system

Abstract

The system according to the embodiment aims to facilitate the sharing and reuse of AI models and to realize efficient collaboration. [Solution] A system according to an embodiment includes a sharing unit, a management unit, a collaboration unit, and an interaction unit. The sharing unit shares and reuses AI models. The management unit provides documents based on the AI ​​models shared by the sharing unit. The collaboration unit allows multiple users to simultaneously edit code based on the documents provided by the management unit. The interaction unit interacts with the AI ​​model based on the code edited by the collaboration unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have made it difficult to share and reuse AI models, making efficient collaboration difficult.

[0005] The system according to the embodiment aims to facilitate the sharing and reuse of AI models and to realize efficient collaboration. [Means for solving the problem]

[0006] The system according to the embodiment includes a sharing unit, a management unit, a collaboration unit, and an interaction unit. The sharing unit shares and reuses AI models. The management unit provides documents based on the AI ​​models shared by the sharing unit. The collaboration unit allows multiple users to simultaneously edit code based on the documents provided by the management unit. The interaction unit interacts with the AI ​​model based on the code edited by the collaboration unit. [Effects of the Invention]

[0007] The system according to the embodiment facilitates the sharing and reuse of AI models, enabling efficient collaboration. [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) The AI ​​model sharing platform according to an embodiment of the present invention is a system for sharing and reusing AI models using generative AI. This system allows users to upload their own AI models and share them with other users. Users can also download and reuse AI models uploaded by other users. Each AI model is accompanied by detailed documentation, which includes information on how to use the model and training data. Multiple users can simultaneously participate in projects and edit code in real time. Finally, users can interact with AI models and check their behavior in real time. This platform is a highly effective tool for AI developers to share information and technologies and streamline projects. For example, users can upload their own AI models and share them with other users. For example, User A can upload his or her trained image recognition model, and User B can download and reuse that model. This allows AI developers to quickly start new projects by leveraging existing models. Each AI model is accompanied by detailed documentation, which includes information on how to use the model and training data. For example, the documentation provides detailed explanations of the model's input data format and how to interpret its output. Users can refer to these documents to gain a deeper understanding of the model. Furthermore, multiple users can participate in a project simultaneously and edit code in real time. For example, User A and User B can participate in the same project and communicate while editing code in real time. This facilitates communication between team members and ensures smooth project progress. Finally, users can interact with AI models and check their behavior in real time. For example, when training a chatbot, users can actually interact with the chatbot to evaluate its performance. This allows users to check the model's performance in real time and make adjustments as necessary.This will enable the AI ​​model sharing platform to enable AI developers to share information and technology and improve project efficiency.

[0029] An AI model sharing platform according to an embodiment includes a sharing unit, a management unit, a collaboration unit, and an interaction unit. The sharing unit uploads and downloads AI models. For example, a user can upload their own trained image recognition model to the sharing unit, allowing other users to download and reuse the model. The sharing unit also properly manages the uploaded AI models and allows users to easily access them. The management unit provides documents containing information on how to use the model and training data. For example, the management unit generates and provides users with documents that explain in detail the format of the model's input data and how to interpret the output results. The collaboration unit provides a function that allows multiple users to simultaneously participate in a project and edit code in real time. For example, User A and User B can participate in the same project and communicate while editing code in real time. This facilitates communication between team members and smooths project progress. The interaction unit provides a function for interactively interacting with an AI model. For example, when training a chatbot, a user can actually interact with the chatbot to evaluate its performance. This allows the user to check the model's performance in real time and make adjustments as necessary. As a result, the AI ​​model sharing platform according to the embodiment enables AI developers to share information and technology and improve project efficiency.

[0030] The sharing unit can upload and download AI models. For example, the sharing unit allows a user to upload his or her trained image recognition model, and other users can download and reuse the model. For example, User A can upload his or her trained image recognition model to the sharing unit, and User B can download and reuse the model. The sharing unit also properly manages uploaded AI models to enable users to easily access them. For example, the sharing unit can organize uploaded AI models by category, allowing users to quickly find the model they need. Furthermore, the sharing unit manages versions of AI models, allowing users to select and download different versions of models. For example, the sharing unit can display the version history of each AI model, allowing users to select and download a specific version. This allows the sharing unit to efficiently upload and download AI models.

[0031] The management unit can provide documents including information on how to use the model and the training data. The management unit generates documents that explain in detail, for example, the format of the model's input data and how to interpret the output results, and provides these to the user. For example, the management unit generates documents that explain how to use the image recognition model and provides these to the user. The management unit can also provide documents including information on the training data. For example, the management unit generates documents that explain details of the dataset used to train the model and provides these to the user. The management unit can also provide documents related to model performance evaluation. For example, the management unit generates documents that explain evaluation metrics such as the model's precision and recall, and provides these to the user. This allows the management unit to provide documents including information on how to use the model and the training data.

