System

Abstract

An object of a system according to an embodiment is to provide appropriate advice in response to a user's worry or question about love.SOLUTION: A system according to an embodiment includes a user input analysis unit and an advice generation unit. The user input analysis unit analyzes an input from a user. The advice generation unit generates advice or a proposal related to love on the basis of the content analyzed by the user input analysis 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 technologies have had the problem of making it difficult to provide appropriate advice to users regarding their worries and questions about love.

[0005] The system according to the embodiment aims to provide appropriate advice to users regarding their worries and questions about love. [Means for solving the problem]

[0006] The system according to the embodiment includes a user input analysis unit and an advice generation unit. The user input analysis unit analyzes input from a user. The advice generation unit generates love advice or suggestions based on the content analyzed by the user input analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate advice to users regarding their worries and questions about love. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The love support tool according to the embodiment of the present invention is a system in which a generation AI provides appropriate answers and suggestions when a user seeks love-related consultation or advice. This allows the love support tool to provide prompt and appropriate advice in response to the user's love consultation.

[0029] A relationship support tool according to an embodiment includes a user input analysis unit and an advice generation unit. The user input analysis unit analyzes user input. For example, when a user inputs a question about love in text format, the user input analysis unit analyzes the content. The user input analysis unit can also analyze voice input. For example, when a user inputs a question by voice, the voice data is analyzed and converted into text data. The user input analysis unit can also analyze image input. For example, when a user uploads an image and asks a question based on that image, the image data is analyzed and related information is extracted. The advice generation unit generates love advice and suggestions based on the content analyzed by the user input analysis unit. For example, the generation AI provides specific advice in response to the user's question. For example, the advice generated may be, "When confessing your love, it's a good idea to choose a place and time that the other person likes and do so in a relaxed atmosphere." The generation AI can also generate optimal advice based on past data and case studies. For example, the generation AI can provide effective advice to the user by referring to past success stories. The generation AI can also provide customized advice tailored to the user's situation. For example, advice tailored to the user's personality and preferences is generated, allowing the love support tool according to the embodiment to provide quick and appropriate advice in response to a user's love consultation.

[0030] The user input analysis unit can refer to the user's past input history and perform analysis optimized for each individual user. For example, the user input analysis unit stores the user's past input history in a database and performs analysis by referring to that history when a new input is received. For example, if a user who previously input "I want to go on a date with my girlfriend" asks a question about dating again, optimal advice is provided based on the past history. The unit also analyzes the user's past input history to find specific patterns and trends. For example, if a user frequently asks about "confessing," detailed advice about confessing is provided taking that trend into consideration. The unit also identifies the user's preferences and interests based on the past input history and performs analysis accordingly. For example, if a user frequently asks about "movie dates," specific advice about movie dates is provided. This makes it possible to provide optimal analysis taking the user's past history into consideration.

[0031] The user input analysis unit can translate the user's input content into other languages ​​and provide love advice in multiple languages. The user input analysis unit, for example, automatically translates the user's input content into other languages ​​and provides love advice in multiple languages. For example, a question entered in English can be translated into Japanese and advice can be provided in Japanese. In addition, a system can be built that translates the user's input content in real time to provide love advice in multiple languages. For example, a question entered in French can be translated into English and advice can be provided in English. In addition, the user's input content can be translated into other languages ​​and provide love advice in multiple languages. For example, a question entered in Chinese can be translated into Japanese and advice can be provided in Japanese. In this way, love advice can be provided in multiple languages.

[0032] The advice generation unit can provide consistent advice by referring to the content of the user's past consultations. For example, the advice generation unit stores the user's past consultations in a database, and when a new consultation is received, refers to the history to provide consistent advice. For example, if a user who previously consulted about "I want to go on a date with my girlfriend" asks another question about dates, the advice generation unit provides optimal advice based on the past history. The advice generation unit also analyzes the user's past consultations to find specific patterns and trends. For example, if a user frequently consults about "confessing oneself," the advice generation unit provides detailed advice about confessing oneself taking that trend into consideration. The advice generation unit also identifies the user's preferences and interests based on the content of the past consultations and provides advice according to those preferences. For example, if a user frequently consults about "movie dates," the advice generation unit provides specific advice about movie dates. This makes it possible to provide consistent advice that takes into account the user's past consultations.

[0033] The advice generation unit enables the generation AI to generate advice that takes into account the user's cultural background and values. For example, if the user belongs to a particular cultural sphere, the generation AI will provide advice appropriate to that culture. The advice generation unit also customizes the advice by taking into account the user's values ​​and beliefs. For example, if the user has religious values, the generation AI will provide advice that takes those values ​​into consideration. The generation AI also understands the user's cultural background and values ​​and generates advice based on them. For example, if the user has a particular social background, the generation AI will provide advice that is appropriate to that background. This makes it possible to provide advice that takes into account the user's cultural background and values.

[0034] The advice generation unit can generate multiple advice options based on the user's input and allow the user to select from them. In the advice generation unit, for example, the generation AI analyzes the user's input and generates multiple advice options. For example, multiple options are provided, such as "When confessing, choose a place and time that the other person likes" and "Do it in a relaxed atmosphere." The generation AI also generates multiple advice options so that the user can select from them. For example, options are provided, such as "When choosing a date location, match it to the other person's hobbies" and "Suggest a place that you like." The generation AI also generates multiple advice options based on the user's input and allows the user to select from them. For example, options are provided, such as "When choosing the timing to confess, consider the other person's mood" and "Express your feelings frankly." This makes it possible to provide multiple advice options from which the user can select.

[0035] The advice generation unit can provide visual and audio advice based on the user's input. In the advice generation unit, for example, the generation AI analyzes the user's input and provides visual and audio advice. For example, a date scenario is displayed visually and specific advice is provided audio. Also, the generation AI provides visual and audio advice based on the user's input. For example, a confession scene is simulated visually and advice is provided audio. Also, the generation AI provides visual and audio advice based on the user's input. For example, a date plan is displayed visually and specific advice is provided audio. In this way, advice can be provided visually and audio.

