system

Abstract

The system according to the embodiment aims to facilitate the use of multiple AI generation services and contract management. [Solution] A system according to an embodiment includes a reception unit, a selection unit, a provision unit, and a visualization unit. The reception unit receives input from a user. The selection unit transmits the input received by the reception unit to multiple generation AI services and selects a response. The provision unit provides the response selected by the selection unit to the user. The visualization unit visualizes the usage status of the generation AI services to which the user has subscribed.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to differentiate between multiple AI generation services and manage contracts, making it difficult to select the most suitable service.

[0005] The system according to the embodiment aims to facilitate the use of multiple AI generation services and contract management. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a selection unit, a provision unit, and a visualization unit. The reception unit receives input from a user. The selection unit transmits the input received by the reception unit to multiple generation AI services and selects a response. The provision unit provides the response selected by the selection unit to the user. The visualization unit visualizes the usage status of the generation AI services subscribed to by the user. [Effects of the Invention]

[0007] The system according to the embodiment makes it easy to differentiate between multiple AI generation services and manage contracts. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A generative AI integration platform according to an embodiment of the present invention is a system that accepts input from a user, provides an optimal response from a generative AI service, and visualizes usage status. In this generative AI integration platform, the user inputs a question or task for the generative AI, and the platform transmits the input to multiple generative AI services, selecting the optimal response and providing it to the user. The platform also lists the generative AI services subscribed to by the user and visualizes their usage status, allowing the user to identify infrequently used services and cancel unnecessary subscriptions. For example, when a user inputs a question or task for the generative AI, they simply access one platform and input the question or task. This information is input into the generative AI integration platform. The platform then transmits the input information to multiple generative AI services, selects the optimal response, and provides it to the user. The platform evaluates the responses from each generative AI service and selects the optimal one to provide to the user. For example, the platform compares summary results from multiple generative AI services and selects the most appropriate summary to provide to the user. Furthermore, the platform lists the generative AI services subscribed to by the user and visualizes their usage status. This allows the user to easily identify infrequently used services and cancel unnecessary subscriptions. For example, the platform displays the number of times and duration of use of each generative AI service in a graph, allowing users to see at a glance how much of each service they are using. This generative AI integrated platform allows users to efficiently use generative AI-related services and easily manage contracts. Users can reduce costs by eliminating the need to select the optimal generative AI service and canceling unnecessary contracts. This allows the generative AI integrated platform to accept user input, provide responses from the optimal generative AI service, and visualize usage status, thereby improving the efficiency of generative AI service usage.

[0029] A generative AI integration platform according to an embodiment includes a reception unit, a selection unit, a provision unit, and a visualization unit. The reception unit receives input from a user. Examples of user input include, but are not limited to, text input, voice input, and image input. The reception unit provides an interface through which a user inputs a question or task for the generative AI. Examples of the interface include, but are not limited to, a web interface, a mobile app, and a voice interface. The selection unit transmits the input received by the reception unit to multiple generative AI services and selects a response. The selection unit evaluates, for example, responses from the multiple generative AI services and selects the optimal one. Evaluation criteria include, but are not limited to, accuracy, relevance, and speed of the response. The selection unit can evaluate the response using, for example, a scoring system, user feedback, a machine learning model, or the like. The provision unit provides the response selected by the selection unit to the user. The provision unit provides, for example, an interface for displaying the selected response to the user. Examples of the interface include, but are not limited to, a web interface, a mobile app, and a voice interface. The visualization unit visualizes the usage status of the generative AI services subscribed to by the user. For example, the visualization unit displays the number of uses and usage time of each generative AI service in a graph, allowing the user to understand at a glance how much of each service the user is using. Methods for measuring the number of uses and usage time include, but are not limited to, daily, weekly, or monthly aggregations. The visualization unit identifies infrequently used services and provides information for canceling unnecessary contracts. Information for cancellation includes, but is not limited to, links to cancellation procedures and explanations of cancellation reasons. As a result, the generative AI integrated platform according to the embodiment can improve the usage efficiency of generative AI services by accepting user input, providing responses from the optimal generative AI service, and visualizing usage status.

[0030] The reception unit can provide an interface through which a user inputs questions or tasks for the generation AI. Examples of interfaces include, but are not limited to, a web interface, a mobile app, and a voice interface. The reception unit can provide an interface through which a user inputs questions or tasks for the generation AI. Examples of interfaces include, but are not limited to, text input, voice input, and image input. For example, if a user inputs a question for the generation AI in text, the question can be input through a web interface. Also, if a user inputs a task for the generation AI by voice, the task can be input through a voice interface. This provides an interface through which a user inputs questions or tasks for the generation AI, thereby improving user convenience.

[0031] The selection unit can evaluate responses from multiple generation AI services and select the optimal one. Evaluation criteria include, but are not limited to, accuracy, relevance, and speed of the response. The selection unit can evaluate responses from multiple generation AI services and select the optimal one. Evaluation methods include, but are not limited to, a scoring system, user feedback, and a machine learning model. For example, the selection unit can evaluate responses from each generation AI service using a scoring system and select the response with the highest score. The selection unit can also evaluate responses based on user feedback and select the response that provides the highest level of user satisfaction. Furthermore, the selection unit can evaluate responses using a machine learning model and select the optimal response. In this way, the optimal response can be provided to the user by evaluating responses from multiple generation AI services and selecting the optimal one.

[0032] The providing unit can provide the response selected by the selecting unit to the user. The providing unit, for example, provides an interface for displaying the selected response to the user. Examples of the interface include, but are not limited to, a web interface, a mobile app, and an audio interface. The providing unit, for example, provides an interface for displaying the selected response to the user. Examples of the interface include, but are not limited to, a text display, an audio output, and an image display. For example, the providing unit can provide a web interface for displaying the selected response in text. The providing unit can also provide an audio interface for outputting the selected response in audio. Furthermore, the providing unit can also provide an image display interface for displaying the selected response as an image. By providing the selected response to the user, the user can obtain optimal information.

[0033] The visualization unit can display the number of uses or usage time of each generated AI service in a graph, allowing the user to understand at a glance how much the user is using each service. Methods for measuring the number of uses and usage time include, but are not limited to, daily, weekly, or monthly aggregations. The visualization unit can display, for example, the number of uses and usage time of each generated AI service in a graph, allowing the user to understand at a glance how much the user is using each service. Types of graphs include, but are not limited to, bar graphs, line graphs, and pie charts. For example, the visualization unit can display the number of uses of each generated AI service in a bar graph to highlight frequently used services. The visualization unit can also display the usage time of each generated AI service in a line graph to highlight services with long usage times. Furthermore, the visualization unit can display the usage status of each generated AI service in a pie chart to show the overall usage percentage. By visualizing the usage status of each generated AI service, the user can more easily identify services that are used less frequently.

[0034] The visualization unit can identify infrequently used services and provide information for canceling unnecessary contracts. Information for cancellation includes, but is not limited to, a link to the cancellation procedure and a reason for cancellation. For example, the visualization unit can identify infrequently used services and provide information for canceling unnecessary contracts. For example, the link to the cancellation procedure includes, but is not limited to, a link to the cancellation page for each generated AI service. Reasons for cancellation include, but are not limited to, reasons for low usage and the need to reduce costs. For example, the visualization unit can identify infrequently used services and provide a link to the cancellation procedure, allowing the user to easily complete the cancellation procedure. Furthermore, the visualization unit can also provide a reason for cancellation to help the user understand the need for cancellation. This allows the user to reduce costs by identifying infrequently used services and canceling unnecessary contracts.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit, for example, analyzes the user's past input history and suggests the optimal input method. Methods for storing the past input history include, but are not limited to, a database format. Analysis methods include, but are not limited to, a machine learning algorithm. For example, the reception unit automatically displays questions and tasks frequently input by the user in the past as candidates. The reception unit can also prioritize and suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest questions and tasks to be used during a specific time period based on the user's past input history. This allows the analysis of the user's past input history to suggest the optimal input method, improving user convenience. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI suggest the optimal input method.

