by a user. The response generation unit can also discover new patterns and generate a response using unsupervised learning. For example, the response generation unit provides a new perspective on an opinion about life choices input by a user. Furthermore, the response generation unit can generate an optimal response using reinforcement learning. For example, the response generation unit proposes an optimal solution to a minor everyday concern input by a user. In this way, an appropriate response can be generated using machine learning. For example, the response generation unit provides specific advice for a work-related concern input by a user using supervised learning. The response generation unit can also provide a new perspective on an opinion about life choices input by a user using unsupervised learning. Furthermore, the response generation unit can propose an optimal solution to a minor everyday concern input by a user using reinforcement learning.

[0033] The providing unit can send the generated response to the user. The providing unit can, for example, display the response as a text message. For example, the providing unit can display advice for a work-related problem entered by the user as a text message. The providing unit can also play the response as an audio response. For example, the providing unit can play an audio response to an opinion about life choices entered by the user. The providing unit can also provide the response using an image or a video. For example, the providing unit can provide a solution to a minor everyday problem entered by the user as an image or a video. This allows the user to interact with themselves by providing the generated response to the user. For example, the providing unit can display advice for a work-related problem entered by the user as a text message. The providing unit can also play an audio response to an opinion about life choices entered by the user. The providing unit can also provide a solution to a minor everyday problem entered by the user using an image or a video.

[0034] The input unit can analyze the user's past input history and suggest the optimal input method. For example, the input unit automatically displays opinions and thoughts that the user has frequently input in the past as candidates. For example, the input unit displays work-related worries that the user has input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, if the user has used voice input in the past, the input unit preferentially suggests voice input. The input unit can also predict and suggest opinions and thoughts to be used in a specific time period based on the user's past input history. For example, the input unit preferentially displays opinions and thoughts that the user has input at night. In this way, the input method that is optimal for the user can be suggested by analyzing the past input history. For example, the input unit automatically displays work-related worries that the user has frequently input in the past as candidates. The input unit can also preferentially suggest voice input that the user has used in the past. The input unit can also predict and suggest opinions and thoughts to be used at night based on the user's past input history.

[0035] The input unit can filter input content based on the user's current situation when inputting. For example, when the user inputs at night, the input unit prioritizes displaying relaxing content. For example, the input unit displays relaxing music and scenery images when inputting at night. Furthermore, when the user inputs at work, the input unit can prioritize displaying work-related content. For example, the input unit displays work-related advice and information when inputting at work. Furthermore, when the user inputs while traveling, the input unit can prioritize displaying travel-related content. For example, when inputting while traveling, the input unit displays tourist information and recommended spots for the travel destination. This allows input content according to the user's current situation to be provided, allowing more appropriate information to be input. For example, the input unit prioritizes displaying relaxing content at night. Furthermore, the input unit can prioritize displaying work-related content when inputting at work. Furthermore, the input unit can prioritize displaying travel-related content when traveling.

[0036] The input unit can select the optimal input means depending on the user's input method when inputting. For example, when the user inputs opinions or thoughts by voice, the input unit preferentially supports voice input. For example, the input unit converts the user's voice into text using voice recognition technology. The input unit can also preferentially support text input when the user inputs opinions or thoughts in text. For example, the input unit supports keyboard input, making it easy for the user to input opinions or thoughts in text. Furthermore, the input unit can also preferentially support gesture input when the user inputs opinions or thoughts by gesture. For example, the input unit analyzes the user's gestures using gesture recognition technology and recognizes the input content. This facilitates input work by providing the optimal input means depending on the user's input method. For example, the input unit preferentially supports voice input. The input unit can also preferentially support text input. Furthermore, the input unit can also preferentially support gesture input.

[0037] The input unit can prioritize acquiring highly relevant input content during input, taking into account the user's geographical location information. For example, when the user is in a specific region, the input unit prioritizes displaying content related to that region. For example, when the user is in a specific city, the input unit displays tourist information and event information for that city. Furthermore, when the user is traveling, the input unit can prioritize displaying content related to the travel destination. For example, when the user inputs information at a travel destination, the input unit displays recommended spots and restaurant information for the travel destination. Furthermore, when the user is at home, the input unit can prioritize displaying content related to the user's home. For example, when the user inputs information at home, the input unit displays relaxation techniques that can be done at home and tips for gardening. This allows the user to input more appropriate information by providing highly relevant input content based on the user's geographical location information. For example, the input unit can prioritize displaying content related to a specific region. Furthermore, the input unit can prioritize displaying content related to a travel destination. Furthermore, the input unit can prioritize displaying content related to the user's home.

[0038] The input unit can analyze the user's social media activity at the time of input and input related opinions and thoughts. The input unit, for example, automatically inputs opinions and thoughts shared by the user on social media. For example, the input unit analyzes content posted by the user on social media and inputs related opinions and thoughts. The input unit can also analyze the content posted by the user on social media and input related opinions and thoughts. For example, the input unit extracts opinions about work worries and life choices from the content posted by the user on social media. The input unit can also input related opinions and thoughts by referring to the activities of the user's friends on social media. For example, the input unit inputs related opinions and thoughts based on information shared by the user's friends on social media. This allows more appropriate information to be input by providing related opinions and thoughts based on the user's social media activity. For example, the input unit automatically inputs opinions and thoughts shared by the user on social media. The input unit can also analyze the content posted by the user on social media and input related opinions and thoughts. The input unit can also input related opinions and thoughts by referring to the activities of the user's friends on social media.

[0039] The input unit can customize the input method by reflecting the user's past feedback when inputting information. The input unit, for example, suggests an optimal input method based on feedback provided by the user in the past. For example, the input unit analyzes the content of feedback provided by the user in the past and suggests an optimal input method. The input unit can also preferentially support a specific input method based on the user's past feedback. For example, the input unit preferentially supports voice input provided by the user in the past. The input unit can also analyze the user's past feedback and customize the input interface. For example, the input unit adjusts the design and functions of the input interface based on the content of feedback provided by the user in the past. This allows more appropriate information to be input by providing an input method based on the user's past feedback. For example, the input unit suggests an optimal input method based on feedback provided by the user in the past. The input unit can also preferentially support a specific input method based on the user's past feedback. The input unit can also analyze the user's past feedback and customize the input interface.

[0040] The analysis unit can improve the accuracy of the analysis by referring to the user's past opinions and thoughts during analysis. The analysis unit, for example, improves the accuracy of the analysis by referring to opinions and thoughts previously input by the user. For example, the analysis unit adjusts the analysis results by referring to work-related worries previously input by the user. The analysis unit can also extract specific patterns from the user's past opinions and thoughts and reflect them in the analysis. For example, the analysis unit customizes the analysis results based on opinions about life choices previously input by the user. The analysis unit can also customize the analysis results based on the user's past opinions and thoughts. For example, the analysis unit adjusts the analysis results based on minor everyday worries previously input by the user. In this way, the accuracy of the analysis is improved by referring to the user's past opinions and thoughts. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past opinions and thoughts. The analysis unit can also extract specific patterns from the user's past opinions and thoughts and reflect them in the analysis. The analysis unit can also customize the analysis results based on the user's past opinions and thoughts.

[0041] During analysis, the analysis unit can apply different analysis methods depending on the category of the user's opinions and thoughts. For example, if the user's opinions and thoughts are work-related, the analysis unit applies an analysis method specialized for work. For example, the analysis unit performs analysis using work-related data based on work-related worries input by the user. Furthermore, if the user's opinions and thoughts are private, the analysis unit can also apply an analysis method specialized for private life. For example, the analysis unit performs analysis using private-related data based on opinions about life choices input by the user. Furthermore, if the user's opinions and thoughts are health-related, the analysis unit can also apply an analysis method specialized for health. For example, the analysis unit performs analysis using health-related data based on trivial daily worries input by the user. This allows for providing an analysis method according to the category of the user's opinions and thoughts, thereby providing more appropriate analysis results. For example, the analysis unit applies a work-specialized analysis method to opinions and thoughts related to work. Furthermore, the analysis unit can apply a private-specialized analysis method to opinions and thoughts related to private life. Furthermore, the analysis unit can apply a health-specialized analysis method to opinions and thoughts related to health.

[0042] The analysis unit can perform the analysis while taking into account the user's attribute information. The analysis unit, for example, takes into account the user's age to provide an appropriate analysis result. For example, if the user is young, the analysis unit provides advice suitable for young people. The analysis unit can also take into account the user's gender to provide an appropriate analysis result. For example, if the user is female, the analysis unit provides advice suitable for women. The analysis unit can also take into account the user's occupation to provide an appropriate analysis result. For example, if the user is an engineer, the analysis unit provides advice suitable for engineers. In this way, by providing an analysis based on the user's attribute information, a more appropriate analysis result can be provided. For example, the analysis unit can take into account the user's age to provide advice suitable for young people. The analysis unit can also take into account the user's gender to provide advice suitable for women. The analysis unit can also take into account the user's occupation to provide advice suitable for engineers.