[0036] The system can refer to the user's past simulation history to provide a more realistic simulation scenario. For example, the system stores the user's past simulation history in a database and, when performing a new simulation, refers to that history to provide a realistic scenario. For example, if a user who has previously performed a "dating simulation" requests another simulation, the system provides an optimal scenario based on the past history. The system can also analyze the user's past simulation history to find specific patterns and trends. For example, if a user frequently performs a "confession simulation," the system can provide a detailed confession scenario taking that trend into account. The system can also identify the user's preferences and interests based on the past simulation history and provide a realistic scenario that matches those preferences. For example, if a user frequently performs a "movie date simulation," the system can provide a specific movie date scenario. This makes it possible to provide a realistic scenario that takes the user's past simulation history into account.

[0037] The system can use a generation AI to generate a simulation scenario that takes into account the user's preferences and interests. For example, the generation AI generates a simulation scenario that takes into account the user's preferences and interests. For example, if the user likes particular movies or music, the system provides a date scenario that matches those preferences. The system also customizes the simulation scenario by taking into account the user's interests. For example, if the user is interested in outdoor activities, the system provides a date scenario that matches those interests. The generation AI also understands the user's preferences and interests and generates a simulation scenario based on them. For example, if the user has a particular hobby, the system provides a date scenario related to that hobby. In this way, it is possible to generate a simulation scenario that takes into account the user's preferences and interests.

[0038] The system can generate multiple simulation scenarios based on user input and allow the user to select from them. For example, the system uses a generation AI to analyze the user's input and generate multiple simulation scenarios. For example, if a user inputs "I want to simulate a date," multiple options such as "movie date," "cafe date," and "park date" are provided. The generation AI also generates multiple simulation scenarios for the user to select from. For example, if a user inputs "I want to simulate a confession," multiple options are provided such as "confess to a friend," "confess at work," and "confess during a date." The generation AI also generates multiple simulation scenarios based on the user's input and allows the user to select from them. For example, if a user inputs "I want to simulate a date," multiple options are provided such as "first date," "anniversary date," and "surprise date." This allows the system to provide multiple simulation scenarios for the user to select from.

[0039] The system can provide a simulation in visual and audio formats based on the user's input. For example, the generation AI analyzes the user's input and provides a simulation in visual and audio formats. For example, a date scenario may be displayed visually and a specific simulation may be provided audio. The generation AI can also provide a simulation in visual and audio formats based on the user's input. For example, a confession scene may be simulated visually and advice may be provided audio. The generation AI can also provide a simulation in visual and audio formats based on the user's input. For example, a date plan may be displayed visually and a specific simulation may be provided audio. This makes it possible to provide a simulation in visual and audio formats.

[0040] The system can automatically categorize the content of a user's past consultations, making them easier to search. For example, the system stores the content of a user's past consultations in a database and builds a system that automatically categorizes them. For example, the content can be categorized into categories such as "confession," "dating," and "repairing relationships," making them easier to search. The system also uses an automatic classification algorithm to categorize the content of a user's past consultations, making them easier to search. For example, if a user asks, "I want to confess my feelings to my boyfriend," the content can be categorized into the "confession" category. The system can also analyze the content of a user's past consultations, find specific patterns or trends, and automatically categorize them. For example, if a user frequently asks about "dating," the content can be categorized into the "dating" category. This allows the system to automatically categorize the content of a user's past consultations, making them easier to search.

[0041] The system can use the generation AI to predict future consultations based on the user's consultation history. For example, the system uses the generation AI to analyze the user's consultation history and predict future consultations. For example, if the user has often consulted about "confessing" in the past, the system predicts that the next consultation topic will also be likely to be related to "confession." Furthermore, a system is constructed in which the generation AI predicts future consultation topics based on the user's consultation history. For example, if the user has often consulted about "dating," the system predicts that the next consultation topic will also be likely to be related to "dating." Furthermore, the generation AI predicts future consultations based on the user's consultation history. For example, if the user has often consulted about "repairing relationships," the system predicts that the next consultation topic will also be likely to be related to "repairing relationships." In this way, future consultations can be predicted based on the user's consultation history.

[0042] The system can synchronize a user's consultation history with other devices, making it accessible anywhere. For example, the system will build a system in which a generation AI stores a user's consultation history in the cloud and synchronizes it with other devices. For example, it will make it accessible from smartphones and tablets. The system will also synchronize a user's consultation history with other devices, making it accessible anywhere. For example, it will make it possible to check the contents of consultations made on a PC on a smartphone. In addition, a system will be developed in which a generation AI will synchronize a user's consultation history with other devices, making it accessible anywhere. For example, it will make it possible for a user to check the contents of past consultations even when they are out and about. This will make it possible to synchronize a user's consultation history with other devices, making it accessible anywhere.

[0043] The system can visualize a user's consultation history, allowing for intuitive understanding. For example, the system builds a system in which a generation AI can visualize a user's consultation history, allowing for intuitive understanding. For example, past consultation details can be displayed in timeline format. The user's consultation history can also be visualized in graphs and charts, allowing for intuitive understanding. For example, each consultation detail can be displayed in a different color. The generation AI can also visualize a user's consultation history, allowing for intuitive understanding. For example, past consultation details can be displayed as a keyword cloud, highlighting frequently occurring keywords. This makes it possible to visualize a user's consultation history, allowing for intuitive understanding.

