System

Abstract

To provide a system that provide appropriate support to users who have difficulty in expressing their intentions.SOLUTION: A system according to the embodiment comprises a reception unit, a generation unit, and a presentation unit. The reception unit inputs expression of a user's intention. The generation unit analyzes the content input by the reception unit, and generates an expression or action. The presentation unit presents the expression or action generated by the generation unit to the user, and allows the user to select one.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have not adequately provided appropriate support to users who have difficulty expressing their intentions, and there is room for improvement.

[0005] The system according to the embodiment aims to provide appropriate support to users who have difficulty expressing their intentions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a presentation unit. The reception unit inputs an expression of intent from a user. The generation unit analyzes the content input by the reception unit and generates an expression or an action. The presentation unit presents the expression or action generated by the generation unit to the user and allows the user to select one. Effect of the Invention

[0007] The system according to the embodiment can provide appropriate assistance to users who have difficulty expressing their intentions. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The intention expression support system according to the embodiment of the present invention is a system that supports the intention expression and learning of children with special needs and elderly people. In this system, a user inputs the content of the intention he / she wants to express, and a generation AI analyzes the input and suggests appropriate expressions and actions. The proposed expressions and actions are presented to the user, and the user expresses his / her intention by selecting them. For example, if a child wants to convey that he / she is "hungry," he / she inputs the content, and the generation AI suggests the expression "I want to eat" or the action "prepare a meal." This mechanism allows children with special needs and elderly people to communicate their intentions more clearly. It can also be used in learning situations, and the generation AI suggests a learning plan so that children can learn at their own pace. In this way, the intention expression support system can support the intention expression and learning of children with special needs and elderly people.

[0029] The intention expression support system according to the embodiment includes a reception unit, a generation unit, and a presentation unit. The reception unit inputs an intention of a user. The intention of the user includes, for example, text input, voice input, gesture, and the like, but is not limited to such examples. The reception unit includes, for example, a keyboard or a touch screen for receiving text input. Also, a microphone or voice recognition technology for receiving voice input can be used. Furthermore, a camera or gesture recognition technology for receiving gesture input can be used. The generation unit uses a generation AI to analyze the content input by the reception unit and generate an appropriate expression or action. The generation AI uses, for example, a text generation AI (for example, LLM) to analyze the content of the user's input and generate an appropriate expression or action. The generation AI can also use a multimodal generation AI to analyze the content of the user's input and generate an appropriate expression or action. For example, when the user inputs "I'm hungry," the generation AI generates an expression of "I want to eat" or an action of "preparing a meal." The presentation unit presents the expression or action generated by the generation unit to the user and allows the user to select it. The presentation unit presents the generated expression and behavior to the user using, for example, a display or a speaker. The presentation unit can also present a plurality of options to make it easier for the user to select. For example, the presentation unit displays the expression "want to eat" and the behavior "prepare a meal" on the display, and the user can select one using a touch screen. In this way, the intention expression support system according to the embodiment can efficiently analyze the intention of the user, generate appropriate expressions and behaviors, and present them.

[0030] The reception unit inputs the user's expression of intention. The user's expression of intention includes, but is not limited to, for example, text input, voice input, gesture, and the like. The reception unit includes, for example, a keyboard and a touch screen for receiving text input. This allows the user to easily input text. Touch screens allow intuitive operation and are widely used, particularly in devices such as smartphones and tablets. In addition, a microphone and voice recognition technology can be used for receiving voice input. Voice recognition technology is a technology that converts the words spoken by a user into text, and is particularly useful for situations in which the hands cannot be used or for visually impaired people. Furthermore, a camera and gesture recognition technology can be used for receiving gesture input. Gesture recognition technology is a technology that detects the movement of the user's hands or body and interprets it as a specific command, and for example, "yes" or "no" can be expressed by waving one's hand. This allows the reception unit to provide a variety of input methods and efficiently receive the user's expression of intention. Furthermore, the reception unit can use these input methods in combination, which can improve the user's convenience. For example, a user can use text input and voice input in combination to more accurately express his or her intention.

[0031] The generation unit uses the generation AI to analyze the content input by the reception unit and generate appropriate expressions and actions. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze the user's input content and generate appropriate expressions and actions. Specifically, the generation AI analyzes the text and voice input by the user and generates an optimal response based on the content. For example, if the user inputs "I'm hungry," the generation AI understands the intention and generates the expression "I want to eat" and the action "prepare a meal." The generation AI can also analyze the user's input content and generate appropriate expressions and actions using a multimodal generation AI. Multimodal generation AI is a technology that analyzes multiple input modes, such as not only text but also voice, images, and gestures, and integrates them to generate an optimal response. For example, if a user indicates "I want to eat" with a gesture, the generation AI can analyze the gesture and generate the action "prepare a meal." In addition, the generation AI can also generate a more personalized response by taking into account the user's past input history and context. This enables the generation unit to efficiently analyze the various input contents of the user and generate appropriate expressions and actions.

[0032] The presentation unit presents the expressions and actions generated by the generation unit to the user and allows the user to select. The presentation unit presents the generated expressions and actions to the user using, for example, a display or a speaker. The display is a device for visually presenting information, and allows the user to intuitively understand the generated expressions and actions. For example, the expression "I want to eat" and the action "prepare a meal" are displayed on the display, and the user can select using a touch screen. The speaker is a device for presenting information by voice, and is particularly useful for visually impaired people and those who cannot use their hands. The speaker can be used to convey the generated expressions and actions to the user by voice. Furthermore, the presentation unit can present multiple options to make it easier for the user to select. For example, when the user inputs "I want to eat," the presentation unit presents multiple options such as "go to a restaurant," "order delivery," and "cook at home," allowing the user to select the most suitable option. In this way, the presentation unit can effectively present the expressions and actions generated by the generation unit to the user and assist the user in making an appropriate selection. Furthermore, the presentation unit can record the user's selection history and optimize the presentation content for the next time and thereafter. This allows the presentation unit to improve user convenience and increase the efficiency of the entire system.

[0033] The intention expression support system has a customization function according to the attributes of the user. The customization function customizes the system according to the attributes of the user. The user attributes include, but are not limited to, for example, age, sex, and interests. The customization function adjusts the content and interface to be displayed according to the age of the user. For example, a visually easy-to-understand interface is provided for children, and the font size is increased for the elderly. The customization function can also preferentially display related information and options based on the user's interests. For example, if a user has a specific hobby or interest, information related to the hobby or interest is preferentially displayed. This allows the intention expression support system to customize the system according to the characteristics of the user.

[0034] The intention expression support system has a function in which the generation AI proposes a study plan. The study plan proposal function uses the generation AI to propose a study plan for the user. For example, when the user inputs the content he or she wants to learn, the generation AI analyzes the content and proposes an appropriate study plan. The study plan includes, but is not limited to, a study schedule, a selection of study materials, and a study method proposal. For example, if the user inputs "I want to learn mathematics," the generation AI proposes a study schedule and study materials according to the user's level. The generation AI can also adjust the study plan according to the user's learning progress. For example, if the user is struggling with a particular task, the generation AI proposes additional study materials and practice problems related to that task. This allows the intention expression support system to have the generation AI propose a study plan in the learning environment.

[0035] The intention expression support system has a function in which the generation AI receives feedback from the user, learns, and improves the proposed content. The feedback learning function uses the generation AI to receive feedback from the user, learns, and improves the proposed content. For example, when the user provides feedback on the proposed content, the generation AI analyzes the feedback and reflects it in the next proposed content. The feedback includes, but is not limited to, the user's evaluation, comments, and behavior history. For example, if the user evaluates the proposal as "useful," the generation AI learns based on the evaluation so as to make similar proposals in the future. Also, if the user evaluates the proposal as "not useful," the generation AI improves the proposed content based on the evaluation. In this way, the intention expression support system allows the generation AI to receive feedback from the user, learn, and improve the proposed content.