[0032] The collaboration unit can provide a function for editing code in real time. For example, the collaboration unit allows user A and user B to participate in the same project and communicate while editing code in real time. For example, the collaboration unit provides an interface for editing code in real time, allowing users to edit code simultaneously. The collaboration unit can also manage the history of code changes and allow users to review past changes. For example, the collaboration unit can display the history of code changes and allow users to select and review specific changes. Furthermore, the collaboration unit can provide a function for resolving code conflicts. For example, when multiple users edit the same part of code at the same time, the collaboration unit detects the conflict and suggests a solution to the user. In this way, the collaboration unit can provide a function for editing code in real time.

[0033] The dialogue unit can provide a function of interacting with the AI ​​model in real time. For example, when a user trains a chatbot, the dialogue unit can actually interact with the chatbot to evaluate its performance. For example, the dialogue unit provides a dialogue interface with the chatbot, allowing the user to interact with the chatbot in real time. The dialogue unit can also save a dialogue log and allow the user to review past dialogues. For example, the dialogue unit can display the dialogue log and allow the user to select and review a specific dialogue. Furthermore, the dialogue unit can provide a function of analyzing the content of the dialogue and providing feedback to the user. For example, the dialogue unit can analyze the content of the dialogue and suggest improvements to the chatbot to the user. In this way, the dialogue unit can provide a function of interacting with the AI ​​model in real time.

[0034] The sharing unit can analyze the user's past upload history and select the optimal upload method. For example, the sharing unit can prioritize and suggest upload methods that the user has frequently used in the past. The sharing unit can also use the generation AI to select the most efficient upload method from the user's past upload history. Furthermore, the sharing unit can also suggest the optimal file format and compression method based on the user's past upload history. This allows the sharing unit to select the optimal upload method based on the user's past upload history.

[0035] The sharing unit can filter AI models based on the user's current project and areas of interest when uploading them. For example, the sharing unit filters the generated AI so that only AI models related to the user's current ongoing project are uploaded. The sharing unit can also prioritize uploading highly relevant AI models based on the user's areas of interest. Furthermore, the sharing unit can upload AI models at appropriate times depending on the progress of the user's project. This allows the sharing unit to filter uploading of AI models based on the user's current project and areas of interest.

[0036] When uploading an AI model, the sharing unit can prioritize uploading a highly relevant model by taking into account the user's geographical location information. For example, if the user is in a specific region, the sharing unit prioritizes uploading an AI model related to that region. The sharing unit can also upload a model including region-specific data based on the user's geographical location information. Furthermore, if the user is moving, the sharing unit can also upload a model that is most relevant to the user's current location. This allows the sharing unit to prioritize uploading a highly relevant model based on the user's geographical location information.

[0037] When uploading an AI model, the sharing unit can analyze the user's social media activity and upload a relevant model. For example, the sharing unit uploads relevant AI models based on the user's social media activity. The sharing unit can also prioritize uploading models related to topics in which the user has shown interest on social media. Furthermore, the sharing unit can upload highly relevant models based on the activity of the user's followers and friends on social media. This allows the sharing unit to upload relevant models based on the user's social media activity.

[0038] The management unit can adjust the level of detail of the document based on the importance of the AI ​​model when providing the document. For example, the management unit can provide detailed documents for AI models with high importance. The management unit can also provide concise documents for AI models with low importance. Furthermore, the management unit can automatically adjust the content of the document according to the importance of the AI ​​model. This allows the management unit to adjust the level of detail of the document based on the importance of the AI ​​model.

[0039] When providing documents, the management unit can apply different document generation algorithms depending on the category of the AI ​​model. For example, the management unit can provide documents specialized for handling image data to an image recognition model. The management unit can also provide documents specialized for handling text data to a natural language processing model. Furthermore, the management unit can also provide documents specialized for handling voice data to a voice recognition model. This allows the management unit to apply different document generation algorithms depending on the category of the AI ​​model.

[0040] When providing documents, the management department can determine the priority of the documents based on the creation date of the AI ​​model. For example, the management department can provide the most detailed documents for the latest AI models. The management department can also provide concise documents for older AI models. Furthermore, the management department can automatically adjust the content of the documents depending on the creation date of the AI ​​model. This allows the management department to determine the priority of the documents based on the creation date of the AI ​​model.

[0041] The management unit can adjust the order of documents based on the relevance of the AI ​​model when providing the documents. For example, the management unit can provide documents of the most relevant AI model first. The management unit can also postpone documents of less relevant AI models. Furthermore, the management unit can automatically adjust the order of documents according to the relevance of the AI ​​model. This allows the management unit to adjust the order of documents based on the relevance of the AI ​​model.

[0042] The collaboration unit can improve the accuracy of collaboration by taking into account the interrelationships between projects during collaboration. The collaboration unit can propose an optimal collaboration method by taking into account, for example, the dependencies between projects. The collaboration unit can also analyze the interrelationships between projects to achieve efficient collaboration. Furthermore, the collaboration unit can determine collaboration priorities based on the interrelationships between projects. In this way, the collaboration unit can improve the accuracy of collaboration by taking into account the interrelationships between projects.