[0043] The analysis unit can take the user's geographical distribution into consideration when performing the analysis. For example, when the user is in a specific region, the analysis unit prioritizes analyzing information related to that region. For example, when the user is in a specific city, the analysis unit analyzes tourist information and event information for that city. Furthermore, when the user is traveling, the analysis unit can prioritize analyzing information related to the travel destination. For example, the analysis unit analyzes tourist information and recommended spots for the travel destination based on content entered by the user at the travel destination. Furthermore, when the user is at home, the analysis unit can prioritize analyzing information related to the user's home. For example, the analysis unit analyzes relaxation methods that can be done at home and tips for home gardening based on content entered by the user at home. This allows for providing an analysis based on the user's geographical distribution, thereby providing more appropriate analysis results. For example, the analysis unit prioritizes analyzing information related to a specific region. Furthermore, the analysis unit can prioritize analyzing information related to the travel destination. Furthermore, the analysis unit can prioritize analyzing information related to the user's home.

[0044] The analysis unit can improve the accuracy of the analysis by referring to related literature and data during analysis. The analysis unit, for example, refers to related academic papers to improve the accuracy of the analysis. For example, the analysis unit searches for academic papers related to the content entered by the user and reflects the contents in the analysis. The analysis unit can also improve the accuracy of the analysis by referring to related databases. For example, the analysis unit searches for databases related to the content entered by the user and reflects the data in the analysis. The analysis unit can also improve the accuracy of the analysis by referring to related statistical data. For example, the analysis unit searches for statistical data related to the content entered by the user and reflects the data in the analysis. In this way, the accuracy of the analysis is improved by referring to related literature and data. For example, the analysis unit can improve the accuracy of the analysis by referring to related academic papers. The analysis unit can also improve the accuracy of the analysis by referring to related databases. The analysis unit can also improve the accuracy of the analysis by referring to related statistical data.

[0045] The analysis unit can perform the analysis taking into account the user's market value. For example, the analysis unit evaluates the market value based on the user's occupation and skills and reflects it in the analysis results. For example, if the user is an engineer, the analysis unit evaluates the user's market value as an engineer and reflects it in the analysis results. The analysis unit can also evaluate the user's market value taking into account the user's past performance and track record and reflect it in the analysis results. For example, the analysis unit can evaluate the user's market value based on the projects and track record that the user has achieved in the past and reflect it in the analysis results. Furthermore, the analysis unit can evaluate the user's market value taking into account current market trends and reflect it in the analysis results. For example, the analysis unit can evaluate the user's market value based on current market trends and reflect it in the analysis results. This allows for providing an analysis based on the user's market value, thereby providing more appropriate analysis results. For example, the analysis unit can evaluate the user's market value based on the user's occupation and skills and reflect it in the analysis results. The analysis unit can also evaluate the user's market value taking into account the user's past performance and track record and reflect it in the analysis results. Furthermore, the analysis unit can evaluate the user's market value taking into account current market trends and reflect it in the analysis results.

[0046] When generating a response, the response generation unit can adjust the level of detail of the response based on the importance of the user's opinion or thought. For example, the response generation unit generates a detailed response when the user's opinion or thought is important. For example, the response generation unit provides detailed advice for an important opinion or thought input by the user. The response generation unit can also generate a concise response when the user's opinion or thought is general. For example, the response generation unit provides concise feedback for a general opinion or thought input by the user. The response generation unit can also generate a quick response when the user's opinion or thought is urgent. For example, the response generation unit provides quick advice for an urgent opinion or thought input by the user. This allows for the generation of a more appropriate response by providing the level of detail of the response according to the importance of the user's opinion or thought. For example, the response generation unit generates a detailed response for an important opinion or thought. The response generation unit can also generate a concise response for a general opinion or thought. The response generation unit can also generate a quick response for an urgent opinion or thought.

[0047] When generating a response, the response generation unit can apply different response algorithms depending on the category of the user's opinion or thought. For example, if the user's opinion or thought is work-related, the response generation unit applies a response algorithm specialized for work. For example, the response generation unit provides work-related advice in response to a work-related concern input by the user. Furthermore, if the user's opinion or thought is related to private life, the response generation unit can also apply a response algorithm specialized for private life. For example, the response generation unit provides private life-related advice in response to an opinion about life choices input by the user. Furthermore, the response generation unit can also apply a health-specific response algorithm in response to a health-related opinion or thought. For example, the response generation unit provides health-related advice in response to a minor daily concern input by the user. In this way, by providing a response algorithm according to the category of the user's opinion or thought, a more appropriate response can be generated. For example, the response generation unit applies a work-specific response algorithm to opinions or thoughts related to work. Furthermore, the response generation unit can also apply a private life-specific response algorithm to opinions or thoughts related to private life. Furthermore, the response generation unit can also apply a health-specific response algorithm to opinions or thoughts related to health.

[0048] When generating a response, the response generation unit can improve the accuracy of the response by referring to the user's past response results. The response generation unit, for example, improves the accuracy of the response by referring to responses received by the user in the past. For example, the response generation unit generates a more accurate response based on advice received by the user in the past. The response generation unit can also extract a specific pattern from the user's past response results and reflect it in the response. For example, the response generation unit customizes the content of the response based on feedback received by the user in the past. The response generation unit can also customize the content of the response based on the user's past response results. For example, the response generation unit adjusts the content of the response based on advice received by the user in the past for minor everyday concerns. In this way, the accuracy of the response is improved by referring to the user's past response results. For example, the response generation unit improves the accuracy of the response by referring to advice received by the user in the past. The response generation unit can also extract a specific pattern from the user's past response results and reflect it in the response. The response generation unit can also customize the content of the response based on the user's past response results.

[0049] When generating a response, the response generation unit can determine the priority of responses based on the time when the user submitted their opinions and thoughts. For example, the response generation unit prioritizes responses to opinions and thoughts recently submitted by the user. For example, the response generation unit prioritizes providing advice for work-related worries recently input by the user. The response generation unit can also prioritize responses to opinions and thoughts submitted by the user during a specific time period. For example, the response generation unit prioritizes providing advice for opinions about life choices input by the user at night. The response generation unit can also prioritize responses to opinions and thoughts urgently submitted by the user. For example, the response generation unit can quickly provide advice for minor everyday worries urgently input by the user. This allows for the generation of more appropriate responses by prioritizing responses according to the time when the user submitted their opinions and thoughts. For example, the response generation unit prioritizes responses to opinions and thoughts recently submitted. The response generation unit can also prioritize responses to opinions and thoughts submitted during a specific time period. The response generation unit can also quickly respond to opinions and thoughts urgently submitted.

[0050] When generating a response, the response generation unit can adjust the order of responses based on the relevance of the user's opinions and thoughts. For example, the response generation unit responds to the user's opinions and thoughts in descending order of relevance. For example, the response generation unit prioritizes responses to opinions and thoughts that are highly relevant to work-related worries input by the user. The response generation unit can also respond to the user's opinions and thoughts by category. For example, the response generation unit responds to opinions about life choices input by the user by category. The response generation unit can also respond to the user's opinions and thoughts in descending order of importance. For example, the response generation unit responds to minor daily worries input by the user in descending order of importance. This allows for the generation of more appropriate responses by providing an order of responses according to the relevance of the user's opinions and thoughts. For example, the response generation unit prioritizes responses to highly relevant opinions and thoughts. The response generation unit can also respond to opinions and thoughts by category. The response generation unit can also respond to opinions and thoughts in descending order of importance.

[0051] When generating a response, the response generation unit can adjust the use of technical terminology in the response depending on the user's level of expertise. For example, if the user has technical expertise, the response generation unit generates a response that uses a lot of technical terminology. For example, if the user is an engineer, the response generation unit provides technical advice that uses a lot of technical terminology. Furthermore, if the user has general knowledge, the response generation unit can generate a response that avoids technical terminology. For example, if the user has general knowledge, the response generation unit provides concise advice that avoids technical terminology. Furthermore, if the user is a beginner, the response generation unit can generate a response in simple language. For example, if the user is a beginner, the response generation unit provides easy-to-understand advice in simple language. This allows for the generation of more appropriate responses by providing technical terminology in the response depending on the user's level of expertise. For example, the response generation unit can generate a response that uses a lot of technical terminology for a user who has technical expertise. Furthermore, the response generation unit can generate a response that avoids technical terminology for a user who has general knowledge. Furthermore, the response generation unit can generate a response in simple language for a novice user.

