system

Abstract

The system according to the embodiment aims to enable users to easily obtain ideas for AI services. [Solution] A system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives requirements or requests for AI services from a user. The generation unit analyzes the requirements or requests received by the reception unit and generates ideas for AI services. The provision unit provides the ideas generated by the generation unit to the user.

Description

[Technical Field]

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

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

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

[0004] Conventional technology requires users to come up with ideas for AI services themselves, which places a heavy creative burden on users.

[0005] The system according to the embodiment aims to enable users to easily obtain ideas for AI services. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives requirements or requests for AI services from a user. The generation unit analyzes the requirements or requests received by the reception unit and generates ideas for AI services. The provision unit provides the ideas generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily get ideas for AI services. [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) In an embodiment of the present invention, an AI service generation system allows a user to input requirements and preferences for an AI service. The AI ​​analyzes the requirements and preferences to generate optimal AI service ideas and provide them to the user. This AI service generation system begins when the user inputs their requirements and preferences for an AI service. For example, the user may input requirements such as "Please think of an AI service that can be used in the medical field" or a preference such as "I want an AI service that assists in the diagnosis of a specific disease." This information is then input to the AI ​​generation system. The AI ​​then analyzes the input requirements and preferences. The AI ​​then generates optimal AI service ideas based on data from past AI services and related information. For example, as "AI services that can be used in the medical field," the AI ​​may generate ideas such as an AI that assists in the diagnosis of a disease or an AI that analyzes patient data and suggests optimal treatments. The generated ideas are then provided to the user. The user can develop AI services based on the generated ideas. For example, an AI service that assists in the diagnosis of a disease can be developed based on the generated ideas. In this way, users can efficiently devise AI services that meet their needs. This mechanism allows users to efficiently devise AI services that meet their needs. For example, when considering an AI service that can be used in the medical field, the AI ​​can provide optimal ideas, allowing users to efficiently develop AI services. In addition, users can devise high-quality AI services because the generative AI generates ideas based on past data and related information. This allows the AI ​​service generation system to generate and provide optimal AI service ideas based on the user's requirements and wishes.

[0029] An AI service generation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives requirements or requests for an AI service from a user. The user's requirements or requests include, but are not limited to, functional requirements, performance requirements, and design preferences. For example, the user can input requirements such as "Please consider an AI service that can be used in the medical field" or requests such as "I want an AI service that assists in diagnosing a specific disease" into the reception unit. The generation unit uses a generation AI to analyze the requirements or requests received by the reception unit and generate ideas for an AI service. The generation AI generates optimal AI service ideas, for example, based on data from past AI services and related information. The generation AI analyzes the requirements and requests and generates ideas using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI generates ideas for "AI services that can be used in the medical field," such as an AI that assists in diagnosing diseases or an AI that analyzes patient data and proposes optimal treatments. The provision unit provides the ideas generated by the generation unit to the user. The provision unit can provide the generated ideas to the user in text format, for example. The providing unit can also provide the generated ideas to the user through a graphical interface, thereby enabling the AI ​​service generation system according to the embodiment to generate and provide optimal AI service ideas based on the user's requirements and wishes.

[0030] The generation unit can generate ideas using an algorithm based on past AI service data and related information. For example, the generation unit references past AI service data and generates ideas using an algorithm based on related information. Past AI service data includes, but is not limited to, user feedback and usage history. For example, the generation unit analyzes user feedback to extract features of AI services that have received high ratings in the past. The generation unit can also identify functions and services frequently used by users based on usage history and generate ideas based on the identified functions and services. Furthermore, the generation unit generates ideas using an algorithm based on related information. For example, the related information includes, but is not limited to, metadata and context information. For example, the generation unit analyzes metadata to identify trends and popular features of AI services. The generation unit can also generate ideas that meet user needs based on context information. This allows the generation unit to utilize past data to generate more accurate AI service ideas. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data from past AI services into the generation AI, which can then analyze the data and generate ideas.

[0031] The providing unit can provide the generated idea to the user in text format. For example, the providing unit provides the generated idea to the user in text format. The text format includes, but is not limited to, for example, the length and format of the sentence. For example, the providing unit can provide the generated idea in concise sentences. The providing unit can also provide the idea in text format including detailed explanations. Furthermore, the providing unit can customize the text format idea according to the user's request. For example, the providing unit provides the text format idea in accordance with the user's desired format or style. In this way, the providing unit can provide the generated idea in text format, making it easier for the user to understand. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the generated idea to the generation AI, which can output the idea in text format.

[0032] The providing unit can provide the generated ideas to the user through a graphical interface. For example, the providing unit provides the generated ideas to the user through a graphical interface. Examples of graphical interfaces include, but are not limited to, dashboards and visual charts. For example, the providing unit can provide a dashboard that visually displays the generated ideas. The providing unit can also visually display an overview of the generated ideas using a visual chart. Furthermore, the providing unit can customize the graphical interface according to the user's request. For example, the providing unit can provide a graphical interface according to the user's desired design and layout. In this way, the providing unit can visually provide the generated ideas, making it easier for the user to intuitively understand them. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the generated ideas to the generation AI, which can then output the ideas through a graphical interface.