[0036] The reception unit can analyze the user's past intention expression history and select an appropriate input method. The reception unit, for example, stores the user's past intention expression history in a database and analyzes the data. The past intention expression history includes, for example, past input data, behavior history, etc., but is not limited to such examples. The reception unit, for example, preferentially suggests an input method that the user has frequently used in the past. The reception unit can also suggest an input method suitable for a specific time period from the user's past history. For example, if the user tends to use a specific input method in a specific time period, the reception unit suggests an input method suitable for that time period. The reception unit can also customize the input method based on the user's past history. For example, if the user prefers a specific input method, the reception unit preferentially suggests that input method. This allows the reception unit to select the optimal input method based on the user's past intention expression history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0037] The reception unit can perform filtering based on the user's current situation and field of interest when inputting the intention. The reception unit, for example, acquires the user's current situation from a sensor or a database and analyzes the data. The current situation includes, for example, the user's location information, activity status, etc., but is not limited to such examples. The reception unit provides, for example, input options according to the user's current situation. The reception unit can also preferentially display related input options based on the user's field of interest. The field of interest includes, for example, past search history, browsing history, etc., but is not limited to such examples. For example, if the user has a specific field of interest, the reception unit preferentially displays input options related to the field of interest. The reception unit can also customize input options taking into account the user's current situation. For example, if the user is in a specific location, the reception unit provides input options related to the location. This allows the reception unit to perform filtering based on the user's current situation and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0038] The reception unit can preferentially acquire a highly relevant expression of intention in consideration of the geographical location information of the user when inputting an expression of intention. For example, the reception unit acquires the geographical location information of the user from GPS data or an IP address, and analyzes the data. The geographical location information includes, for example, the user's current location, past visited places, and the like, but is not limited to such examples. For example, when the user is in a specific location, the reception unit preferentially acquires an expression of intention related to the location. In addition, the reception unit can also suggest a related expression of intention based on the user's current location. For example, when the user is in a specific area, information and options related to the area are preferentially displayed. In addition, the reception unit can also acquire an optimal expression of intention in consideration of the geographical location information of the user. For example, when the user is in a specific area, the reception unit preferentially acquires an expression of intention related to the location. As a result, the reception unit can preferentially acquire an expression of intention in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0039] The reception unit can analyze the user's online activity at the time of inputting the intention, and acquire the related intention. The reception unit, for example, acquires the user's social media activity and browsing history from a database, and analyzes the data. The online activity includes, for example, social media posts, the number of followers, browsing history, and the like, but is not limited to such examples. The reception unit, for example, proposes the related intention from the user's social media activity. The reception unit can also analyze the user's online activity and acquire the optimal intention. For example, if the user is interested in a specific topic, the intention related to the topic is preferentially acquired. The reception unit can also preferentially acquire the related intention based on the user's social media activity. For example, if the user frequently uses a specific hashtag, the intention related to the hashtag is preferentially acquired. This allows the reception unit to acquire the related intention based on the user's online activity. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0040] The generation unit can adjust the level of detail of the generation based on the importance of the intention at the time of generation. The generation unit, for example, acquires the importance of the intention of the user from a database and analyzes the data. The importance includes, for example, the content of the intention, the urgency, the user's evaluation, etc., but is not limited to such examples. For example, the generation unit generates a detailed expression in the case of an important intention. In addition, in the case of a general intention, it can also generate a concise expression. In addition, in the case of an urgent intention, it can also generate an expression that can be quickly understood. For example, the generation unit generates a detailed explanation and a specific action for an intention marked by the user as "urgent". In addition, it can also generate a concise explanation and an action for an intention marked by the user as "general". In this way, the generation unit can adjust the level of detail of the generation based on the importance of the intention. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0041] The generating unit can apply different generating algorithms depending on the category of the intention expression at the time of generation. For example, the generating unit acquires the category of the intention expression of the user from a database and analyzes the data. The categories include, for example, emotion expression, action suggestion, learning plan, etc., but are not limited to such examples. For example, in the case of emotion expression, the generating unit applies a generating algorithm specialized for emotion. In addition, in the case of action suggestion, a generating algorithm specialized for action can also be applied. In addition, in the case of learning plan, a generating algorithm specialized for learning can also be applied. For example, the generating unit generates an emotional expression using an emotion estimation algorithm for the emotion expression. In addition, in the case of action suggestion, a specific action can also be generated using an action estimation algorithm. In addition, in the learning plan, an effective learning plan can also be generated using a learning estimation algorithm. Thereby, the generating unit can apply different generating algorithms depending on the category of the intention expression. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI.

[0042] The generation unit can determine the priority of generation based on the submission time of the intention at the time of generation. The generation unit, for example, obtains the submission time of the intention of the user from a database and analyzes the data. The submission time includes, for example, a timestamp, calendar information, etc., but is not limited to such examples. For example, the generation unit generates the intention of an urgent intention with the highest priority. In addition, in the case of a regular intention, it can be generated with normal priority. In addition, in the case of a future intention, it can be generated later. For example, the generation unit generates the intention of an intention marked by the user as "urgent" with the highest priority. In addition, in the case of an intention marked by the user as "regular", it can be generated with normal priority. In addition, in the case of an intention marked by the user as "future", it can be generated later. In this way, the generation unit can determine the priority of generation based on the submission time of the intention. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0043] The generation unit can adjust the order of generation based on the relevance of the intention expression at the time of generation. For example, the generation unit obtains the relevance of the intention expression of the user from a database and analyzes the data. The relevance includes, for example, the degree of agreement of the contents, related keywords, and the like, but is not limited to such examples. For example, the generation unit generates an intention expression of high relevance preferentially. In addition, it can also generate an intention expression of medium relevance next. In addition, it can also generate an intention expression of low relevance last. For example, the generation unit generates an intention expression marked by the user as "high relevance" preferentially. In addition, it can also generate an intention expression marked by the user as "medium relevance" next. In addition, it can also generate an intention expression marked by the user as "low relevance" last. In this way, the generation unit can adjust the order of generation based on the relevance of the intention expression. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0044] The presentation unit can select the optimal display method by referring to the user's past selection history at the time of presentation. The presentation unit, for example, acquires the user's past selection history from a database and analyzes the data. The selection history includes, for example, past selection data, click history, etc., but is not limited to such examples. The presentation unit, for example, preferentially provides the display method selected by the user in the past. The presentation unit can also suggest the optimal display method from the user's past selection history. The presentation unit can also customize the display method based on the user's past selection history. For example, if the user prefers a specific display method, the display method is preferentially provided. This allows the presentation unit to select the optimal display method based on the user's past selection history. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI.

[0045] The presentation unit can customize the means of presentation based on the user's current situation at the time of presentation. The presentation unit, for example, acquires the user's current situation from a sensor or a database and analyzes the data. The current situation includes, for example, the user's location information, activity status, etc., but is not limited to such examples. The presentation unit, for example, provides a display method according to the user's current situation. The presentation unit can also propose an optimal display means in consideration of the user's current situation. The presentation unit can also customize the display means based on the user's current situation. For example, when the user is in a specific location, a display means related to the location is provided. This allows the presentation unit to customize the means of presentation based on the user's current situation. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI.

[0046] The presentation unit can select the optimal presentation method in consideration of the geographical location information of the user at the time of presentation. The presentation unit, for example, acquires the geographical location information of the user from GPS data or an IP address, and analyzes the data. The geographical location information includes, for example, the user's current location, past visited places, and the like, but is not limited to such examples. For example, when the user is in a specific location, the presentation unit preferentially presents information related to the location. The presentation unit can also suggest the optimal presentation method based on the user's current location. The presentation unit can also select the optimal presentation method in consideration of the geographical location information of the user. For example, when the user is in a specific area, information and options related to the area are preferentially displayed. This allows the presentation unit to select the optimal presentation method in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI.

[0047] The presentation unit can analyze the user's social media activity and suggest a means of presentation at the time of presentation. The presentation unit, for example, acquires the user's social media activity from a database and analyzes the data. The social media activity includes, for example, the content of posts, the number of followers, browsing history, etc., but is not limited to such examples. The presentation unit, for example, preferentially presents related information from the user's social media activity. The presentation unit can also analyze the user's social media activity and suggest the optimal presentation means. The presentation unit can also select the optimal presentation means based on the user's social media activity. For example, if the user is interested in a specific topic, the presentation unit preferentially presents information related to the topic. Also, if the user frequently uses a specific hashtag, the presentation unit can also preferentially present information related to the hashtag. This allows the presentation unit to suggest the optimal presentation means based on the user's social media activity. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI.

[0048] The customization function can select the optimal customization method by referring to the user's past usage history during customization. The customization function, for example, acquires the user's past usage history from a database and analyzes the data. The usage history includes, for example, past operation data, usage frequency, etc., but is not limited to such examples. The customization function, for example, preferentially suggests a customization option that the user has used in the past. The customization function can also suggest the optimal customization method from the user's past usage history. The customization function can also customize the customization method based on the user's past usage history. For example, if the user prefers a specific customization option, the option is preferentially suggested. This allows the customization function to select the optimal customization method based on the user's past usage history. A part or all of the above-mentioned processing in the customization function may be performed, for example, using AI, or may be performed without using AI.