[0052] The providing unit can select the optimal delivery method by referring to the user's past response history when providing a response. The providing unit, for example, selects the optimal delivery method based on the response history received by the user in the past. For example, the providing unit selects the optimal delivery method based on advice received by the user in the past. The providing unit can also preferentially select a specific delivery method from the user's past response history. For example, the providing unit preferentially selects a specific delivery method based on feedback received by the user in the past. The providing unit can also analyze the user's past response history and customize the delivery method. For example, the providing unit adjusts the delivery method based on advice received by the user in the past for minor everyday concerns. This allows for the provision of a more appropriate response by providing the optimal delivery method based on the user's past response history. For example, the providing unit selects the optimal delivery method based on advice received by the user in the past. The providing unit can also preferentially select a specific delivery method from the user's past response history. The providing unit can also analyze the user's past response history and customize the delivery method.

[0053] The providing unit can customize the provided content according to the user's current task when providing the content. For example, when the user is at work, the providing unit provides a work-related response. For example, the providing unit provides work-related advice in response to content input by the user while at work. Furthermore, the providing unit can also provide a relaxing response when the user is on a break. For example, the providing unit provides relaxing advice in response to content input by the user while on a break. Furthermore, the providing unit can also provide a travel-related response when the user is traveling. For example, the providing unit provides tourist information about the travel destination and recommended spots in response to content input by the user while traveling. This allows the content to be provided according to the user's current task, thereby providing a more appropriate response. For example, the providing unit provides a work-related response in response to content input by the user while at work. Furthermore, the providing unit can also provide a relaxing response in response to content input by the user while on a break. Furthermore, the providing unit can also provide a travel-related response in response to content input by the user while traveling.

[0054] The providing unit can improve the delivery method by reflecting user feedback when providing a response. The providing unit improves the delivery method, for example, by providing feedback for a response provided by the user. For example, the providing unit provides feedback for advice provided by the user and improves the delivery method based on the feedback. The providing unit can also preferentially select a specific delivery method based on user feedback. For example, the providing unit preferentially selects a specific delivery method based on feedback provided by the user in the past. The providing unit can also analyze user feedback and customize the delivery method. For example, the providing unit adjusts the design and functions of the delivery method based on feedback provided by the user. This makes it possible to provide a more appropriate response by providing a delivery method based on user feedback. For example, the providing unit improves the delivery method by providing feedback for advice provided by the user. The providing unit can also preferentially select a specific delivery method based on user feedback. The providing unit can also analyze user feedback and customize the delivery method.

[0055] The providing unit can select the optimal providing method by taking into consideration the user's device information when providing advice. For example, if the user is using a smartphone, the providing unit provides a providing method tailored to the screen size. For example, the providing unit provides advice optimized for the smartphone's screen size in response to content entered by the user on the smartphone. Furthermore, if the user is using a tablet, the providing unit can also provide a providing method optimized for a large screen. For example, the providing unit provides advice optimized for a large screen in response to content entered by the user on the tablet. Furthermore, if the user is using a smartwatch, the providing unit can also provide a concise and highly visible providing method. For example, the providing unit provides concise and highly visible advice in response to content entered by the user on the smartwatch. This allows for a more appropriate response by providing the optimal providing method based on the user's device information. For example, the providing unit can provide a providing method tailored to the screen size in response to a user using a smartphone. Furthermore, the providing unit can also provide a providing method optimized for a large screen in response to a user using a tablet. Furthermore, the providing unit can also provide a concise and highly visible providing method in response to a user using a smartwatch.

[0056] The providing unit can provide the content in multiple languages ​​according to the user's language setting when providing the content. The providing unit, for example, automatically sets the content to be provided based on the language setting of the user's device. For example, if the user sets the device's language setting to English, the providing unit provides advice in English. The providing unit can also provide a language switching function when the user uses multiple languages. For example, if the user uses English and Japanese, the providing unit can provide a language switching function and switch languages ​​as needed. Furthermore, if the user selects a specific language, the providing unit can provide the content in that language. For example, if the user selects Spanish, the providing unit provides advice in Spanish. This allows for more appropriate responses by providing content in multiple languages ​​based on the user's language setting. For example, the providing unit can automatically set the content to be provided based on the device's language setting. The providing unit can also provide a language switching function for users who use multiple languages. Furthermore, the providing unit can provide content in a specific language for users who select a specific language.

[0057] The providing unit can analyze the user's lifestyle rhythm when providing advice and suggest the optimal timing for providing the advice. The providing unit, for example, analyzes the user's lifestyle rhythm and suggests the optimal timing for providing the advice. For example, the providing unit analyzes the user's sleep patterns and activity times and provides advice at the optimal timing. Furthermore, if the user is more likely to receive responses during a specific time period, the providing unit can provide the response during that time period. For example, if the user is more likely to receive responses in the morning, the providing unit can provide the advice in the morning. Furthermore, the providing unit can customize the timing for providing the response to match the user's lifestyle rhythm. For example, the providing unit can provide advice at night so that the user can relax at night. In this way, by providing the optimal timing for providing the advice based on the user's lifestyle rhythm, a more appropriate response can be provided. For example, the providing unit can analyze the user's lifestyle rhythm and suggest the optimal timing for providing the advice. Furthermore, the providing unit can provide a response during a specific time period for a user who is more likely to receive responses during that time period. Furthermore, the providing unit can customize the timing for providing the response to match the user's lifestyle rhythm.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The dialogue support system can further analyze the user's past dialogue history to learn the user's preferences and tendencies. For example, the analysis unit analyzes what topics the user has talked about in the past and prioritizes analysis of topics that interest the user. The response generation unit can also generate responses using expressions and phrases preferred by the user based on the past dialogue history. The provision unit can also refer to the past dialogue history and provide responses in a format preferred by the user. In this way, by utilizing the user's past dialogue history, more personalized dialogue can be realized. For example, the analysis unit prioritizes analysis of topics that the user has talked about frequently in the past. The response generation unit can also generate responses using expressions preferred by the user. The provision unit can also provide responses in a format preferred by the user.

[0060] The input unit can monitor the user's input speed and input frequency and provide optimal input assistance. For example, if the user is taking a long time to input, the input unit can provide an input completion function, thereby making the input work more efficient. The input unit can also learn words and phrases that the user frequently inputs and provide a predictive input function. Furthermore, the input unit can dynamically change the layout of the input interface according to the user's input frequency. This makes it possible to provide optimal input assistance according to the user's input speed and input frequency and make the input work more efficient. For example, the input unit can provide an input completion function. The input unit can also provide a predictive input function. Furthermore, the input unit can dynamically change the layout of the input interface.

[0061] The analysis unit can complement the analysis results by referencing related external databases based on the user's input. For example, the analysis unit can refer to a related industry database for a work-related concern entered by the user and reflect the latest industry trends in the analysis results. The analysis unit can also refer to a medical database for a health-related concern entered by the user and reflect the latest medical information in the analysis results. Furthermore, the analysis unit can refer to an academic paper database for an academic question entered by the user and reflect the latest research results in the analysis results. In this way, by utilizing external databases, it is possible to provide more accurate analysis results. For example, the analysis unit can refer to an industry database. The analysis unit can also refer to a medical database. The analysis unit can also refer to an academic paper database.

[0062] The answer generation unit can generate multiple answer candidates based on the user's input and provide the user with options. For example, the answer generation unit can generate multiple solutions to a work-related problem input by the user and provide the user with options. The answer generation unit can also generate advice from multiple perspectives in response to an opinion about life choices input by the user. Furthermore, the answer generation unit can generate multiple solutions to a minor everyday problem input by the user and provide the user with options. This allows the user to select a more appropriate answer by providing multiple options. For example, the answer generation unit can generate multiple solutions. The answer generation unit can also generate advice from multiple perspectives. Furthermore, the answer generation unit can generate multiple solutions.

[0063] The providing unit can dynamically change the method of providing the response depending on the usage status of the user's device. For example, if the user is using a smartphone, the providing unit can provide the response as a short text message. Furthermore, if the user is using a personal computer, the providing unit can provide the response including detailed text and graphs. Furthermore, if the user is using a smartwatch, the providing unit can provide the response in the form of a concise notification. This makes it possible to provide more appropriate information by providing an optimal response depending on the usage status of the user's device. For example, the providing unit can provide the response as a short text message. Furthermore, the providing unit can provide the response including detailed text and graphs. Furthermore, the providing unit can provide the response in the form of a concise notification.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The input unit receives input of opinions and thoughts from the user. Examples of opinions and thoughts that the user may input include, but are not limited to, work-related worries and opinions about life choices. The input unit can input the user's opinions and thoughts using, for example, a keyboard or a voice input device. The input unit can also accept handwritten input using a touch screen. Step 2: The analysis unit analyzes the information input by the input unit. The analysis unit understands the user's opinions and thoughts, for example, using natural language processing technology. The analysis unit divides the input text using morphological analysis and performs grammatical analysis, for example. The analysis unit can also understand the meaning of the input opinions and thoughts using semantic analysis. Furthermore, the analysis unit can also estimate the user's emotions using sentiment analysis. Step 3: The response generation unit generates a response based on the information analyzed by the analysis unit. The response generation unit generates an appropriate response using, for example, machine learning. The response generation unit generates a response based on a model previously trained using, for example, supervised learning. The response generation unit can also discover new patterns using unsupervised learning and generate a response. Furthermore, the response generation unit can also generate an optimal response using reinforcement learning. Step 4: The providing unit provides the response generated by the response generating unit to the user. For example, the providing unit displays the response as a text message. The providing unit can also play the response as a voice response. Furthermore, the providing unit can also provide the response using an image or video.