[0043] The collaboration unit can take into consideration the attribute information of the project participants when collaborating. The collaboration unit can propose the optimal collaboration method based on, for example, the areas of expertise of the project participants. The collaboration unit can also realize efficient collaboration based on the experience of the project participants. Furthermore, the collaboration unit can determine the allocation of collaboration roles based on the attribute information of the project participants. This allows the collaboration unit to carry out optimal collaboration based on the attribute information of the project participants.

[0044] The collaboration department can take into account the geographical distribution of the project when collaborating. For example, the collaboration department can propose an optimal collaboration method by taking into account the geographical distribution of project participants. The collaboration department can also provide tools to facilitate communication between geographically distant participants. Furthermore, the collaboration department can adjust the collaboration schedule based on the geographical distribution. This allows the collaboration department to perform optimal collaboration based on the geographical distribution of the project.

[0045] The collaboration unit can improve the accuracy of collaboration by referring to literature related to the project during collaboration. For example, the collaboration unit automatically refers to the latest research papers related to the project. The collaboration unit can also suggest related literature that will be useful in progressing the project. Furthermore, the collaboration unit can search for related literature based on the project theme and use it in collaboration. In this way, the collaboration unit can improve the accuracy of collaboration by referring to literature related to the project.

[0046] During a dialogue, the dialogue unit can predict the current dialogue by referring to past dialogue data. For example, the dialogue unit presents the next predicted question and answer based on the user's past dialogue data. The dialogue unit can also analyze the past dialogue data and propose an optimal dialogue flow. Furthermore, the dialogue unit can customize the content of the dialogue by referring to the user's past dialogue data. This allows the dialogue unit to predict the current dialogue based on the past dialogue data.

[0047] The dialogue unit can apply different dialogue analysis methods to each category of AI model during dialogue. For example, the dialogue unit applies an image-related dialogue analysis method to an image recognition model. The dialogue unit can also apply a text-related dialogue analysis method to a natural language processing model. Furthermore, the dialogue unit can also apply a voice-related dialogue analysis method to a voice recognition model. This allows the dialogue unit to apply a dialogue analysis method according to the AI ​​model category.

[0048] The dialogue unit can analyze changes in the dialogue during the dialogue based on the creation date of the AI ​​model. The dialogue unit updates the content of the dialogue based on, for example, the latest AI model. The dialogue unit can also simplify the content of the dialogue based on an older AI model. Furthermore, the dialogue unit can automatically adjust the content of the dialogue depending on the creation date of the AI ​​model. This allows the dialogue unit to analyze changes in the dialogue based on the creation date of the AI ​​model.

[0049] The dialogue unit can analyze the dialogue by referring to market data related to the AI ​​model during the dialogue. The dialogue unit can customize the content of the dialogue based on the market data, for example. The dialogue unit can also update the content of the dialogue by referring to market trends. Furthermore, the dialogue unit can optimize the content of the dialogue based on the market data. This allows the dialogue unit to analyze the dialogue based on market data related to the AI ​​model.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The sharing unit can analyze a user's past upload history and select the optimal upload method. For example, it can prioritize and suggest upload methods that the user has frequently used in the past. The sharing unit can also use the generation AI to select the most efficient upload method from the user's past upload history. Furthermore, the sharing unit can also suggest the optimal file format and compression method based on the user's past upload history. This allows the sharing unit to select the optimal upload method based on the user's past upload history.

[0052] When uploading an AI model, the sharing unit can filter the AI ​​models based on the user's current project or areas of interest. For example, the generation AI filters so that only AI models related to the user's current ongoing project are uploaded. The sharing unit can also prioritize uploading highly relevant AI models based on the user's areas of interest. Furthermore, the sharing unit can upload AI models at appropriate times depending on the progress of the user's project. This allows the sharing unit to filter uploading of AI models based on the user's current project or areas of interest.

[0053] When uploading an AI model, the sharing unit can prioritize uploading highly relevant models by taking into account the user's geographic location information. For example, if the user is in a specific region, the sharing unit can prioritize uploading AI models related to that region. The sharing unit can also upload models including region-specific data based on the user's geographic location information. Furthermore, if the user is moving, the sharing unit can also upload the model most relevant to the user's current location. This allows the sharing unit to prioritize uploading highly relevant models based on the user's geographic location information.

[0054] When providing documents, the management unit can adjust the level of detail of the documents based on the importance of the AI ​​model. For example, detailed documents are provided for AI models with high importance. The management unit can also provide concise documents for AI models with low importance. Furthermore, the management unit can automatically adjust the content of the documents according to the importance of the AI ​​model. This allows the management unit to adjust the level of detail of the documents based on the importance of the AI ​​model.

[0055] When providing documents, the management unit can apply different document generation algorithms depending on the category of the AI ​​model. For example, for an image recognition model, the management unit can provide documents specialized for handling image data. Furthermore, the management unit can also provide documents specialized for handling text data for a natural language processing model. Furthermore, the management unit can also provide documents specialized for handling voice data for a voice recognition model. This allows the management unit to apply different document generation algorithms depending on the category of the AI ​​model.