[0033] The reception unit can analyze the user's past input history of requirements and preferences and suggest an input format. For example, the reception unit can analyze the user's past input history of requirements and preferences and suggest an optimal input format. The analysis of the input history is performed using technologies such as data mining and pattern recognition. For example, the reception unit can automatically display requirements and preferences that the user has frequently input 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 requirements and preferences to be used during a specific time period based on the user's past input history. This allows the reception unit to utilize the past input history to suggest an optimal input format to the user, thereby improving input efficiency. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past input history data into a generation AI, which can analyze the data and suggest an optimal input format.

[0034] The reception unit can filter the input content based on the user's current project or area of ​​interest when the user inputs requirements or preferences. For example, the reception unit filters the input content based on the user's current project or area of ​​interest when the user inputs requirements or preferences. Filtering the input content is performed using techniques such as keyword matching and category classification. For example, the reception unit can prioritize and display requirements and preferences related to the user's current project. The reception unit can also filter and display related requirements and preferences based on the user's area of ​​interest. Furthermore, the reception unit can suggest appropriate requirements and preferences based on the progress of the user's project. This allows the reception unit to efficiently input highly relevant requirements and preferences by filtering the input content based on the user's project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI. For example, the reception unit can input the user's project data and area of ​​interest data into a generation AI, which can analyze the data and filter the input content.

[0035] The reception unit can prioritize acquiring highly relevant requirements and preferences by taking into account the user's geographical location information when inputting requirements and preferences. For example, the reception unit prioritizes acquiring highly relevant requirements and preferences by taking into account the user's geographical location information when inputting requirements and preferences. Consideration of geographical location information is performed, for example, using technologies such as GPS data and IP addresses. For example, if the user is in a specific area, the reception unit can prioritize displaying requirements and preferences related to that area. The reception unit can also suggest requirements and preferences related to nearby resources and services based on the user's current location. Furthermore, the reception unit can prioritize acquiring region-specific requirements and preferences based on the user's geographical location information. This allows the reception unit to efficiently acquire region-specific requirements and preferences by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's geographical location information data into a generation AI, which can analyze the data to acquire highly relevant requirements and preferences.

[0036] The reception unit can analyze the user's social media activity when the user inputs requirements or preferences, and acquire related requirements or preferences. For example, the reception unit can analyze the user's social media activity when the user inputs requirements or preferences, and acquire related requirements or preferences. The analysis of social media activity can be performed using techniques such as analysis of post content and analysis of followers. For example, the reception unit can analyze the user's social media posts and suggest related requirements or preferences. The reception unit can also display requirements or preferences that the user may be interested in based on the user's social media activity history. Furthermore, the reception unit can acquire related requirements or preferences by referring to the activities of the user's followers and friends on social media. In this way, the reception unit can efficiently acquire requirements and preferences based on the user's interests by analyzing social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's social media data into a generation AI, which can analyze the data and acquire related requirements or preferences.

[0037] The generation unit can adjust the level of detail of the generated ideas based on the importance of the requirements and desires when generating ideas. For example, the generation unit adjusts the level of detail of the generated ideas based on the importance of the requirements and desires when generating ideas. The evaluation of importance is performed using criteria such as user priorities and business impact. For example, the generation unit generates detailed ideas for requirements and desires with high importance. The generation unit can also generate concise ideas for requirements and desires with low importance. Furthermore, the generation unit can gradually adjust the level of detail of the generated ideas depending on the importance of the requirements and desires. This allows the generation unit to provide ideas at an appropriate level by adjusting the level of detail of the generated ideas depending on the importance of the requirements and desires. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the importance of the requirements and desires into the generation AI, which can analyze the data and adjust the level of detail of the generated ideas.

[0038] The generation unit can apply different generation algorithms depending on the requirements and desired categories when generating ideas. For example, the generation unit applies different generation algorithms depending on the requirements and desired categories when generating ideas. Categories are classified using criteria such as technology category and business category. For example, the generation unit can apply a medical-specialized generation algorithm to requirements and desires in the medical field. The generation unit can also apply an education-specialized generation algorithm to requirements and desires in the education field. Furthermore, the generation unit can apply an entertainment-specialized generation algorithm to requirements and desires in the entertainment field. In this way, the generation unit can generate more appropriate ideas by applying a generation algorithm depending on the category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input requirement and desired category data into a generation AI, which analyzes the data and applies an appropriate generation algorithm.

[0039] The generation unit can determine the generation priority based on the requirements and desired submission dates when generating ideas. For example, the generation unit determines the generation priority based on the requirements and desired submission dates when generating ideas. The evaluation of the submission dates is performed using criteria such as the submission date and deadline. For example, the generation unit prioritizes the generation of ideas for requirements and requests with upcoming submission deadlines. The generation unit can also postpone the generation of ideas for requirements and requests with distant submission deadlines. Furthermore, the generation unit can gradually adjust the priority of the ideas to be generated depending on the submission dates. This allows the generation unit to provide timely ideas by determining the generation priority based on the submission dates. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on requirements and desired submission dates into a generation AI, which can analyze the data and determine the generation priority.