[0066] (Example 2) A system according to an embodiment of the present invention allows people to listen to their own opinions without feeling lonely. In this system, a user inputs their own opinions and thoughts, and the system analyzes the input and generates an appropriate response based on the user's opinions and thoughts. This allows the user to converse with themselves, avoiding loneliness and gaining confidence in their own judgment. The system also allows the user to converse with themselves, allowing them to treat themselves as their best partner. For example, when making important life decisions, listening to one's own opinions helps one make decisions without regrets. Furthermore, talking to oneself can help one find solutions to minor everyday worries. Thus, the present invention provides a system that allows users to converse with themselves, thereby eliminating loneliness and gaining confidence in their own judgment.

[0067] A dialogue support system according to an embodiment includes an input unit, an analysis unit, a response generation unit, and a provision unit. The input unit receives input from a user, and the input unit receives input from the user. The input unit receives input from the user about work-related worries, life choices, and the like, but is not limited to these examples. The input unit can receive input from the user using, for example, a keyboard or a voice input device. The input unit can also receive handwritten input using a touchscreen. The analysis unit analyzes the information input by the input unit. The analysis unit understands the user's opinions and thoughts using, for example, natural language processing technology. The analysis unit segments the input text using, for example, morphological analysis and performs grammatical analysis. The analysis unit can also understand the meaning of the input opinions and thoughts using semantic analysis. The analysis unit can also estimate the user's emotions using sentiment analysis. The response generation unit generates a response based on the information analyzed by the analysis unit. The response generation unit generates an appropriate response using, for example, machine learning. The response generation unit generates a response based on a model previously trained using, for example, supervised learning. The response generation unit can also discover new patterns using unsupervised learning and generate a response. Furthermore, the response generation unit can also generate an optimal response using reinforcement learning. The providing unit provides the response generated by the response generation unit to the user. The providing unit displays the response as, for example, a text message. The providing unit can also play the response as an audio response. Furthermore, the providing unit can also provide the response using an image or video. This allows the dialogue support system according to the embodiment to enable the user to have a dialogue with themselves, avoid feeling lonely, and gain confidence in their own judgment. For example, if a user inputs a problem about work, the system generates advice for that problem and provides it to the user. This allows the user to organize their thoughts and make appropriate decisions.

[0068] The input unit allows the user to input opinions and thoughts. For example, the input unit allows the user to input opinions and thoughts using a keyboard. The input unit can also allow the user to input opinions and thoughts using a voice input device. Furthermore, the input unit can also accept handwritten input using a touch screen. This allows the user to freely input opinions and thoughts, making the system easier to use. For example, the user can input work-related worries using a keyboard. The user can also input opinions about life choices using a voice input device. Furthermore, the user can input minor everyday worries by handwriting using a touch screen.

[0069] The analysis unit can analyze a user's opinions and thoughts using natural language processing technology. For example, the analysis unit divides input text using morphological analysis and performs grammatical analysis. For example, the analysis unit divides a sentence input by the user into words and identifies the part of speech of each word. The analysis unit can also understand the meaning of the input opinions and thoughts using semantic analysis. For example, the analysis unit understands the context of the sentence input by the user and extracts the appropriate meaning. Furthermore, the analysis unit can estimate the user's emotions using sentiment analysis. For example, the analysis unit estimates emotions from the sentence input by the user and adjusts the analysis results based on the emotions. In this way, the use of natural language processing technology allows the user's opinions and thoughts to be accurately understood. For example, the analysis unit uses morphological analysis to analyze work-related worries input by the user. The analysis unit can also use semantic analysis to understand opinions about life choices input by the user. Furthermore, the analysis unit can estimate emotions about minor everyday worries input by the user using sentiment analysis.

[0070] The response generation unit can generate a response using machine learning. The response generation unit generates a response based on a model previously trained using, for example, supervised learning. For example, the response generation unit generates advice for a work-related concern input by a user. The response generation unit can also discover new patterns and generate a response using unsupervised learning. For example, the response generation unit provides a new perspective on an opinion about life choices input by a user. Furthermore, the response generation unit can generate an optimal response using reinforcement learning. For example, the response generation unit proposes an optimal solution to a minor everyday concern input by a user. In this way, an appropriate response can be generated using machine learning. For example, the response generation unit provides specific advice for a work-related concern input by a user using supervised learning. The response generation unit can also provide a new perspective on an opinion about life choices input by a user using unsupervised learning. Furthermore, the response generation unit can propose an optimal solution to a minor everyday concern input by a user using reinforcement learning.

[0071] The providing unit can send the generated response to the user. The providing unit can, for example, display the response as a text message. For example, the providing unit can display advice for a work-related problem entered by the user as a text message. The providing unit can also play the response as an audio response. For example, the providing unit can play an audio response to an opinion about life choices entered by the user. The providing unit can also provide the response using an image or a video. For example, the providing unit can provide a solution to a minor everyday problem entered by the user as an image or a video. This allows the user to interact with themselves by providing the generated response to the user. For example, the providing unit can display advice for a work-related problem entered by the user as a text message. The providing unit can also play an audio response to an opinion about life choices entered by the user. The providing unit can also provide a solution to a minor everyday problem entered by the user using an image or a video.

[0072] The input unit can estimate the user's emotions and dynamically change the design of the input interface based on the estimated user emotions. For example, if the user is sad, the input unit provides a calm interface to reduce visual stress. For example, the input unit changes the background color when the user is typing to blue. Furthermore, if the user is happy, the input unit can provide a bright interface to make inputting more enjoyable. For example, the input unit changes the background color when the user is typing to yellow. Furthermore, if the user is tired, the input unit can provide a simple, highly visible interface to make inputting easier. For example, the input unit increases the font size when the user is typing to improve visibility. This reduces stress for the user by changing the input interface according to the user's emotions and makes inputting more enjoyable. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the input unit can capture the user's facial expression with a camera, input it into the generative AI, and estimate the emotion. The input unit can also record the user's voice, input it into the generation AI, and estimate emotions.Furthermore, the input unit can collect the user's biometric data (heart rate and electrodermal activity) using sensors, input it into the generation AI, and estimate emotions.

[0073] The input unit can analyze the user's past input history and suggest the optimal input method. For example, the input unit automatically displays opinions and thoughts that the user has frequently input in the past as candidates. For example, the input unit displays work-related worries that the user has input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, if the user has used voice input in the past, the input unit preferentially suggests voice input. The input unit can also predict and suggest opinions and thoughts to be used in a specific time period based on the user's past input history. For example, the input unit preferentially displays opinions and thoughts that the user has input at night. In this way, the input method that is optimal for the user can be suggested by analyzing the past input history. For example, the input unit automatically displays work-related worries that the user has frequently input in the past as candidates. The input unit can also preferentially suggest voice input that the user has used in the past. The input unit can also predict and suggest opinions and thoughts to be used at night based on the user's past input history.

[0074] The input unit can filter input content based on the user's current situation when inputting. For example, when the user inputs at night, the input unit prioritizes displaying relaxing content. For example, the input unit displays relaxing music and scenery images when inputting at night. Furthermore, when the user inputs at work, the input unit can prioritize displaying work-related content. For example, the input unit displays work-related advice and information when inputting at work. Furthermore, when the user inputs while traveling, the input unit can prioritize displaying travel-related content. For example, when inputting while traveling, the input unit displays tourist information and recommended spots for the travel destination. This allows input content according to the user's current situation to be provided, allowing more appropriate information to be input. For example, the input unit prioritizes displaying relaxing content at night. Furthermore, the input unit can prioritize displaying work-related content when inputting at work. Furthermore, the input unit can prioritize displaying travel-related content when traveling.