[0044] The system can refer to a user's past information search history to provide highly relevant information. For example, the system stores a user's past information search history in a database and, when providing new information, refers to that history to provide highly relevant information. For example, the system provides the latest date spot information to a user who previously searched for "date spots." The system can also analyze a user's past information search history to find specific patterns and trends. For example, if a user frequently searches for "how to confess," the system can provide detailed information about confessions taking that trend into consideration. The system can also identify a user's preferences and interests based on the past information search history and provide highly relevant information accordingly. For example, if a user frequently searches for "movie date spots," the system can provide the latest information about movie dates. This makes it possible to provide highly relevant information by referring to a user's past information search history.

[0045] The system can use the generation AI to provide information that takes into account the user's interests and concerns. For example, the generation AI provides information by taking into account the user's interests and concerns. For example, if the user likes a particular movie or music, the system provides information on date spots that match those preferences. The system also customizes information by taking into account the user's interests and concerns. For example, if the user is interested in outdoor activities, the system provides information on date spots that match those interests. The generation AI also understands the user's interests and concerns and provides information based on them. For example, if the user has a particular hobby, the system provides information on date spots related to that hobby. This makes it possible to provide information that takes into account the user's interests and concerns.

[0046] The system can provide multiple information options based on the user's input, allowing the user to select from them. For example, the system uses a generation AI to analyze the user's input and provide multiple information options. For example, if a user inputs "Tell me about date spots," multiple options such as "movie theater," "cafe," and "park" are provided. The generation AI also provides multiple information options so that the user can select from them. For example, if a user inputs "Tell me how to confess," multiple options such as "confess by letter," "confess in person," and "have a friend tell her." The generation AI also provides multiple information options based on the user's input, allowing the user to select from them. For example, if a user inputs "Tell me about date plans," multiple options such as "movie date," "cafe date," and "outdoor date" are provided. This makes it possible to provide multiple information options for the user to select from.

[0047] The system can provide information visually and audibly based on the user's input. For example, the generation AI analyzes the user's input and provides information visually and audibly. For example, information about date spots is displayed visually and specific explanations are provided audibly. The generation AI also provides information visually and audibly based on the user's input. For example, a visual simulation of how to confess is performed and specific advice is provided audibly. The generation AI also provides information visually and audibly based on the user's input. For example, a date plan is displayed visually and specific explanations are provided audibly. In this way, information can be provided visually and audibly.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The user input analysis unit can estimate the user's health condition based on the user's input and provide health-conscious advice. For example, if a user inputs, "I've been feeling tired easily lately," the generation AI can use that information to suggest a relaxing date plan. It can also extract information about the user's diet and exercise habits from the user's input and provide healthy date plans. For example, if a user inputs, "I haven't been getting enough exercise lately," the generation AI can suggest a walking date or a sports date. It can also provide advice to reduce stress, taking the user's health condition into consideration. For example, if a user inputs, "I'm getting stressed at work," the generation AI can suggest a date plan to a relaxing cafe or spa. This makes it possible to provide advice that takes the user's health condition into consideration.

[0050] The user input analysis unit can also infer the user's hobbies and interests based on the user's input and provide advice accordingly. For example, if a user inputs "I like movies," the generation AI can use that information to suggest a movie date plan. It can also extract specific hobbies and interests from the user's input and provide related date plans. For example, if a user inputs "I like the outdoors," the generation AI can suggest a hiking or camping date plan. It can also suggest special events and activities taking the user's hobbies and interests into consideration. For example, if a user inputs "I like music," the generation AI can suggest a concert or live show date plan. This makes it possible to provide advice tailored to the user's hobbies and interests.

[0051] The user input analysis unit can also estimate the user's cultural background based on their input and provide advice accordingly. For example, if a user inputs "I like Japanese culture," the generation AI can use that information to suggest date plans for Japanese restaurants and traditional festivals. It can also extract specific cultural backgrounds from the user's input and provide date plans related to those. For example, if a user inputs "I like French culture," the generation AI can suggest date plans for French restaurants and art museums. It can also suggest special events and activities taking the user's cultural background into consideration. For example, if a user inputs "I like Indian culture," the generation AI can suggest date plans for Indian cooking classes and Bollywood movies. This makes it possible to provide advice tailored to the user's cultural background.

[0052] The user input analysis unit can also estimate the user's lifestyle based on their input and provide advice accordingly. For example, if a user inputs "busy," the generation AI can use that information to suggest a date plan that can be enjoyed in a short amount of time. It can also extract specific lifestyles from the user's input and provide date plans related to that. For example, if a user inputs "night owl," the generation AI can suggest date plans at restaurants and bars that are open late. It can also suggest special events and activities taking the user's lifestyle into consideration. For example, if a user inputs "outdoorsy," the generation AI can suggest date plans for camping and hiking. This makes it possible to provide advice tailored to the user's lifestyle.

[0053] The user input analysis unit can also estimate the user's financial situation based on the user's input and provide advice accordingly. For example, if a user inputs "I'm trying to save money," the generation AI can use that information to suggest a low-cost date plan that can be enjoyed. It can also extract specific financial situations from the user's input and provide date plans related to that. For example, if a user inputs "I want to splurge," the generation AI can suggest date plans at high-end restaurants and resorts. It can also suggest special events and activities taking the user's financial situation into consideration. For example, if a user inputs "I want to have fun within my budget," the generation AI can suggest date plans that use free events and discount coupons. This makes it possible to provide advice tailored to the user's financial situation.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The user input analysis unit analyzes input from the user. For example, if the user inputs a question about love in text format, the content is analyzed. It can also analyze voice input and convert the voice data into text data. It can also analyze image input and extract related information. Step 2: The advice generator generates love-related advice and suggestions based on the content analyzed by the user input analyzer. For example, the generator AI can provide specific advice in response to a user's question and generate optimal advice based on past data and case studies. It can also provide customized advice tailored to the user's situation.