[0049] The customization function can select the optimal customization method in consideration of the user's device information at the time of customization. The customization function, for example, acquires the user's device information from a database and analyzes the data. The device information includes, for example, the type of device, the OS version, and the like, but is not limited to such examples. For example, when the user is using a smartphone, the customization function provides customization options that match the screen size. In addition, when the user is using a tablet, the customization function can also provide customization options optimized for a large screen. In addition, when the user is using a smart watch, the customization function can also provide simple and highly visible customization options. This allows the customization function to select the optimal customization method based on the user's device information. A part or all of the above-mentioned processing in the customization function may be performed, for example, using AI, or may be performed without using AI.

[0050] The study plan proposal function can provide an optimal proposal by referring to the user's past study history when proposing a study plan. The study plan proposal function, for example, obtains the user's past study history from a database and analyzes the data. The study history includes, for example, past study data, grade information, etc., but is not limited to such examples. The study plan proposal function, for example, proposes an optimal study plan based on the content that the user has studied in the past. The study plan proposal function can also propose an effective study plan from the user's past study history. The study plan proposal function can also analyze the user's past study history and propose the most efficient study plan. For example, if the user is struggling with a particular subject, additional learning materials and practice questions related to that subject are proposed. Also, if the user excels in a particular subject, an advanced study plan related to that subject can be proposed. This allows the study plan proposal function to propose an optimal study plan based on the user's past study history. A part or all of the above-mentioned processing in the study plan proposal function may be performed, for example, using AI, or may be performed without using AI.

[0051] The study plan proposal function can provide an optimal proposal in consideration of the user's geographical location information when proposing a study plan. The study plan proposal function, for example, obtains the user's geographical location information from GPS data or an IP address, and analyzes the data. The geographical location information includes, for example, the user's current location, past visited places, etc., but is not limited to such examples. For example, when the user is in a specific location, the study plan proposal function preferentially proposes a study plan related to that location. In addition, the study plan proposal function can also propose an optimal study plan based on the user's current location. In addition, the study plan proposal function can also propose an optimal study plan in consideration of the user's geographical location information. For example, when the user is in a specific area, the study plan related to that area is preferentially proposed. In this way, the study plan proposal function can propose an optimal study plan based on the user's geographical location information. A part or all of the above-mentioned processing in the study plan proposal function may be performed, for example, using AI, or may be performed without using AI.

[0052] The feedback learning function can select the optimal learning method by referring to the user's past feedback history during feedback learning. The feedback learning function, for example, acquires the user's past feedback history from a database and analyzes the data. The feedback history includes, for example, past feedback data, evaluation history, etc., but is not limited to such examples. The feedback learning function, for example, suggests the optimal learning method based on the feedback the user has received in the past. The feedback learning function can also suggest an effective learning method from the user's past feedback history. The feedback learning function can also analyze the user's past feedback history and suggest the most efficient learning method. For example, if a user gives a high rating to a specific feedback, a learning method related to that feedback is suggested. Also, if a user gives a low rating to a specific feedback, the learning method related to that feedback can be improved. This allows the feedback learning function to select the optimal learning method based on the user's past feedback history. A part or all of the above-mentioned processing in the feedback learning function may be performed, for example, using AI, or may be performed without using AI.

[0053] The feedback learning function can select the optimal learning method in consideration of the device information of the user during feedback learning. The feedback learning function, for example, acquires the device information of the user from a database and analyzes the data. The device information includes, for example, the type of device, the OS version, and the like, but is not limited to such examples. For example, when the user is using a smartphone, the feedback learning function provides a learning method that matches the screen size. In addition, when the user is using a tablet, the feedback learning function can also provide a learning method optimized for a large screen. In addition, when the user is using a smartwatch, the feedback learning function can also provide a learning method that is concise and highly visible. This allows the feedback learning function to select the optimal learning method based on the device information of the user. A part or all of the above-mentioned processing in the feedback learning function may be performed, for example, using AI, or may be performed without using AI.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The intention expression support system can further include an entertainment unit that provides entertainment content customized based on the user's hobbies and preferences. The entertainment unit analyzes the user's past viewing history and evaluation data, and suggests movies, music, games, etc. that the user may be interested in. For example, if the user likes movies of a particular genre, it can suggest new movies related to that genre. Also, if the user likes music by a particular artist, it can suggest new songs by that artist and songs by related artists. In this way, the intention expression support system can provide entertainment content according to the user's hobbies and preferences.

[0056] The intention expression support system can further include a learning style adaptation unit that suggests a learning method according to the user's learning style. The learning style adaptation unit analyzes the user's past learning history and feedback, and suggests the most effective learning method for the user. For example, if the user prefers visual learning, it can suggest learning materials that make extensive use of visual aids. Also, if the user prefers auditory learning, it can suggest audio learning materials or podcasts. Furthermore, if the user prefers practical learning, it can suggest experiments or project-based learning. In this way, the intention expression support system can provide the optimal learning method according to the user's learning style.

[0057] The intention expression support system may further include a social relationship analysis unit that adjusts the content of the suggestions taking into account the user's social relationships. The social relationship analysis unit analyzes the user's social media activities and contact data to suggest events and activities that the user may be interested in. For example, if the user frequently interacts with a specific friend, it may suggest events that the friend will participate in. Also, if the user belongs to a specific group, it may suggest activities and events related to the group. This allows the intention expression support system to make appropriate suggestions according to the user's social relationships.

[0058] The intention expression support system can further include a prediction unit that predicts future intentions based on the user's past intention history. The prediction unit analyzes the user's past input data and behavior history, and predicts what intention the user will express next. For example, if the user inputs "I'm hungry" at the same time every day, the system can automatically suggest a meal at that time. Also, if the user inputs "I'm nervous" before a specific event, the system can suggest relaxation before the event. This allows the intention expression support system to predict future intentions based on the user's past history and make appropriate suggestions.

[0059] The intention expression support system may further include an environment adaptation unit that adjusts the content of the proposal based on the user's current situation and environment. The environment adaptation unit analyzes the user's location information and surrounding environmental data, and makes proposals according to the user's current location and situation. For example, when the user is out, it can make suggestions of nearby restaurants and cafes. Also, when the user is at home, it can make suggestions of activities and relaxation methods that can be done at home. Furthermore, when the user is in an office, it can make suggestions to improve work efficiency. In this way, the intention expression support system can make appropriate suggestions according to the user's current situation and environment.

[0060] The intention expression support system can further include a region information providing unit that provides region-specific information based on the user's geographical location information. The region information providing unit analyzes the user's current location and past visited places, and provides information related to the region. For example, if the user is in a tourist spot, it can provide tourist spot and event information for that region. Also, if the user moves to a new region, it can provide lifestyle and community information for that region. Furthermore, if the user frequently visits a particular region, it can provide recommended restaurant and shop information for that region. In this way, the intention expression support system can provide region-specific information based on the user's geographical location information.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The reception unit inputs a user's expression of intent. The user's expression of intent includes, for example, text input, voice input, gestures, etc. The reception unit can use a keyboard or touch screen for receiving text input, a microphone or voice recognition technology for receiving voice input, or a camera or gesture recognition technology for receiving gesture input. Step 2: The generation unit uses a generation AI to analyze the content entered by the reception unit and generate appropriate expressions and actions. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the user's input and generate appropriate expressions and actions. For example, if the user enters "I'm hungry," it generates the expression "I want to eat" and the action "Prepare a meal." Step 3: The presentation unit presents the expressions and actions generated by the generation unit to the user and allows the user to select one. The presentation unit presents the generated expressions and actions to the user using a display and a speaker. The presentation unit can also present multiple options to make it easier for the user to select. For example, the expression "I want to eat" and the action "Prepare a meal" can be displayed on the display, and the user can select one using the touch screen.

[0063] (Example 2) The intention expression support system according to the embodiment of the present invention is a system that supports the intention expression and learning of children with special needs and elderly people. In this system, a user inputs the content of the intention he / she wants to express, and a generation AI analyzes the input and suggests appropriate expressions and actions. The proposed expressions and actions are presented to the user, and the user expresses his / her intention by selecting them. For example, if a child wants to convey that he / she is "hungry," he / she inputs the content, and the generation AI suggests the expression "I want to eat" or the action "prepare a meal." This mechanism allows children with special needs and elderly people to communicate their intentions more clearly. It can also be used in learning situations, and the generation AI suggests a learning plan so that children can learn at their own pace. In this way, the intention expression support system can support the intention expression and learning of children with special needs and elderly people.

