System

Abstract

An object of the system according to the embodiment is to easily fine-tune a model and share the model with other users when a user is not satisfied with the accuracy of an AI model.SOLUTION: A system according to an embodiment includes a using unit, a providing unit, a fine-tuning unit, a distributing unit, and a fee management unit. The utilization unit is used by a user to utilize a service of the AI model. The providing unit provides the correct answer data when the accuracy of the service used by the using unit is not satisfactory (for example, when the accuracy falls below a certain standard). The fine-tuning unit fine-tunes the AI model based on the correct answer data provided by the providing unit. The distribution unit distributes the model fine-tuned by the fine-tuning unit to another user. The fee management unit manages a fee for using the model distributed by the distribution unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not provide a means for users to easily improve an AI model if they are dissatisfied with its accuracy, leaving room for improvement.

[0005] The system according to the embodiment aims to allow a user to easily fine-tune an AI model and share it with other users when the user is not satisfied with its accuracy. [Means for solving the problem]

[0006] The system according to the embodiment includes a utilization unit, a providing unit, a fine-tuning unit, a distribution unit, and a fee management unit. The utilization unit allows a user to utilize the service of an AI model. The providing unit provides correct answer data when the accuracy of the service utilized by the utilization unit is not satisfactory (for example, when the accuracy falls below a certain standard). The fine-tuning unit fine-tunes the AI ​​model based on the correct answer data provided by the providing unit. The distribution unit distributes the model fine-tuned by the fine-tuning unit to other users. The fee management unit manages fees for using the model distributed by the distribution unit. [Effects of the Invention]

[0007] In the system according to the embodiment, if a user is not satisfied with the accuracy of an AI model, the user can easily fine-tune the model and share it with other users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention not only provides AI model services but also provides an environment for fine-tuning the AI ​​model. This system allows users to use the AI ​​model service, provide ground truth data for fine-tuning, distribute the model to other users, and manage fees. This allows users to customize AI models to suit their needs and share the results with other users. For example, users can earn revenue by creating AI models specialized for specific industries or applications and providing them to other users. Other users can also efficiently perform tasks by using high-precision AI models that have already been fine-tuned.

[0029] An AI model service system according to an embodiment includes a utilization unit, a providing unit, a fine-tuning unit, a distribution unit, and a fee management unit. The utilization unit allows a user to utilize an AI model service. For example, the utilization unit performs a specific task using an AI model such as image recognition or natural language processing. The providing unit provides correct answer data when the user is dissatisfied with the accuracy of the service. For example, in the case of image recognition, the user provides image data correctly labeled. The fine-tuning unit fine-tunes the AI ​​model based on the correct answer data provided by the providing unit. For example, the AI ​​model is retrained based on the correct answer data provided by the user, thereby improving its accuracy. The distribution unit distributes the fine-tuned model to other users. For example, other users can use the fine-tuned model when performing the same task. The fee management unit manages fees for using the model distributed by the distribution unit. For example, when other users use the fine-tuned model, the fee is paid to the service provider and the user who provided the correct answer data. This allows the AI ​​model service system according to an embodiment to allow users to utilize the AI ​​model service, provide correct answer data to fine-tune the model, distribute the model to other users, and manage fees.

[0030] The providing unit includes an interface for the user to upload the correct answer data. Examples of the interface include, but are not limited to, a web interface and a mobile app. For example, the providing unit provides a web interface that allows the user to easily upload the correct answer data. The providing unit can also allow the user to upload the correct answer data using a mobile app. For example, the user can take a photo of the correct answer data using a smartphone and upload it through the app. This allows the user to easily upload the correct answer data.

[0031] The fine-tuning unit includes a re-learning unit that re-trains the AI ​​model. The re-learning unit re-trains the AI ​​model using, for example, correct answer data provided by the user. Re-learning may include, for example, but is not limited to, the dataset to be used and the frequency of re-learning. The re-learning unit adjusts the parameters of the AI ​​model using, for example, the correct answer data provided by the user, to improve accuracy. Furthermore, by periodically re-learning the AI ​​model, the re-learning unit can maintain a highly accurate model based on the latest data. For example, the re-learning unit can re-train the AI ​​model using a new dataset every week. In this way, re-learning the AI ​​model can improve the accuracy of the model.

[0032] The fine-tuning unit includes an evaluation unit that evaluates the results of the fine-tuning. The evaluation unit uses, for example, evaluation metrics for evaluating the results of the fine-tuning. Evaluation metrics include, but are not limited to, precision, recall, and F1 score. The evaluation unit evaluates the accuracy of the fine-tuned AI model, for example, and provides the results as feedback to the user. The evaluation unit can also periodically evaluate the results of the fine-tuning to continuously check the performance of the model. For example, the evaluation unit can evaluate the accuracy of the fine-tuned model monthly and provide the results as a report. This allows the performance of the model to be checked by evaluating the results of the fine-tuning.

[0033] The fee management unit includes a calculation unit that calculates fees. The calculation unit calculates fees based on, for example, a fee calculation method or criteria. Fee calculation includes, for example, a fee calculation formula and discount application conditions, but is not limited to these examples. The calculation unit calculates, for example, fees for using the fine-tuned model and notifies the user of the result. The calculation unit can also automate fee calculations to efficiently manage fees. For example, the calculation unit can automatically calculate fees and generate an invoice each time a user uses the fine-tuned model. This allows efficient fee management by automating fee calculations.