[0056] The collaboration department can improve the accuracy of collaboration by taking into account the interrelationships between projects during collaboration. For example, the collaboration department can propose an optimal collaboration method by taking into account the dependencies between projects. The collaboration department can also analyze the interrelationships between projects to achieve efficient collaboration. Furthermore, the collaboration department can determine collaboration priorities based on the interrelationships between projects. In this way, the collaboration department can improve the accuracy of collaboration by taking into account the interrelationships between projects.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The sharing unit handles uploading and downloading of AI models. For example, a user can upload their own trained image recognition model to the sharing unit, and other users can download and reuse the model. The sharing unit also properly manages the uploaded AI models and makes them easily accessible to users. Step 2: The management unit provides documentation including information on how to use the model and the training data. For example, the management unit generates documentation that provides detailed information on the format of the model's input data and how to interpret the output results, and provides it to the user. Step 3: The collaboration section allows multiple users to simultaneously participate in a project and edit code in real time. For example, user A and user B can participate in the same project and communicate while editing code in real time. This facilitates communication between team members and makes the project progress more smoothly. Step 4: The dialogue unit provides the ability to interact with the AI ​​model. For example, when training a chatbot, users can actually interact with the chatbot to evaluate its performance. This allows users to check the model's performance in real time and make adjustments as needed.

[0059] (Example 2) The AI ​​model sharing platform according to an embodiment of the present invention is a system for sharing and reusing AI models using generative AI. This system allows users to upload their own AI models and share them with other users. Users can also download and reuse AI models uploaded by other users. Each AI model is accompanied by detailed documentation, which includes information on how to use the model and training data. Multiple users can simultaneously participate in projects and edit code in real time. Finally, users can interact with AI models and check their behavior in real time. This platform is a highly effective tool for AI developers to share information and technologies and streamline projects. For example, users can upload their own AI models and share them with other users. For example, User A can upload his or her trained image recognition model, and User B can download and reuse that model. This allows AI developers to quickly start new projects by leveraging existing models. Each AI model is accompanied by detailed documentation, which includes information on how to use the model and training data. For example, the documentation provides detailed explanations of the model's input data format and how to interpret its output. Users can refer to these documents to gain a deeper understanding of the model. Furthermore, multiple users can participate in a project simultaneously and edit code in real time. For example, User A and User B can participate in the same project and communicate while editing code in real time. This facilitates communication between team members and ensures smooth project progress. Finally, users can interact with AI models and check their behavior in real time. For example, when training a chatbot, users can actually interact with the chatbot to evaluate its performance. This allows users to check the model's performance in real time and make adjustments as necessary.This will enable the AI ​​model sharing platform to enable AI developers to share information and technology and improve project efficiency.

[0060] An AI model sharing platform according to an embodiment includes a sharing unit, a management unit, a collaboration unit, and an interaction unit. The sharing unit uploads and downloads AI models. For example, a user can upload their own trained image recognition model to the sharing unit, allowing other users to download and reuse the model. The sharing unit also properly manages the uploaded AI models and allows users to easily access them. The management unit provides documents containing information on how to use the model and training data. For example, the management unit generates and provides users with documents that explain in detail the format of the model's input data and how to interpret the output results. The collaboration unit provides a function that allows multiple users to simultaneously participate in a project and edit code in real time. For example, User A and User B can participate in the same project and communicate while editing code in real time. This facilitates communication between team members and smooths project progress. The interaction unit provides a function for interactively interacting with an AI model. For example, when training a chatbot, a user can actually interact with the chatbot to evaluate its performance. This allows the user to check the model's performance in real time and make adjustments as necessary. As a result, the AI ​​model sharing platform according to the embodiment enables AI developers to share information and technology and improve project efficiency.

[0061] The sharing unit can upload and download AI models. For example, the sharing unit allows a user to upload his or her trained image recognition model, and other users can download and reuse the model. For example, User A can upload his or her trained image recognition model to the sharing unit, and User B can download and reuse the model. The sharing unit also properly manages uploaded AI models to enable users to easily access them. For example, the sharing unit can organize uploaded AI models by category, allowing users to quickly find the model they need. Furthermore, the sharing unit manages versions of AI models, allowing users to select and download different versions of models. For example, the sharing unit can display the version history of each AI model, allowing users to select and download a specific version. This allows the sharing unit to efficiently upload and download AI models.

[0062] The management unit can provide documents including information on how to use the model and the training data. The management unit generates documents that explain in detail, for example, the format of the model's input data and how to interpret the output results, and provides these to the user. For example, the management unit generates documents that explain how to use the image recognition model and provides these to the user. The management unit can also provide documents including information on the training data. For example, the management unit generates documents that explain details of the dataset used to train the model and provides these to the user. The management unit can also provide documents related to model performance evaluation. For example, the management unit generates documents that explain evaluation metrics such as the model's precision and recall, and provides these to the user. This allows the management unit to provide documents including information on how to use the model and the training data.