[0040] The generation unit can adjust the order of ideas to be generated based on the relevance of requirements and desires when generating ideas. The generation unit, for example, adjusts the order of ideas to be generated based on the relevance of requirements and desires when generating ideas. The relevance evaluation is performed using criteria such as topic modeling and interest scoring. For example, the generation unit can prioritize generating ideas for highly relevant requirements and desires. The generation unit can also postpone generating ideas for less relevant requirements and desires. Furthermore, the generation unit can gradually adjust the order of ideas to be generated according to the relevance of requirements and desires. In this way, the generation unit can prioritize providing the most relevant ideas to the user by adjusting the order of generation based on the relevance. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input relevance data of requirements and desires into the generation AI, which can analyze the data and adjust the order of generation.

[0041] The providing unit can select the optimal delivery method by referring to the user's past feedback when providing an idea. For example, the providing unit selects the optimal delivery method by referring to the user's past feedback when providing an idea. The feedback can be referenced using criteria such as surveys and reviews. For example, the providing unit selects the optimal delivery method based on delivery methods that the user has previously preferred. The providing unit can also select a delivery method that reflects improvements based on the user's past feedback. Furthermore, the providing unit can analyze the user's past feedback and select the most effective delivery method. In this way, the providing unit can select the optimal delivery method for the user by utilizing the past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's feedback data into the generation AI, which can analyze the data and select the optimal delivery method.

[0042] The providing unit can customize the provided content based on the user's current project status when providing an idea. For example, the providing unit customizes the provided content based on the user's current project status when providing an idea. The project status is evaluated using criteria such as progress and resource usage. For example, the providing unit prioritizes providing ideas related to the user's current project. The providing unit can also customize and provide appropriate ideas based on the progress of the user's project. Furthermore, the providing unit can provide optimal ideas tailored to the user's project goals. In this way, the providing unit can provide the most appropriate idea for the user by customizing the provided content based on the project status. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's project data into the generation AI, which can analyze the data and customize the provided content.

[0043] The providing unit can select the optimal providing method by taking into account the user's geographical location information when providing ideas. For example, the providing unit selects the optimal providing method by taking into account the user's geographical location information when providing ideas. The geographical location information can be taken into account using technologies such as GPS data and IP addresses. For example, if the user is in a specific area, the providing unit can prioritize providing ideas related to that area. The providing unit can also suggest ideas related to nearby resources and services based on the user's current location. Furthermore, the providing unit can prioritize providing ideas specific to the area based on the user's geographical location information. This allows the providing unit to efficiently provide ideas specific to the area by taking into account the geographical location information. Some or all of the above-described processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's geographical location information data into the generation AI, which can analyze the data and select the optimal providing method.

[0044] The providing unit can customize the provided content by analyzing the user's social media activity when providing ideas. For example, the providing unit can customize the provided content by analyzing the user's social media activity when providing ideas. The analysis of social media activity is performed using techniques such as analysis of posted content and analysis of followers. For example, the providing unit can analyze the user's social media posts and suggest related ideas. The providing unit can also display ideas that the user may be interested in based on the user's social media activity history. Furthermore, the providing unit can provide related ideas by referring to the activities of the user's followers and friends on social media. In this way, the providing unit can efficiently provide ideas based on the user's interests by analyzing social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's social media data into the generation AI, which can analyze the data and customize the provided content.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The reception unit can analyze the user's past behavioral history and automatically suggest requirements and preferences previously entered by the user. For example, if the user has previously requested AI services in the medical field, the reception unit can automatically display related requirements and preferences based on that information. The reception unit can also prioritize and suggest frequently used requirements and preferences based on the user's previously entered requirements and preferences. Furthermore, the reception unit can analyze the user's behavioral history and suggest optimal requirements and preferences according to specific time periods and situations. In this way, the reception unit can utilize the user's past behavioral history to support efficient and appropriate input of requirements and preferences.

[0047] The generation unit can retrieve information from relevant external databases based on the user's input and use it to generate ideas. For example, when generating an AI service in the medical field, the generation unit retrieves information from the latest medical research database and generates ideas based on that information. The generation unit can also retrieve trend data from specific industries and generate optimal ideas based on the user's requirements and wishes. Furthermore, the generation unit can reference patent databases related to the user's input and generate new ideas based on existing technologies and ideas. This allows the generation unit to utilize external databases to generate richer and more accurate ideas.

[0048] The providing unit can customize the generated ideas to suit the user's learning style. For example, the provided ideas can be provided in infographics or visual charts to visual learners. The provided ideas can also be provided in audio format to auditory learners. Furthermore, the provided ideas can be provided through interactive simulations or demonstrations to tactile learners. In this way, the providing unit can deepen the user's understanding by providing ideas in an optimal way according to the user's learning style.

[0049] The providing unit can update the generated ideas in real time based on user feedback. For example, when a user inputs comments or corrections to a generated idea, the providing unit immediately reflects the feedback and provides the updated idea again. Also, if a user wants to change part of an idea, the providing unit can generate and provide a new idea based on the changes. Furthermore, the providing unit can analyze user feedback and generate ideas that reflect common improvements and requests. This allows the providing unit to utilize user feedback to provide more appropriate and satisfying ideas.