[0034] The fee management unit includes a payment unit that pays rewards. The payment unit pays rewards based on, for example, a reward payment method and criteria. Reward payments include, for example, but are not limited to, payment timing and payment method. The payment unit, for example, pays rewards to users who provide correct answer data. Furthermore, the payment unit can automate reward payments, thereby efficiently managing rewards. For example, the payment unit can automatically calculate monthly rewards and transfer the amount to the user's bank account. In this way, automating reward payments allows efficient reward management.

[0035] The utilization unit can analyze the user's past usage history and suggest an appropriate usage method. The utilization unit, for example, uses a data analysis algorithm to analyze the user's past usage history. The usage history includes, for example, but is not limited to, the number of times of use and the content of use in the past. For example, the utilization unit can automatically display services that the user has frequently used in the past as candidates. The utilization unit can also prioritize and suggest usage methods (voice, text, etc.) that the user has used in the past. Furthermore, the utilization unit can predict and suggest services that will be used in a specific time period based on the user's past usage history. This improves user convenience by suggesting optimal usage methods based on the user's past usage history.

[0036] The utilization unit can provide a customized usage method based on the user's current task or purpose when using a service. The utilization unit, for example, uses a data analysis algorithm to identify the user's current task or purpose. Examples of the task or purpose include, but are not limited to, user input and a pre-questionnaire. For example, when the user is performing a specific task, the utilization unit can suggest a service that is optimal for that task. The utilization unit can also prioritize and display related services according to the user's purpose. Furthermore, the utilization unit can customize an optimal usage method based on the user's current task. This improves user convenience by providing a usage method customized according to the user's current task or purpose.

[0037] When using a service, the utilization unit can select an appropriate utilization means according to the user's input method. The utilization unit, for example, uses a data analysis algorithm to identify the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the utilization unit can provide the service by utilizing voice recognition technology. Furthermore, when the user uses text input, the utilization unit can also provide the service by utilizing text analysis technology. Furthermore, when the user uses image input, the utilization unit can also provide the service by utilizing image recognition technology. This improves user convenience by selecting the optimal utilization means according to the user's input method.

[0038] When using a service, the utilization unit can provide highly relevant services with priority in consideration of the user's geographical location information. The utilization unit, for example, uses a location information service to acquire the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, when the user is in a specific area, the utilization unit can provide services related to that area with priority. The utilization unit can also provide services related to nearby stores and facilities based on the user's current location. Furthermore, the utilization unit can suggest optimal services based on the user's geographical location information. This improves user convenience by providing highly relevant services with consideration of the user's geographical location information.

[0039] The utilization unit can analyze the user's social media activity and suggest related services when using the service. The utilization unit, for example, uses a data analysis algorithm to analyze the user's social media activity. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The utilization unit, for example, can suggest services related to places where the user has checked in on social media. The utilization unit can also analyze the content of the user's social media posts and suggest related services. Furthermore, the utilization unit can suggest related services based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity and suggesting related services, the utilization unit improves user convenience.

[0040] The utilization unit can customize the usage method by reflecting the user's past feedback when using the service. The utilization unit, for example, uses a data analysis algorithm to reflect the user's past feedback. The feedback includes, for example, but is not limited to, the user's evaluation comments and survey results. The utilization unit customizes the usage method based on, for example, the feedback provided by the user in the past. The utilization unit can also suggest a usage method that enhances specific functions based on the user's past feedback. Furthermore, the utilization unit can reflect the user's feedback and provide an optimal usage method. In this way, customizing the usage method by reflecting the user's past feedback improves user convenience.

[0041] When providing correct data, the providing unit can analyze the user's past data provision history and propose an appropriate provision method. The providing unit, for example, uses a data analysis algorithm for analyzing the user's past data provision history. The data provision history includes, for example, the content of past provided data and the number of times it was provided, but is not limited to such examples. The providing unit, for example, automatically displays correct data that the user has frequently provided in the past as candidates. The providing unit can also preferentially propose a provision method (such as voice or text) that the user has used in the past. Furthermore, the providing unit can predict and propose correct data to be provided in a specific time period based on the user's past data provision history. This improves user convenience by proposing the optimal provision method based on the user's past data provision history.

[0042] When providing the correct answer data, the providing unit can provide a customized providing method based on the user's current task or purpose. The providing unit, for example, uses a data analysis algorithm to identify the user's current task or purpose. Examples of the task or purpose include, but are not limited to, the user's input content and a pre-questionnaire. For example, when the user is performing a specific task, the providing unit provides the correct answer data that is optimal for that task. The providing unit can also prioritize displaying related correct answer data according to the user's purpose. Furthermore, the providing unit can customize the optimal providing method based on the user's current task. This improves user convenience by providing a providing method customized according to the user's current task or purpose.

[0043] When providing the correct answer data, the providing unit can select an appropriate providing means depending on the user's input method. The providing unit, for example, uses a data analysis algorithm to identify the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the providing unit provides the correct answer data using voice recognition technology. Furthermore, when the user uses text input, the providing unit can also provide the correct answer data using text analysis technology. Furthermore, when the user uses image input, the providing unit can also provide the correct answer data using image recognition technology. This improves user convenience by selecting the optimal providing means depending on the user's input method.

[0044] When providing correct answer data, the providing unit can prioritize providing highly relevant data by taking into account the user's geographical location information. The providing unit, for example, uses a location information service to acquire the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the providing unit prioritizes providing correct answer data related to that area. The providing unit can also provide correct answer data related to nearby stores and facilities based on the user's current location. Furthermore, the providing unit can suggest optimal correct answer data based on the user's geographical location information. This improves user convenience by providing highly relevant data by taking into account the user's geographical location information.