[0064] The intention expression support system according to the embodiment includes a reception unit, a generation unit, and a presentation unit. The reception unit inputs an intention of a user. The intention of the user includes, for example, text input, voice input, gesture, and the like, but is not limited to such examples. The reception unit includes, for example, a keyboard or a touch screen for receiving text input. Also, a microphone or voice recognition technology for receiving voice input can be used. Furthermore, a camera or gesture recognition technology for receiving gesture input can be used. The generation unit uses a generation AI to analyze the content input by the reception unit and generate an appropriate expression or action. The generation AI uses, for example, a text generation AI (for example, LLM) to analyze the content of the user's input and generate an appropriate expression or action. The generation AI can also use a multimodal generation AI to analyze the content of the user's input and generate an appropriate expression or action. For example, when the user inputs "I'm hungry," the generation AI generates an expression of "I want to eat" or an action of "preparing a meal." The presentation unit presents the expression or action generated by the generation unit to the user and allows the user to select it. The presentation unit presents the generated expression and behavior to the user using, for example, a display or a speaker. The presentation unit can also present a plurality of options to make it easier for the user to select. For example, the presentation unit displays the expression "want to eat" and the behavior "prepare a meal" on the display, and the user can select one using a touch screen. In this way, the intention expression support system according to the embodiment can efficiently analyze the intention of the user, generate appropriate expressions and behaviors, and present them.

[0065] The reception unit inputs the user's expression of intention. The user's expression of intention includes, but is not limited to, for example, text input, voice input, gesture, and the like. The reception unit includes, for example, a keyboard and a touch screen for receiving text input. This allows the user to easily input text. Touch screens allow intuitive operation and are widely used, particularly in devices such as smartphones and tablets. In addition, a microphone and voice recognition technology can be used for receiving voice input. Voice recognition technology is a technology that converts the words spoken by a user into text, and is particularly useful for situations in which the hands cannot be used or for visually impaired people. Furthermore, a camera and gesture recognition technology can be used for receiving gesture input. Gesture recognition technology is a technology that detects the movement of the user's hands or body and interprets it as a specific command, and for example, "yes" or "no" can be expressed by waving one's hand. This allows the reception unit to provide a variety of input methods and efficiently receive the user's expression of intention. Furthermore, the reception unit can use these input methods in combination, which can improve the user's convenience. For example, a user can use text input and voice input in combination to more accurately express his or her intention.

[0066] The generation unit uses the generation AI to analyze the content input by the reception unit and generate appropriate expressions and actions. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze the user's input content and generate appropriate expressions and actions. Specifically, the generation AI analyzes the text and voice input by the user and generates an optimal response based on the content. For example, if the user inputs "I'm hungry," the generation AI understands the intention and generates the expression "I want to eat" and the action "prepare a meal." The generation AI can also analyze the user's input content and generate appropriate expressions and actions using a multimodal generation AI. Multimodal generation AI is a technology that analyzes multiple input modes, such as not only text but also voice, images, and gestures, and integrates them to generate an optimal response. For example, if a user indicates "I want to eat" with a gesture, the generation AI can analyze the gesture and generate the action "prepare a meal." In addition, the generation AI can also generate a more personalized response by taking into account the user's past input history and context. This enables the generation unit to efficiently analyze the various input contents of the user and generate appropriate expressions and actions.

[0067] The presentation unit presents the expressions and actions generated by the generation unit to the user and allows the user to select. The presentation unit presents the generated expressions and actions to the user using, for example, a display or a speaker. The display is a device for visually presenting information, and allows the user to intuitively understand the generated expressions and actions. For example, the expression "I want to eat" and the action "prepare a meal" are displayed on the display, and the user can select using a touch screen. The speaker is a device for presenting information by voice, and is particularly useful for visually impaired people and those who cannot use their hands. The speaker can be used to convey the generated expressions and actions to the user by voice. Furthermore, the presentation unit can present multiple options to make it easier for the user to select. For example, when the user inputs "I want to eat," the presentation unit presents multiple options such as "go to a restaurant," "order delivery," and "cook at home," allowing the user to select the most suitable option. In this way, the presentation unit can effectively present the expressions and actions generated by the generation unit to the user and assist the user in making an appropriate selection. Furthermore, the presentation unit can record the user's selection history and optimize the presentation content for the next time and thereafter. This allows the presentation unit to improve user convenience and increase the efficiency of the entire system.

[0068] The intention expression support system has a customization function according to the attributes of the user. The customization function customizes the system according to the attributes of the user. The user attributes include, but are not limited to, for example, age, sex, and interests. The customization function adjusts the content and interface to be displayed according to the age of the user. For example, a visually easy-to-understand interface is provided for children, and the font size is increased for the elderly. The customization function can also preferentially display related information and options based on the user's interests. For example, if a user has a specific hobby or interest, information related to the hobby or interest is preferentially displayed. This allows the intention expression support system to customize the system according to the characteristics of the user.

[0069] The intention expression support system has a function in which the generation AI proposes a study plan. The study plan proposal function uses the generation AI to propose a study plan for the user. For example, when the user inputs the content he or she wants to learn, the generation AI analyzes the content and proposes an appropriate study plan. The study plan includes, but is not limited to, a study schedule, a selection of study materials, and a study method proposal. For example, if the user inputs "I want to learn mathematics," the generation AI proposes a study schedule and study materials according to the user's level. The generation AI can also adjust the study plan according to the user's learning progress. For example, if the user is struggling with a particular task, the generation AI proposes additional study materials and practice problems related to that task. This allows the intention expression support system to have the generation AI propose a study plan in the learning environment.

[0070] The intention expression support system has a function in which the generation AI receives feedback from the user, learns, and improves the proposed content. The feedback learning function uses the generation AI to receive feedback from the user, learns, and improves the proposed content. For example, when the user provides feedback on the proposed content, the generation AI analyzes the feedback and reflects it in the next proposed content. The feedback includes, but is not limited to, the user's evaluation, comments, and behavior history. For example, if the user evaluates the proposal as "useful," the generation AI learns based on the evaluation so as to make similar proposals in the future. Also, if the user evaluates the proposal as "not useful," the generation AI improves the proposed content based on the evaluation. In this way, the intention expression support system allows the generation AI to receive feedback from the user, learn, and improve the proposed content.

[0071] The reception unit can analyze the user's emotion and adjust the input timing of the intention expression based on the analyzed emotion of the user. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the change in the facial expression. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to adjust the input timing of the intention expression according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, if the user is nervous, the reception unit waits for input until the user relaxes. Also, if the user is excited, it can prompt the user to input immediately. In addition, if the user is tired, the system can prompt the user to input after taking a break. This makes it possible to adjust the timing of inputting the user's intention depending on the user's emotions.

[0072] The reception unit can analyze the user's past intention expression history and select an appropriate input method. The reception unit, for example, stores the user's past intention expression history in a database and analyzes the data. The past intention expression history includes, for example, past input data, behavior history, etc., but is not limited to such examples. The reception unit, for example, preferentially suggests an input method that the user has frequently used in the past. The reception unit can also suggest an input method suitable for a specific time period from the user's past history. For example, if the user tends to use a specific input method in a specific time period, the reception unit suggests an input method suitable for that time period. The reception unit can also customize the input method based on the user's past history. For example, if the user prefers a specific input method, the reception unit preferentially suggests that input method. This allows the reception unit to select the optimal input method based on the user's past intention expression history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0073] The reception unit can perform filtering based on the user's current situation and field of interest when inputting the intention. The reception unit, for example, acquires the user's current situation from a sensor or a database and analyzes the data. The current situation includes, for example, the user's location information, activity status, etc., but is not limited to such examples. The reception unit provides, for example, input options according to the user's current situation. The reception unit can also preferentially display related input options based on the user's field of interest. The field of interest includes, for example, past search history, browsing history, etc., but is not limited to such examples. For example, if the user has a specific field of interest, the reception unit preferentially displays input options related to the field of interest. The reception unit can also customize input options taking into account the user's current situation. For example, if the user is in a specific location, the reception unit provides input options related to the location. This allows the reception unit to perform filtering based on the user's current situation and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0074] The reception unit can estimate the user's emotion and determine the priority of the intention to be input based on the estimated user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the change in the facial expression. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the reception unit analyzes the tone and speed of the voice and calculates the emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates the emotion score based on the fluctuation of the heart rate. This makes it possible to determine the priority of the intention to be input according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the reception unit prioritizes important intentions when the user is nervous. Also, if the user is relaxed, a detailed expression of intention can be prioritized, whereas if the user is in a hurry, a quick expression of intention can be prioritized.In this way, the priority of the expressions of intention to be input can be determined according to the user's emotions.