[0075] The input unit can select the optimal input means depending on the user's input method when inputting. For example, when the user inputs opinions or thoughts by voice, the input unit preferentially supports voice input. For example, the input unit converts the user's voice into text using voice recognition technology. The input unit can also preferentially support text input when the user inputs opinions or thoughts in text. For example, the input unit supports keyboard input, making it easy for the user to input opinions or thoughts in text. Furthermore, the input unit can also preferentially support gesture input when the user inputs opinions or thoughts by gesture. For example, the input unit analyzes the user's gestures using gesture recognition technology and recognizes the input content. This facilitates input work by providing the optimal input means depending on the user's input method. For example, the input unit preferentially supports voice input. The input unit can also preferentially support text input. Furthermore, the input unit can also preferentially support gesture input.

[0076] The input unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, if the user is feeling stressed, the input unit can prioritize displaying content that helps reduce stress. For example, the input unit can display relaxing music or advice on stress reduction. Furthermore, if the user is feeling relaxed, the input unit can prioritize displaying relaxing content. For example, the input unit can display relaxing landscape images or relaxation tips. Furthermore, if the user is feeling excited, the input unit can prioritize displaying content that helps reduce excitement. For example, the input unit can display deep breathing techniques or relaxation exercises. This allows for more appropriate information to be provided by prioritizing input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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. For example, the input unit can capture the user's facial expression with a camera, input the captured image into the generation AI, and estimate the emotion. The input unit can also record the user's voice, input it into the generation AI, and estimate emotions.Furthermore, the input unit can collect the user's biometric data (heart rate and electrodermal activity) using sensors, input it into the generation AI, and estimate emotions.

[0077] The input unit can prioritize acquiring highly relevant input content during input, taking into account the user's geographical location information. For example, when the user is in a specific region, the input unit prioritizes displaying content related to that region. For example, when the user is in a specific city, the input unit displays tourist information and event information for that city. Furthermore, when the user is traveling, the input unit can prioritize displaying content related to the travel destination. For example, when the user inputs information at a travel destination, the input unit displays recommended spots and restaurant information for the travel destination. Furthermore, when the user is at home, the input unit can prioritize displaying content related to the user's home. For example, when the user inputs information at home, the input unit displays relaxation techniques that can be done at home and tips for gardening. This allows the user to input more appropriate information by providing highly relevant input content based on the user's geographical location information. For example, the input unit can prioritize displaying content related to a specific region. Furthermore, the input unit can prioritize displaying content related to a travel destination. Furthermore, the input unit can prioritize displaying content related to the user's home.

[0078] The input unit can analyze the user's social media activity at the time of input and input related opinions and thoughts. The input unit, for example, automatically inputs opinions and thoughts shared by the user on social media. For example, the input unit analyzes content posted by the user on social media and inputs related opinions and thoughts. The input unit can also analyze the content posted by the user on social media and input related opinions and thoughts. For example, the input unit extracts opinions about work worries and life choices from the content posted by the user on social media. The input unit can also input related opinions and thoughts by referring to the activities of the user's friends on social media. For example, the input unit inputs related opinions and thoughts based on information shared by the user's friends on social media. This allows more appropriate information to be input by providing related opinions and thoughts based on the user's social media activity. For example, the input unit automatically inputs opinions and thoughts shared by the user on social media. The input unit can also analyze the content posted by the user on social media and input related opinions and thoughts. The input unit can also input related opinions and thoughts by referring to the activities of the user's friends on social media.

[0079] The input unit can customize the input method by reflecting the user's past feedback when inputting information. The input unit, for example, suggests an optimal input method based on feedback provided by the user in the past. For example, the input unit analyzes the content of feedback provided by the user in the past and suggests an optimal input method. The input unit can also preferentially support a specific input method based on the user's past feedback. For example, the input unit preferentially supports voice input provided by the user in the past. The input unit can also analyze the user's past feedback and customize the input interface. For example, the input unit adjusts the design and functions of the input interface based on the content of feedback provided by the user in the past. This allows more appropriate information to be input by providing an input method based on the user's past feedback. For example, the input unit suggests an optimal input method based on feedback provided by the user in the past. The input unit can also preferentially support a specific input method based on the user's past feedback. The input unit can also analyze the user's past feedback and customize the input interface.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is sad, the analysis unit provides an analysis result that soothes the user's emotions. For example, the analysis unit analyzes the content entered by the user and provides advice to soothe the user's emotions. The analysis unit can also provide a positive analysis result if the user is happy. For example, the analysis unit analyzes the content entered by the user and provides positive feedback. Furthermore, the analysis unit can provide a calm analysis result if the user is angry. For example, the analysis unit analyzes the content entered by the user and provides calm advice. This allows for providing an analysis algorithm that corresponds to the user's emotions, thereby providing a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, 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. For example, the analysis unit can capture the user's facial expression with a camera, input it into the generation AI, and estimate the emotion. The analysis unit can also record the user's voice, input it into the generation AI, and estimate emotions.Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) using sensors, input it into the generation AI, and estimate emotions.

[0081] The analysis unit can improve the accuracy of the analysis by referring to the user's past opinions and thoughts during analysis. The analysis unit, for example, improves the accuracy of the analysis by referring to opinions and thoughts previously input by the user. For example, the analysis unit adjusts the analysis results by referring to work-related worries previously input by the user. The analysis unit can also extract specific patterns from the user's past opinions and thoughts and reflect them in the analysis. For example, the analysis unit customizes the analysis results based on opinions about life choices previously input by the user. The analysis unit can also customize the analysis results based on the user's past opinions and thoughts. For example, the analysis unit adjusts the analysis results based on minor everyday worries previously input by the user. In this way, the accuracy of the analysis is improved by referring to the user's past opinions and thoughts. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past opinions and thoughts. The analysis unit can also extract specific patterns from the user's past opinions and thoughts and reflect them in the analysis. The analysis unit can also customize the analysis results based on the user's past opinions and thoughts.

[0082] During analysis, the analysis unit can apply different analysis methods depending on the category of the user's opinions and thoughts. For example, if the user's opinions and thoughts are work-related, the analysis unit applies an analysis method specialized for work. For example, the analysis unit performs analysis using work-related data based on work-related worries input by the user. Furthermore, if the user's opinions and thoughts are private, the analysis unit can also apply an analysis method specialized for private life. For example, the analysis unit performs analysis using private-related data based on opinions about life choices input by the user. Furthermore, if the user's opinions and thoughts are health-related, the analysis unit can also apply an analysis method specialized for health. For example, the analysis unit performs analysis using health-related data based on trivial daily worries input by the user. This allows for providing an analysis method according to the category of the user's opinions and thoughts, thereby providing more appropriate analysis results. For example, the analysis unit applies a work-specialized analysis method to opinions and thoughts related to work. Furthermore, the analysis unit can apply a private-specialized analysis method to opinions and thoughts related to private life. Furthermore, the analysis unit can apply a health-specialized analysis method to opinions and thoughts related to health.

[0083] The analysis unit can perform the analysis while taking into account the user's attribute information. The analysis unit, for example, takes into account the user's age to provide an appropriate analysis result. For example, if the user is young, the analysis unit provides advice suitable for young people. The analysis unit can also take into account the user's gender to provide an appropriate analysis result. For example, if the user is female, the analysis unit provides advice suitable for women. The analysis unit can also take into account the user's occupation to provide an appropriate analysis result. For example, if the user is an engineer, the analysis unit provides advice suitable for engineers. In this way, by providing an analysis based on the user's attribute information, a more appropriate analysis result can be provided. For example, the analysis unit can take into account the user's age to provide advice suitable for young people. The analysis unit can also take into account the user's gender to provide advice suitable for women. The analysis unit can also take into account the user's occupation to provide advice suitable for engineers.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, the analysis unit displays the content entered by the user in a simple text format. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, the analysis unit displays the content entered by the user in a detailed graph or chart. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, the analysis unit displays the main points of the content entered by the user in bullet points. This allows for providing more appropriate information by providing a display method of the analysis results that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, 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. For example, the analysis unit can capture the user's facial expression with a camera, input it into the generation AI, and estimate the emotion. The analysis unit can also record the user's voice, input it into the generation AI, and estimate emotions.Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) using sensors, input it into the generation AI, and estimate emotions.

[0085] The analysis unit can take the user's geographical distribution into consideration when performing the analysis. For example, when the user is in a specific region, the analysis unit prioritizes analyzing information related to that region. For example, when the user is in a specific city, the analysis unit analyzes tourist information and event information for that city. Furthermore, when the user is traveling, the analysis unit can prioritize analyzing information related to the travel destination. For example, the analysis unit analyzes tourist information and recommended spots for the travel destination based on content entered by the user at the travel destination. Furthermore, when the user is at home, the analysis unit can prioritize analyzing information related to the user's home. For example, the analysis unit analyzes relaxation methods that can be done at home and tips for home gardening based on content entered by the user at home. This allows for providing an analysis based on the user's geographical distribution, thereby providing more appropriate analysis results. For example, the analysis unit prioritizes analyzing information related to a specific region. Furthermore, the analysis unit can prioritize analyzing information related to the travel destination. Furthermore, the analysis unit can prioritize analyzing information related to the user's home.