[0045] When providing the correct data, the providing unit can analyze the user's social media activity and provide related data. The providing unit, for example, uses a data analysis algorithm to analyze the user's social media activity. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The providing unit can provide correct data related to, for example, locations where the user checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide related correct data. Furthermore, the providing unit can provide related correct data by referring to the activities of the user's friends on social media. This improves user convenience by analyzing the user's social media activity and providing related data.

[0046] When providing correct answer data, the providing unit can customize the providing method by reflecting the user's past feedback. The providing unit, for example, uses a data analysis algorithm for reflecting the user's past feedback. The feedback includes, for example, but is not limited to, the user's evaluation comments and survey results. The providing unit customizes the providing method based on, for example, feedback provided by the user in the past. The providing unit can also suggest a providing method with enhanced specific functions based on the user's past feedback. Furthermore, the providing unit can also provide an optimal providing method by reflecting the user's feedback. In this way, customizing the providing method by reflecting the user's past feedback improves user convenience.

[0047] During fine tuning, the fine tuning unit can analyze the user's past data provision history and propose an appropriate fine tuning method. The fine tuning unit, for example, uses a data analysis algorithm to analyze the user's past data provision history. The data provision history includes, for example, but is not limited to, the content of past provided data and the number of times it was provided. The fine tuning unit can propose an optimal fine tuning method, for example, based on correct answer data that the user frequently provided in the past. The fine tuning unit can also prioritize and propose fine tuning methods (such as voice and text) that the user has used in the past. Furthermore, the fine tuning unit can predict and propose an optimal fine tuning method for a specific time period based on the user's past data provision history. This improves user convenience by proposing an optimal fine tuning method based on the user's past data provision history.

[0048] During fine tuning, the fine tuning unit can provide a customized fine tuning method based on the user's current task or purpose. The fine tuning unit, for example, uses a data analysis algorithm to identify the user's current task or purpose. Examples of the task or purpose include, but are not limited to, user input and a pre-questionnaire. For example, when the user is performing a specific task, the fine tuning unit provides a fine tuning method optimal for that task. The fine tuning unit can also preferentially display related fine tuning methods according to the user's purpose. Furthermore, the fine tuning unit can customize an optimal fine tuning method based on the user's current task. This improves user convenience by providing a fine tuning method customized according to the user's current task or purpose.

[0049] During fine tuning, the fine tuning unit can select an appropriate fine tuning means according to the user's input method. The fine tuning unit, for example, uses a data analysis algorithm to identify the user's input method. The input method includes, but is not limited to, voice input, text input, and image input. For example, when the user uses voice input, the fine tuning unit performs fine tuning using voice recognition technology. Furthermore, when the user uses text input, the fine tuning unit can also perform fine tuning using text analysis technology. Furthermore, when the user uses image input, the fine tuning unit can also perform fine tuning using image recognition technology. This improves user convenience by selecting an optimal fine tuning means according to the user's input method.

[0050] During fine tuning, the fine tuning unit can prioritize the use of highly relevant data by taking into account the user's geographical location information. The fine tuning unit, for example, uses a location information service to acquire the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the fine tuning unit can prioritize the use of data related to that area. The fine tuning unit can also use data related to nearby stores and facilities based on the user's current location. Furthermore, the fine tuning unit can suggest optimal data based on the user's geographical location information. This improves user convenience by using highly relevant data by taking into account the user's geographical location information.

[0051] During fine tuning, the fine tuning unit may analyze the user's social media activities and use related data. The fine tuning unit may, for example, use a data analysis algorithm to analyze the user's social media activities. Social media activities include, but are not limited to, the content of posts and the number of likes. The fine tuning unit may, for example, use data related to the locations where the user checked in on social media. The fine tuning unit may also analyze the content of the user's social media posts and use related data. Furthermore, the fine tuning unit may use related data based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activities and using related data, user convenience is improved.

[0052] During fine tuning, the fine-tuning unit can customize the fine-tuning method by reflecting the user's past feedback. The fine-tuning unit, for example, uses a data analysis algorithm to reflect the user's past feedback. The feedback includes, for example, but is not limited to, the user's evaluation comments and survey results. The fine-tuning unit customizes the fine-tuning method based on, for example, feedback provided by the user in the past. The fine-tuning unit can also suggest a fine-tuning method that enhances a specific function based on the user's past feedback. Furthermore, the fine-tuning unit can also provide an optimal fine-tuning method by reflecting the user's feedback. In this way, customizing the fine-tuning method by reflecting the user's past feedback improves user convenience.

[0053] The distribution unit can analyze the user's past usage history and suggest an appropriate distribution method at the time of distribution. The distribution unit, for example, uses a data analysis algorithm to analyze the user's past usage history. The usage history includes, for example, but is not limited to, the number of times of use and the content of use in the past. For example, the distribution unit can automatically display as candidates distribution methods that the user has frequently used in the past. The distribution unit can also preferentially suggest distribution methods (audio, text, etc.) that the user has used in the past. Furthermore, the distribution unit can predict and suggest the optimal distribution method for a specific time period based on the user's past usage history. This improves user convenience by suggesting the optimal distribution method based on the user's past usage history.