[0075] The reception unit can preferentially acquire a highly relevant expression of intention in consideration of the geographical location information of the user when inputting an expression of intention. For example, the reception unit acquires the geographical location information of the user from GPS data or an IP address, and analyzes the data. The geographical location information includes, for example, the user's current location, past visited places, and the like, but is not limited to such examples. For example, when the user is in a specific location, the reception unit preferentially acquires an expression of intention related to the location. In addition, the reception unit can also suggest a related expression of intention based on the user's current location. For example, when the user is in a specific area, information and options related to the area are preferentially displayed. In addition, the reception unit can also acquire an optimal expression of intention in consideration of the geographical location information of the user. For example, when the user is in a specific area, the reception unit preferentially acquires an expression of intention related to the location. As a result, the reception unit can preferentially acquire an expression of intention in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0076] The reception unit can analyze the user's online activity at the time of inputting the intention, and acquire the related intention. The reception unit, for example, acquires the user's social media activity and browsing history from a database, and analyzes the data. The online activity includes, for example, social media posts, the number of followers, browsing history, and the like, but is not limited to such examples. The reception unit, for example, proposes the related intention from the user's social media activity. The reception unit can also analyze the user's online activity and acquire the optimal intention. For example, if the user is interested in a specific topic, the intention related to the topic is preferentially acquired. The reception unit can also preferentially acquire the related intention based on the user's social media activity. For example, if the user frequently uses a specific hashtag, the intention related to the hashtag is preferentially acquired. This allows the reception unit to acquire the related intention based on the user's online activity. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0077] The generation unit can estimate the user's emotion and adjust the expression and behavior expression method to be generated based on the estimated user's emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on the change in the facial expression. The generation unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the generation unit analyzes the tone and speed of the voice and calculates the emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates the emotion score based on the fluctuation of the heart rate. This makes it possible to adjust the expression and behavior expression method to be generated according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the generation unit generates a calm expression when the user is relaxed. In addition, if the user is excited, an active expression can be generated. In addition, if the user is nervous, a calm expression can be generated. In this way, the expression to be generated and the method of expressing the behavior can be adjusted according to the user's emotions.

[0078] The generation unit can adjust the level of detail of the generation based on the importance of the intention at the time of generation. The generation unit, for example, acquires the importance of the intention of the user from a database and analyzes the data. The importance includes, for example, the content of the intention, the urgency, the user's evaluation, etc., but is not limited to such examples. For example, the generation unit generates a detailed expression in the case of an important intention. In addition, in the case of a general intention, it can also generate a concise expression. In addition, in the case of an urgent intention, it can also generate an expression that can be quickly understood. For example, the generation unit generates a detailed explanation and a specific action for an intention marked by the user as "urgent". In addition, it can also generate a concise explanation and an action for an intention marked by the user as "general". In this way, the generation unit can adjust the level of detail of the generation based on the importance of the intention. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0079] The generating unit can apply different generating algorithms depending on the category of the intention expression at the time of generation. For example, the generating unit acquires the category of the intention expression of the user from a database and analyzes the data. The categories include, for example, emotion expression, action suggestion, learning plan, etc., but are not limited to such examples. For example, in the case of emotion expression, the generating unit applies a generating algorithm specialized for emotion. In addition, in the case of action suggestion, a generating algorithm specialized for action can also be applied. In addition, in the case of learning plan, a generating algorithm specialized for learning can also be applied. For example, the generating unit generates an emotional expression using an emotion estimation algorithm for the emotion expression. In addition, in the case of action suggestion, a specific action can also be generated using an action estimation algorithm. In addition, in the learning plan, an effective learning plan can also be generated using a learning estimation algorithm. Thereby, the generating unit can apply different generating algorithms depending on the category of the intention expression. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI.

[0080] The generation unit can estimate the user's emotion and adjust the length of the expression or action to be generated based on the estimated user's emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on the change in facial expression. The generation unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to adjust the length of the expression or action to be generated according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the generation unit generates a short expression when the user is in a hurry. Also, it can generate a detailed expression when the user is relaxed. In addition, if the user is excited, a visually stimulating expression can be generated. This allows the length of the expression or action to be adjusted according to the user's emotions.

[0081] The generation unit can determine the priority of generation based on the submission time of the intention at the time of generation. The generation unit, for example, obtains the submission time of the intention of the user from a database and analyzes the data. The submission time includes, for example, a timestamp, calendar information, etc., but is not limited to such examples. For example, the generation unit generates the intention of an urgent intention with the highest priority. In addition, in the case of a regular intention, it can be generated with normal priority. In addition, in the case of a future intention, it can be generated later. For example, the generation unit generates the intention of an intention marked by the user as "urgent" with the highest priority. In addition, in the case of an intention marked by the user as "regular", it can be generated with normal priority. In addition, in the case of an intention marked by the user as "future", it can be generated later. In this way, the generation unit can determine the priority of generation based on the submission time of the intention. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0082] The generation unit can adjust the order of generation based on the relevance of the intention expression at the time of generation. For example, the generation unit obtains the relevance of the intention expression of the user from a database and analyzes the data. The relevance includes, for example, the degree of agreement of the contents, related keywords, and the like, but is not limited to such examples. For example, the generation unit generates an intention expression of high relevance preferentially. In addition, it can also generate an intention expression of medium relevance next. In addition, it can also generate an intention expression of low relevance last. For example, the generation unit generates an intention expression marked by the user as "high relevance" preferentially. In addition, it can also generate an intention expression marked by the user as "medium relevance" next. In addition, it can also generate an intention expression marked by the user as "low relevance" last. In this way, the generation unit can adjust the order of generation based on the relevance of the intention expression. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0083] The presentation unit can estimate the user's emotion and adjust the presentation display method based on the estimated user's emotion. For example, the presentation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the presentation unit calculates an emotion score based on the change in facial expression. The presentation unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the presentation unit analyzes the tone and speed of the voice and calculates an emotion score. The presentation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the presentation unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to adjust the presentation display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the presentation unit provides a simple and highly visible display method when the user is nervous. Also, when the user is relaxed, it can provide a display method including detailed information. Also, if the user is in a hurry, a more to-the-point display can be provided, allowing the presentation display to be tailored according to the user's emotions.

[0084] The presentation unit can select the optimal display method by referring to the user's past selection history at the time of presentation. The presentation unit, for example, acquires the user's past selection history from a database and analyzes the data. The selection history includes, for example, past selection data, click history, etc., but is not limited to such examples. The presentation unit, for example, preferentially provides the display method selected by the user in the past. The presentation unit can also suggest the optimal display method from the user's past selection history. The presentation unit can also customize the display method based on the user's past selection history. For example, if the user prefers a specific display method, the display method is preferentially provided. This allows the presentation unit to select the optimal display method based on the user's past selection history. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI.

[0085] The presentation unit can customize the means of presentation based on the user's current situation at the time of presentation. The presentation unit, for example, acquires the user's current situation from a sensor or a database and analyzes the data. The current situation includes, for example, the user's location information, activity status, etc., but is not limited to such examples. The presentation unit, for example, provides a display method according to the user's current situation. The presentation unit can also propose an optimal display means in consideration of the user's current situation. The presentation unit can also customize the display means based on the user's current situation. For example, when the user is in a specific location, a display means related to the location is provided. This allows the presentation unit to customize the means of presentation based on the user's current situation. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI.

[0086] The presentation unit can estimate the user's emotion and determine the presentation priority based on the estimated user's emotion. The presentation unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the presentation unit calculates an emotion score based on the change in facial expression. The presentation unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the presentation unit analyzes the tone and speed of the voice and calculates the emotion score. The presentation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the presentation unit calculates the emotion score based on the fluctuation of the heart rate. This makes it possible to determine the presentation priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the presentation unit preferentially presents important information when the user is nervous. Also, when the user is relaxed, detailed information can be preferentially presented. Also, if the user is in a hurry, information that can be quickly understood can be presented with priority. This makes it possible to determine the presentation priority according to the user's emotions.