[0056] (Example 2) The love support tool according to the embodiment of the present invention is a system in which a generation AI provides appropriate answers and suggestions when a user seeks love-related consultation or advice. This allows the love support tool to provide prompt and appropriate advice in response to the user's love consultation.

[0057] A relationship support tool according to an embodiment includes a user input analysis unit and an advice generation unit. The user input analysis unit analyzes user input. For example, when a user inputs a question about love in text format, the user input analysis unit analyzes the content. The user input analysis unit can also analyze voice input. For example, when a user inputs a question by voice, the voice data is analyzed and converted into text data. The user input analysis unit can also analyze image input. For example, when a user uploads an image and asks a question based on that image, the image data is analyzed and related information is extracted. The advice generation unit generates love advice and suggestions based on the content analyzed by the user input analysis unit. For example, the generation AI provides specific advice in response to the user's question. For example, the advice generated may be, "When confessing your love, it's a good idea to choose a place and time that the other person likes and do so in a relaxed atmosphere." The generation AI can also generate optimal advice based on past data and case studies. For example, the generation AI can provide effective advice to the user by referring to past success stories. The generation AI can also provide customized advice tailored to the user's situation. For example, advice tailored to the user's personality and preferences is generated, allowing the love support tool according to the embodiment to provide quick and appropriate advice in response to a user's love consultation.

[0058] The user input analysis unit performs emotional analysis on the user's input and can adjust the tone of the advice based on the intensity and type of emotion. For example, the user input analysis unit analyzes the text entered by the user and performs emotional analysis. For example, if the user enters, "I've been fighting a lot with my girlfriend lately," the generation AI detects negative emotion from the text and adjusts the tone of the advice to be gentler. The unit also references the user's past input history when performing emotional analysis to understand emotional fluctuations. For example, if a user who has previously entered many positive inputs suddenly starts entering negative inputs, the advice can be adjusted to take that fluctuation into account. The tone of the advice can also be adjusted in real time based on the results of the emotional analysis. For example, if a user enters, "I really want to confess my feelings to him, but I'm scared," the generation AI will provide advice in a gentler tone to ease the fear. This allows the generation AI to provide advice in an appropriate tone based on the user's emotions.

[0059] The user input analysis unit can refer to the user's past input history and perform analysis optimized for each individual user. For example, the user input analysis unit stores the user's past input history in a database and performs analysis by referring to that history when a new input is received. For example, if a user who previously input "I want to go on a date with my girlfriend" asks a question about dating again, optimal advice is provided based on the past history. The unit also analyzes the user's past input history to find specific patterns and trends. For example, if a user frequently asks about "confessing," detailed advice about confessing is provided taking that trend into consideration. The unit also identifies the user's preferences and interests based on the past input history and performs analysis accordingly. For example, if a user frequently asks about "movie dates," specific advice about movie dates is provided. This makes it possible to provide optimal analysis taking the user's past history into consideration.

[0060] The user input analysis unit can use the emotion estimation function to estimate the user's emotion at the time of input in real time and provide analysis results according to the emotion. The user input analysis unit, for example, uses the emotion estimation function to analyze the emotion in real time when the user is inputting. For example, if the user inputs "I'm looking forward to seeing him," the unit detects the positive emotion and suggests a fun date plan. The emotion estimation function also monitors the user's emotion at the time of input in real time and performs analysis based on the results. For example, if the user inputs "I've been fighting a lot with my girlfriend lately," the unit detects the negative emotion and provides advice on repairing the relationship. The emotion estimation function also analyzes the user's emotion at the time of input in real time and provides advice according to the emotion. For example, if the user inputs "I want to confess my feelings to him, but I'm scared," the unit provides advice in a gentle tone to ease the fear. This makes it possible to provide analysis results according to the user's emotion in real time.

[0061] The user input analysis unit can analyze the user's voice input, infer emotions from the voice tone and voice speed, and reflect the emotions in the analysis. The user input analysis unit, for example, analyzes the content of the user's voice input and infers emotions from the voice tone and voice speed. For example, if the user speaks in a bright tone, such as "I'm looking forward to seeing him," the unit detects the positive emotion and suggests a fun date plan. When analyzing the voice input, the unit also detects changes in the voice tone and voice speed to infer emotions. For example, if the user speaks in a calm tone, such as "I've been fighting a lot with my girlfriend lately," the unit detects the negative emotion and provides advice on repairing the relationship. The unit also analyzes the voice input in real time and infers emotions from the voice tone and voice speed to reflect the emotions in the analysis. For example, if the user speaks in a nervous tone, such as "I want to confess my feelings to him, but I'm scared," the unit detects the fear and provides advice in a gentle tone. In this way, emotions can be inferred from the user's voice input and reflected in the analysis.

[0062] The user input analysis unit can translate the user's input content into other languages ​​and provide love advice in multiple languages. The user input analysis unit, for example, automatically translates the user's input content into other languages ​​and provides love advice in multiple languages. For example, a question entered in English can be translated into Japanese and advice can be provided in Japanese. In addition, a system can be built that translates the user's input content in real time to provide love advice in multiple languages. For example, a question entered in French can be translated into English and advice can be provided in English. In addition, the user's input content can be translated into other languages ​​and provide love advice in multiple languages. For example, a question entered in Chinese can be translated into Japanese and advice can be provided in Japanese. In this way, love advice can be provided in multiple languages.

[0063] The user input analysis unit can use the emotion estimation function to provide input support to draw out positive emotions based on the emotions expressed by the user at the time of input. The user input analysis unit, for example, uses the emotion estimation function to analyze the emotions expressed by the user at the time of input in real time and provide input support to draw out positive emotions. For example, if the user inputs, "I want to confess my feelings to him, but I'm scared," an encouraging message is displayed. The emotion estimation function can also be used to provide input support to draw out positive emotions based on the emotions expressed by the user at the time of input. For example, if the user inputs, "I've been fighting a lot with my girlfriend lately," positive advice for repairing the relationship is provided. The emotion estimation function can also be used to provide input support to draw out positive emotions based on the emotions expressed by the user at the time of input. For example, if the user inputs, "I'm looking forward to seeing him," advice to further enhance that positive emotion is provided. In this way, input support can be provided to draw out positive emotions from the user.