[0036] The reception unit may add a function to automatically complete input content based on the user's current task or project. The reception unit, for example, automatically completes input content based on the user's current task or project. Methods for automatic completion include, but are not limited to, predictive algorithms. For example, when a user inputs a project name, the reception unit automatically completes related tasks and questions. Furthermore, when a user inputs a specific task, the reception unit can automatically complete detailed information related to the task. Furthermore, the reception unit can automatically complete input content based on similar tasks the user has performed in the past. This improves input efficiency by automatically completing input content based on the user's current task or project. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's current task or project data into a generation AI and have the generation AI perform automatic completion of the input content.

[0037] The reception unit can select the optimal input means depending on the user's input method. The reception unit selects the optimal input means depending on, for example, the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the reception unit inputs a question or task using voice recognition technology. Furthermore, if the user selects text input, the reception unit can provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can input a question or task using image recognition technology. This allows for improved input efficiency by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input method data to a generation AI and cause the generation AI to select the optimal input means.

[0038] The reception unit can prioritize accepting highly relevant input content by taking into account the user's geographical location information. The reception unit, for example, prioritizes accepting highly relevant input content by taking into account the user's geographical location information. Methods for acquiring geographical location information include, but are not limited to, GPS data, IP addresses, etc. For example, if the user is in a specific area, the reception unit can prioritize accepting questions and tasks related to that area. Furthermore, if the user is traveling, the reception unit can prioritize accepting questions and tasks related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize accepting questions and tasks related to the event. In this way, by taking the user's geographical location information into account, highly relevant input content can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize accepting highly relevant input content.

[0039] The reception unit can analyze the user's social media activity and suggest related input content. The reception unit can, for example, analyze the user's social media activity and suggest related input content. Methods for analyzing social media activity include, but are not limited to, post content, the number of likes, and the number of followers. For example, the reception unit can analyze content posted by the user on social media and suggest related questions and tasks. The reception unit can also suggest related questions and tasks based on the user's social media friends' activities. Furthermore, the reception unit can suggest related questions and tasks based on the user's social media check-in information. In this way, by analyzing the user's social media activity, related input content can be suggested, improving user convenience. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest related input content.

[0040] The reception unit can customize the input interface by reflecting the user's past feedback. The reception unit, for example, customizes the input interface by reflecting the user's past feedback. Methods for collecting past feedback include, but are not limited to, survey results and user reviews. For example, the reception unit adjusts the interface design based on feedback previously provided by the user. The reception unit can also suggest an optimal input method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and improve the function of the input interface. This allows the input interface to be customized by reflecting the user's past feedback, thereby improving user convenience. Some or all of the above-described processing by the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the input interface.

[0041] The selection unit can optimize the algorithm for selecting the optimal response by referring to the past performance data of each generation AI service. The selection unit, for example, optimizes the algorithm for selecting the optimal response by referring to the past performance data of each generation AI service. Methods for collecting past performance data include, but are not limited to, response time and success rate. For example, the selection unit selects the optimal response based on the accuracy of past responses of each generation AI service. The selection unit can also select the optimal response based on the speed of past responses of each generation AI service. Furthermore, the selection unit can select the optimal response based on user satisfaction with past responses of each generation AI service. By referring to the past performance data of each generation AI service, the algorithm for selecting the optimal response can be optimized and the accuracy of the response can be improved. Some or all of the above-described processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can input past performance data of each generation AI service into the generation AI and cause the generation AI to optimize the algorithm for selecting the optimal response.

[0042] The selection unit can customize the response selection criteria based on the specialty of each generation AI service. The selection unit customizes the response selection criteria, for example, based on the specialty of each generation AI service. Definitions of specialty include, but are not limited to, medical, legal, and technical. For example, the selection unit selects the optimal response based on the specialty of each generation AI service (e.g., medical, legal, etc.). The selection unit can also adjust the response evaluation criteria based on the specialty of each generation AI service. Furthermore, the selection unit can determine the priority of responses based on the specialty of each generation AI service. This allows for the provision of specialized responses by customizing the response selection criteria based on the specialty of each generation AI service. Some or all of the above-described processing in the selection unit may be performed, for example, using AI or without AI. For example, the selection unit can input specialty data of each generation AI service into the generation AI and cause the generation AI to customize the response selection criteria.

[0043] The selection unit can improve the accuracy of reply selection by referring to the user's past selection results. The selection unit improves the accuracy of reply selection by referring to, for example, the user's past selection results. Methods of collecting past selection results include, but are not limited to, selection history and user feedback. For example, the selection unit selects the optimal reply based on replies selected by the user in the past. The selection unit can also adjust the reply evaluation criteria based on the user's past selection results. Furthermore, the selection unit can analyze the user's past selection results and optimize the reply selection algorithm. This can improve the accuracy of reply selection by referring to the user's past selection results. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's past selection result data into the generation AI and cause the generation AI to improve the accuracy of reply selection.

[0044] The selection unit can select the optimal response by taking into account the geographic distribution of each generation AI service. The selection unit can select the optimal response by taking into account, for example, the geographic distribution of each generation AI service. Methods for obtaining the geographic distribution include, but are not limited to, regional usage data and geographic trends. For example, the selection unit can select the optimal response based on the geographic distribution of each generation AI service. The selection unit can also adjust the response evaluation criteria based on the geographic distribution of each generation AI service. Furthermore, the selection unit can determine the priority of responses based on the geographic distribution of each generation AI service. This makes it possible to provide responses appropriate for each region by taking into account the geographic distribution of each generation AI service. Some or all of the above-described processing in the selection unit can be performed, for example, using AI or without using AI. For example, the selection unit can input geographic distribution data of each generation AI service into the generation AI and cause the generation AI to select the optimal response.

[0045] The selection unit can improve the accuracy of response selection by referring to related literature for each generation AI service. The selection unit can improve the accuracy of response selection by referring to related literature for each generation AI service, for example. Methods for referring to related literature include, but are not limited to, academic papers, technical reports, etc. For example, the selection unit selects the optimal response based on the related literature for each generation AI service. The selection unit can also adjust the response evaluation criteria based on the related literature for each generation AI service. Furthermore, the selection unit can determine the priority of responses based on the related literature for each generation AI service. This allows the accuracy of response selection to be improved by referring to the related literature for each generation AI service. Some or all of the above-described processing in the selection unit can be performed, for example, using AI or without AI. For example, the selection unit can input related literature data for each generation AI service into the generation AI and cause the generation AI to improve the accuracy of response selection.

[0046] The selection unit can adjust the response selection criteria taking into account the market value of each generation AI service. The selection unit, for example, adjusts the response selection criteria taking into account the market value of each generation AI service. Market value evaluation criteria include, but are not limited to, sales data, market share, etc. For example, the selection unit selects the optimal response based on the market value of each generation AI service. The selection unit can also adjust the response evaluation criteria based on the market value of each generation AI service. Furthermore, the selection unit can determine the priority of responses based on the market value of each generation AI service. This makes it possible to provide highly valuable responses by taking into account the market value of each generation AI service. Some or all of the above-described processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can input market value data of each generation AI service into the generation AI and cause the generation AI to adjust the response selection criteria.