[0063] The collaboration unit can provide a function for editing code in real time. For example, the collaboration unit allows user A and user B to participate in the same project and communicate while editing code in real time. For example, the collaboration unit provides an interface for editing code in real time, allowing users to edit code simultaneously. The collaboration unit can also manage the history of code changes and allow users to review past changes. For example, the collaboration unit can display the history of code changes and allow users to select and review specific changes. Furthermore, the collaboration unit can provide a function for resolving code conflicts. For example, when multiple users edit the same part of code at the same time, the collaboration unit detects the conflict and suggests a solution to the user. In this way, the collaboration unit can provide a function for editing code in real time.

[0064] The dialogue unit can provide a function of interacting with the AI ​​model in real time. For example, when a user trains a chatbot, the dialogue unit can actually interact with the chatbot to evaluate its performance. For example, the dialogue unit provides a dialogue interface with the chatbot, allowing the user to interact with the chatbot in real time. The dialogue unit can also save a dialogue log and allow the user to review past dialogues. For example, the dialogue unit can display the dialogue log and allow the user to select and review a specific dialogue. Furthermore, the dialogue unit can provide a function of analyzing the content of the dialogue and providing feedback to the user. For example, the dialogue unit can analyze the content of the dialogue and suggest improvements to the chatbot to the user. In this way, the dialogue unit can provide a function of interacting with the AI ​​model in real time.

[0065] The sharing unit can estimate the user's emotions and adjust the upload timing of the AI ​​model based on the estimated user emotions. For example, when the user is feeling stressed, the sharing unit allows the generation AI to automatically select the optimal timing, thereby reducing the user's burden. Furthermore, when the user is relaxed, the sharing unit can increase the user's freedom by allowing the generation AI to choose the upload timing. Furthermore, when the user is in a hurry, the sharing unit can also enable the generation AI to upload immediately, thereby achieving rapid sharing. This allows the sharing unit to adjust the upload timing of the AI ​​model according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0066] The sharing unit can analyze the user's past upload history and select the optimal upload method. For example, the sharing unit can prioritize and suggest upload methods that the user has frequently used in the past. The sharing unit can also use the generation AI to select the most efficient upload method from the user's past upload history. Furthermore, the sharing unit can also suggest the optimal file format and compression method based on the user's past upload history. This allows the sharing unit to select the optimal upload method based on the user's past upload history.

[0067] The sharing unit can filter AI models based on the user's current project and areas of interest when uploading them. For example, the sharing unit filters the generated AI so that only AI models related to the user's current ongoing project are uploaded. The sharing unit can also prioritize uploading highly relevant AI models based on the user's areas of interest. Furthermore, the sharing unit can upload AI models at appropriate times depending on the progress of the user's project. This allows the sharing unit to filter uploading of AI models based on the user's current project and areas of interest.

[0068] The sharing unit can estimate the user's emotions and determine the priority of AI models to download based on the estimated user emotions. For example, if the user is feeling stressed, the sharing unit can prioritize downloading a model that the generation AI can use most easily. Also, if the user is relaxed, the sharing unit can prioritize downloading a model that matches the user's interests. Furthermore, if the user is in a hurry, the sharing unit can select a model that the generation AI can download most quickly. In this way, the sharing unit can prioritize AI models to download based on 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 can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] When uploading an AI model, the sharing unit can prioritize uploading a highly relevant model by taking into account the user's geographical location information. For example, if the user is in a specific region, the sharing unit prioritizes uploading an AI model related to that region. The sharing unit can also upload a model including region-specific data based on the user's geographical location information. Furthermore, if the user is moving, the sharing unit can also upload a model that is most relevant to the user's current location. This allows the sharing unit to prioritize uploading a highly relevant model based on the user's geographical location information.

[0070] When uploading an AI model, the sharing unit can analyze the user's social media activity and upload a relevant model. For example, the sharing unit uploads relevant AI models based on the user's social media activity. The sharing unit can also prioritize uploading models related to topics in which the user has shown interest on social media. Furthermore, the sharing unit can upload highly relevant models based on the activity of the user's followers and friends on social media. This allows the sharing unit to upload relevant models based on the user's social media activity.

[0071] The management unit can estimate the user's emotions and adjust the way the document is presented based on the estimated user's emotions. For example, if the user is nervous, the management unit can provide a simple, highly visible document. If the user is relaxed, the management unit can also provide a document containing detailed information. Furthermore, if the user is in a hurry, the management unit can also provide a concise document that focuses on the main points. This allows the management unit to adjust the way the document is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0072] The management unit can adjust the level of detail of the document based on the importance of the AI ​​model when providing the document. For example, the management unit can provide detailed documents for AI models with high importance. The management unit can also provide concise documents for AI models with low importance. Furthermore, the management unit can automatically adjust the content of the document according to the importance of the AI ​​model. This allows the management unit to adjust the level of detail of the document based on the importance of the AI ​​model.