[0064] The advice generation unit can use the emotion estimation function to generate advice according to the user's emotions. For example, the generation AI in the advice generation unit uses the emotion estimation function to generate advice according to the user's emotions. For example, if the user inputs, "I want to confess my feelings to him, but I'm scared," advice is provided in a gentle tone that eases the fear. The emotion estimation function can also be used to analyze the user's emotions in real time and generate advice based on the results. For example, if the user inputs, "I've been fighting a lot with my girlfriend lately," the generation AI can detect the negative emotion and provide advice on repairing the relationship. The generation AI can also use the emotion estimation function to generate advice according to the user's emotions. For example, if the user inputs, "I'm looking forward to seeing him," advice that further enhances the positive emotion can be provided. In this way, advice according to the user's emotions can be generated.

[0065] The advice generation unit can provide consistent advice by referring to the content of the user's past consultations. For example, the advice generation unit stores the user's past consultations in a database, and when a new consultation is received, refers to the history to provide consistent advice. For example, if a user who previously consulted about "I want to go on a date with my girlfriend" asks another question about dates, the advice generation unit provides optimal advice based on the past history. The advice generation unit also analyzes the user's past consultations to find specific patterns and trends. For example, if a user frequently consults about "confessing oneself," the advice generation unit provides detailed advice about confessing oneself taking that trend into consideration. The advice generation unit also identifies the user's preferences and interests based on the content of the past consultations and provides advice according to those preferences. For example, if a user frequently consults about "movie dates," the advice generation unit provides specific advice about movie dates. This makes it possible to provide consistent advice that takes into account the user's past consultations.

[0066] The advice generation unit enables the generation AI to generate advice that takes into account the user's cultural background and values. For example, if the user belongs to a particular cultural sphere, the generation AI will provide advice appropriate to that culture. The advice generation unit also customizes the advice by taking into account the user's values ​​and beliefs. For example, if the user has religious values, the generation AI will provide advice that takes those values ​​into consideration. The generation AI also understands the user's cultural background and values ​​and generates advice based on them. For example, if the user has a particular social background, the generation AI will provide advice that is appropriate to that background. This makes it possible to provide advice that takes into account the user's cultural background and values.

[0067] The advice generation unit can generate multiple advice options based on the user's input and allow the user to select from them. In the advice generation unit, for example, the generation AI analyzes the user's input and generates multiple advice options. For example, multiple options are provided, such as "When confessing, choose a place and time that the other person likes" and "Do it in a relaxed atmosphere." The generation AI also generates multiple advice options so that the user can select from them. For example, options are provided, such as "When choosing a date location, match it to the other person's hobbies" and "Suggest a place that you like." The generation AI also generates multiple advice options based on the user's input and allows the user to select from them. For example, options are provided, such as "When choosing the timing to confess, consider the other person's mood" and "Express your feelings frankly." This makes it possible to provide multiple advice options from which the user can select.

[0068] The advice generation unit can provide visual and audio advice based on the user's input. In the advice generation unit, for example, the generation AI analyzes the user's input and provides visual and audio advice. For example, a date scenario is displayed visually and specific advice is provided audio. Also, the generation AI provides visual and audio advice based on the user's input. For example, a confession scene is simulated visually and advice is provided audio. Also, the generation AI provides visual and audio advice based on the user's input. For example, a date plan is displayed visually and specific advice is provided audio. In this way, advice can be provided visually and audio.

[0069] The advice generation unit uses the emotion estimation function to collect feedback on advice according to the user's emotions, thereby improving the quality of the advice. The advice generation unit, for example, uses the emotion estimation function to collect emotional reactions of the user when receiving advice in real time. For example, if the user shows positive emotions after receiving advice, the quality of the advice is highly rated. Furthermore, a system for improving the quality of advice is constructed based on the user's emotional reaction data. For example, advice with many negative emotional reactions is improved, and advice with many positive reactions is preferentially provided. Furthermore, the emotion estimation function is used to collect feedback on advice according to the user's emotions, and the quality of the advice is improved based on the data. For example, if a user has a high emotion score after receiving advice, the advice is also provided to other users. In this way, feedback according to the user's emotions can be collected, thereby improving the quality of advice.

[0070] The system can use the generation AI to generate a simulation scenario that corresponds to the user's emotions. For example, the generation AI uses an emotion estimation function to generate a simulation scenario that corresponds to the user's emotions. For example, if a user inputs, "I'm looking forward to seeing him," the system provides a fun date scenario that reflects that positive emotion. The emotion estimation function also analyzes the user's emotions in real time and generates a simulation scenario based on the results. For example, if a user inputs, "I've been fighting a lot with my girlfriend lately," the system provides a relationship repair scenario that reflects that negative emotion. The generation AI also uses the emotion estimation function to generate a simulation scenario that corresponds to the user's emotions. For example, if a user inputs, "I want to confess my feelings to him, but I'm scared," the system provides a confession scenario in a gentle tone that eases the fear. In this way, it is possible to generate a simulation scenario that corresponds to the user's emotions.

[0071] The system can refer to the user's past simulation history to provide a more realistic simulation scenario. For example, the system stores the user's past simulation history in a database and, when performing a new simulation, refers to that history to provide a realistic scenario. For example, if a user who has previously performed a "dating simulation" requests another simulation, the system provides an optimal scenario based on the past history. The system can also analyze the user's past simulation history to find specific patterns and trends. For example, if a user frequently performs a "confession simulation," the system can provide a detailed confession scenario taking that trend into account. The system can also identify the user's preferences and interests based on the past simulation history and provide a realistic scenario that matches those preferences. For example, if a user frequently performs a "movie date simulation," the system can provide a specific movie date scenario. This makes it possible to provide a realistic scenario that takes the user's past simulation history into account.