[0050] The providing unit can link the generated ideas with the user's project management tool. For example, the providing unit can automatically add the generated ideas to a project management tool (e.g., Trello, Asana, etc.) used by the user. The providing unit can also assign the generated ideas as appropriate tasks depending on the progress of the project. Furthermore, the providing unit can link with the user's project management tool to update the progress of the generated ideas in real time. In this way, the providing unit can support efficient project management by linking with the user's project management tool.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The reception unit accepts the user's requirements or requests for an AI service. User requirements and requests include, for example, functional requirements, performance requirements, and design preferences. Specifically, the user can input requirements such as "Please consider an AI service that can be used in the medical field" or requests such as "I want an AI service that helps diagnose a specific disease." Step 2: The generation unit uses the generation AI to analyze the requirements or requests received by the reception unit and generate ideas for AI services. The generation AI generates optimal AI service ideas based on data from past AI services and related information. For example, the generation AI generates ideas for "AI services that can be used in the medical field," such as AI that assists in diagnosing diseases and AI that analyzes patient data and suggests optimal treatments. Step 3: The providing unit provides the idea generated by the generating unit to the user. The providing unit can provide the generated idea to the user in a text format. The providing unit can also provide the generated idea to the user in a graphical interface.

[0053] (Example 2) In an embodiment of the present invention, an AI service generation system allows a user to input requirements and preferences for an AI service. The AI ​​analyzes the requirements and preferences to generate optimal AI service ideas and provide them to the user. This AI service generation system begins when the user inputs their requirements and preferences for an AI service. For example, the user may input requirements such as "Please think of an AI service that can be used in the medical field" or a preference such as "I want an AI service that assists in the diagnosis of a specific disease." This information is then input to the AI ​​generation system. The AI ​​then analyzes the input requirements and preferences. The AI ​​generation system generates optimal AI service ideas based on data from past AI services and related information. For example, as "AI services that can be used in the medical field," it generates ideas such as an AI that assists in disease diagnosis or an AI that analyzes patient data and suggests optimal treatments. The generated ideas are provided to the user. The user can develop AI services based on the generated ideas. For example, an AI service that assists in disease diagnosis can be developed based on the generated ideas. In this way, users can efficiently devise AI services that meet their needs. This mechanism allows users to efficiently devise AI services that meet their needs. For example, when considering AI services that can be used in the medical field, the AI ​​generation system can provide optimal ideas, allowing users to efficiently develop AI services. In addition, users can devise high-quality AI services because the generative AI generates ideas based on past data and related information. This allows the AI ​​service generation system to generate and provide optimal AI service ideas based on the user's requirements and wishes.

[0054] An AI service generation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives requirements or requests for an AI service from a user. The user's requirements or requests include, but are not limited to, functional requirements, performance requirements, and design preferences. For example, the user can input requirements such as "Please consider an AI service that can be used in the medical field" or requests such as "I want an AI service that assists in diagnosing a specific disease" into the reception unit. The generation unit uses a generation AI to analyze the requirements or requests received by the reception unit and generate ideas for an AI service. The generation AI generates optimal AI service ideas, for example, based on data from past AI services and related information. The generation AI analyzes the requirements and requests and generates ideas using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI generates ideas for "AI services that can be used in the medical field," such as an AI that assists in diagnosing diseases or an AI that analyzes patient data and proposes optimal treatments. The provision unit provides the ideas generated by the generation unit to the user. The provision unit can provide the generated ideas to the user in text format, for example. The providing unit can also provide the generated ideas to the user through a graphical interface, thereby enabling the AI ​​service generation system according to the embodiment to generate and provide optimal AI service ideas based on the user's requirements and wishes.

[0055] The generation unit can generate ideas using an algorithm based on past AI service data and related information. For example, the generation unit references past AI service data and generates ideas using an algorithm based on related information. Past AI service data includes, but is not limited to, user feedback and usage history. For example, the generation unit analyzes user feedback to extract features of AI services that have received high ratings in the past. The generation unit can also identify functions and services frequently used by users based on usage history and generate ideas based on the identified functions and services. Furthermore, the generation unit generates ideas using an algorithm based on related information. For example, the related information includes, but is not limited to, metadata and context information. For example, the generation unit analyzes metadata to identify trends and popular features of AI services. The generation unit can also generate ideas that meet user needs based on context information. This allows the generation unit to utilize past data to generate more accurate AI service ideas. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data from past AI services into the generation AI, which can then analyze the data and generate ideas.

[0056] The providing unit can provide the generated idea to the user in text format. For example, the providing unit provides the generated idea to the user in text format. The text format includes, but is not limited to, for example, the length and format of the sentence. For example, the providing unit can provide the generated idea in concise sentences. The providing unit can also provide the idea in text format including detailed explanations. Furthermore, the providing unit can customize the text format idea according to the user's request. For example, the providing unit provides the text format idea in accordance with the user's desired format or style. In this way, the providing unit can provide the generated idea in text format, making it easier for the user to understand. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the generated idea to the generation AI, which can output the idea in text format.