[0047] The providing unit can select the optimal display method by referring to the user's past response history. The providing unit, for example, selects the optimal display method by referring to the user's past response history. Methods for collecting the past response history include, but are not limited to, the content of the responses and the timing of the responses. For example, the providing unit provides the optimal display method based on display methods previously selected by the user. The providing unit can also suggest the optimal display method from the user's past response history. Furthermore, the providing unit can analyze the user's past response history and optimize the display method. In this way, the optimal display method can be provided by referring to the user's past response history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past response history data into the generation AI and cause the generation AI to select the optimal display method.

[0048] The providing unit can customize the response content based on the user's current task or project. The providing unit customizes the response content based on, for example, the user's current task or project. Definitions of a current task or project include, but are not limited to, project management tools and task management apps. For example, if the user is working on a specific project, the providing unit can prioritize displaying responses related to that project. Furthermore, if the user is performing a specific task, the providing unit can prioritize displaying responses related to that task. Furthermore, the providing unit can customize the response content based on the user's current task or project. This allows the response content to be customized based on the user's current task or project, thereby providing the optimal response for the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's current task or project data into a generation AI and cause the generation AI to customize the response content.

[0049] The providing unit can improve the response display method by reflecting user feedback. The providing unit, for example, improves the response display method by reflecting user feedback. Methods of collecting feedback include, but are not limited to, survey results and user reviews. For example, the providing unit adjusts the display method based on feedback provided by the user. The providing unit can also analyze the user feedback and identify areas for improvement in the display method. Furthermore, the providing unit can reflect user feedback to continuously improve the display method. In this way, the response display method can be continuously improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to improve the response display method.

[0050] The providing unit can display the optimal response by taking into account the user's geographical location information. The providing unit can, for example, display the optimal response by taking into account the user's geographical location information. Methods for acquiring geographical location information include, but are not limited to, GPS data, IP addresses, etc. For example, if the user is in a specific area, the providing unit can prioritize displaying responses related to that area. Furthermore, if the user is traveling, the providing unit can prioritize displaying responses related to the travel destination. Furthermore, if the user is participating in a specific event, the providing unit can prioritize displaying responses related to the event. In this way, by taking the user's geographical location information into account, highly relevant responses can be provided. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's geographical location information data into a generation AI and cause the generation AI to display the optimal response.

[0051] The providing unit can analyze the user's social media activity and display related replies. The providing unit, for example, analyzes the user's social media activity and displays related replies. Methods for analyzing social media activity include, but are not limited to, post content, the number of likes, and the number of followers. For example, the providing unit analyzes content posted by the user on social media and displays related replies. The providing unit can also display related replies based on the activity of the user's friends on social media. Furthermore, the providing unit can display related replies based on the user's check-in information on social media. In this way, related replies can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to display related replies.

[0052] The providing unit can customize the response display method by reflecting the user's past feedback. The providing unit, for example, customizes the response display method by reflecting the user's past feedback. Methods for collecting past feedback include, but are not limited to, survey results and user reviews. For example, the providing unit adjusts the display method based on feedback previously provided by the user. The providing unit can also suggest an optimal display method based on the user's past feedback. Furthermore, the providing unit can analyze the user's past feedback and optimize the display method. This makes it possible to optimize the response display method by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the response display method.

[0053] The visualization unit can select the optimal visualization method by referring to the usage data of each generation AI service. The visualization unit, for example, selects the optimal visualization method by referring to the usage data of each generation AI service. Methods for collecting usage data include, but are not limited to, access logs and usage time. For example, the visualization unit displays a graph highlighting frequently used services based on the number of times each generation AI service is used. The visualization unit can also display a graph highlighting long-used services based on the usage time of each generation AI service. Furthermore, the visualization unit can comprehensively evaluate the usage of each generation AI service and select the optimal visualization method. This makes it possible to provide the optimal visualization method by referring to the usage data of each generation AI service. Some or all of the above-described processing in the visualization unit may be performed, for example, using AI, or may be performed without using AI. For example, the visualization unit can input usage data of each generation AI service into the generation AI and cause the generation AI to select the optimal visualization method.

[0054] The visualization unit may be added with a function to analyze a user's past usage and automatically identify infrequently used services. The visualization unit, for example, analyzes a user's past usage and automatically identifies infrequently used services. Methods for collecting past usage include, but are not limited to, usage history and usage frequency. For example, the visualization unit may automatically identify infrequently used services based on the user's past usage count. The visualization unit may also automatically identify infrequently used services based on the user's past usage time. Furthermore, the visualization unit may comprehensively evaluate a user's past usage and automatically identify infrequently used services. This allows for the automatic identification of infrequently used services by analyzing a user's past usage, thereby contributing to cost reduction for the user. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or without AI. For example, the visualization unit may input a user's past usage data into a generation AI and cause the generation AI to identify infrequently used services.

[0055] The visualization unit can improve the display method of the visualization by reflecting user feedback. For example, the visualization unit improves the display method of the visualization by reflecting user feedback. Methods of collecting feedback include, but are not limited to, survey results and user reviews. For example, the visualization unit adjusts the display method based on feedback provided by the user. The visualization unit can also analyze the user feedback and identify areas for improvement in the display method. Furthermore, the visualization unit can reflect user feedback to continuously improve the display method. In this way, the display method of the visualization can be continuously improved by reflecting user feedback. Some or all of the above-mentioned processing in the visualization unit may be performed, for example, using AI or without using AI. For example, the visualization unit can input user feedback data into a generation AI and cause the generation AI to improve the display method of the visualization.

[0056] The visualization unit can visualize the usage status by taking into account the geographical distribution of each generation AI service. The visualization unit visualizes the usage status by taking into account, for example, the geographical distribution of each generation AI service. Methods for obtaining the geographical distribution include, but are not limited to, regional usage data and geographical trends. For example, the visualization unit displays the usage status on a map based on the geographical distribution of each generation AI service. The visualization unit can also display the usage status by region based on the geographical distribution of each generation AI service. Furthermore, the visualization unit can display the usage status by country based on the geographical distribution of each generation AI service. This makes it possible to visualize the usage status by region by taking into account the geographical distribution of each generation AI service. Some or all of the above-described processing in the visualization unit may be performed, for example, using AI, or may be performed without using AI. For example, the visualization unit can input geographical distribution data of each generation AI service into the generation AI and cause the generation AI to visualize the usage status.

[0057] The visualization unit can improve the accuracy of the usage status visualization by referring to related literature for each generation AI service. The visualization unit can improve the accuracy of the usage status visualization by referring to related literature for each generation AI service, for example. Methods for referring to related literature include, but are not limited to, academic papers, technical reports, etc. For example, the visualization unit can improve the accuracy of the usage status visualization based on the related literature for each generation AI service. The visualization unit can also adjust the usage status evaluation criteria based on the related literature for each generation AI service. Furthermore, the visualization unit can determine the priority of the usage status based on the related literature for each generation AI service. In this way, the accuracy of the usage status visualization can be improved by referring to the related literature for each generation AI service. Some or all of the above-mentioned processing in the visualization unit can be performed, for example, using AI or without AI. For example, the visualization unit can input related literature data for each generation AI service into the generation AI and cause the generation AI to improve the accuracy of the usage status visualization.