[0072] The system can use a generation AI to generate a simulation scenario that takes into account the user's preferences and interests. For example, the generation AI generates a simulation scenario that takes into account the user's preferences and interests. For example, if the user likes particular movies or music, the system provides a date scenario that matches those preferences. The system also customizes the simulation scenario by taking into account the user's interests. For example, if the user is interested in outdoor activities, the system provides a date scenario that matches those interests. The generation AI also understands the user's preferences and interests and generates a simulation scenario based on them. For example, if the user has a particular hobby, the system provides a date scenario related to that hobby. In this way, it is possible to generate a simulation scenario that takes into account the user's preferences and interests.

[0073] The system can generate multiple simulation scenarios based on user input and allow the user to select from them. For example, the system uses a generation AI to analyze the user's input and generate multiple simulation scenarios. For example, if a user inputs "I want to simulate a date," multiple options such as "movie date," "cafe date," and "park date" are provided. The generation AI also generates multiple simulation scenarios for the user to select from. For example, if a user inputs "I want to simulate a confession," multiple options are provided such as "confess to a friend," "confess at work," and "confess during a date." The generation AI also generates multiple simulation scenarios based on the user's input and allows the user to select from them. For example, if a user inputs "I want to simulate a date," multiple options are provided such as "first date," "anniversary date," and "surprise date." This allows the system to provide multiple simulation scenarios for the user to select from.

[0074] The system can provide a simulation in visual and audio formats based on the user's input. For example, the generation AI analyzes the user's input and provides a simulation in visual and audio formats. For example, a date scenario may be displayed visually and a specific simulation may be provided audio. The generation AI can also provide a simulation in visual and audio formats based on the user's input. For example, a confession scene may be simulated visually and advice may be provided audio. The generation AI can also provide a simulation in visual and audio formats based on the user's input. For example, a date plan may be displayed visually and a specific simulation may be provided audio. This makes it possible to provide a simulation in visual and audio formats.

[0075] The system uses an emotion estimation function to collect simulation feedback according to the user's emotions and improve the quality of the simulation. For example, the system uses the emotion estimation function to collect emotional responses in real time when a user performs a simulation. For example, if a user shows positive emotions after performing a simulation, the system rates the quality of the simulation highly. Furthermore, a system is constructed that improves the quality of simulations based on the user's emotional response data. For example, simulations with many negative emotional responses are improved, and simulations with many positive responses are preferentially provided. Furthermore, the emotion estimation function is used to collect simulation feedback according to the user's emotions and improve the quality of the simulation based on the data. For example, if a user performs a simulation with a high emotional score, that simulation is provided to other users as well. In this way, feedback according to the user's emotions can be collected and the quality of the simulation can be improved.

[0076] The system can use the emotion estimation function to manage history according to the user's emotions. For example, if a user inputs, "I want to confess my feelings to him, but I'm scared," the system provides advice in a gentle tone to ease the fear and saves the history. The system also uses the emotion estimation function to analyze the user's emotions in real time and manages the history based on the results. For example, if a user inputs, "I've been fighting a lot with my girlfriend lately," the system detects the negative emotion, provides advice on repairing the relationship, and saves the history. The system also uses the emotion estimation function to manage history according to the user's emotions. For example, if a user inputs, "I'm looking forward to seeing him," the system provides a fun date scenario that reflects the user's positive emotion and saves the history. This allows history management according to the user's emotions.

[0077] The system can automatically categorize the content of a user's past consultations, making them easier to search. For example, the system stores the content of a user's past consultations in a database and builds a system that automatically categorizes them. For example, the content can be categorized into categories such as "confession," "dating," and "repairing relationships," making them easier to search. The system also uses an automatic classification algorithm to categorize the content of a user's past consultations, making them easier to search. For example, if a user asks, "I want to confess my feelings to my boyfriend," the content can be categorized into the "confession" category. The system can also analyze the content of a user's past consultations, find specific patterns or trends, and automatically categorize them. For example, if a user frequently asks about "dating," the content can be categorized into the "dating" category. This allows the system to automatically categorize the content of a user's past consultations, making them easier to search.

[0078] The system can use the generation AI to predict future consultations based on the user's consultation history. For example, the system uses the generation AI to analyze the user's consultation history and predict future consultations. For example, if the user has often consulted about "confessing" in the past, the system predicts that the next consultation topic will also be likely to be related to "confession." Furthermore, a system is constructed in which the generation AI predicts future consultation topics based on the user's consultation history. For example, if the user has often consulted about "dating," the system predicts that the next consultation topic will also be likely to be related to "dating." Furthermore, the generation AI predicts future consultations based on the user's consultation history. For example, if the user has often consulted about "repairing relationships," the system predicts that the next consultation topic will also be likely to be related to "repairing relationships." In this way, future consultations can be predicted based on the user's consultation history.

[0079] The system can synchronize a user's consultation history with other devices, making it accessible anywhere. For example, the system will build a system in which a generation AI stores a user's consultation history in the cloud and synchronizes it with other devices. For example, it will make it accessible from smartphones and tablets. The system will also synchronize a user's consultation history with other devices, making it accessible anywhere. For example, it will make it possible to check the contents of consultations made on a PC on a smartphone. In addition, a system will be developed in which a generation AI will synchronize a user's consultation history with other devices, making it accessible anywhere. For example, it will make it possible for a user to check the contents of past consultations even when they are out and about. This will make it possible to synchronize a user's consultation history with other devices, making it accessible anywhere.