[0057] The providing unit can provide the generated ideas to the user through a graphical interface. For example, the providing unit provides the generated ideas to the user through a graphical interface. Examples of graphical interfaces include, but are not limited to, dashboards and visual charts. For example, the providing unit can provide a dashboard that visually displays the generated ideas. The providing unit can also visually display an overview of the generated ideas using a visual chart. Furthermore, the providing unit can customize the graphical interface according to the user's request. For example, the providing unit can provide a graphical interface according to the user's desired design and layout. In this way, the providing unit can visually provide the generated ideas, making it easier for the user to intuitively understand them. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the generated ideas to the generation AI, which can then output the ideas through a graphical interface.

[0058] The reception unit can estimate the user's emotions and adjust the requirements and desired input method based on the estimated user emotions. For example, the reception unit estimates the user's emotions and adjusts the requirements and desired input method based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotion using a facial expression recognition algorithm. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the reception unit adjusts the requirements and desired input method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit prioritizes voice input, allowing the user to quickly enter their requirements and preferences. As a result, the reception unit adjusts the input method according to the user's emotion, thereby reducing the user's stress and enabling efficient input. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI, which then estimates the emotion and adjusts the input method.

[0059] The reception unit can analyze the user's past input history of requirements and preferences and suggest an input format. For example, the reception unit can analyze the user's past input history of requirements and preferences and suggest an optimal input format. The analysis of the input history is performed using technologies such as data mining and pattern recognition. For example, the reception unit can automatically display requirements and preferences that the user has frequently input 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 requirements and preferences to be used during a specific time period based on the user's past input history. This allows the reception unit to utilize the past input history to suggest an optimal input format to the user, thereby improving input efficiency. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past input history data into a generation AI, which can analyze the data and suggest an optimal input format.

[0060] The reception unit can filter the input content based on the user's current project or area of ​​interest when the user inputs requirements or preferences. For example, the reception unit filters the input content based on the user's current project or area of ​​interest when the user inputs requirements or preferences. Filtering the input content is performed using techniques such as keyword matching and category classification. For example, the reception unit can prioritize and display requirements and preferences related to the user's current project. The reception unit can also filter and display related requirements and preferences based on the user's area of ​​interest. Furthermore, the reception unit can suggest appropriate requirements and preferences based on the progress of the user's project. This allows the reception unit to efficiently input highly relevant requirements and preferences by filtering the input content based on the user's project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI. For example, the reception unit can input the user's project data and area of ​​interest data into a generation AI, which can analyze the data and filter the input content.

[0061] The reception unit can estimate the user's emotions and determine the priority of the input requirements and requests based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of the input requirements and requests based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotion using a facial expression recognition algorithm. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the reception unit determines the priority of the input requirements and requests based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize important requirements and requests. Also, if the user is relaxed, the reception unit can prioritize detailed requirements and requests. Furthermore, if the user is in a hurry, the reception unit can prioritize requirements and requests that need to be processed quickly. This allows the reception unit to prioritize requirements and wishes based on the user's emotions, thereby allowing important requirements and wishes to be processed with priority. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be 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 may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI, which then estimates the emotions and determines the priority.

[0062] The reception unit can prioritize acquiring highly relevant requirements and preferences by taking into account the user's geographical location information when inputting requirements and preferences. For example, the reception unit prioritizes acquiring highly relevant requirements and preferences by taking into account the user's geographical location information when inputting requirements and preferences. Consideration of geographical location information is performed, for example, using technologies such as GPS data and IP addresses. For example, if the user is in a specific area, the reception unit can prioritize displaying requirements and preferences related to that area. The reception unit can also suggest requirements and preferences related to nearby resources and services based on the user's current location. Furthermore, the reception unit can prioritize acquiring region-specific requirements and preferences based on the user's geographical location information. This allows the reception unit to efficiently acquire region-specific requirements and preferences by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's geographical location information data into a generation AI, which can analyze the data to acquire highly relevant requirements and preferences.

[0063] The reception unit can analyze the user's social media activity when the user inputs requirements or preferences, and acquire related requirements or preferences. For example, the reception unit can analyze the user's social media activity when the user inputs requirements or preferences, and acquire related requirements or preferences. The analysis of social media activity can be performed using techniques such as analysis of post content and analysis of followers. For example, the reception unit can analyze the user's social media posts and suggest related requirements or preferences. The reception unit can also display requirements or preferences that the user may be interested in based on the user's social media activity history. Furthermore, the reception unit can acquire related requirements or preferences by referring to the activities of the user's followers and friends on social media. In this way, the reception unit can efficiently acquire requirements and preferences based on the user's interests by analyzing social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's social media data into a generation AI, which can analyze the data and acquire related requirements or preferences.