[0086] The analysis unit can improve the accuracy of the analysis by referring to related literature and data during analysis. The analysis unit, for example, refers to related academic papers to improve the accuracy of the analysis. For example, the analysis unit searches for academic papers related to the content entered by the user and reflects the contents in the analysis. The analysis unit can also improve the accuracy of the analysis by referring to related databases. For example, the analysis unit searches for databases related to the content entered by the user and reflects the data in the analysis. The analysis unit can also improve the accuracy of the analysis by referring to related statistical data. For example, the analysis unit searches for statistical data related to the content entered by the user and reflects the data in the analysis. In this way, the accuracy of the analysis is improved by referring to related literature and data. For example, the analysis unit can improve the accuracy of the analysis by referring to related academic papers. The analysis unit can also improve the accuracy of the analysis by referring to related databases. The analysis unit can also improve the accuracy of the analysis by referring to related statistical data.

[0087] The analysis unit can perform the analysis taking into account the user's market value. For example, the analysis unit evaluates the market value based on the user's occupation and skills and reflects it in the analysis results. For example, if the user is an engineer, the analysis unit evaluates the user's market value as an engineer and reflects it in the analysis results. The analysis unit can also evaluate the user's market value taking into account the user's past performance and track record and reflect it in the analysis results. For example, the analysis unit can evaluate the user's market value based on the projects and track record that the user has achieved in the past and reflect it in the analysis results. Furthermore, the analysis unit can evaluate the user's market value taking into account current market trends and reflect it in the analysis results. For example, the analysis unit can evaluate the user's market value based on current market trends and reflect it in the analysis results. This allows for providing an analysis based on the user's market value, thereby providing more appropriate analysis results. For example, the analysis unit can evaluate the user's market value based on the user's occupation and skills and reflect it in the analysis results. The analysis unit can also evaluate the user's market value taking into account the user's past performance and track record and reflect it in the analysis results. Furthermore, the analysis unit can evaluate the user's market value taking into account current market trends and reflect it in the analysis results.

[0088] The response generation unit can estimate the user's emotions and adjust the way a response is expressed based on the estimated user's emotions. For example, if the user is sad, the response generation unit generates a response in kind words. For example, the response generation unit provides advice in kind words in response to the content entered by the user. Furthermore, if the user is happy, the response generation unit can generate a response in cheerful words. For example, the response generation unit provides feedback in cheerful words in response to the content entered by the user. Furthermore, if the user is angry, the response generation unit can generate a response in calm words. For example, the response generation unit provides advice in calm words in response to the content entered by the user. This allows for the generation of a more appropriate response by providing a way to express a response according to the user's emotions. Emotion estimation is realized using an emotion estimation function, 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. For example, the response generation unit can capture the user's facial expression with a camera, input it into the generation AI, and estimate the emotion. The response generation unit can also record the user's voice, input it into the generation AI, and estimate emotions.Furthermore, the response generation unit can collect the user's biometric data (heart rate and electrodermal activity) using sensors, input it into the generation AI, and estimate emotions.

[0089] When generating a response, the response generation unit can adjust the level of detail of the response based on the importance of the user's opinion or thought. For example, the response generation unit generates a detailed response when the user's opinion or thought is important. For example, the response generation unit provides detailed advice for an important opinion or thought input by the user. The response generation unit can also generate a concise response when the user's opinion or thought is general. For example, the response generation unit provides concise feedback for a general opinion or thought input by the user. The response generation unit can also generate a quick response when the user's opinion or thought is urgent. For example, the response generation unit provides quick advice for an urgent opinion or thought input by the user. This allows for the generation of a more appropriate response by providing the level of detail of the response according to the importance of the user's opinion or thought. For example, the response generation unit generates a detailed response for an important opinion or thought. The response generation unit can also generate a concise response for a general opinion or thought. The response generation unit can also generate a quick response for an urgent opinion or thought.

[0090] When generating a response, the response generation unit can apply different response algorithms depending on the category of the user's opinion or thought. For example, if the user's opinion or thought is work-related, the response generation unit applies a response algorithm specialized for work. For example, the response generation unit provides work-related advice in response to a work-related concern input by the user. Furthermore, if the user's opinion or thought is related to private life, the response generation unit can also apply a response algorithm specialized for private life. For example, the response generation unit provides private life-related advice in response to an opinion about life choices input by the user. Furthermore, the response generation unit can also apply a health-specific response algorithm in response to a health-related opinion or thought. For example, the response generation unit provides health-related advice in response to a minor daily concern input by the user. In this way, by providing a response algorithm according to the category of the user's opinion or thought, a more appropriate response can be generated. For example, the response generation unit applies a work-specific response algorithm to opinions or thoughts related to work. Furthermore, the response generation unit can also apply a private life-specific response algorithm to opinions or thoughts related to private life. Furthermore, the response generation unit can also apply a health-specific response algorithm to opinions or thoughts related to health.

[0091] When generating a response, the response generation unit can improve the accuracy of the response by referring to the user's past response results. The response generation unit, for example, improves the accuracy of the response by referring to responses received by the user in the past. For example, the response generation unit generates a more accurate response based on advice received by the user in the past. The response generation unit can also extract a specific pattern from the user's past response results and reflect it in the response. For example, the response generation unit customizes the content of the response based on feedback received by the user in the past. The response generation unit can also customize the content of the response based on the user's past response results. For example, the response generation unit adjusts the content of the response based on advice received by the user in the past for minor everyday concerns. In this way, the accuracy of the response is improved by referring to the user's past response results. For example, the response generation unit improves the accuracy of the response by referring to advice received by the user in the past. The response generation unit can also extract a specific pattern from the user's past response results and reflect it in the response. The response generation unit can also customize the content of the response based on the user's past response results.

[0092] The response generation unit can estimate the user's emotions and adjust the length of the response based on the estimated user emotions. For example, if the user is in a hurry, the response generation unit generates a short and to-the-point response. For example, the response generation unit provides short and to-the-point advice in response to the content entered by the user. Furthermore, if the user is relaxed, the response generation unit can generate a longer response including detailed explanations. For example, the response generation unit provides advice including detailed explanations in response to the content entered by the user. Furthermore, if the user is excited, the response generation unit can generate a response that adds visually stimulating effects. For example, the response generation unit provides advice that adds visually stimulating effects in response to the content entered by the user. This allows the length of the response to be adjusted according to the user's emotions, thereby generating a more appropriate response. Emotion estimation is achieved using an emotion estimation function, 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. For example, the response generation unit can capture the user's facial expression with a camera, input it into the generation AI, and infer emotions. The response generation unit can also record the user's voice, input it into the generation AI, and infer emotions. Furthermore, the response generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, input it into the generation AI, and infer emotions.

[0093] When generating a response, the response generation unit can determine the priority of responses based on the time when the user submitted their opinions and thoughts. For example, the response generation unit prioritizes responses to opinions and thoughts recently submitted by the user. For example, the response generation unit prioritizes providing advice for work-related worries recently input by the user. The response generation unit can also prioritize responses to opinions and thoughts submitted by the user during a specific time period. For example, the response generation unit prioritizes providing advice for opinions about life choices input by the user at night. The response generation unit can also prioritize responses to opinions and thoughts urgently submitted by the user. For example, the response generation unit can quickly provide advice for minor everyday worries urgently input by the user. This allows for the generation of more appropriate responses by prioritizing responses according to the time when the user submitted their opinions and thoughts. For example, the response generation unit prioritizes responses to opinions and thoughts recently submitted. The response generation unit can also prioritize responses to opinions and thoughts submitted during a specific time period. The response generation unit can also quickly respond to opinions and thoughts urgently submitted.

[0094] When generating a response, the response generation unit can adjust the order of responses based on the relevance of the user's opinions and thoughts. For example, the response generation unit responds to the user's opinions and thoughts in descending order of relevance. For example, the response generation unit prioritizes responses to opinions and thoughts that are highly relevant to work-related worries input by the user. The response generation unit can also respond to the user's opinions and thoughts by category. For example, the response generation unit responds to opinions about life choices input by the user by category. The response generation unit can also respond to the user's opinions and thoughts in descending order of importance. For example, the response generation unit responds to minor daily worries input by the user in descending order of importance. This allows for the generation of more appropriate responses by providing an order of responses according to the relevance of the user's opinions and thoughts. For example, the response generation unit prioritizes responses to highly relevant opinions and thoughts. The response generation unit can also respond to opinions and thoughts by category. The response generation unit can also respond to opinions and thoughts in descending order of importance.