[0080] The system can visualize a user's consultation history, allowing for intuitive understanding. For example, the system builds a system in which a generation AI can visualize a user's consultation history, allowing for intuitive understanding. For example, past consultation details can be displayed in timeline format. The user's consultation history can also be visualized in graphs and charts, allowing for intuitive understanding. For example, each consultation detail can be displayed in a different color. The generation AI can also visualize a user's consultation history, allowing for intuitive understanding. For example, past consultation details can be displayed as a keyword cloud, highlighting frequently occurring keywords. This makes it possible to visualize a user's consultation history, allowing for intuitive understanding.

[0081] The system uses the emotion estimation function to collect history feedback according to the user's emotions and improve the quality of history management. For example, the system uses the emotion estimation function to collect emotional responses in real time when the user checks the consultation history. For example, if the user shows positive emotions when looking at the past consultation content, the quality of the history is highly rated. Furthermore, a system is constructed to improve the quality of history management based on the user's emotional response data. For example, history with many negative emotional responses is improved, and history with many positive responses is preferentially displayed. Furthermore, the emotion estimation function is used to collect history feedback according to the user's emotions and improve the quality of history management based on that data. For example, if a user looks at the past consultation content and receives a high emotional score, that history is provided to other users as a reference. In this way, feedback according to the user's emotions can be collected and the quality of history management can be improved.

[0082] The system can use the emotion estimation function to provide information that corresponds to the user's emotions. For example, if a user inputs, "I'm looking forward to seeing him," the system provides information about fun date spots that reflect that positive emotion. The system also uses the emotion estimation function to analyze the user's emotions in real time and provide information based on the results. For example, if a user inputs, "I've been fighting a lot with my girlfriend lately," the system provides information for repairing the relationship that reflects that negative emotion. The system also uses the emotion estimation function to provide information that corresponds to the user's emotions. For example, if a user inputs, "I want to confess my feelings to him, but I'm scared," the system provides confession advice in a gentle tone that eases the fear. This makes it possible to provide information that corresponds to the user's emotions.

[0083] The system can refer to a user's past information search history to provide highly relevant information. For example, the system stores a user's past information search history in a database and, when providing new information, refers to that history to provide highly relevant information. For example, the system provides the latest date spot information to a user who previously searched for "date spots." The system can also analyze a user's past information search history to find specific patterns and trends. For example, if a user frequently searches for "how to confess," the system can provide detailed information about confessions taking that trend into consideration. The system can also identify a user's preferences and interests based on the past information search history and provide highly relevant information accordingly. For example, if a user frequently searches for "movie date spots," the system can provide the latest information about movie dates. This makes it possible to provide highly relevant information by referring to a user's past information search history.

[0084] The system can use the generation AI to provide information that takes into account the user's interests and concerns. For example, the generation AI provides information by taking into account the user's interests and concerns. For example, if the user likes a particular movie or music, the system provides information on date spots that match those preferences. The system also customizes information by taking into account the user's interests and concerns. For example, if the user is interested in outdoor activities, the system provides information on date spots that match those interests. The generation AI also understands the user's interests and concerns and provides information based on them. For example, if the user has a particular hobby, the system provides information on date spots related to that hobby. This makes it possible to provide information that takes into account the user's interests and concerns.

[0085] The system can provide multiple information options based on the user's input, allowing the user to select from them. For example, the system uses a generation AI to analyze the user's input and provide multiple information options. For example, if a user inputs "Tell me about date spots," multiple options such as "movie theater," "cafe," and "park" are provided. The generation AI also provides multiple information options so that the user can select from them. For example, if a user inputs "Tell me how to confess," multiple options such as "confess by letter," "confess in person," and "have a friend tell her." The generation AI also provides multiple information options based on the user's input, allowing the user to select from them. For example, if a user inputs "Tell me about date plans," multiple options such as "movie date," "cafe date," and "outdoor date" are provided. This makes it possible to provide multiple information options for the user to select from.

[0086] The system can provide information visually and audibly based on the user's input. For example, the generation AI analyzes the user's input and provides information visually and audibly. For example, information about date spots is displayed visually and specific explanations are provided audibly. The generation AI also provides information visually and audibly based on the user's input. For example, a visual simulation of how to confess is performed and specific advice is provided audibly. The generation AI also provides information visually and audibly based on the user's input. For example, a date plan is displayed visually and specific explanations are provided audibly. In this way, information can be provided visually and audibly.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The user input analysis unit can estimate the user's health condition based on the user's input and provide health-conscious advice. For example, if a user inputs, "I've been feeling tired easily lately," the generation AI can use that information to suggest a relaxing date plan. It can also extract information about the user's diet and exercise habits from the user's input and provide healthy date plans. For example, if a user inputs, "I haven't been getting enough exercise lately," the generation AI can suggest a walking date or a sports date. It can also provide advice to reduce stress, taking the user's health condition into consideration. For example, if a user inputs, "I'm getting stressed at work," the generation AI can suggest a date plan to a relaxing cafe or spa. This makes it possible to provide advice that takes the user's health condition into consideration.

[0089] The user input analysis unit can also infer the user's hobbies and interests based on the user's input and provide advice accordingly. For example, if a user inputs "I like movies," the generation AI can use that information to suggest a movie date plan. It can also extract specific hobbies and interests from the user's input and provide related date plans. For example, if a user inputs "I like the outdoors," the generation AI can suggest a hiking or camping date plan. It can also suggest special events and activities taking the user's hobbies and interests into consideration. For example, if a user inputs "I like music," the generation AI can suggest a concert or live show date plan. This makes it possible to provide advice tailored to the user's hobbies and interests.