[0054] The distribution unit can provide a customized distribution method based on the user's current task or purpose at the time of distribution. The distribution unit, for example, uses a data analysis algorithm to identify the user's current task or purpose. Examples of tasks and purposes include, but are not limited to, user input and a pre-questionnaire. For example, when the user is performing a specific task, the distribution unit provides the optimal distribution method for that task. The distribution unit can also prioritize displaying related distribution methods according to the user's purpose. Furthermore, the distribution unit can customize the optimal distribution method based on the user's current task. This improves user convenience by providing a distribution method customized according to the user's current task or purpose.

[0055] The distribution unit can select an appropriate distribution means according to the user's input method at the time of distribution. The distribution unit, for example, uses a data analysis algorithm to identify the user's input method. The input method includes, but is not limited to, for example, voice input, text input, and image input. For example, when the user uses voice input, the distribution unit performs distribution using voice recognition technology. Furthermore, when the user uses text input, the distribution unit can also perform distribution using text analysis technology. Furthermore, when the user uses image input, the distribution unit can also perform distribution using image recognition technology. This improves user convenience by selecting the optimal distribution means according to the user's input method.

[0056] During distribution, the distribution unit can prioritize distribution of highly relevant models taking into account the user's geographical location information. The distribution unit, for example, uses a location information service to acquire the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the distribution unit prioritizes distribution of models related to that area. The distribution unit can also distribute models related to nearby stores and facilities based on the user's current location. Furthermore, the distribution unit can suggest an optimal model based on the user's geographical location information. This improves user convenience by distributing highly relevant models taking into account the user's geographical location information.

[0057] During distribution, the distribution unit can analyze the user's social media activity and distribute a related model. The distribution unit, for example, uses a data analysis algorithm to analyze the user's social media activity. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The distribution unit, for example, distributes a model related to a location where the user checked in on social media. The distribution unit can also analyze the content of the user's social media posts and distribute a related model. Furthermore, the distribution unit can distribute a related model based on the activity of the user's friends on social media. In this way, analyzing the user's social media activity and distributing a related model improves user convenience.

[0058] The distribution unit can customize the distribution method by reflecting the user's past feedback during distribution. The distribution unit, for example, uses a data analysis algorithm to reflect the user's past feedback. The feedback includes, for example, but is not limited to, the user's evaluation comments and survey results. The distribution unit customizes the distribution method based on, for example, feedback provided by the user in the past. The distribution unit can also suggest a distribution method that enhances specific functions based on the user's past feedback. Furthermore, the distribution unit can also provide an optimal distribution method by reflecting the user's feedback. In this way, customizing the distribution method by reflecting the user's past feedback improves user convenience.

[0059] The fee management unit can analyze the user's past usage history and propose an appropriate fee management method during fee management. The fee management unit, for example, uses a data analysis algorithm to analyze the user's past usage history. The usage history includes, for example, but is not limited to, the number of times and content of past usage. The fee management unit, for example, automatically displays as candidates fee management methods that the user has frequently used in the past. The fee management unit can also prioritize and propose fee management methods (such as voice and text) that the user has used in the past. Furthermore, the fee management unit can predict and propose the optimal fee management method for a specific time period based on the user's past usage history. This improves user convenience by proposing the optimal fee management method based on the user's past usage history.

[0060] The fee management unit can provide a fee management method customized based on the user's current task or purpose during fee management. The fee management unit, for example, uses a data analysis algorithm to identify the user's current task or purpose. Examples of tasks and purposes include, but are not limited to, user input and a pre-questionnaire. For example, when the user is performing a specific task, the fee management unit provides the fee management method optimal for that task. The fee management unit can also prioritize displaying related fee management methods according to the user's purpose. Furthermore, the fee management unit can customize the optimal fee management method based on the user's current task. This improves user convenience by providing a fee management method customized according to the user's current task or purpose.

[0061] The fee management unit can select an appropriate fee management means depending on the user's input method during fee management. The fee management unit, for example, uses a data analysis algorithm to identify the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the fee management unit performs fee management using voice recognition technology. Furthermore, when the user uses text input, the fee management unit can also perform fee management using text analysis technology. Furthermore, when the user uses image input, the fee management unit can also perform fee management using image recognition technology. This improves user convenience by selecting the optimal fee management means depending on the user's input method.

[0062] During fee management, the fee management unit can prioritize providing a highly relevant fee management method by taking into account the user's geographical location information. The fee management unit, for example, uses a location information service to acquire the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. For example, if the user is in a specific area, the fee management unit prioritizes providing a fee management method related to that area. The fee management unit can also provide a fee management method related to nearby stores or facilities based on the user's current location. Furthermore, the fee management unit can suggest an optimal fee management method based on the user's geographical location information. This improves user convenience by providing a highly relevant fee management method by taking into account the user's geographical location information.

[0063] The fee management unit may analyze the user's social media activity during fee management and provide a related fee management method. The fee management unit may, for example, use a data analysis algorithm to analyze the user's social media activity. Social media activity may include, but is not limited to, the content of posts and the number of likes. The fee management unit may, for example, provide a fee management method related to the locations where the user has checked in on social media. The fee management unit may also analyze the content of the user's social media posts and provide a related fee management method. Furthermore, the fee management unit may refer to the activities of the user's friends on social media to provide a related fee management method. This improves user convenience by analyzing the user's social media activity and providing a related fee management method.