[0064] The generation unit can estimate the user's emotions and adjust the expression method of the generated idea based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the expression method of the generated idea based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotion using a facial expression recognition algorithm. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the generation unit adjusts the expression method of the generated idea based on the estimated user emotions. For example, if the user is relaxed, the generation unit uses a detailed and polite expression method. On the other hand, if the user is in a hurry, the generation unit can use a concise and to-the-point expression method. On the other hand, if the user is excited, the generation unit uses a visually stimulating expression method. In this way, the generation unit can provide ideas that are easy for the user to understand by adjusting the expression method of the idea according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be 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 generation unit may be performed using, or without, the generation AI. For example, the generation unit inputs the user's facial expression data into the generation AI, which then estimates the emotion and adjusts the expression method.

[0065] The generation unit can adjust the level of detail of the generated ideas based on the importance of the requirements and desires when generating ideas. For example, the generation unit adjusts the level of detail of the generated ideas based on the importance of the requirements and desires when generating ideas. The evaluation of importance is performed using criteria such as user priorities and business impact. For example, the generation unit generates detailed ideas for requirements and desires with high importance. The generation unit can also generate concise ideas for requirements and desires with low importance. Furthermore, the generation unit can gradually adjust the level of detail of the generated ideas depending on the importance of the requirements and desires. This allows the generation unit to provide ideas at an appropriate level by adjusting the level of detail of the generated ideas depending on the importance of the requirements and desires. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the importance of the requirements and desires into the generation AI, which can analyze the data and adjust the level of detail of the generated ideas.

[0066] The generation unit can apply different generation algorithms depending on the requirements and desired categories when generating ideas. For example, the generation unit applies different generation algorithms depending on the requirements and desired categories when generating ideas. Categories are classified using criteria such as technology category and business category. For example, the generation unit can apply a medical-specialized generation algorithm to requirements and desires in the medical field. The generation unit can also apply an education-specialized generation algorithm to requirements and desires in the education field. Furthermore, the generation unit can apply an entertainment-specialized generation algorithm to requirements and desires in the entertainment field. In this way, the generation unit can generate more appropriate ideas by applying a generation algorithm depending on the category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input requirement and desired category data into a generation AI, which analyzes the data and applies an appropriate generation algorithm.

[0067] The generation unit can estimate the user's emotions and adjust the length of the generated ideas based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the length of the generated ideas based on the estimated user emotions. The emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotion using a facial expression recognition algorithm. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the generation unit adjusts the length of the generated ideas based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates short, concise ideas. Furthermore, if the user is relaxed, the generation unit can generate longer ideas with detailed explanations. Furthermore, if the user is excited, the generation unit generates ideas with visually stimulating effects. In this way, the generation unit can adjust the length of ideas according to the user's emotions, thereby providing ideas of an appropriate length for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit may input the user's facial expression data into the generation AI, which may then estimate the emotion and adjust the length of the idea.

[0068] The generation unit can determine the generation priority based on the requirements and desired submission dates when generating ideas. For example, the generation unit determines the generation priority based on the requirements and desired submission dates when generating ideas. The evaluation of the submission dates is performed using criteria such as the submission date and deadline. For example, the generation unit prioritizes the generation of ideas for requirements and requests with upcoming submission deadlines. The generation unit can also postpone the generation of ideas for requirements and requests with distant submission deadlines. Furthermore, the generation unit can gradually adjust the priority of the ideas to be generated depending on the submission dates. This allows the generation unit to provide timely ideas by determining the generation priority based on the submission dates. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on requirements and desired submission dates into a generation AI, which can analyze the data and determine the generation priority.

[0069] The generation unit can adjust the order of ideas to be generated based on the relevance of requirements and desires when generating ideas. The generation unit, for example, adjusts the order of ideas to be generated based on the relevance of requirements and desires when generating ideas. The relevance evaluation is performed using criteria such as topic modeling and interest scoring. For example, the generation unit can prioritize generating ideas for highly relevant requirements and desires. The generation unit can also postpone generating ideas for less relevant requirements and desires. Furthermore, the generation unit can gradually adjust the order of ideas to be generated according to the relevance of requirements and desires. In this way, the generation unit can prioritize providing the most relevant ideas to the user by adjusting the order of generation based on the relevance. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input relevance data of requirements and desires into the generation AI, which can analyze the data and adjust the order of generation.

[0070] The providing unit can estimate the user's emotions and adjust the idea presentation method based on the estimated user's emotions. For example, the providing unit estimates the user's emotions and adjusts the idea presentation method based on the estimated user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the providing unit adjusts the idea presentation method based on the estimated user's emotions. For example, if the user is nervous, the providing unit provides a simple and highly visible presentation method. Furthermore, if the user is relaxed, the providing unit can provide a presentation method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit provides a presentation method that focuses on the main points. In this way, the providing unit can adjust the presentation method according to the user's emotions and provide ideas in an optimal way for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit may input the user's facial expression data into the generation AI, which may then estimate the emotion and adjust the presentation method.