[0058] The visualization unit can adjust the usage visualization criteria taking into account the market value of each generating AI service. The visualization unit, for example, adjusts the usage visualization criteria taking into account the market value of each generating AI service. Market value evaluation criteria include, but are not limited to, sales data, market share, etc. For example, the visualization unit adjusts the usage visualization criteria based on the market value of each generating AI service. The visualization unit can also adjust the usage evaluation criteria based on the market value of each generating AI service. Furthermore, the visualization unit can determine usage priorities based on the market value of each generating AI service. This allows the usage of high-value services to be highlighted by taking into account the market value of each generating AI service. Some or all of the above-described processing in the visualization unit may be performed using, or without, AI. For example, the visualization unit can input market value data of each generating AI service into the generating AI and cause the generating AI to adjust the usage visualization criteria.

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

[0060] The reception unit can automatically search for related past data based on the user's input and present it to the user. For example, if the user inputs a specific question, if a similar question has been asked in the past, the answer to that question can be automatically displayed. Also, if the user inputs a specific task, if a similar task has been done in the past, the progress and results of that task can be displayed. Furthermore, if the user inputs information about a specific project, if a similar project has been done in the past, detailed information about that project can be displayed. This allows the user to efficiently obtain information by referring to past data.

[0061] The selection unit can search related external databases and select the most appropriate answer based on the user's input. For example, if the user inputs a medical question, the selection unit can search a medical database and select the most appropriate answer. If the user inputs a legal question, the selection unit can search a legal database and select the most appropriate answer. If the user inputs a technical question, the selection unit can search a technical database and select the most appropriate answer. This allows the user to obtain specialized information.

[0062] The providing unit can automatically generate and provide related visual content to the user based on the user's input. For example, if the user inputs a specific question, graphs and diagrams related to that question can be automatically generated and displayed. Also, if the user inputs a specific task, flowcharts and mind maps related to that task can be automatically generated and displayed. Furthermore, if the user inputs information about a specific project, presentation materials related to that project can be automatically generated and displayed. This makes it easier for the user to understand information visually.

[0063] The visualization unit can visualize data in real time based on user input. For example, if a user inputs a specific data set, the statistical information for that data set can be converted into graphs and charts in real time and displayed. Also, if a user specifies a specific time range, data within that time range can be aggregated and displayed in real time. Furthermore, if a user sets specific conditions, data can be filtered based on those conditions and displayed in real time. This allows the user to grasp fluctuations in data in real time.

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

[0065] Step 1: The reception unit receives input from the user. User input can include text input, voice input, image input, etc. The reception unit provides an interface through which the user can input questions and tasks for the generation AI. The interface can be a web interface, a mobile app, a voice interface, etc. Step 2: The selector sends the input received by the receiver to multiple generative AI services to select a response. The selector evaluates the responses from the multiple generative AI services and selects the best one. Evaluation criteria include the accuracy, relevance, and speed of the response. The selector can evaluate the responses using a scoring system, user feedback, machine learning models, etc. Step 3: The providing unit provides the answer selected by the selecting unit to the user. The providing unit provides an interface for displaying the selected answer to the user. The interface may include a web interface, a mobile app, a voice interface, etc. Step 4: The visualization unit visualizes the usage status of the generation AI services subscribed to by the user. The visualization unit displays the number of times and duration of use of each generation AI service in a graph, allowing the user to see at a glance how much of each service the user is using. Methods for measuring the number of times and duration of use include daily, weekly, and monthly aggregations. The visualization unit identifies services that are used infrequently and provides information for canceling unnecessary contracts. The information for cancellation includes a link to the cancellation procedure and a description of the reason for cancellation.

[0066] (Example 2) A generative AI integration platform according to an embodiment of the present invention is a system that accepts input from a user, provides an optimal response from a generative AI service, and visualizes usage status. In this generative AI integration platform, the user inputs a question or task for the generative AI, and the platform transmits the input to multiple generative AI services, selecting the optimal response and providing it to the user. The platform also lists the generative AI services subscribed to by the user and visualizes their usage status, allowing the user to identify infrequently used services and cancel unnecessary subscriptions. For example, when a user inputs a question or task for the generative AI, they simply access one platform and input the question or task. This information is input into the generative AI integration platform. The platform then transmits the input information to multiple generative AI services, selects the optimal response, and provides it to the user. The platform evaluates the responses from each generative AI service and selects the optimal one to provide to the user. For example, the platform compares summary results from multiple generative AI services and selects the most appropriate summary to provide to the user. Furthermore, the platform lists the generative AI services subscribed to by the user and visualizes their usage status. This allows the user to easily identify infrequently used services and cancel unnecessary subscriptions. For example, the platform displays the number of times and duration of use of each generative AI service in a graph, allowing users to see at a glance how much of each service they are using. This generative AI integrated platform allows users to efficiently use generative AI-related services and easily manage contracts. Users can reduce costs by eliminating the need to select the optimal generative AI service and canceling unnecessary contracts. This allows the generative AI integrated platform to accept user input, provide responses from the optimal generative AI service, and visualize usage status, thereby improving the efficiency of generative AI service usage.

[0067] A generative AI integration platform according to an embodiment includes a reception unit, a selection unit, a provision unit, and a visualization unit. The reception unit receives input from a user. Examples of user input include, but are not limited to, text input, voice input, and image input. The reception unit provides an interface through which a user inputs a question or task for the generative AI. Examples of the interface include, but are not limited to, a web interface, a mobile app, and a voice interface. The selection unit transmits the input received by the reception unit to multiple generative AI services and selects a response. The selection unit evaluates, for example, responses from the multiple generative AI services and selects the optimal one. Evaluation criteria include, but are not limited to, accuracy, relevance, and speed of the response. The selection unit can evaluate the response using, for example, a scoring system, user feedback, a machine learning model, or the like. The provision unit provides the response selected by the selection unit to the user. The provision unit provides, for example, an interface for displaying the selected response to the user. Examples of the interface include, but are not limited to, a web interface, a mobile app, and a voice interface. The visualization unit visualizes the usage status of the generative AI services subscribed to by the user. For example, the visualization unit displays the number of uses and usage time of each generative AI service in a graph, allowing the user to understand at a glance how much of each service the user is using. Methods for measuring the number of uses and usage time include, but are not limited to, daily, weekly, or monthly aggregations. The visualization unit identifies infrequently used services and provides information for canceling unnecessary contracts. Information for cancellation includes, but is not limited to, links to cancellation procedures and explanations of cancellation reasons. As a result, the generative AI integrated platform according to the embodiment can improve the usage efficiency of generative AI services by accepting user input, providing responses from the optimal generative AI service, and visualizing usage status.

[0068] The reception unit can provide an interface through which a user inputs questions or tasks for the generation AI. Examples of interfaces include, but are not limited to, a web interface, a mobile app, and a voice interface. The reception unit can provide an interface through which a user inputs questions or tasks for the generation AI. Examples of interfaces include, but are not limited to, text input, voice input, and image input. For example, if a user inputs a question for the generation AI in text, the question can be input through a web interface. Also, if a user inputs a task for the generation AI by voice, the task can be input through a voice interface. This provides an interface through which a user inputs questions or tasks for the generation AI, thereby improving user convenience.