[0087] The presentation unit can select the optimal presentation method in consideration of the geographical location information of the user at the time of presentation. The presentation unit, for example, acquires the geographical location information of the user from GPS data or an IP address, and analyzes the data. The geographical location information includes, for example, the user's current location, past visited places, and the like, but is not limited to such examples. For example, when the user is in a specific location, the presentation unit preferentially presents information related to the location. The presentation unit can also suggest the optimal presentation method based on the user's current location. The presentation unit can also select the optimal presentation method in consideration of the geographical location information of the user. For example, when the user is in a specific area, information and options related to the area are preferentially displayed. This allows the presentation unit to select the optimal presentation method in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI.

[0088] The presentation unit can analyze the user's social media activity and suggest a means of presentation at the time of presentation. The presentation unit, for example, acquires the user's social media activity from a database and analyzes the data. The social media activity includes, for example, the content of posts, the number of followers, browsing history, etc., but is not limited to such examples. The presentation unit, for example, preferentially presents related information from the user's social media activity. The presentation unit can also analyze the user's social media activity and suggest the optimal presentation means. The presentation unit can also select the optimal presentation means based on the user's social media activity. For example, if the user is interested in a specific topic, the presentation unit preferentially presents information related to the topic. Also, if the user frequently uses a specific hashtag, the presentation unit can also preferentially present information related to the hashtag. This allows the presentation unit to suggest the optimal presentation means based on the user's social media activity. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI.

[0089] The customization function can estimate the user's emotion and adjust the customization content based on the estimated user's emotion. For example, the customization function captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the customization function calculates an emotion score based on the change in facial expression. The customization function can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the customization function analyzes the tone and speed of the voice and calculates an emotion score. The customization function can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the customization function calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to adjust the customization content according to the user's emotion. The emotion estimation is realized using the emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the customization function provides a simple customization option when the user is nervous. Also, the customization function can provide a detailed customization option when the user is relaxed. Also, the customization function can provide an option for quick customization when the user is in a hurry. This allows the customization content to be adjusted according to the user's emotions.

[0090] The customization function can select the optimal customization method by referring to the user's past usage history during customization. The customization function, for example, acquires the user's past usage history from a database and analyzes the data. The usage history includes, for example, past operation data, usage frequency, etc., but is not limited to such examples. The customization function, for example, preferentially suggests a customization option that the user has used in the past. The customization function can also suggest the optimal customization method from the user's past usage history. The customization function can also customize the customization method based on the user's past usage history. For example, if the user prefers a specific customization option, the option is preferentially suggested. This allows the customization function to select the optimal customization method based on the user's past usage history. A part or all of the above-mentioned processing in the customization function may be performed, for example, using AI, or may be performed without using AI.

[0091] The customization function can estimate the user's emotion and determine the priority of customization based on the estimated user's emotion. For example, the customization function captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the customization function calculates an emotion score based on the change in facial expression. The customization function can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the customization function analyzes the tone and speed of the voice and calculates an emotion score. The customization function can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the customization function calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to determine the priority of customization according to the user's emotion. The emotion estimation is realized using the emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the customization function prioritizes important customization when the user is nervous. Also, when the user is relaxed, it can prioritize detailed customization. Also, if the user is in a hurry, the option that allows quick customization can be prioritized. This allows the priority of customization to be determined according to the user's emotions.

[0092] The customization function can select the optimal customization method in consideration of the user's device information at the time of customization. The customization function, for example, acquires the user's device information from a database and analyzes the data. The device information includes, for example, the type of device, the OS version, and the like, but is not limited to such examples. For example, when the user is using a smartphone, the customization function provides customization options that match the screen size. In addition, when the user is using a tablet, the customization function can also provide customization options optimized for a large screen. In addition, when the user is using a smart watch, the customization function can also provide simple and highly visible customization options. This allows the customization function to select the optimal customization method based on the user's device information. A part or all of the above-mentioned processing in the customization function may be performed, for example, using AI, or may be performed without using AI.

[0093] The study plan proposal function can estimate the user's emotions and adjust the method of proposing a study plan based on the estimated user's emotions. For example, the study plan proposal function captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the study plan proposal function calculates an emotion score based on changes in facial expressions. The study plan proposal function can also record the user's voice and estimate the emotions using voice analysis technology. For example, the study plan proposal function analyzes the tone and speed of the voice and calculates an emotion score. The study plan proposal function can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the study plan proposal function calculates an emotion score based on heart rate fluctuations. This makes it possible to adjust the method of proposing a study plan according to the user's emotions. The emotion estimation is realized using the emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. For example, the study plan proposal function proposes a simple and highly visible study plan when the user is nervous. Also, if the user is relaxed, a detailed study plan can be proposed, and if the user is in a hurry, a quick study plan can be proposed. This allows the method of proposing a study plan to be adjusted according to the user's emotions.

[0094] The study plan proposal function can provide an optimal proposal by referring to the user's past study history when proposing a study plan. The study plan proposal function, for example, obtains the user's past study history from a database and analyzes the data. The study history includes, for example, past study data, grade information, etc., but is not limited to such examples. The study plan proposal function, for example, proposes an optimal study plan based on the content that the user has studied in the past. The study plan proposal function can also propose an effective study plan from the user's past study history. The study plan proposal function can also analyze the user's past study history and propose the most efficient study plan. For example, if the user is struggling with a particular subject, additional learning materials and practice questions related to that subject are proposed. Also, if the user excels in a particular subject, an advanced study plan related to that subject can be proposed. This allows the study plan proposal function to propose an optimal study plan based on the user's past study history. A part or all of the above-mentioned processing in the study plan proposal function may be performed, for example, using AI, or may be performed without using AI.

[0095] The study plan proposal function can estimate the user's emotions and determine the priority of study plans based on the estimated user's emotions. For example, the study plan proposal function captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the study plan proposal function calculates an emotion score based on changes in facial expressions. The study plan proposal function can also record the user's voice and estimate the emotions using voice analysis technology. For example, the study plan proposal function analyzes the tone and speed of the voice and calculates an emotion score. The study plan proposal function can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the study plan proposal function calculates an emotion score based on heart rate fluctuations. This makes it possible to determine the priority of study plans according to the user's emotions. The emotion estimation is realized using the emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. For example, the study plan proposal function preferentially suggests important learning content when the user is nervous. In addition, when the user is relaxed, detailed study content can be given priority. When the user is in a hurry, study content that can be quickly understood can be given priority. In this way, the priority of the study plan can be determined according to the user's emotions.

[0096] The study plan proposal function can provide an optimal proposal in consideration of the user's geographical location information when proposing a study plan. The study plan proposal function, for example, obtains the user's geographical location information from GPS data or an IP address, and analyzes the data. The geographical location information includes, for example, the user's current location, past visited places, etc., but is not limited to such examples. For example, when the user is in a specific location, the study plan proposal function preferentially proposes a study plan related to that location. In addition, the study plan proposal function can also propose an optimal study plan based on the user's current location. In addition, the study plan proposal function can also propose an optimal study plan in consideration of the user's geographical location information. For example, when the user is in a specific area, the study plan related to that area is preferentially proposed. In this way, the study plan proposal function can propose an optimal study plan based on the user's geographical location information. A part or all of the above-mentioned processing in the study plan proposal function may be performed, for example, using AI, or may be performed without using AI.

[0097] The feedback learning function can estimate the user's emotion and adjust the content of the feedback based on the estimated user's emotion. For example, the feedback learning function captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the feedback learning function calculates an emotion score based on the change in facial expression. The feedback learning function can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the feedback learning function analyzes the tone and speed of the voice and calculates an emotion score. The feedback learning function can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the feedback learning function calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to adjust the content of the feedback according to the user's emotion. The emotion estimation is realized using the emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the feedback learning function provides simple and highly visible feedback when the user is nervous. Also, detailed feedback can be provided when the user is relaxed. Also, if the user is in a hurry, the system can provide quick and understandable feedback, allowing the content of the feedback to be adjusted according to the user's emotions.