[0071] The providing unit can select the optimal delivery method by referring to the user's past feedback when providing an idea. For example, the providing unit selects the optimal delivery method by referring to the user's past feedback when providing an idea. The feedback can be referenced using criteria such as surveys and reviews. For example, the providing unit selects the optimal delivery method based on delivery methods that the user has previously preferred. The providing unit can also select a delivery method that reflects improvements based on the user's past feedback. Furthermore, the providing unit can analyze the user's past feedback and select the most effective delivery method. In this way, the providing unit can select the optimal delivery method for the user by utilizing the past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's feedback data into the generation AI, which can analyze the data and select the optimal delivery method.

[0072] The providing unit can customize the provided content based on the user's current project status when providing an idea. For example, the providing unit customizes the provided content based on the user's current project status when providing an idea. The project status is evaluated using criteria such as progress and resource usage. For example, the providing unit prioritizes providing ideas related to the user's current project. The providing unit can also customize and provide appropriate ideas based on the progress of the user's project. Furthermore, the providing unit can provide optimal ideas tailored to the user's project goals. In this way, the providing unit can provide the most appropriate idea for the user by customizing the provided content based on the project status. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's project data into the generation AI, which can analyze the data and customize the provided content.

[0073] The providing unit can estimate the user's emotions and adjust the order in which ideas are provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the order in which ideas are provided based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the providing unit adjusts the order in which ideas are provided based on the estimated user emotions. For example, if the user is nervous, the providing unit can prioritize providing important ideas. Also, if the user is relaxed, the providing unit can prioritize providing detailed ideas. Furthermore, if the user is in a hurry, the providing unit prioritizes providing ideas that need to be provided quickly. In this way, the providing unit can adjust the order in which ideas are provided according to the user's emotions, thereby providing ideas in an optimal order for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be 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 may be performed using, or without, the generation AI. For example, the providing unit may input the user's facial expression data into the generation AI, which may then estimate the emotion and adjust the provision order.

[0074] The providing unit can select the optimal providing method by taking into account the user's geographical location information when providing ideas. For example, the providing unit selects the optimal providing method by taking into account the user's geographical location information when providing ideas. The geographical location information can be taken into account using technologies such as GPS data and IP addresses. For example, if the user is in a specific area, the providing unit can prioritize providing ideas related to that area. The providing unit can also suggest ideas related to nearby resources and services based on the user's current location. Furthermore, the providing unit can prioritize providing ideas specific to the area based on the user's geographical location information. This allows the providing unit to efficiently provide ideas specific to the area by taking into account the geographical location information. Some or all of the above-described processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's geographical location information data into the generation AI, which can analyze the data and select the optimal providing method.

[0075] The providing unit can customize the provided content by analyzing the user's social media activity when providing ideas. For example, the providing unit can customize the provided content by analyzing the user's social media activity when providing ideas. The analysis of social media activity is performed using techniques such as analysis of posted content and analysis of followers. For example, the providing unit can analyze the user's social media posts and suggest related ideas. The providing unit can also display ideas that the user may be interested in based on the user's social media activity history. Furthermore, the providing unit can provide related ideas by referring to the activities of the user's followers and friends on social media. In this way, the providing unit can efficiently provide ideas based on the user's interests by analyzing social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's social media data into the generation AI, which can analyze the data and customize the provided content. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, allowing the user to input requirements and requests for an AI service. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzing the requirements and requests using a generation AI and generating ideas for an AI service. The provision unit is realized, for example, by the output device 40 of the smart device 14, and providing the generated ideas to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, allowing the user to input requirements and requests for AI services by voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the requirements and requests using a generation AI to generate ideas for AI services. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides the generated ideas to the user by voice. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, allowing the user to input requirements and requests for AI services by voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the requirements and requests using a generation AI to generate ideas for AI services. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314, and visually presents the generated ideas to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, allowing the user to input requirements and requests for AI services by voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the requirements and requests using a generation AI to generate ideas for AI services. The provision unit is realized, for example, by the speaker 240 of the robot 414, and provides the generated ideas to the user by voice.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The reception unit can analyze the user's past behavioral history and automatically suggest requirements and preferences previously entered by the user. For example, if the user has previously requested AI services in the medical field, the reception unit can automatically display related requirements and preferences based on that information. The reception unit can also prioritize and suggest frequently used requirements and preferences based on the user's previously entered requirements and preferences. Furthermore, the reception unit can analyze the user's behavioral history and suggest optimal requirements and preferences according to specific time periods and situations. In this way, the reception unit can utilize the user's past behavioral history to support efficient and appropriate input of requirements and preferences.

[0078] The generation unit can retrieve information from relevant external databases based on the user's input and use it to generate ideas. For example, when generating an AI service in the medical field, the generation unit retrieves information from the latest medical research database and generates ideas based on that information. The generation unit can also retrieve trend data from specific industries and generate optimal ideas based on the user's requirements and wishes. Furthermore, the generation unit can reference patent databases related to the user's input and generate new ideas based on existing technologies and ideas. This allows the generation unit to utilize external databases to generate richer and more accurate ideas.

[0079] The providing unit can customize the generated ideas to suit the user's learning style. For example, the provided ideas can be provided in infographics or visual charts to visual learners. The provided ideas can also be provided in audio format to auditory learners. Furthermore, the provided ideas can be provided through interactive simulations or demonstrations to tactile learners. In this way, the providing unit can deepen the user's understanding by providing ideas in an optimal way according to the user's learning style.