[0069] The selection unit can evaluate responses from multiple generation AI services and select the optimal one. Evaluation criteria include, but are not limited to, accuracy, relevance, and speed of the response. The selection unit can evaluate responses from multiple generation AI services and select the optimal one. Evaluation methods include, but are not limited to, a scoring system, user feedback, and a machine learning model. For example, the selection unit can evaluate responses from each generation AI service using a scoring system and select the response with the highest score. The selection unit can also evaluate responses based on user feedback and select the response that provides the highest level of user satisfaction. Furthermore, the selection unit can evaluate responses using a machine learning model and select the optimal response. In this way, the optimal response can be provided to the user by evaluating responses from multiple generation AI services and selecting the optimal one.

[0070] The providing unit can provide the response selected by the selecting unit to the user. The providing unit, for example, provides an interface for displaying the selected response to the user. Examples of the interface include, but are not limited to, a web interface, a mobile app, and an audio interface. The providing unit, for example, provides an interface for displaying the selected response to the user. Examples of the interface include, but are not limited to, a text display, an audio output, and an image display. For example, the providing unit can provide a web interface for displaying the selected response in text. The providing unit can also provide an audio interface for outputting the selected response in audio. Furthermore, the providing unit can also provide an image display interface for displaying the selected response as an image. By providing the selected response to the user, the user can obtain optimal information.

[0071] The visualization unit can display the number of uses or usage time of each generated AI service in a graph, allowing the user to understand at a glance how much the user is using each service. Methods for measuring the number of uses and usage time include, but are not limited to, daily, weekly, or monthly aggregations. The visualization unit can display, for example, the number of uses and usage time of each generated AI service in a graph, allowing the user to understand at a glance how much the user is using each service. Types of graphs include, but are not limited to, bar graphs, line graphs, and pie charts. For example, the visualization unit can display the number of uses of each generated AI service in a bar graph to highlight frequently used services. The visualization unit can also display the usage time of each generated AI service in a line graph to highlight services with long usage times. Furthermore, the visualization unit can display the usage status of each generated AI service in a pie chart to show the overall usage percentage. By visualizing the usage status of each generated AI service, the user can more easily identify services that are used less frequently.

[0072] The visualization unit can identify infrequently used services and provide information for canceling unnecessary contracts. Information for cancellation includes, but is not limited to, a link to the cancellation procedure and a reason for cancellation. For example, the visualization unit can identify infrequently used services and provide information for canceling unnecessary contracts. For example, the link to the cancellation procedure includes, but is not limited to, a link to the cancellation page for each generated AI service. Reasons for cancellation include, but are not limited to, reasons for low usage and the need to reduce costs. For example, the visualization unit can identify infrequently used services and provide a link to the cancellation procedure, allowing the user to easily complete the cancellation procedure. Furthermore, the visualization unit can also provide a reason for cancellation to help the user understand the need for cancellation. This allows the user to reduce costs by identifying infrequently used services and canceling unnecessary contracts.

[0073] The reception unit can estimate a user's emotion and dynamically change the design of the input interface based on the estimated user emotion. The reception unit, for example, estimates a user's emotion and dynamically changes the design of the input interface based on the estimated user emotion. Emotion estimation can be performed using, but is not limited to, technologies such as facial expression recognition, voice analysis, and text analysis. For example, when a user is stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, when a user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, when a user is in a hurry, the reception unit can prioritize voice input to enable quick input of questions or tasks. This improves user convenience by changing the design of the input interface according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0074] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit, for example, analyzes the user's past input history and suggests the optimal input method. Methods for storing the past input history include, but are not limited to, a database format. Analysis methods include, but are not limited to, a machine learning algorithm. For example, the reception unit automatically displays questions and tasks frequently input by the user in the past as candidates. The reception unit can also prioritize and suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest questions and tasks to be used during a specific time period based on the user's past input history. This allows the analysis of the user's past input history to suggest the optimal input method, improving user convenience. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI suggest the optimal input method.

[0075] The reception unit may add a function to automatically complete input content based on the user's current task or project. The reception unit, for example, automatically completes input content based on the user's current task or project. Methods for automatic completion include, but are not limited to, predictive algorithms. For example, when a user inputs a project name, the reception unit automatically completes related tasks and questions. Furthermore, when a user inputs a specific task, the reception unit can automatically complete detailed information related to the task. Furthermore, the reception unit can automatically complete input content based on similar tasks the user has performed in the past. This improves input efficiency by automatically completing input content based on the user's current task or project. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's current task or project data into a generation AI and have the generation AI perform automatic completion of the input content.

[0076] The reception unit can select the optimal input means depending on the user's input method. The reception unit selects the optimal input means depending on, for example, the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the reception unit inputs a question or task using voice recognition technology. Furthermore, if the user selects text input, the reception unit can provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can input a question or task using image recognition technology. This allows for improved input efficiency by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input method data to a generation AI and cause the generation AI to select the optimal input means.

[0077] The reception unit can estimate the user's emotions and prioritize the input contents based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and prioritizes the input contents based on the estimated user emotions. Emotion estimation can be performed using, but is not limited to, technologies such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the reception unit can prioritize important questions or tasks. Furthermore, when the user is relaxed, the reception unit can prioritize detailed input contents. Furthermore, when the user is in a hurry, the reception unit can prioritize input contents that require quick processing. Thus, by prioritizing the input contents according to the user's emotions, important input contents can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of the input content.

[0078] The reception unit can prioritize accepting highly relevant input content by taking into account the user's geographical location information. The reception unit, for example, prioritizes accepting highly relevant input content by taking into account the user's geographical location information. Methods for acquiring geographical location information include, but are not limited to, GPS data, IP addresses, etc. For example, if the user is in a specific area, the reception unit can prioritize accepting questions and tasks related to that area. Furthermore, if the user is traveling, the reception unit can prioritize accepting questions and tasks related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize accepting questions and tasks related to the event. In this way, by taking the user's geographical location information into account, highly relevant input content can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize accepting highly relevant input content.

[0079] The reception unit can analyze the user's social media activity and suggest related input content. The reception unit can, for example, analyze the user's social media activity and suggest related input content. Methods for analyzing social media activity include, but are not limited to, post content, the number of likes, and the number of followers. For example, the reception unit can analyze content posted by the user on social media and suggest related questions and tasks. The reception unit can also suggest related questions and tasks based on the user's social media friends' activities. Furthermore, the reception unit can suggest related questions and tasks based on the user's social media check-in information. In this way, by analyzing the user's social media activity, related input content can be suggested, improving user convenience. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest related input content.

[0080] The reception unit can customize the input interface by reflecting the user's past feedback. The reception unit, for example, customizes the input interface by reflecting the user's past feedback. Methods for collecting past feedback include, but are not limited to, survey results and user reviews. For example, the reception unit adjusts the interface design based on feedback previously provided by the user. The reception unit can also suggest an optimal input method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and improve the function of the input interface. This allows the input interface to be customized by reflecting the user's past feedback, thereby improving user convenience. Some or all of the above-described processing by the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the input interface.

[0081] The selection unit can estimate the user's emotions and dynamically change the evaluation criteria for responses based on the estimated user emotions. The selection unit, for example, estimates the user's emotions and dynamically changes the evaluation criteria for responses based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, and other technologies, but is not limited to these examples. For example, if the user is stressed, the selection unit can prioritize selecting simple and intuitive responses. Furthermore, if the user is relaxed, the selection unit can prioritize selecting responses that include detailed information. Furthermore, if the user is in a hurry, the selection unit can prioritize selecting responses that require quick processing. This allows the optimal response to be provided by changing the evaluation criteria for responses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the selection unit can be performed using, for example, an AI, or without an AI. For example, the selection unit can input the user's emotional data into the generation AI and cause the generation AI to dynamically change the evaluation criteria for the response.