[0064] The fee management unit can customize the fee management method by reflecting the user's past feedback during fee management. The fee management unit, for example, uses a data analysis algorithm to reflect the user's past feedback. Feedback includes, for example, but is not limited to, the user's evaluation comments and survey results. The fee management unit customizes the fee management method based on, for example, feedback provided by the user in the past. The fee management unit can also propose a fee management method with enhanced specific functions based on the user's past feedback. Furthermore, the fee management unit can also reflect the user's feedback and provide an optimal fee management method. In this way, customizing the fee management method by reflecting the user's past feedback improves user convenience.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The usage unit can analyze the user's past usage history and suggest appropriate usage methods. For example, it can automatically display services that the user has frequently used in the past as candidates. It can also prioritize and suggest usage methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest services that will be used during specific time periods based on the user's past usage history. This improves user convenience by suggesting optimal usage methods based on the user's past usage history.

[0067] When providing correct answer data, the providing unit can analyze the user's past data provision history and suggest an appropriate provision method. For example, correct answer data that the user has frequently provided in the past can be automatically displayed as a candidate. It can also preferentially suggest a provision method (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest correct answer data to be provided in a specific time period based on the user's past data provision history. This improves user convenience by suggesting the optimal provision method based on the user's past data provision history.

[0068] During fine tuning, the fine tuning unit can analyze the user's past data provision history and suggest an appropriate fine tuning method. For example, the unit can suggest the optimal fine tuning method based on the correct answer data that the user has frequently provided in the past. It can also preferentially suggest fine tuning methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the optimal fine tuning method for a specific time period based on the user's past data provision history. This improves user convenience by suggesting the optimal fine tuning method based on the user's past data provision history.

[0069] The distribution unit can analyze the user's past usage history and suggest an appropriate distribution method at the time of distribution. For example, distribution methods that the user has frequently used in the past can be automatically displayed as candidates. It can also prioritize and suggest distribution methods (audio, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the optimal distribution method for a specific time period based on the user's past usage history. This improves user convenience by suggesting the optimal distribution method based on the user's past usage history.

[0070] The fee management unit can analyze the user's past usage history and suggest an appropriate fee management method when managing fees. For example, fee management methods that the user has frequently used in the past can be automatically displayed as candidates. It can also prioritize and suggest fee management methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the optimal fee management method for a specific time period based on the user's past usage history. This improves user convenience by suggesting the optimal fee management method based on the user's past usage history.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: In the user unit, the user uses the AI ​​model's services. For example, the user uses an AI model for image recognition or natural language processing to perform a specific task. Step 2: The provider provides correct answer data if the user is not satisfied with the accuracy of the service. For example, in the case of image recognition, the user provides image data with the correct labels. Step 3: The fine-tuning unit fine-tunes the AI ​​model based on the correct answer data provided by the providing unit. For example, the AI ​​model is retrained based on the correct answer data provided by the user, thereby improving its accuracy. Step 4: The distribution unit distributes the fine-tuned model to other users, so that other users can use the fine-tuned model when performing the same task. Step 5: The fee management unit manages the fees for using the model distributed by the distribution unit. For example, when other users use the fine-tuned model, the fee is paid to the service provider and the user who provided the correct answer data.

[0073] (Example 2) A system according to an embodiment of the present invention not only provides AI model services but also provides an environment for fine-tuning the AI ​​model. This system allows users to use the AI ​​model service, provide ground truth data for fine-tuning, distribute the model to other users, and manage fees. This allows users to customize AI models to suit their needs and share the results with other users. For example, users can earn revenue by creating AI models specialized for specific industries or applications and providing them to other users. Other users can also efficiently perform tasks by using high-precision AI models that have already been fine-tuned.

[0074] An AI model service system according to an embodiment includes a utilization unit, a providing unit, a fine-tuning unit, a distribution unit, and a fee management unit. The utilization unit allows a user to utilize an AI model service. For example, the utilization unit performs a specific task using an AI model such as image recognition or natural language processing. The providing unit provides correct answer data when the user is dissatisfied with the accuracy of the service. For example, in the case of image recognition, the user provides image data correctly labeled. The fine-tuning unit fine-tunes the AI ​​model based on the correct answer data provided by the providing unit. For example, the AI ​​model is retrained based on the correct answer data provided by the user, thereby improving its accuracy. The distribution unit distributes the fine-tuned model to other users. For example, other users can use the fine-tuned model when performing the same task. The fee management unit manages fees for using the model distributed by the distribution unit. For example, when other users use the fine-tuned model, the fee is paid to the service provider and the user who provided the correct answer data. This allows the AI ​​model service system according to an embodiment to allow users to utilize the AI ​​model service, provide correct answer data to fine-tune the model, distribute the model to other users, and manage fees.

[0075] The providing unit includes an interface for the user to upload the correct answer data. Examples of the interface include, but are not limited to, a web interface and a mobile app. For example, the providing unit provides a web interface that allows the user to easily upload the correct answer data. The providing unit can also allow the user to upload the correct answer data using a mobile app. For example, the user can take a photo of the correct answer data using a smartphone and upload it through the app. This allows the user to easily upload the correct answer data.

[0076] The fine-tuning unit includes a re-learning unit that re-trains the AI ​​model. The re-learning unit re-trains the AI ​​model using, for example, correct answer data provided by the user. Re-learning may include, for example, but is not limited to, the dataset to be used and the frequency of re-learning. The re-learning unit adjusts the parameters of the AI ​​model using, for example, the correct answer data provided by the user, to improve accuracy. Furthermore, by periodically re-learning the AI ​​model, the re-learning unit can maintain a highly accurate model based on the latest data. For example, the re-learning unit can re-train the AI ​​model using a new dataset every week. In this way, re-learning the AI ​​model can improve the accuracy of the model.