[0095] When generating a response, the response generation unit can adjust the use of technical terminology in the response depending on the user's level of expertise. For example, if the user has technical expertise, the response generation unit generates a response that uses a lot of technical terminology. For example, if the user is an engineer, the response generation unit provides technical advice that uses a lot of technical terminology. Furthermore, if the user has general knowledge, the response generation unit can generate a response that avoids technical terminology. For example, if the user has general knowledge, the response generation unit provides concise advice that avoids technical terminology. Furthermore, if the user is a beginner, the response generation unit can generate a response in simple language. For example, if the user is a beginner, the response generation unit provides easy-to-understand advice in simple language. This allows for the generation of more appropriate responses by providing technical terminology in the response depending on the user's level of expertise. For example, the response generation unit can generate a response that uses a lot of technical terminology for a user who has technical expertise. Furthermore, the response generation unit can generate a response that avoids technical terminology for a user who has general knowledge. Furthermore, the response generation unit can generate a response in simple language for a novice user.

[0096] The providing unit can estimate the user's emotions and adjust the response provision method based on the estimated user emotions. For example, if the user is sad, the providing unit can provide a response in a gentle voice. For example, the providing unit can provide advice in a gentle voice in response to content entered by the user. Furthermore, if the user is happy, the providing unit can provide a response in a cheerful voice. For example, the providing unit can provide feedback in a cheerful voice in response to content entered by the user. Furthermore, if the user is angry, the providing unit can provide a response in a calm voice. For example, the providing unit can provide advice in a calm voice in response to content entered by the user. This provides a response provision method according to the user's emotions, thereby providing a more appropriate response. Emotion estimation is realized using an emotion estimation function, 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. For example, the providing unit can capture the user's facial expression with a camera, input it into the generation AI, and estimate the emotion. The providing unit can also record the user's voice, input it into the generating AI, and estimate emotions. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) using sensors, input it into the generating AI, and estimate emotions.

[0097] The providing unit can select the optimal delivery method by referring to the user's past response history when providing a response. The providing unit, for example, selects the optimal delivery method based on the response history received by the user in the past. For example, the providing unit selects the optimal delivery method based on advice received by the user in the past. The providing unit can also preferentially select a specific delivery method from the user's past response history. For example, the providing unit preferentially selects a specific delivery method based on feedback received by the user in the past. The providing unit can also analyze the user's past response history and customize the delivery method. For example, the providing unit adjusts the delivery method based on advice received by the user in the past for minor everyday concerns. This allows for the provision of a more appropriate response by providing the optimal delivery method based on the user's past response history. For example, the providing unit selects the optimal delivery method based on advice received by the user in the past. The providing unit can also preferentially select a specific delivery method from the user's past response history. The providing unit can also analyze the user's past response history and customize the delivery method.

[0098] The providing unit can customize the provided content according to the user's current task when providing the content. For example, when the user is at work, the providing unit provides a work-related response. For example, the providing unit provides work-related advice in response to content input by the user while at work. Furthermore, the providing unit can also provide a relaxing response when the user is on a break. For example, the providing unit provides relaxing advice in response to content input by the user while on a break. Furthermore, the providing unit can also provide a travel-related response when the user is traveling. For example, the providing unit provides tourist information about the travel destination and recommended spots in response to content input by the user while traveling. This allows the content to be provided according to the user's current task, thereby providing a more appropriate response. For example, the providing unit provides a work-related response in response to content input by the user while at work. Furthermore, the providing unit can also provide a relaxing response in response to content input by the user while on a break. Furthermore, the providing unit can also provide a travel-related response in response to content input by the user while traveling.

[0099] The providing unit can improve the delivery method by reflecting user feedback when providing a response. The providing unit improves the delivery method, for example, by providing feedback for a response provided by the user. For example, the providing unit provides feedback for advice provided by the user and improves the delivery method based on the feedback. The providing unit can also preferentially select a specific delivery method based on user feedback. For example, the providing unit preferentially selects a specific delivery method based on feedback provided by the user in the past. The providing unit can also analyze user feedback and customize the delivery method. For example, the providing unit adjusts the design and functions of the delivery method based on feedback provided by the user. This makes it possible to provide a more appropriate response by providing a delivery method based on user feedback. For example, the providing unit improves the delivery method by providing feedback for advice provided by the user. The providing unit can also preferentially select a specific delivery method based on user feedback. The providing unit can also analyze user feedback and customize the delivery method.

[0100] 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 nervous, the providing unit can provide a response at a calm timing. For example, the providing unit can provide advice in response to content entered by the user at a calm timing. Furthermore, the providing unit can provide a response immediately if the user is relaxed. For example, the providing unit can provide advice immediately in response to content entered by the user. Furthermore, if the user is in a hurry, the providing unit can provide a response quickly. For example, the providing unit can provide advice quickly in response to content entered by the user. This allows for a more appropriate response to be provided by timing the response to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, 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. For example, the providing unit can capture the user's facial expression with a camera, input it into the generation AI, and estimate the emotion. The providing unit can also record the user's voice, input it into the generation AI, and estimate the emotion. In addition, the provider can collect the user's biometric data (heart rate and electrical skin activity) using sensors, input it into the generation AI, and estimate emotions.

[0101] The providing unit can select the optimal providing method by taking into consideration the user's device information when providing advice. For example, if the user is using a smartphone, the providing unit provides a providing method tailored to the screen size. For example, the providing unit provides advice optimized for the smartphone's screen size in response to content entered by the user on the smartphone. Furthermore, if the user is using a tablet, the providing unit can also provide a providing method optimized for a large screen. For example, the providing unit provides advice optimized for a large screen in response to content entered by the user on the tablet. Furthermore, if the user is using a smartwatch, the providing unit can also provide a concise and highly visible providing method. For example, the providing unit provides concise and highly visible advice in response to content entered by the user on the smartwatch. This allows for a more appropriate response by providing the optimal providing method based on the user's device information. For example, the providing unit can provide a providing method tailored to the screen size in response to a user using a smartphone. Furthermore, the providing unit can also provide a providing method optimized for a large screen in response to a user using a tablet. Furthermore, the providing unit can also provide a concise and highly visible providing method in response to a user using a smartwatch.

[0102] The providing unit can provide the content in multiple languages ​​according to the user's language setting when providing the content. The providing unit, for example, automatically sets the content to be provided based on the language setting of the user's device. For example, if the user sets the device's language setting to English, the providing unit provides advice in English. The providing unit can also provide a language switching function when the user uses multiple languages. For example, if the user uses English and Japanese, the providing unit can provide a language switching function and switch languages ​​as needed. Furthermore, if the user selects a specific language, the providing unit can provide the content in that language. For example, if the user selects Spanish, the providing unit provides advice in Spanish. This allows for more appropriate responses by providing content in multiple languages ​​based on the user's language setting. For example, the providing unit can automatically set the content to be provided based on the device's language setting. The providing unit can also provide a language switching function for users who use multiple languages. Furthermore, the providing unit can provide content in a specific language for users who select a specific language.

[0103] The providing unit can analyze the user's lifestyle rhythm when providing advice and suggest the optimal timing for providing the advice. The providing unit, for example, analyzes the user's lifestyle rhythm and suggests the optimal timing for providing the advice. For example, the providing unit analyzes the user's sleep patterns and activity times and provides advice at the optimal timing. Furthermore, if the user is more likely to receive responses during a specific time period, the providing unit can provide the response during that time period. For example, if the user is more likely to receive responses in the morning, the providing unit can provide the advice in the morning. Furthermore, the providing unit can customize the timing for providing the response to match the user's lifestyle rhythm. For example, the providing unit can provide advice at night so that the user can relax at night. In this way, by providing the optimal timing for providing the advice based on the user's lifestyle rhythm, a more appropriate response can be provided. For example, the providing unit can analyze the user's lifestyle rhythm and suggest the optimal timing for providing the advice. Furthermore, the providing unit can provide a response during a specific time period for a user who is more likely to receive responses during that time period. Furthermore, the providing unit can customize the timing for providing the response to match the user's lifestyle rhythm. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, analysis unit, response generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit can input a user's opinions and thoughts using a keyboard or a voice input device of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and understands the user's opinions and thoughts using natural language processing technology. The response generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an appropriate response using machine learning. The provision unit provides a response to the user using, for example, the display 40A or speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned input unit, analysis unit, response 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 input unit can input a user's opinions and thoughts using the microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and understands the user's opinions and thoughts using natural language processing technology. The response generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an appropriate response using machine learning. The provision unit provides a response to the user using, for example, the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, response 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 input unit can input the user's opinions and thoughts using the microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and understands the user's opinions and thoughts using natural language processing technology. The response generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an appropriate response using machine learning. The provision unit provides the response to the user using, for example, the display 343 or speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, response 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 input unit can input the user's opinions and thoughts using the microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and understands the user's opinions and thoughts using natural language processing technology. The response generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an appropriate response using machine learning. The provision unit provides the response to the user using, for example, the speaker 240 or display device of the robot 414.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The dialogue support system can further analyze the user's past dialogue history to learn the user's preferences and tendencies. For example, the analysis unit analyzes what topics the user has talked about in the past and prioritizes analysis of topics that interest the user. The response generation unit can also generate responses using expressions and phrases preferred by the user based on the past dialogue history. The provision unit can also refer to the past dialogue history and provide responses in a format preferred by the user. In this way, by utilizing the user's past dialogue history, more personalized dialogue can be realized. For example, the analysis unit prioritizes analysis of topics that the user has talked about frequently in the past. The response generation unit can also generate responses using expressions preferred by the user. The provision unit can also provide responses in a format preferred by the user.