[0073] When providing documents, the management unit can apply different document generation algorithms depending on the category of the AI ​​model. For example, the management unit can provide documents specialized for handling image data to an image recognition model. The management unit can also provide documents specialized for handling text data to a natural language processing model. Furthermore, the management unit can also provide documents specialized for handling voice data to a voice recognition model. This allows the management unit to apply different document generation algorithms depending on the category of the AI ​​model.

[0074] The management unit can estimate the user's emotions and adjust the length of the document based on the estimated user emotions. For example, if the user is nervous, the management unit can provide a short, to-the-point document. If the user is relaxed, the management unit can also provide a longer document with detailed information. Furthermore, if the user is in a hurry, the management unit can also provide a concise, quickly understandable document. This allows the management unit to adjust the length of the document according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] When providing documents, the management department can determine the priority of the documents based on the creation date of the AI ​​model. For example, the management department can provide the most detailed documents for the latest AI models. The management department can also provide concise documents for older AI models. Furthermore, the management department can automatically adjust the content of the documents depending on the creation date of the AI ​​model. This allows the management department to determine the priority of the documents based on the creation date of the AI ​​model.

[0076] The management unit can adjust the order of documents based on the relevance of the AI ​​model when providing the documents. For example, the management unit can provide documents of the most relevant AI model first. The management unit can also postpone documents of less relevant AI models. Furthermore, the management unit can automatically adjust the order of documents according to the relevance of the AI ​​model. This allows the management unit to adjust the order of documents based on the relevance of the AI ​​model.

[0077] The collaboration unit can estimate the user's emotions and adjust the collaboration criteria based on the estimated user's emotions. For example, if the user is nervous, the collaboration unit provides a simple and highly visible collaboration interface. Furthermore, if the user is relaxed, the collaboration unit can also provide a collaboration interface that includes detailed information. Furthermore, if the user is in a hurry, the collaboration unit can also provide a collaboration interface that can be operated quickly. This allows the collaboration unit to adjust the collaboration criteria according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] The collaboration unit can improve the accuracy of collaboration by taking into account the interrelationships between projects during collaboration. The collaboration unit can propose an optimal collaboration method by taking into account, for example, the dependencies between projects. The collaboration unit can also analyze the interrelationships between projects to achieve efficient collaboration. Furthermore, the collaboration unit can determine collaboration priorities based on the interrelationships between projects. In this way, the collaboration unit can improve the accuracy of collaboration by taking into account the interrelationships between projects.

[0079] The collaboration unit can take into consideration the attribute information of the project participants when collaborating. The collaboration unit can propose the optimal collaboration method based on, for example, the areas of expertise of the project participants. The collaboration unit can also realize efficient collaboration based on the experience of the project participants. Furthermore, the collaboration unit can determine the allocation of collaboration roles based on the attribute information of the project participants. This allows the collaboration unit to carry out optimal collaboration based on the attribute information of the project participants.

[0080] The collaboration unit can estimate the user's emotions and adjust the order in which collaboration results are displayed based on the estimated user's emotions. For example, if the user is nervous, the collaboration unit can display the most important results first. Also, if the user is relaxed, the collaboration unit can sequentially display detailed results. Furthermore, if the user is in a hurry, the collaboration unit can quickly display results that focus on the main points. This allows the collaboration unit to adjust the order in which collaboration results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The collaboration department can take into account the geographical distribution of the project when collaborating. For example, the collaboration department can propose an optimal collaboration method by taking into account the geographical distribution of project participants. The collaboration department can also provide tools to facilitate communication between geographically distant participants. Furthermore, the collaboration department can adjust the collaboration schedule based on the geographical distribution. This allows the collaboration department to perform optimal collaboration based on the geographical distribution of the project.

[0082] The collaboration unit can improve the accuracy of collaboration by referring to literature related to the project during collaboration. For example, the collaboration unit automatically refers to the latest research papers related to the project. The collaboration unit can also suggest related literature that will be useful in progressing the project. Furthermore, the collaboration unit can search for related literature based on the project theme and use it in collaboration. In this way, the collaboration unit can improve the accuracy of collaboration by referring to literature related to the project.

[0083] The dialogue unit can estimate the user's emotions and adjust the dialogue display method based on the estimated user emotions. For example, if the user is nervous, the dialogue unit provides a simple, highly visible display method. If the user is relaxed, the dialogue unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the dialogue unit can also provide a display method that focuses on the main points. This allows the dialogue unit to adjust the dialogue display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0084] During a dialogue, the dialogue unit can predict the current dialogue by referring to past dialogue data. For example, the dialogue unit presents the next predicted question and answer based on the user's past dialogue data. The dialogue unit can also analyze the past dialogue data and propose an optimal dialogue flow. Furthermore, the dialogue unit can customize the content of the dialogue by referring to the user's past dialogue data. This allows the dialogue unit to predict the current dialogue based on the past dialogue data.