[0080] The providing unit can update the generated ideas in real time based on user feedback. For example, when a user inputs comments or corrections to a generated idea, the providing unit immediately reflects the feedback and provides the updated idea again. Also, if a user wants to change part of an idea, the providing unit can generate and provide a new idea based on the changes. Furthermore, the providing unit can analyze user feedback and generate ideas that reflect common improvements and requests. This allows the providing unit to utilize user feedback to provide more appropriate and satisfying ideas.

[0081] The reception unit can estimate the user's emotions and adjust the interface design based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can provide an interface with a simple and calm design. If the user is excited, the reception unit can provide an interface with a colorful and lively design. Furthermore, if the user is relaxed, the reception unit can provide an interface including detailed information that the user can freely customize. In this way, the reception unit can improve the user's experience by providing an optimal interface according to the user's emotions.

[0082] The generation unit can estimate the user's emotions and adjust the tone of the generated ideas based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate ideas with a friendly and approachable tone. If the user is nervous, the generation unit can also generate ideas with a formal and reliable tone. Furthermore, if the user is excited, the generation unit can also generate ideas with an energetic and stimulating tone. In this way, the generation unit can provide ideas in a tone that corresponds to the user's emotions, thereby achieving optimal communication for the user.

[0083] The providing unit can estimate the user's emotions and adjust the timing of providing ideas based on the estimated emotions. For example, if the user is feeling stressed, the providing unit temporarily delays providing ideas to give the user time to relax. Also, if the user is relaxed, the providing unit can provide ideas immediately and provide information while the user is concentrating. Furthermore, if the user is in a hurry, the providing unit can quickly provide ideas and provide information at a timing that meets the user's needs. In this way, the providing unit can improve user satisfaction by providing ideas at the optimal timing according to the user's emotions.

[0084] The reception unit can estimate the user's emotions and determine the priority of input requirements and requests based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can prioritize important requirements and requests. Also, if the user is relaxed, the reception unit can prioritize detailed requirements and requests. Furthermore, if the user is in a hurry, the reception unit prioritizes requirements and requests that need to be processed quickly. In this way, the reception unit can prioritize important requirements and requests by determining the priority of requirements and requests according to the user's emotions.

[0085] The providing unit can estimate the user's emotions and adjust the order in which ideas are provided based on the estimated emotions. For example, if the user is nervous, the providing unit can provide important ideas with priority. Also, if the user is relaxed, the providing unit can provide detailed ideas with priority. Furthermore, if the user is in a hurry, the providing unit can provide ideas that need to be provided quickly with priority. In this way, the providing unit can provide ideas in the order that is optimal for the user by adjusting the providing order according to the user's emotions.

[0086] The providing unit can link the generated ideas with the user's project management tool. For example, the providing unit can automatically add the generated ideas to a project management tool (e.g., Trello, Asana, etc.) used by the user. The providing unit can also assign the generated ideas as appropriate tasks depending on the progress of the project. Furthermore, the providing unit can link with the user's project management tool to update the progress of the generated ideas in real time. In this way, the providing unit can support efficient project management by linking with the user's project management tool.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The reception unit accepts the user's requirements or requests for an AI service. User requirements and requests include, for example, functional requirements, performance requirements, and design preferences. Specifically, the user can input requirements such as "Please consider an AI service that can be used in the medical field" or requests such as "I want an AI service that helps diagnose a specific disease." Step 2: The generation unit uses the generation AI to analyze the requirements or requests received by the reception unit and generate ideas for AI services. The generation AI generates optimal AI service ideas based on data from past AI services and related information. For example, the generation AI generates ideas for "AI services that can be used in the medical field," such as AI that assists in diagnosing diseases and AI that analyzes patient data and suggests optimal treatments. Step 3: The providing unit provides the idea generated by the generating unit to the user. The providing unit can provide the generated idea to the user in a text format. The providing unit can also provide the generated idea to the user in a graphical interface.

[0089] 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.

[0090] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0091] 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.

[0092] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] [Explanation of symbols]

[0161] 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 requirements or requests for AI services from users; A generation unit that analyzes the requirements or requests received by the reception unit and generates ideas for AI services; a providing unit that provides the idea generated by the generating unit to a user. A system characterized by:

2. The generation unit Based on past AI service data, ideas are generated using algorithms based on relevant information.

2. The system of claim 1.

3. The providing unit Provide generated ideas to users in text format 2. The system of claim 1.

4. The providing unit Generated ideas are presented to the user through a graphical interface 2. The system of claim 1.

5. The reception unit To estimate a user's emotion and adjust a requirement or a desired input method based on the estimated user's emotion.

2. The system of claim 1.

6. The reception unit Analyze the user's past requirements or desired input history and suggest input formats 2. The system of claim 1.

7. The reception unit When entering requirements or desires, filter your input based on your current projects or areas of interest 2. The system of claim 1.

8. The reception unit Estimate the user's emotions and prioritize the input requirements and wishes based on the estimated user emotions.

2. The system of claim 1.

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