[0106] The input unit can monitor the user's input speed and input frequency and provide optimal input assistance. For example, if the user is taking a long time to input, the input unit can provide an input completion function, thereby making the input work more efficient. The input unit can also learn words and phrases that the user frequently inputs and provide a predictive input function. Furthermore, the input unit can dynamically change the layout of the input interface according to the user's input frequency. This makes it possible to provide optimal input assistance according to the user's input speed and input frequency and make the input work more efficient. For example, the input unit can provide an input completion function. The input unit can also provide a predictive input function. Furthermore, the input unit can dynamically change the layout of the input interface.

[0107] The analysis unit can complement the analysis results by referencing related external databases based on the user's input. For example, the analysis unit can refer to a related industry database for a work-related concern entered by the user and reflect the latest industry trends in the analysis results. The analysis unit can also refer to a medical database for a health-related concern entered by the user and reflect the latest medical information in the analysis results. Furthermore, the analysis unit can refer to an academic paper database for an academic question entered by the user and reflect the latest research results in the analysis results. In this way, by utilizing external databases, it is possible to provide more accurate analysis results. For example, the analysis unit can refer to an industry database. The analysis unit can also refer to a medical database. The analysis unit can also refer to an academic paper database.

[0108] The answer generation unit can generate multiple answer candidates based on the user's input and provide the user with options. For example, the answer generation unit can generate multiple solutions to a work-related problem input by the user and provide the user with options. The answer generation unit can also generate advice from multiple perspectives in response to an opinion about life choices input by the user. Furthermore, the answer generation unit can generate multiple solutions to a minor everyday problem input by the user and provide the user with options. This allows the user to select a more appropriate answer by providing multiple options. For example, the answer generation unit can generate multiple solutions. The answer generation unit can also generate advice from multiple perspectives. Furthermore, the answer generation unit can generate multiple solutions.

[0109] The providing unit can dynamically change the method of providing the response depending on the usage status of the user's device. For example, if the user is using a smartphone, the providing unit can provide the response as a short text message. Furthermore, if the user is using a personal computer, the providing unit can provide the response including detailed text and graphs. Furthermore, if the user is using a smartwatch, the providing unit can provide the response in the form of a concise notification. This makes it possible to provide more appropriate information by providing an optimal response depending on the usage status of the user's device. For example, the providing unit can provide the response as a short text message. Furthermore, the providing unit can provide the response including detailed text and graphs. Furthermore, the providing unit can provide the response in the form of a concise notification.

[0110] The input unit can estimate the user's emotions and provide feedback on the input content based on the estimated user emotions. For example, if the user feels anxious, the input unit can display an encouraging message to support the input task. Furthermore, if the user feels happy, the input unit can provide positive feedback to make the input task more enjoyable. Furthermore, if the user feels tired, the input unit can display a message encouraging the user to take a break to reduce the burden of the input task. This can support the input task by providing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the input unit can capture the user's facial expression with a camera, input it into the generative AI, and estimate the emotion. The input unit can also record the user's voice, input it into the generative AI, and estimate the emotion. Furthermore, the input unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, input it into the generative AI, and estimate the emotion.

[0111] The analysis unit can estimate the user's emotions and evaluate the reliability of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can evaluate the reliability of the analysis results as low and perform reanalysis. Alternatively, if the user is relaxed, the analysis unit can evaluate the reliability of the analysis results as high and provide them as is. Furthermore, if the user is excited, the analysis unit can evaluate the reliability of the analysis results as medium and perform additional confirmation. This allows for evaluating the reliability of the analysis results according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. For example, the analysis unit can capture the user's facial expressions with a camera, input them into the generation AI, and estimate emotions. The analysis unit can also record the user's voice, input them into the generation AI, and estimate emotions. In addition, the analysis unit can collect the user's biometric data (heart rate and electrical skin activity) using sensors, input it into the generation AI, and estimate emotions.

[0112] The response generation unit can estimate the user's emotions and adjust the tone of the response based on the estimated user's emotions. For example, if the user is sad, the response generation unit can generate a response in a gentle tone. If the user is happy, the response generation unit can also generate a response in a bright tone. If the user is angry, the response generation unit can also generate a response in a calm tone. This allows for more appropriate dialogue by providing a response in a tone that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, 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. For example, the response generation unit can capture the user's facial expression with a camera and input it into the generation AI to estimate emotions. The response generation unit can also record the user's voice and input it into the generation AI to estimate emotions. The response generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor, input it into the generation AI, and estimate emotions.

[0113] The providing unit can estimate the user's emotions and adjust the format of the response based on the estimated user's emotions. For example, if the user is sad, the providing unit can provide the response as a text message. If the user is happy, the providing unit can also provide the response as a voice message. Furthermore, if the user is angry, the providing unit can provide the response using an image or video. This allows for providing more appropriate information by providing a response in a format appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, 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. For example, the providing unit can capture the user's facial expression with a camera and input it into the generation AI to estimate the emotion. The providing unit can also record the user's voice and input it into the generation AI to estimate the emotion. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and input it into the generation AI to estimate the emotion.

[0114] 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 nervous, the providing unit can provide a response at a calm timing. Furthermore, if the user is relaxed, the providing unit can also provide a response immediately. Furthermore, if the user is in a hurry, the providing unit can also provide a response quickly. This allows for a more appropriate response by providing a timing for providing a response according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the providing unit can capture the user's facial expression with a camera and input it into the generation AI to estimate the emotion. The providing unit can also record the user's voice and input it into the generation AI to estimate the emotion. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and input it into the generation AI to estimate the emotion.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The input unit receives input of opinions and thoughts from the user. Examples of opinions and thoughts that the user may input include, but are not limited to, work-related worries and opinions about life choices. The input unit can input the user's opinions and thoughts using, for example, a keyboard or a voice input device. The input unit can also accept handwritten input using a touch screen. Step 2: The analysis unit analyzes the information input by the input unit. The analysis unit understands the user's opinions and thoughts, for example, using natural language processing technology. The analysis unit divides the input text using morphological analysis and performs grammatical analysis, for example. The analysis unit can also understand the meaning of the input opinions and thoughts using semantic analysis. Furthermore, the analysis unit can also estimate the user's emotions using sentiment analysis. Step 3: The response generation unit generates a response based on the information analyzed by the analysis unit. The response generation unit generates an appropriate response using, for example, machine learning. The response generation unit generates a response based on a model previously trained using, for example, supervised learning. The response generation unit can also discover new patterns using unsupervised learning and generate a response. Furthermore, the response generation unit can also generate an optimal response using reinforcement learning. Step 4: The providing unit provides the response generated by the response generating unit to the user. For example, the providing unit displays the response as a text message. The providing unit can also play the response as a voice response. Furthermore, the providing unit can also provide the response using an image or video.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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. an input unit for inputting user opinions and thoughts; an analysis unit that analyzes the information input by the input unit; a response generation unit that generates a response based on the information analyzed by the analysis unit; a providing unit that provides the response generated by the response generating unit; Equipped with A system characterized by:

2. The input unit Users can input their opinions and thoughts 2. The system of claim 1.

3. The analysis unit Analyzing user opinions and thoughts using natural language processing technology 2. The system of claim 1.

4. The response generation unit Use machine learning to generate responses 2. The system of claim 1.

5. The providing unit Sends the generated response to the user 2. The system of claim 1.

6. The input unit Estimate user emotions and dynamically change the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

7. The input unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

8. The input unit As you type, filter your input based on the user's current context 2. The system of claim 1.

9. The input unit When inputting, select the most appropriate input method depending on the user's input method 2. The system of claim 1.

10. The input unit Estimate the user's emotions and prioritize input content based on the estimated user emotions.

2. The system of claim 1.

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