[0085] The dialogue unit can apply different dialogue analysis methods to each category of AI model during dialogue. For example, the dialogue unit applies an image-related dialogue analysis method to an image recognition model. The dialogue unit can also apply a text-related dialogue analysis method to a natural language processing model. Furthermore, the dialogue unit can also apply a voice-related dialogue analysis method to a voice recognition model. This allows the dialogue unit to apply a dialogue analysis method according to the AI ​​model category.

[0086] The dialogue unit can estimate the user's emotions and adjust the importance of the dialogue based on the estimated user's emotions. For example, if the user is nervous, the dialogue unit can prioritize displaying important information. Furthermore, if the user is relaxed, the dialogue unit can sequentially display detailed information. Furthermore, if the user is in a hurry, the dialogue unit can quickly display information that focuses on the main points. This allows the dialogue unit to adjust the importance of the dialogue 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 can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] The dialogue unit can analyze changes in the dialogue during the dialogue based on the creation date of the AI ​​model. The dialogue unit updates the content of the dialogue based on, for example, the latest AI model. The dialogue unit can also simplify the content of the dialogue based on an older AI model. Furthermore, the dialogue unit can automatically adjust the content of the dialogue depending on the creation date of the AI ​​model. This allows the dialogue unit to analyze changes in the dialogue based on the creation date of the AI ​​model.

[0088] The dialogue unit can analyze the dialogue by referring to market data related to the AI ​​model during the dialogue. The dialogue unit can customize the content of the dialogue based on the market data, for example. The dialogue unit can also update the content of the dialogue by referring to market trends. Furthermore, the dialogue unit can optimize the content of the dialogue based on the market data. This allows the dialogue unit to analyze the dialogue based on market data related to the AI ​​model. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned sharing unit, management unit, collaboration unit, and dialogue unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the sharing unit is realized by the control unit 46A of the smart device 14 and provides a function for a user to upload and download an AI model. The management unit is realized by the specific processing unit 290 of the data processing device 12 and generates and provides documentation for the AI ​​model. The collaboration unit is realized by the control unit 46A of the smart device 14 and provides a function for multiple users to edit code in real time. The dialogue unit is realized by the specific processing unit 290 of the data processing device 12 and provides a function for a user to interact with the AI ​​model. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned sharing unit, management unit, collaboration unit, and dialogue unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the sharing unit is realized by the control unit 46A of the smart glasses 214 and provides a function for a user to upload and download an AI model. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates and provides documentation for the AI ​​model. The collaboration unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides a function for multiple users to edit code in real time. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a function for a user to interact with the AI ​​model. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned sharing unit, management unit, collaboration unit, and dialogue unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the sharing unit is realized by the control unit 46A of the headset type terminal 314 and provides a function for a user to upload and download an AI model. The management unit is realized by the specific processing unit 290 of the data processing device 12 and generates and provides documentation of the AI ​​model. The collaboration unit is realized by the control unit 46A of the headset type terminal 314 and provides a function for multiple users to edit code in real time. The dialogue unit is realized by the specific processing unit 290 of the data processing device 12 and provides a function for a user to interact with the AI ​​model. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned sharing unit, management unit, collaboration unit, and dialogue unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the sharing unit is realized by the control unit 46A of the robot 414 and provides a function for a user to upload and download an AI model. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates and provides documentation for the AI ​​model. The collaboration unit is realized, for example, by the control unit 46A of the robot 414 and provides a function for multiple users to edit code in real time. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a function for a user to interact with the AI ​​model.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The sharing unit can estimate the user's emotions and adjust the upload timing of the AI ​​model based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI automatically selects the optimal timing, reducing the user's burden. Furthermore, if the user is relaxed, the sharing unit can increase the user's freedom by having the generation AI let the user choose the upload timing. Furthermore, if the user is in a hurry, the sharing unit can also have the generation AI upload immediately, enabling rapid sharing. This allows the sharing unit to adjust the upload timing of the AI ​​model according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The sharing unit can analyze a user's past upload history and select the optimal upload method. For example, it can prioritize and suggest upload methods that the user has frequently used in the past. The sharing unit can also use the generation AI to select the most efficient upload method from the user's past upload history. Furthermore, the sharing unit can also suggest the optimal file format and compression method based on the user's past upload history. This allows the sharing unit to select the optimal upload method based on the user's past upload history.

[0092] When uploading an AI model, the sharing unit can filter the AI ​​models based on the user's current project or areas of interest. For example, the generation AI filters so that only AI models related to the user's current ongoing project are uploaded. The sharing unit can also prioritize uploading highly relevant AI models based on the user's areas of interest. Furthermore, the sharing unit can upload AI models at appropriate times depending on the progress of the user's project. This allows the sharing unit to filter uploading of AI models based on the user's current project or areas of interest.