[0090] The user input analysis unit can also estimate the user's cultural background based on their input and provide advice accordingly. For example, if a user inputs "I like Japanese culture," the generation AI can use that information to suggest date plans for Japanese restaurants and traditional festivals. It can also extract specific cultural backgrounds from the user's input and provide date plans related to those. For example, if a user inputs "I like French culture," the generation AI can suggest date plans for French restaurants and art museums. It can also suggest special events and activities taking the user's cultural background into consideration. For example, if a user inputs "I like Indian culture," the generation AI can suggest date plans for Indian cooking classes and Bollywood movies. This makes it possible to provide advice tailored to the user's cultural background.

[0091] The user input analysis unit can also estimate the user's lifestyle based on their input and provide advice accordingly. For example, if a user inputs "busy," the generation AI can use that information to suggest a date plan that can be enjoyed in a short amount of time. It can also extract specific lifestyles from the user's input and provide date plans related to that. For example, if a user inputs "night owl," the generation AI can suggest date plans at restaurants and bars that are open late. It can also suggest special events and activities taking the user's lifestyle into consideration. For example, if a user inputs "outdoorsy," the generation AI can suggest date plans for camping and hiking. This makes it possible to provide advice tailored to the user's lifestyle.

[0092] The user input analysis unit can also estimate the user's financial situation based on the user's input and provide advice accordingly. For example, if a user inputs "I'm trying to save money," the generation AI can use that information to suggest a low-cost date plan that can be enjoyed. It can also extract specific financial situations from the user's input and provide date plans related to that. For example, if a user inputs "I want to splurge," the generation AI can suggest date plans at high-end restaurants and resorts. It can also suggest special events and activities taking the user's financial situation into consideration. For example, if a user inputs "I want to have fun within my budget," the generation AI can suggest date plans that use free events and discount coupons. This makes it possible to provide advice tailored to the user's financial situation.

[0093] The advice generation unit can also estimate the user's emotions and provide advice to change the user's emotions to positive ones based on the estimated emotions. For example, if the user inputs, "I've been fighting a lot with my girlfriend lately," the generation AI can detect this negative emotion and provide positive advice to repair the relationship. Also, if the user inputs, "I really want to confess my feelings to him, but I'm scared," the generation AI can provide an encouraging message to ease the fear. Furthermore, if the user inputs, "I'm looking forward to seeing him," the generation AI can provide advice to further increase the positive emotion. In this way, it is possible to provide advice to change the user's emotions to positive ones.

[0094] The advice generation unit can also estimate the user's emotions and provide advice to stabilize the user's emotions based on the estimated emotions. For example, if the user inputs, "I've been fighting a lot with my girlfriend lately," the generation AI can detect the negative emotion and provide advice to calm down. Also, if the user inputs, "I really want to confess my feelings to him, but I'm scared," the generation AI can suggest relaxation methods to ease the fear. Furthermore, if the user inputs, "I'm looking forward to seeing him," the generation AI can provide advice to maintain the positive emotions. In this way, advice to stabilize the user's emotions can be provided.

[0095] The advice generation unit can also estimate the user's emotions and provide advice to support the user's emotions based on the estimated emotions. For example, if the user inputs, "I've been fighting a lot with my girlfriend lately," the generation AI can detect this negative emotion and provide emotional support. Also, if the user inputs, "I really want to confess my feelings to him, but I'm scared," it can provide a support message to ease the fear. Furthermore, if the user inputs, "I'm looking forward to seeing him," it can provide advice to support the positive emotion. In this way, it is possible to provide advice to support the user's emotions.

[0096] The advice generation unit can also estimate the user's emotions and provide advice to understand the user's emotions based on the estimated emotions. For example, if the user inputs, "I've been fighting a lot with my girlfriend lately," the generation AI can detect this negative emotion and provide advice to help the user better understand the emotion. Also, if the user inputs, "I really want to confess my feelings to him, but I'm scared," the generation AI can provide advice to help the user understand the fear. Furthermore, if the user inputs, "I'm looking forward to seeing him," the generation AI can provide advice to help the user understand the positive emotion. In this way, it is possible to provide advice to help the user understand the emotion.

[0097] The advice generation unit can also estimate the user's emotions and provide advice for sharing the user's emotions based on the estimated emotions. For example, if the user inputs "I've been fighting a lot with my girlfriend lately," the generation AI can detect this negative emotion and provide advice for sharing the emotion. Also, if the user inputs "I really want to confess my feelings to him, but I'm scared," the generation AI can provide advice for sharing the fear. Furthermore, if the user inputs "I'm looking forward to seeing him," the generation AI can provide advice for sharing the positive emotion. In this way, advice for sharing the user's emotions can be provided.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The user input analysis unit analyzes input from the user. For example, if the user inputs a question about love in text format, the content is analyzed. It can also analyze voice input and convert the voice data into text data. It can also analyze image input and extract related information. Step 2: The advice generator generates love-related advice and suggestions based on the content analyzed by the user input analyzer. For example, the generator AI can provide specific advice in response to a user's question and generate optimal advice based on past data and case studies. It can also provide customized advice tailored to the user's situation.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a user input analysis unit that analyzes input from a user; an advice generation unit that generates advice or suggestions about love based on the content analyzed by the user input analysis unit; A system characterized by:

2. The user input analysis unit Performing a sentiment analysis on the user's input and adjusting the tone of the advice based on the intensity and type of sentiment 2. The system of claim 1.

3. The user input analysis unit Analyzing the user's voice input and inferring emotions from the voice tone and rate to reflect in the analysis.

2. The system of claim 1.

4. The advice generation unit Generating advice according to the user's feelings 2. The system of claim 1.

5. The system comprises: Using a generative AI, a simulation scenario is generated according to the user's emotions.

2. The system of claim 1.

6. The system comprises: Collecting feedback on the simulation according to the user's emotions and improving the quality of the simulation 2. The system of claim 1.

7. The system comprises: History management according to the user's emotions 2. The system of claim 1.

8. The system comprises: Providing information according to the user's emotions 2. The system of claim 1.

Images