[0077] The fine-tuning unit includes an evaluation unit that evaluates the results of the fine-tuning. The evaluation unit uses, for example, evaluation metrics for evaluating the results of the fine-tuning. Evaluation metrics include, but are not limited to, precision, recall, and F1 score. The evaluation unit evaluates the accuracy of the fine-tuned AI model, for example, and provides the results as feedback to the user. The evaluation unit can also periodically evaluate the results of the fine-tuning to continuously check the performance of the model. For example, the evaluation unit can evaluate the accuracy of the fine-tuned model monthly and provide the results as a report. This allows the performance of the model to be checked by evaluating the results of the fine-tuning.

[0078] The fee management unit includes a calculation unit that calculates fees. The calculation unit calculates fees based on, for example, a fee calculation method or criteria. Fee calculation includes, for example, a fee calculation formula and discount application conditions, but is not limited to these examples. The calculation unit calculates, for example, fees for using the fine-tuned model and notifies the user of the result. The calculation unit can also automate fee calculations to efficiently manage fees. For example, the calculation unit can automatically calculate fees and generate an invoice each time a user uses the fine-tuned model. This allows efficient fee management by automating fee calculations.

[0079] The fee management unit includes a payment unit that pays rewards. The payment unit pays rewards based on, for example, a reward payment method and criteria. Reward payments include, for example, but are not limited to, payment timing and payment method. The payment unit, for example, pays rewards to users who provide correct answer data. Furthermore, the payment unit can automate reward payments, thereby efficiently managing rewards. For example, the payment unit can automatically calculate monthly rewards and transfer the amount to the user's bank account. In this way, automating reward payments allows efficient reward management.

[0080] The utilization unit can estimate the user's emotions and adjust the service usage method based on the estimated user emotions. The utilization unit, for example, uses an emotion estimation algorithm to estimate the user's emotions. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the utilization unit can provide a simple interface and minimize usage steps. Furthermore, if the user is relaxed, the utilization unit can provide detailed usage options and suggest customizable usage methods. Furthermore, if the user is in a hurry, the utilization unit can prioritize voice input to enable quick service access. This allows for more appropriate service by adjusting the service usage method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, 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.

[0081] The utilization unit can analyze the user's past usage history and suggest an appropriate usage method. The utilization unit, for example, uses a data analysis algorithm to analyze the user's past usage history. The usage history includes, for example, but is not limited to, the number of times of use and the content of use in the past. For example, the utilization unit can automatically display services that the user has frequently used in the past as candidates. The utilization unit can also prioritize and suggest usage methods (voice, text, etc.) that the user has used in the past. Furthermore, the utilization unit can predict and suggest services that will be used in a specific time period based on the user's past usage history. This improves user convenience by suggesting optimal usage methods based on the user's past usage history.

[0082] The utilization unit can provide a customized usage method based on the user's current task or purpose when using a service. The utilization unit, for example, uses a data analysis algorithm to identify the user's current task or purpose. Examples of the task or purpose include, but are not limited to, user input and a pre-questionnaire. For example, when the user is performing a specific task, the utilization unit can suggest a service that is optimal for that task. The utilization unit can also prioritize and display related services according to the user's purpose. Furthermore, the utilization unit can customize an optimal usage method based on the user's current task. This improves user convenience by providing a usage method customized according to the user's current task or purpose.

[0083] When using a service, the utilization unit can select an appropriate utilization means according to the user's input method. The utilization unit, for example, uses a data analysis algorithm to identify the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the utilization unit can provide the service by utilizing voice recognition technology. Furthermore, when the user uses text input, the utilization unit can also provide the service by utilizing text analysis technology. Furthermore, when the user uses image input, the utilization unit can also provide the service by utilizing image recognition technology. This improves user convenience by selecting the optimal utilization means according to the user's input method.

[0084] The utilization unit can estimate the user's emotions and determine the priority of services to be used based on the estimated user emotions. The utilization unit uses, for example, an emotion estimation algorithm to estimate the user's emotions. Emotion estimation includes, but is not limited to, technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the utilization unit can prioritize providing relaxing services. Furthermore, if the user is in a hurry, the utilization unit can prioritize quickly available services. Furthermore, if the user is enjoying themselves, the utilization unit can prioritize entertainment services. This allows for more appropriate services to be provided by determining the priority of services 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] When using a service, the utilization unit can provide highly relevant services with priority in consideration of the user's geographical location information. The utilization unit, for example, uses a location information service to acquire the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, when the user is in a specific area, the utilization unit can provide services related to that area with priority. The utilization unit can also provide services related to nearby stores and facilities based on the user's current location. Furthermore, the utilization unit can suggest optimal services based on the user's geographical location information. This improves user convenience by providing highly relevant services with consideration of the user's geographical location information.

[0086] The utilization unit can analyze the user's social media activity and suggest related services when using the service. The utilization unit, for example, uses a data analysis algorithm to analyze the user's social media activity. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The utilization unit, for example, can suggest services related to places where the user has checked in on social media. The utilization unit can also analyze the content of the user's social media posts and suggest related services. Furthermore, the utilization unit can suggest related services based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity and suggesting related services, the utilization unit improves user convenience.