[0082] The selection unit can optimize the algorithm for selecting the optimal response by referring to the past performance data of each generation AI service. The selection unit, for example, optimizes the algorithm for selecting the optimal response by referring to the past performance data of each generation AI service. Methods for collecting past performance data include, but are not limited to, response time and success rate. For example, the selection unit selects the optimal response based on the accuracy of past responses of each generation AI service. The selection unit can also select the optimal response based on the speed of past responses of each generation AI service. Furthermore, the selection unit can select the optimal response based on user satisfaction with past responses of each generation AI service. By referring to the past performance data of each generation AI service, the algorithm for selecting the optimal response can be optimized and the accuracy of the response can be improved. Some or all of the above-described processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can input past performance data of each generation AI service into the generation AI and cause the generation AI to optimize the algorithm for selecting the optimal response.

[0083] The selection unit can customize the response selection criteria based on the specialty of each generation AI service. The selection unit customizes the response selection criteria, for example, based on the specialty of each generation AI service. Definitions of specialty include, but are not limited to, medical, legal, and technical. For example, the selection unit selects the optimal response based on the specialty of each generation AI service (e.g., medical, legal, etc.). The selection unit can also adjust the response evaluation criteria based on the specialty of each generation AI service. Furthermore, the selection unit can determine the priority of responses based on the specialty of each generation AI service. This allows for the provision of specialized responses by customizing the response selection criteria based on the specialty of each generation AI service. Some or all of the above-described processing in the selection unit may be performed, for example, using AI or without AI. For example, the selection unit can input specialty data of each generation AI service into the generation AI and cause the generation AI to customize the response selection criteria.

[0084] The selection unit can improve the accuracy of reply selection by referring to the user's past selection results. The selection unit improves the accuracy of reply selection by referring to, for example, the user's past selection results. Methods of collecting past selection results include, but are not limited to, selection history and user feedback. For example, the selection unit selects the optimal reply based on replies selected by the user in the past. The selection unit can also adjust the reply evaluation criteria based on the user's past selection results. Furthermore, the selection unit can analyze the user's past selection results and optimize the reply selection algorithm. This can improve the accuracy of reply selection by referring to the user's past selection results. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's past selection result data into the generation AI and cause the generation AI to improve the accuracy of reply selection.

[0085] The selection unit can estimate the user's emotion and adjust the display order of replies based on the estimated user's emotion. The selection unit, for example, estimates the user's emotion and adjusts the display order of replies based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, or other technologies, but is not limited to these examples. For example, when the user is stressed, the selection unit can prioritize displaying important replies. Furthermore, when the user is relaxed, the selection unit can prioritize displaying detailed replies. Furthermore, when the user is in a hurry, the selection unit can prioritize displaying replies that require quick processing. By adjusting the display order of replies according to the user's emotion, replies can be displayed in an order optimal for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the selection unit can be performed using, for example, an AI, or without using an AI. For example, the selection unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the display order of the replies.

[0086] The selection unit can select the optimal response by taking into account the geographic distribution of each generation AI service. The selection unit can select the optimal response by taking into account, for example, the geographic distribution of each generation AI service. Methods for obtaining the geographic distribution include, but are not limited to, regional usage data and geographic trends. For example, the selection unit can select the optimal response based on the geographic distribution of each generation AI service. The selection unit can also adjust the response evaluation criteria based on the geographic distribution of each generation AI service. Furthermore, the selection unit can determine the priority of responses based on the geographic distribution of each generation AI service. This makes it possible to provide responses appropriate for each region by taking into account the geographic distribution of each generation AI service. Some or all of the above-described processing in the selection unit can be performed, for example, using AI or without using AI. For example, the selection unit can input geographic distribution data of each generation AI service into the generation AI and cause the generation AI to select the optimal response.

[0087] The selection unit can improve the accuracy of response selection by referring to related literature for each generation AI service. The selection unit can improve the accuracy of response selection by referring to related literature for each generation AI service, for example. Methods for referring to related literature include, but are not limited to, academic papers, technical reports, etc. For example, the selection unit selects the optimal response based on the related literature for each generation AI service. The selection unit can also adjust the response evaluation criteria based on the related literature for each generation AI service. Furthermore, the selection unit can determine the priority of responses based on the related literature for each generation AI service. This allows the accuracy of response selection to be improved by referring to the related literature for each generation AI service. Some or all of the above-described processing in the selection unit can be performed, for example, using AI or without AI. For example, the selection unit can input related literature data for each generation AI service into the generation AI and cause the generation AI to improve the accuracy of response selection.

[0088] The selection unit can adjust the response selection criteria taking into account the market value of each generation AI service. The selection unit, for example, adjusts the response selection criteria taking into account the market value of each generation AI service. Market value evaluation criteria include, but are not limited to, sales data, market share, etc. For example, the selection unit selects the optimal response based on the market value of each generation AI service. The selection unit can also adjust the response evaluation criteria based on the market value of each generation AI service. Furthermore, the selection unit can determine the priority of responses based on the market value of each generation AI service. This makes it possible to provide highly valuable responses by taking into account the market value of each generation AI service. Some or all of the above-described processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can input market value data of each generation AI service into the generation AI and cause the generation AI to adjust the response selection criteria.

[0089] The providing unit can estimate the user's emotion and dynamically change the response display method based on the estimated user emotion. The providing unit, for example, estimates the user's emotion and dynamically changes the response display method based on the estimated user emotion. Emotion estimation can use, for example, facial expression recognition, voice analysis, text analysis, and other technologies, but is not limited to these examples. For example, if the user is stressed, the providing unit can provide a simple and intuitive display method. Furthermore, if the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can prioritize displaying responses that require quick processing. This allows the optimal display method for the user to be provided by changing the response display method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotional data into the generating AI and cause the generating AI to dynamically change the way the response is displayed.

[0090] The providing unit can select the optimal display method by referring to the user's past response history. The providing unit, for example, selects the optimal display method by referring to the user's past response history. Methods for collecting the past response history include, but are not limited to, the content of the responses and the timing of the responses. For example, the providing unit provides the optimal display method based on display methods previously selected by the user. The providing unit can also suggest the optimal display method from the user's past response history. Furthermore, the providing unit can analyze the user's past response history and optimize the display method. In this way, the optimal display method can be provided by referring to the user's past response history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past response history data into the generation AI and cause the generation AI to select the optimal display method.

[0091] The providing unit can customize the response content based on the user's current task or project. The providing unit customizes the response content based on, for example, the user's current task or project. Definitions of a current task or project include, but are not limited to, project management tools and task management apps. For example, if the user is working on a specific project, the providing unit can prioritize displaying responses related to that project. Furthermore, if the user is performing a specific task, the providing unit can prioritize displaying responses related to that task. Furthermore, the providing unit can customize the response content based on the user's current task or project. This allows the response content to be customized based on the user's current task or project, thereby providing the optimal response for the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's current task or project data into a generation AI and cause the generation AI to customize the response content.

[0092] The providing unit can improve the response display method by reflecting user feedback. The providing unit, for example, improves the response display method by reflecting user feedback. Methods of collecting feedback include, but are not limited to, survey results and user reviews. For example, the providing unit adjusts the display method based on feedback provided by the user. The providing unit can also analyze the user feedback and identify areas for improvement in the display method. Furthermore, the providing unit can reflect user feedback to continuously improve the display method. In this way, the response display method can be continuously improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to improve the response display method.