[0093] The sharing unit can estimate the user's emotions and prioritize the AI ​​models to download based on the estimated user emotions. For example, if the user is feeling stressed, the sharing unit can prioritize downloading a model that the generation AI can use most easily. In addition, if the user is relaxed, the sharing unit can also prioritize downloading a model that matches the user's interests. Furthermore, if the user is in a hurry, the sharing unit can select a model that the generation AI can download most quickly. In this way, the sharing unit can prioritize the AI ​​models to download based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] When uploading an AI model, the sharing unit can prioritize uploading highly relevant models by taking into account the user's geographic location information. For example, if the user is in a specific region, the sharing unit can prioritize uploading AI models related to that region. The sharing unit can also upload models including region-specific data based on the user's geographic location information. Furthermore, if the user is moving, the sharing unit can also upload the model most relevant to the user's current location. This allows the sharing unit to prioritize uploading highly relevant models based on the user's geographic location information.

[0095] The management unit can estimate the user's emotions and adjust the way the document is presented based on the estimated user's emotions. For example, if the user is nervous, the management unit can provide a simple, highly readable document. If the user is relaxed, the management unit can also provide a document containing detailed information. If the user is in a hurry, the management unit can also provide a concise document that focuses on the main points. This allows the management unit to adjust the way the document is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] When providing documents, the management unit can adjust the level of detail of the documents based on the importance of the AI ​​model. For example, detailed documents are provided for AI models with high importance. The management unit can also provide concise documents for AI models with low importance. Furthermore, the management unit can automatically adjust the content of the documents according to the importance of the AI ​​model. This allows the management unit to adjust the level of detail of the documents based on the importance of the AI ​​model.

[0097] When providing documents, the management unit can apply different document generation algorithms depending on the category of the AI ​​model. For example, for an image recognition model, the management unit can provide documents specialized for handling image data. Furthermore, the management unit can also provide documents specialized for handling text data for a natural language processing model. Furthermore, the management unit can also provide documents specialized for handling voice data for a voice recognition model. This allows the management unit to apply different document generation algorithms depending on the category of the AI ​​model.

[0098] The collaboration unit can estimate the user's emotions and adjust the collaboration criteria based on the estimated user emotions. For example, if the user is nervous, it can provide a simple, highly visible collaboration interface. If the user is relaxed, it can also provide a collaboration interface that includes detailed information. Furthermore, if the user is in a hurry, it can also provide a collaboration interface that can be operated quickly. This allows the collaboration unit to adjust the collaboration criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0099] The collaboration department can improve the accuracy of collaboration by taking into account the interrelationships between projects during collaboration. For example, the collaboration department can propose an optimal collaboration method by taking into account the dependencies between projects. The collaboration department can also analyze the interrelationships between projects to achieve efficient collaboration. Furthermore, the collaboration department can determine collaboration priorities based on the interrelationships between projects. In this way, the collaboration department can improve the accuracy of collaboration by taking into account the interrelationships between projects.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The sharing unit handles uploading and downloading of AI models. For example, a user can upload their own trained image recognition model to the sharing unit, and other users can download and reuse the model. The sharing unit also properly manages the uploaded AI models and makes them easily accessible to users. Step 2: The management unit provides documentation including information on how to use the model and the training data. For example, the management unit generates documentation that provides detailed information on the format of the model's input data and how to interpret the output results, and provides it to the user. Step 3: The collaboration section allows multiple users to simultaneously participate in a project and edit code in real time. For example, user A and user B can participate in the same project and communicate while editing code in real time. This facilitates communication between team members and makes the project progress more smoothly. Step 4: The dialogue unit provides the ability to interact with the AI ​​model. For example, when training a chatbot, users can actually interact with the chatbot to evaluate its performance. This allows users to check the model's performance in real time and make adjustments as needed.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

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

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

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

[0125] The 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.

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

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

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

[0129] Fig. 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.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0132] In the 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.

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

[0134] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0136] 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.

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

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

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

[0141] The 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.

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

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 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 sharing section that shares and reuses AI models; a management unit that provides documents based on the AI ​​model shared by the sharing unit; a collaboration section in which multiple users simultaneously edit code based on documents provided by the management section; a dialogue unit that dialogues with an AI model based on the code edited by the collaboration unit. A system characterized by:

2. The common part is Upload and download AI models 2. The system of claim 1.

3. The management unit Provide documentation including information on how to use the model and the training data 2. The system of claim 1.

4. The collaboration unit Provides the ability to edit code in real time 2. The system of claim 1.

5. The dialogue unit Providing the ability to interact with AI models in real time 2. The system of claim 1.

6. The common part is Estimate user emotions and adjust the timing of uploading AI models based on the estimated user emotions.

2. The system of claim 1.

7. The common part is Analyze the user's upload history and select the optimal upload method 2. The system of claim 1.

8. The common part is When uploading an AI model, filtering will be performed based on the user's current project or area of ​​interest.

2. The system of claim 1.

9. The common part is Estimate the user's emotions and determine the priority of the AI ​​models to download based on the estimated user emotions.

2. The system of claim 1.

10. The common part is When uploading AI models, the system takes into account the user's geographic location information and prioritizes uploading highly relevant models.

2. The system of claim 1.

Images