[0087] The utilization unit can customize the usage method by reflecting the user's past feedback when using the service. The utilization unit, for example, uses a data analysis algorithm to reflect the user's past feedback. The feedback includes, for example, but is not limited to, the user's evaluation comments and survey results. The utilization unit customizes the usage method based on, for example, the feedback provided by the user in the past. The utilization unit can also suggest a usage method that enhances specific functions based on the user's past feedback. Furthermore, the utilization unit can reflect the user's feedback and provide an optimal usage method. In this way, customizing the usage method by reflecting the user's past feedback improves user convenience.

[0088] The providing unit can estimate the user's emotion and adjust the method for providing correct answer data based on the estimated user emotion. The providing unit, for example, uses an emotion estimation algorithm to estimate the user's emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the providing unit provides a simple interface and minimizes the steps for providing correct answer data. Furthermore, when the user is relaxed, the providing unit can provide detailed delivery options and suggest a customizable delivery method. Furthermore, when the user is in a hurry, the providing unit can prioritize voice input and quickly provide correct answer data. This enables more appropriate data to be provided by adjusting the method for providing correct answer data according to the user's emotion. 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.

[0089] When providing correct data, the providing unit can analyze the user's past data provision history and propose an appropriate provision method. The providing unit, for example, uses a data analysis algorithm for analyzing the user's past data provision history. The data provision history includes, for example, the content of past provided data and the number of times it was provided, but is not limited to such examples. The providing unit, for example, automatically displays correct data that the user has frequently provided in the past as candidates. The providing unit can also preferentially propose a provision method (such as voice or text) that the user has used in the past. Furthermore, the providing unit can predict and propose correct data to be provided in a specific time period based on the user's past data provision history. This improves user convenience by proposing the optimal provision method based on the user's past data provision history.

[0090] When providing the correct answer data, the providing unit can provide a customized providing method based on the user's current task or purpose. The providing unit, for example, uses a data analysis algorithm to identify the user's current task or purpose. Examples of the task or purpose include, but are not limited to, the user's input content and a pre-questionnaire. For example, when the user is performing a specific task, the providing unit provides the correct answer data that is optimal for that task. The providing unit can also prioritize displaying related correct answer data according to the user's purpose. Furthermore, the providing unit can customize the optimal providing method based on the user's current task. This improves user convenience by providing a providing method customized according to the user's current task or purpose.

[0091] When providing the correct answer data, the providing unit can select an appropriate providing means depending on the user's input method. The providing unit, for example, uses a data analysis algorithm to identify the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the providing unit provides the correct answer data using voice recognition technology. Furthermore, when the user uses text input, the providing unit can also provide the correct answer data using text analysis technology. Furthermore, when the user uses image input, the providing unit can also provide the correct answer data using image recognition technology. This improves user convenience by selecting the optimal providing means depending on the user's input method.

[0092] The providing unit can estimate the user's emotion and determine the priority of the correct answer data to be provided based on the estimated user emotion. The providing unit uses, for example, an emotion estimation algorithm to estimate the user's emotion. Emotion estimation includes, for example, facial expression recognition, voice analysis, text analysis, and other technologies, but is not limited to these examples. For example, if the user is stressed, the providing unit can prioritize providing relaxing correct answer data. Furthermore, if the user is in a hurry, the providing unit can prioritize providing quick correct answer data. Furthermore, if the user is enjoying themselves, the providing unit can prioritize providing entertainment-related correct answer data. This enables more appropriate data to be provided by determining the priority of correct answer data according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] When providing correct answer data, the providing unit can prioritize providing highly relevant data by taking into account the user's geographical location information. The providing unit, for example, uses a location information service to acquire the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the providing unit prioritizes providing correct answer data related to that area. The providing unit can also provide correct answer data related to nearby stores and facilities based on the user's current location. Furthermore, the providing unit can suggest optimal correct answer data based on the user's geographical location information. This improves user convenience by providing highly relevant data by taking into account the user's geographical location information.

[0094] When providing the correct data, the providing unit can analyze the user's social media activity and provide related data. The providing unit, for example, uses a data analysis algorithm to analyze the user's social media activity. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The providing unit can provide correct data related to, for example, locations where the user checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide related correct data. Furthermore, the providing unit can provide related correct data by referring to the activities of the user's friends on social media. This improves user convenience by analyzing the user's social media activity and providing related data.

[0095] When providing correct answer data, the providing unit can customize the providing method by reflecting the user's past feedback. The providing unit, for example, uses a data analysis algorithm for reflecting the user's past feedback. The feedback includes, for example, but is not limited to, the user's evaluation comments and survey results. The providing unit customizes the providing method based on, for example, feedback provided by the user in the past. The providing unit can also suggest a providing method with enhanced specific functions based on the user's past feedback. Furthermore, the providing unit can also provide an optimal providing method by reflecting the user's feedback. In this way, customizing the providing method by reflecting the user's past feedback improves user convenience.

[0096] The fine-tuning unit can estimate the user's emotion and adjust the fine-tuning method based on the estimated user emotion. The fine-tuning unit uses, for example, an emotion estimation algorithm to estimate the user's emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the fine-tuning unit provides a simple interface and minimizes the fine-tuning procedure. Furthermore, when the user is relaxed, the fine-tuning unit can provide detailed fine-tuning options and suggest a customizable method. Furthermore, when the user is in a hurry, the fine-tuning unit can prioritize voice input and enable quick fine-tuning. This allows for more appropriate fine-tuning by adjusting the fine-tuning method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] During fine tuning, the fine tuning unit can analyze the user's past data provision history and propose an appropriate fine tuning method. The fine tuning unit, for example, uses a data analysis algorithm to analyze the user's past data provision history. The data provision history includes, for example, but is not limited to, the content of past provided data and the number of times it was provided. The fine tuning unit can propose an optimal fine tuning method, for example, based on correct answer data that the user frequently provided in the past. The fine tuning unit can also prioritize and propose fine tuning methods (such as voice and text) that the user has used in the past. Furthermore, the fine tuning unit can predict and propose an optimal fine tuning method for a specific time period based on the user's past data provision history. This improves user convenience by proposing an optimal fine tuning method based on the user's past data provision history.