[0093] The providing unit can estimate the user's emotions and determine the priority of replies based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of replies based on the estimated user emotions. Emotion estimation can be performed using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the providing unit can prioritize displaying important replies. Furthermore, when the user is relaxed, the providing unit can prioritize displaying detailed replies. Furthermore, when the user is in a hurry, the providing unit can prioritize displaying replies that require quick processing. Thus, by determining the priority of replies according to the user's emotions, important replies can be prioritized and displayed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of responses.

[0094] The providing unit can display the optimal response by taking into account the user's geographical location information. The providing unit can, for example, display the optimal response by taking into account the user's geographical location information. Methods for acquiring geographical location information include, but are not limited to, GPS data, IP addresses, etc. For example, if the user is in a specific area, the providing unit can prioritize displaying responses related to that area. Furthermore, if the user is traveling, the providing unit can prioritize displaying responses related to the travel destination. Furthermore, if the user is participating in a specific event, the providing unit can prioritize displaying responses related to the event. In this way, by taking the user's geographical location information into account, highly relevant responses can be provided. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's geographical location information data into a generation AI and cause the generation AI to display the optimal response.

[0095] The providing unit can analyze the user's social media activity and display related replies. The providing unit, for example, analyzes the user's social media activity and displays related replies. Methods for analyzing social media activity include, but are not limited to, post content, the number of likes, and the number of followers. For example, the providing unit analyzes content posted by the user on social media and displays related replies. The providing unit can also display related replies based on the activity of the user's friends on social media. Furthermore, the providing unit can display related replies based on the user's check-in information on social media. In this way, related replies can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to display related replies.

[0096] The providing unit can customize the response display method by reflecting the user's past feedback. The providing unit, for example, customizes the response display method by reflecting the user's past feedback. Methods for collecting past feedback include, but are not limited to, survey results and user reviews. For example, the providing unit adjusts the display method based on feedback previously provided by the user. The providing unit can also suggest an optimal display method based on the user's past feedback. Furthermore, the providing unit can analyze the user's past feedback and optimize the display method. This makes it possible to optimize the response display method by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the response display method.

[0097] The visualization unit can estimate a user's emotion and dynamically change the display method of the visualization based on the estimated user's emotion. The visualization unit, for example, estimates a user's emotion and dynamically changes the display method of the visualization based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, or other technologies, but is not limited to these examples. For example, if the user is stressed, the visualization unit can display a simple and intuitive graph. Furthermore, if the user is relaxed, the visualization unit can display a graph containing detailed data. Furthermore, if the user is in a hurry, the visualization unit can display a graph that focuses on the main points so that the user can understand it quickly. This allows the optimal display method for the user to be provided by changing the display method of the visualization according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the visualization unit can be performed using, for example, an AI, or without an AI. For example, the visualization unit can input user emotion data into the generation AI and cause the generation AI to dynamically change the way the visualization is displayed.

[0098] The visualization unit can select the optimal visualization method by referring to the usage data of each generation AI service. The visualization unit, for example, selects the optimal visualization method by referring to the usage data of each generation AI service. Methods for collecting usage data include, but are not limited to, access logs and usage time. For example, the visualization unit displays a graph highlighting frequently used services based on the number of times each generation AI service is used. The visualization unit can also display a graph highlighting long-used services based on the usage time of each generation AI service. Furthermore, the visualization unit can comprehensively evaluate the usage of each generation AI service and select the optimal visualization method. This makes it possible to provide the optimal visualization method by referring to the usage data of each generation AI service. Some or all of the above-described processing in the visualization unit may be performed, for example, using AI, or may be performed without using AI. For example, the visualization unit can input usage data of each generation AI service into the generation AI and cause the generation AI to select the optimal visualization method.

[0099] The visualization unit may be added with a function to analyze a user's past usage and automatically identify infrequently used services. The visualization unit, for example, analyzes a user's past usage and automatically identifies infrequently used services. Methods for collecting past usage include, but are not limited to, usage history and usage frequency. For example, the visualization unit may automatically identify infrequently used services based on the user's past usage count. The visualization unit may also automatically identify infrequently used services based on the user's past usage time. Furthermore, the visualization unit may comprehensively evaluate a user's past usage and automatically identify infrequently used services. This allows for the automatic identification of infrequently used services by analyzing a user's past usage, thereby contributing to cost reduction for the user. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or without AI. For example, the visualization unit may input a user's past usage data into a generation AI and cause the generation AI to identify infrequently used services.

[0100] The visualization unit can improve the display method of the visualization by reflecting user feedback. For example, the visualization unit improves the display method of the visualization by reflecting user feedback. Methods of collecting feedback include, but are not limited to, survey results and user reviews. For example, the visualization unit adjusts the display method based on feedback provided by the user. The visualization unit can also analyze the user feedback and identify areas for improvement in the display method. Furthermore, the visualization unit can reflect user feedback to continuously improve the display method. In this way, the display method of the visualization can be continuously improved by reflecting user feedback. Some or all of the above-mentioned processing in the visualization unit may be performed, for example, using AI or without using AI. For example, the visualization unit can input user feedback data into a generation AI and cause the generation AI to improve the display method of the visualization.

[0101] The visualization unit can estimate the user's emotions and determine the visualization priority based on the estimated user emotions. The visualization unit, for example, estimates the user's emotions and determines the visualization priority based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, or other technologies, but is not limited to these examples. For example, the visualization unit can prioritize displaying important data when the user is stressed. Furthermore, the visualization unit can prioritize displaying detailed data when the user is relaxed. Furthermore, the visualization unit can prioritize displaying data that is easy to understand quickly when the user is in a hurry. Thus, by determining the visualization priority based on the user's emotions, important data can be prioritized and displayed. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the visualization unit can be performed using, for example, an AI, or without an AI. For example, the visualization unit can input the user's emotional data into the generation AI and have the generation AI determine the visualization priority.

[0102] The visualization unit can visualize the usage status by taking into account the geographical distribution of each generation AI service. The visualization unit visualizes the usage status by taking into account, for example, the geographical distribution of each generation AI service. Methods for obtaining the geographical distribution include, but are not limited to, regional usage data and geographical trends. For example, the visualization unit displays the usage status on a map based on the geographical distribution of each generation AI service. The visualization unit can also display the usage status by region based on the geographical distribution of each generation AI service. Furthermore, the visualization unit can display the usage status by country based on the geographical distribution of each generation AI service. This makes it possible to visualize the usage status by region by taking into account the geographical distribution of each generation AI service. Some or all of the above-described processing in the visualization unit may be performed, for example, using AI, or may be performed without using AI. For example, the visualization unit can input geographical distribution data of each generation AI service into the generation AI and cause the generation AI to visualize the usage status.

[0103] The visualization unit can improve the accuracy of the usage status visualization by referring to related literature for each generation AI service. The visualization unit can improve the accuracy of the usage status visualization by referring to related literature for each generation AI service, for example. Methods for referring to related literature include, but are not limited to, academic papers, technical reports, etc. For example, the visualization unit can improve the accuracy of the usage status visualization based on the related literature for each generation AI service. The visualization unit can also adjust the usage status evaluation criteria based on the related literature for each generation AI service. Furthermore, the visualization unit can determine the priority of the usage status based on the related literature for each generation AI service. In this way, the accuracy of the usage status visualization can be improved by referring to the related literature for each generation AI service. Some or all of the above-mentioned processing in the visualization unit can be performed, for example, using AI or without AI. For example, the visualization unit can input related literature data for each generation AI service into the generation AI and cause the generation AI to improve the accuracy of the usage status visualization.