[0098] The feedback learning function can select the optimal learning method by referring to the user's past feedback history during feedback learning. The feedback learning function, for example, acquires the user's past feedback history from a database and analyzes the data. The feedback history includes, for example, past feedback data, evaluation history, etc., but is not limited to such examples. The feedback learning function, for example, suggests the optimal learning method based on the feedback the user has received in the past. The feedback learning function can also suggest an effective learning method from the user's past feedback history. The feedback learning function can also analyze the user's past feedback history and suggest the most efficient learning method. For example, if a user gives a high rating to a specific feedback, a learning method related to that feedback is suggested. Also, if a user gives a low rating to a specific feedback, the learning method related to that feedback can be improved. This allows the feedback learning function to select the optimal learning method based on the user's past feedback history. A part or all of the above-mentioned processing in the feedback learning function may be performed, for example, using AI, or may be performed without using AI.

[0099] The feedback learning function can estimate the user's emotion and determine the priority of feedback based on the estimated user's emotion. For example, the feedback learning function captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the feedback learning function calculates an emotion score based on the change in the facial expression. The feedback learning function can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the feedback learning function analyzes the tone and speed of the voice and calculates an emotion score. The feedback learning function can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the feedback learning function calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to determine the priority of feedback according to the user's emotion. The emotion estimation is realized using the emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the feedback learning function can provide important feedback preferentially when the user is nervous. Also, when the user is relaxed, detailed feedback can be provided preferentially. In addition, if the user is in a hurry, feedback that can be quickly understood can be given priority. This makes it possible to determine the priority of feedback according to the user's emotions.

[0100] The feedback learning function can select the optimal learning method in consideration of the device information of the user during feedback learning. The feedback learning function, for example, acquires the device information of the user from a database and analyzes the data. The device information includes, for example, the type of device, the OS version, and the like, but is not limited to such examples. For example, when the user is using a smartphone, the feedback learning function provides a learning method that matches the screen size. In addition, when the user is using a tablet, the feedback learning function can also provide a learning method optimized for a large screen. In addition, when the user is using a smartwatch, the feedback learning function can also provide a learning method that is concise and highly visible. This allows the feedback learning function to select the optimal learning method based on the device information of the user. A part or all of the above-mentioned processing in the feedback learning function may be performed, for example, using AI, or may be performed without using AI.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The intention expression support system can further include a health management unit that monitors the user's health condition. The health management unit collects the user's biometric data (e.g., heart rate, blood pressure, body temperature, etc.) using a sensor and analyzes the data. For example, if the user's heart rate is high, the system can determine that the user may be feeling stressed and make suggestions to relax. Also, if the user's body temperature is high, the system can determine that the user may have a fever and suggest that the user take a rest. This allows the intention expression support system to make appropriate suggestions according to the user's health condition.

[0103] The intention expression support system can further include an entertainment unit that provides entertainment content customized based on the user's hobbies and preferences. The entertainment unit analyzes the user's past viewing history and evaluation data, and suggests movies, music, games, etc. that the user may be interested in. For example, if the user likes movies of a particular genre, it can suggest new movies related to that genre. Also, if the user likes music by a particular artist, it can suggest new songs by that artist and songs by related artists. In this way, the intention expression support system can provide entertainment content according to the user's hobbies and preferences.

[0104] The intention expression support system can further include a learning style adaptation unit that suggests a learning method according to the user's learning style. The learning style adaptation unit analyzes the user's past learning history and feedback, and suggests the most effective learning method for the user. For example, if the user prefers visual learning, it can suggest learning materials that make extensive use of visual aids. Also, if the user prefers auditory learning, it can suggest audio learning materials or podcasts. Furthermore, if the user prefers practical learning, it can suggest experiments or project-based learning. In this way, the intention expression support system can provide the optimal learning method according to the user's learning style.

[0105] The intention expression support system may further include a social relationship analysis unit that adjusts the content of the suggestions taking into account the user's social relationships. The social relationship analysis unit analyzes the user's social media activities and contact data to suggest events and activities that the user may be interested in. For example, if the user frequently interacts with a specific friend, it may suggest events that the friend will participate in. Also, if the user belongs to a specific group, it may suggest activities and events related to the group. This allows the intention expression support system to make appropriate suggestions according to the user's social relationships.

[0106] The intention expression support system can further include a relaxation unit that estimates the user's emotions and provides relaxation content based on the estimated emotions. The relaxation unit analyzes the user's facial expressions, voice, and biometric data, and provides relaxation content when the user is feeling stressed. For example, when the user is tense, relaxation music or a meditation guide can be provided. Also, when the user is tired, relaxing videos or aromatherapy can be suggested. Furthermore, when the user is feeling anxious, relaxation exercises or a guide to deep breathing can be provided. In this way, the intention expression support system can provide relaxation content according to the user's emotions.

[0107] The intention expression support system can further include a prediction unit that predicts future intentions based on the user's past intention history. The prediction unit analyzes the user's past input data and behavior history, and predicts what intention the user will express next. For example, if the user inputs "I'm hungry" at the same time every day, the system can automatically suggest a meal at that time. Also, if the user inputs "I'm nervous" before a specific event, the system can suggest relaxation before the event. This allows the intention expression support system to predict future intentions based on the user's past history and make appropriate suggestions.

[0108] The intention expression support system may further include an environment adaptation unit that adjusts the content of the proposal based on the user's current situation and environment. The environment adaptation unit analyzes the user's location information and surrounding environmental data, and makes proposals according to the user's current location and situation. For example, when the user is out, it can make suggestions of nearby restaurants and cafes. Also, when the user is at home, it can make suggestions of activities and relaxation methods that can be done at home. Furthermore, when the user is in an office, it can make suggestions to improve work efficiency. In this way, the intention expression support system can make appropriate suggestions according to the user's current situation and environment.

[0109] The intention expression support system can further include a communication adjustment unit that estimates the user's emotions and adjusts the communication method based on the estimated emotions. The communication adjustment unit analyzes the user's facial expressions, voice, and biometric data to determine the user's emotional state. For example, if the user is angry, the system can suggest communication in a calm tone. If the user is sad, the system can also suggest communication in a gentle tone. If the user is happy, the system can also suggest communication in a bright tone. In this way, the intention expression support system can provide the optimal communication method according to the user's emotions.

[0110] The intention expression support system can further include a region information providing unit that provides region-specific information based on the user's geographical location information. The region information providing unit analyzes the user's current location and past visited places, and provides information related to the region. For example, if the user is in a tourist spot, it can provide tourist spot and event information for that region. Also, if the user moves to a new region, it can provide lifestyle and community information for that region. Furthermore, if the user frequently visits a particular region, it can provide recommended restaurant and shop information for that region. In this way, the intention expression support system can provide region-specific information based on the user's geographical location information.

[0111] The intention expression support system may further include an exercise suggestion unit that estimates the user's emotions and proposes an exercise plan based on the estimated emotions. The exercise suggestion unit analyzes the user's facial expressions, voice, and biological data to determine the user's emotional state. For example, if the user is stressed, it may suggest relaxation exercises or yoga. If the user is energetic, it may suggest high intensity training (HIIT) or running. Furthermore, if the user is tired, it may suggest light stretching or walking. In this way, the intention expression support system can provide an optimal exercise plan according to the user's emotions.

[0112] The process flow of the second embodiment will be briefly described below.

[0113] Step 1: The reception unit inputs a user's expression of intent. The user's expression of intent includes, for example, text input, voice input, gestures, etc. The reception unit can use a keyboard or touch screen for receiving text input, a microphone or voice recognition technology for receiving voice input, or a camera or gesture recognition technology for receiving gesture input. Step 2: The generation unit uses a generation AI to analyze the content entered by the reception unit and generate appropriate expressions and actions. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the user's input and generate appropriate expressions and actions. For example, if the user enters "I'm hungry," it generates the expression "I want to eat" and the action "Prepare a meal." Step 3: The presentation unit presents the expressions and actions generated by the generation unit to the user and allows the user to select one. The presentation unit presents the generated expressions and actions to the user using a display and a speaker. The presentation unit can also present multiple options to make it easier for the user to select. For example, the expression "I want to eat" and the action "Prepare a meal" can be displayed on the display, and the user can select one using the touch screen.

[0114] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0116] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements including the above-mentioned reception unit, generation unit, presentation unit, customization function, learning plan proposal function, and feedback learning function is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit inputs the user's intention using a keyboard, touch screen, or microphone of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input content using a generation AI to generate appropriate expressions and actions. The presentation unit presents the generated expressions and actions to the user using the display and speaker of the smart device 14. The customization function is realized by the specific processing unit 290 of the data processing device 12, and adjusts the system according to the user's attributes. The learning plan proposal function is realized by the specific processing unit 290 of the data processing device 12, and proposes a learning plan for the user using a generation AI. The feedback learning function is realized by the specific processing unit 290 of the data processing device 12, and improves the proposal content by receiving user feedback. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[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, the RAM 30, and the 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 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.