[0104] The visualization unit can adjust the usage visualization criteria taking into account the market value of each generating AI service. The visualization unit, for example, adjusts the usage visualization criteria taking into account the market value of each generating AI service. Market value evaluation criteria include, but are not limited to, sales data, market share, etc. For example, the visualization unit adjusts the usage visualization criteria based on the market value of each generating AI service. The visualization unit can also adjust the usage evaluation criteria based on the market value of each generating AI service. Furthermore, the visualization unit can determine usage priorities based on the market value of each generating AI service. This allows the usage of high-value services to be highlighted by taking into account the market value of each generating AI service. Some or all of the above-described processing in the visualization unit may be performed using, or without, AI. For example, the visualization unit can input market value data of each generating AI service into the generating AI and cause the generating AI to adjust the usage visualization criteria. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, selection unit, provision unit, and visualization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives input from a user. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates responses from multiple generation AI services and selects the most appropriate one. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the selected response to the user. The visualization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and visualizes the usage status of the generation AI service. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, selection unit, provision unit, and visualization unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives input from a user. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates responses from multiple generation AI services and selects the optimal one. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the selected response to the user. The visualization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and visualizes the usage status of the generation AI service. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, selection unit, provision unit, and visualization unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives input from the user. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates responses from multiple generation AI services and selects the most appropriate one. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the selected response to the user. The visualization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and visualizes the usage status of the generation AI service. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, selection unit, provision unit, and visualization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives input from a user. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates responses from multiple generation AI services and selects the most appropriate one. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the selected response to the user. The visualization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and visualizes the usage status of the generation AI service.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The reception unit can automatically search for related past data based on the user's input and present it to the user. For example, if the user inputs a specific question, if a similar question has been asked in the past, the answer to that question can be automatically displayed. Also, if the user inputs a specific task, if a similar task has been done in the past, the progress and results of that task can be displayed. Furthermore, if the user inputs information about a specific project, if a similar project has been done in the past, detailed information about that project can be displayed. This allows the user to efficiently obtain information by referring to past data.

[0107] The selection unit can search related external databases and select the most appropriate answer based on the user's input. For example, if the user inputs a medical question, the selection unit can search a medical database and select the most appropriate answer. If the user inputs a legal question, the selection unit can search a legal database and select the most appropriate answer. If the user inputs a technical question, the selection unit can search a technical database and select the most appropriate answer. This allows the user to obtain specialized information.

[0108] The providing unit can automatically generate and provide related visual content to the user based on the user's input. For example, if the user inputs a specific question, graphs and diagrams related to that question can be automatically generated and displayed. Also, if the user inputs a specific task, flowcharts and mind maps related to that task can be automatically generated and displayed. Furthermore, if the user inputs information about a specific project, presentation materials related to that project can be automatically generated and displayed. This makes it easier for the user to understand information visually.

[0109] The visualization unit can visualize data in real time based on user input. For example, if a user inputs a specific data set, the statistical information for that data set can be converted into graphs and charts in real time and displayed. Also, if a user specifies a specific time range, data within that time range can be aggregated and displayed in real time. Furthermore, if a user sets specific conditions, data can be filtered based on those conditions and displayed in real time. This allows the user to grasp fluctuations in data in real time.

[0110] The reception unit can estimate the user's emotions and provide feedback on the input content based on the estimated user emotions. For example, if the user is feeling stressed, an encouraging message can be displayed in response to the input content. If the user is relaxed, detailed feedback can be provided in response to the input content. Furthermore, if the user is in a hurry, quick feedback can be provided in response to the input content. In this way, the user can receive feedback according to their emotions.

[0111] The selection unit can estimate the user's emotion and adjust the tone of the reply based on the estimated user's emotion. For example, if the user is feeling stressed, the tone of the reply can be made gentle. If the user is relaxed, the tone of the reply can be made friendly. Furthermore, if the user is in a hurry, the tone of the reply can be made concise. In this way, the user can receive a reply in a tone that matches their emotion.

[0112] The providing unit can estimate the user's emotion and dynamically change the format of the reply based on the estimated user's emotion. For example, if the user is feeling stressed, the reply can be provided in bullet points. If the user is relaxed, the reply can be provided in detailed sentences. Furthermore, if the user is in a hurry, the reply can be provided in summary form. This allows the user to receive a reply in a format that suits their emotion.

[0113] The visualization unit can estimate the user's emotions and dynamically change the color of the visualization based on the estimated user's emotions. For example, if the user is feeling stressed, a graph with calm colors can be displayed. If the user is relaxed, a graph with bright colors can be displayed. Furthermore, if the user is in a hurry, a graph with simple colors can be displayed. In this way, the user can receive visualization with colors that correspond to their emotions.

[0114] The visualization unit can estimate the user's emotions and dynamically change the visualization layout based on the estimated user emotions. For example, if the user is feeling stressed, a graph with a simple layout can be displayed. If the user is relaxed, a graph with a detailed layout can be displayed. Furthermore, if the user is in a hurry, a graph with a layout that focuses on the main points can be displayed. In this way, the user can receive visualization with a layout that suits their emotions.

[0115] The visualization unit can estimate the user's emotion and dynamically change the animation effect of the visualization based on the estimated user's emotion. For example, if the user is feeling stressed, a simple animation effect can be used. If the user is relaxed, a rich animation effect can be used. Furthermore, if the user is in a hurry, the animation effect can be minimized. This allows the user to receive visualization with animation effects that correspond to their emotion.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The reception unit receives input from the user. User input can include text input, voice input, image input, etc. The reception unit provides an interface through which the user can input questions and tasks for the generation AI. The interface can be a web interface, a mobile app, a voice interface, etc. Step 2: The selector sends the input received by the receiver to multiple generative AI services to select a response. The selector evaluates the responses from the multiple generative AI services and selects the best one. Evaluation criteria include the accuracy, relevance, and speed of the response. The selector can evaluate the responses using a scoring system, user feedback, machine learning models, etc. Step 3: The providing unit provides the answer selected by the selecting unit to the user. The providing unit provides an interface for displaying the selected answer to the user. The interface may include a web interface, a mobile app, a voice interface, etc. Step 4: The visualization unit visualizes the usage status of the generation AI services subscribed to by the user. The visualization unit displays the number of times and duration of use of each generation AI service in a graph, allowing the user to see at a glance how much of each service the user is using. Methods for measuring the number of times and duration of use include daily, weekly, and monthly aggregations. The visualization unit identifies services that are used infrequently and provides information for canceling unnecessary contracts. The information for cancellation includes a link to the cancellation procedure and a description of the reason for cancellation.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] 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.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] 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.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] 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.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0181] 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.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user; a selection unit that transmits the input received by the reception unit to a plurality of generation AI services and selects a response; a providing unit that provides the response selected by the selecting unit to the user; A visualization unit that visualizes the usage status of the generation AI service to which the user has subscribed. A system characterized by:

2. The reception unit Provide an interface for users to input questions or tasks for the generated AI 2. The system of claim 1.

3. The selection unit Evaluate and select responses from multiple generative AI services 2. The system of claim 1.

4. The providing unit Providing the response selected by the selection unit to the user 2. The system of claim 1.

5. The visualization unit The number of times or duration of use of each generated AI service is displayed in a graph, allowing users to see at a glance how much of each service they are using.

2. The system of claim 1.

6. The visualization unit Identify infrequently used services and provide information to cancel unnecessary subscriptions 2. The system of claim 1.

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

2. The system of claim 1.

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

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