[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 the voice according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average 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 performed in a secure state.

[0125] Fig. 4 shows an example of 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.

[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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0130] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0132] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements including the above-mentioned reception unit, generation unit, presentation unit, customization function, learning plan proposal function, and feedback learning function is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit inputs the user's intention using a microphone or a camera of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input content using a generation AI to generate appropriate expressions and actions. The presentation unit presents the generated expressions and actions to the user using the display and speaker of the smart glasses 214. The customization function is realized by the specific processing unit 290 of the data processing device 12, and adjusts the system according to the user's attributes. The learning plan proposal function is realized by the specific processing unit 290 of the data processing device 12, and proposes a learning plan for the user using a generation AI. The feedback learning function is realized by the specific processing unit 290 of the data processing device 12, and improves the proposal content by receiving user feedback. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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, the RAM 30, and the 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.

[0137] 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.

[0138] 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 the voice according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0140] 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 performed in a secure state.

[0141] 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.

[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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0144] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0146] 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 voice indicating a user input for 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0148] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements including the above-mentioned reception unit, generation unit, presentation unit, customization function, learning plan proposal function, and feedback learning function is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit inputs the user's intention using a microphone or a camera of the headset type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input content using a generation AI to generate appropriate expressions and actions. The presentation unit presents the generated expressions and actions to the user using a display or a speaker of the headset type terminal 314. The customization function is realized by the specific processing unit 290 of the data processing device 12, and adjusts the system according to the attributes of the user. The learning plan proposal function is realized by the specific processing unit 290 of the data processing device 12, and proposes a learning plan for the user using a generation AI. The feedback learning function is realized by the specific processing unit 290 of the data processing device 12, and improves the proposal content by receiving feedback from the user. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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, the RAM 30, and the 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.

[0153] 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.

[0154] 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 the voice according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0156] 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 performed in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0158] 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.

[0159] 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.

[0160] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0161] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0162] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0163] 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 a voice indicating a user input for 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.

[0164] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0165] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0166] Each of the multiple elements including the above-mentioned reception unit, generation unit, presentation unit, customization function, study plan proposal function, and feedback learning function is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit inputs the user's intention using a microphone or a camera of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input content using a generation AI to generate appropriate expressions and actions. The presentation unit presents the generated expressions and actions to the user using a display or a speaker of the robot 414. The customization function is realized by the specific processing unit 290 of the data processing device 12, and adjusts the system according to the user's attributes. The study plan proposal function is realized by the specific processing unit 290 of the data processing device 12, and proposes a study plan for the user using a generation AI. The feedback learning function is realized by the specific processing unit 290 of the data processing device 12, and improves the proposal content by receiving feedback from the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0168] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0170] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0171] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0172] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0174] 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.

[0175] 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 Universal Serial Bus (USB) 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.

[0176] In addition, 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 upon request from the data processing device 12.

[0177] 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.

[0178] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0179] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0180] As an example of a configuration using one 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 configuration in which a processor is used that realizes the functions of the 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.

[0181] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0183] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0185] (Appendix 1) A reception unit for inputting a user's intention; a generation unit that analyzes the content input by the reception unit and generates an expression or an action; A presentation unit presents the expression or action generated by the generation unit to a user and allows the user to select the expression or action. A system characterized by: (Appendix 2) Equipped with customization functions according to user attributes 2. The system of claim 1, (Appendix 3) Equipped with a function that uses generative AI to suggest study plans 2. The system of claim 1, (Appendix 4) The generative AI will have the ability to learn from user feedback and improve its suggestions 2. The system of claim 1, (Appendix 5) The reception unit is Analyze the user's emotions and adjust the timing of input of intentions based on the analyzed user emotions 2. The system of claim 1, (Appendix 6) The reception unit is Analyze the user's past intention history and select the appropriate input method 2. The system of claim 1, (Appendix 7) The reception unit is Filtering intent input based on the user's current situation or interests 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's emotions and determine the priority of the input intentions based on the analyzed user's emotions. 2. The system of claim 1, (Appendix 9) The reception unit is When inputting a message, the message with higher relevance is preferentially obtained based on the user's geographic location information. 2. The system of claim 1, (Appendix 10) The reception unit is When a user enters a statement of intent, the user's online activity is analyzed to obtain related statements of intent. 2. The system of claim 1, (Appendix 11) The generation unit is Estimate the user's emotions and adjust the expressions and behaviors to be generated based on the estimated user emotions. 2. The system of claim 1, (Appendix 12) The generation unit is At generation time, adjust the level of detail of the generation based on the importance of the intent 2. The system of claim 1, (Appendix 13) The generation unit is During generation, different generation algorithms are applied depending on the category of the expression of intent. 2. The system of claim 1, (Appendix 14) The generation unit is Estimate the user's emotions and adjust the length of expressions and actions to be generated based on the estimated user emotions. 2. The system of claim 1, (Appendix 15) The generation unit is At the time of generation, the priority of generation is determined based on the time of submission of the notice of intent. 2. The system of claim 1, (Appendix 16) The generation unit is At generation time, adjust the order of generation based on the relevance of the expressions of intent 2. The system of claim 1, (Appendix 17) The presentation unit is Inferring a user's emotion and adjusting the presentation method based on the estimated user's emotion 2. The system of claim 1, (Appendix 18) The presentation unit is When presenting the information, the system selects the optimal display method by referring to the user's past selection history. 2. The system of claim 1, (Appendix 19) The presentation unit is At presentation time, customize the presentation based on the user's current situation 2. The system of claim 1, (Appendix 20) The presentation unit is Estimate the user's emotions and determine the presentation priority based on the estimated user's emotions. 2. The system of claim 1, (Appendix 21) The presentation unit is When presenting, the optimal presentation method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 22) The presentation unit is At the time of presentation, the system analyzes the user's social media activity to suggest presentation methods. 2. The system of claim 1, (Appendix 23) The customization feature is Inferring a user's emotions and adjusting customization content based on the estimated user's emotions 3. The system of claim 2, (Appendix 24) The customization feature is When customizing, select the optimal customization method by referring to the user's past usage history. 3. The system of claim 2, (Appendix 25) The customization feature is To estimate a user's emotion and determine a priority of customization based on the estimated user's emotion. 3. The system of claim 2, (Appendix 26) The customization feature is When customizing, select the optimal customization method taking into account the user's device information 3. The system of claim 2, (Appendix 27) The learning plan suggestion function is Infer user emotions and adjust the way in which learning plans are proposed based on the estimated user emotions 4. The system of claim 3. (Appendix 28) The learning plan suggestion function is When proposing a study plan, the system provides optimal suggestions by referring to the user's past study history. 4. The system of claim 3. (Appendix 29) The learning plan suggestion function is Infer user emotions and prioritize learning plans based on the inferred user emotions 4. The system of claim 3. (Appendix 30) The learning plan suggestion function is When proposing study plans, the system takes into account the user's geographic location to provide optimal suggestions. 4. The system of claim 3. (Appendix 31) The feedback learning function is Inferring user emotions and adjusting feedback based on the estimated user emotions 5. The system of claim 4. (Appendix 32) The feedback learning function is When learning feedback, select the optimal learning method by referring to the user's past feedback history. 5. The system of claim 4. (Appendix 33) The feedback learning function is Estimate user emotions and prioritize feedback based on the estimated user emotions 5. The system of claim 4. (Appendix 34) The feedback learning function is During feedback learning, the optimal learning method is selected taking into account the user's device information. 5. The system of claim 4. [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit for inputting a user's intention; a generation unit that analyzes the content input by the reception unit and generates an expression or an action; A presentation unit presents the expression or action generated by the generation unit to a user and allows the user to select the expression or action. A system characterized by:

2. Equipped with customization functions according to user attributes 2. The system of claim 1 .

3. Equipped with a function that suggests learning plans using generative AI 2. The system of claim 1 .

4. The generative AI will have the ability to learn from user feedback and improve its suggestions.

2. The system of claim 1 .

5. The reception unit is Analyze the user's emotions and adjust the timing of input of intentions based on the analyzed user emotions 2. The system of claim 1 .

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

7. The reception unit is Filtering intent input based on the user's current situation or interests 2. The system of claim 1 .

8. The reception unit is Analyze the user's emotions and determine the priority of the input intentions based on the analyzed user's emotions.

2. The system of claim 1 .

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