[0098] During fine tuning, the fine tuning unit can provide a customized fine tuning method based on the user's current task or purpose. The fine tuning unit, for example, uses a data analysis algorithm to identify the user's current task or purpose. Examples of the task or purpose include, but are not limited to, user input and a pre-questionnaire. For example, when the user is performing a specific task, the fine tuning unit provides a fine tuning method optimal for that task. The fine tuning unit can also preferentially display related fine tuning methods according to the user's purpose. Furthermore, the fine tuning unit can customize an optimal fine tuning method based on the user's current task. This improves user convenience by providing a fine tuning method customized according to the user's current task or purpose.

[0099] During fine tuning, the fine tuning unit can select an appropriate fine tuning means according to the user's input method. The fine tuning unit, for example, uses a data analysis algorithm to identify the user's input method. The input method includes, but is not limited to, voice input, text input, and image input. For example, when the user uses voice input, the fine tuning unit performs fine tuning using voice recognition technology. Furthermore, when the user uses text input, the fine tuning unit can also perform fine tuning using text analysis technology. Furthermore, when the user uses image input, the fine tuning unit can also perform fine tuning using image recognition technology. This improves user convenience by selecting an optimal fine tuning means according to the user's input method.

[0100] The fine-tuning unit can estimate the user's emotions and determine fine-tuning priorities based on the estimated user emotions. The fine-tuning unit uses, for example, an emotion estimation algorithm to estimate the user's emotions. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is stressed, the fine-tuning unit can prioritize providing a fine-tuning method that allows for relaxation. Furthermore, if the user is in a hurry, the fine-tuning unit can prioritize providing a method that allows for quick fine-tuning. Furthermore, if the user is enjoying themselves, the fine-tuning unit can prioritize providing an entertainment-related fine-tuning method. This enables more appropriate fine-tuning by determining the fine-tuning priorities according to the user's emotions. 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.

[0101] During fine tuning, the fine tuning unit can prioritize the use of highly relevant data by taking into account the user's geographical location information. The fine tuning unit, for example, uses a location information service to acquire the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the fine tuning unit can prioritize the use of data related to that area. The fine tuning unit can also use data related to nearby stores and facilities based on the user's current location. Furthermore, the fine tuning unit can suggest optimal data based on the user's geographical location information. This improves user convenience by using highly relevant data by taking into account the user's geographical location information.

[0102] During fine tuning, the fine tuning unit may analyze the user's social media activities and use related data. The fine tuning unit may, for example, use a data analysis algorithm to analyze the user's social media activities. Social media activities include, but are not limited to, the content of posts and the number of likes. The fine tuning unit may, for example, use data related to the locations where the user checked in on social media. The fine tuning unit may also analyze the content of the user's social media posts and use related data. Furthermore, the fine tuning unit may use related data based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activities and using related data, user convenience is improved.

[0103] During fine tuning, the fine-tuning unit can customize the fine-tuning method by reflecting the user's past feedback. The fine-tuning unit, for example, uses a data analysis algorithm to reflect the user's past feedback. The feedback includes, for example, but is not limited to, the user's evaluation comments and survey results. The fine-tuning unit customizes the fine-tuning method based on, for example, feedback provided by the user in the past. The fine-tuning unit can also suggest a fine-tuning method that enhances a specific function based on the user's past feedback. Furthermore, the fine-tuning unit can also provide an optimal fine-tuning method by reflecting the user's feedback. In this way, customizing the fine-tuning method by reflecting the user's past feedback improves user convenience.

[0104] The distribution unit can estimate the user's emotion and adjust the distribution method based on the estimated user's emotion. The distribution unit uses, for example, an emotion estimation algorithm to estimate the user's emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the distribution unit can provide a simple interface and minimize the distribution procedure. Furthermore, if the user is relaxed, the distribution unit can provide detailed distribution options and suggest a customizable distribution method. Furthermore, if the user is in a hurry, the distribution unit can prioritize voice input and enable quick distribution. This enables more appropriate distribution by adjusting the distribution method according to the user's emotion. 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.

[0105] The distribution unit can analyze the user's past usage history and suggest an appropriate distribution method at the time of distribution. The distribution unit, for example, uses a data analysis algorithm to analyze the user's past usage history. The usage history includes, for example, but is not limited to, the number of times of use and the content of use in the past. For example, the distribution unit can automatically display as candidates distribution methods that the user has frequently used in the past. The distribution unit can also preferentially suggest distribution methods (audio, text, etc.) that the user has used in the past. Furthermore, the distribution unit can predict and suggest the optimal distribution method for a specific time period based on the user's past usage history. This improves user convenience by suggesting the optimal distribution method based on the user's past usage history.

[0106] The distribution unit can provide a customized distribution method based on the user's current task or purpose at the time of distribution. The distribution unit, for example, uses a data analysis algorithm to identify the user's current task or purpose. Examples of tasks and purposes include, but are not limited to, user input and a pre-questionnaire. For example, when the user is performing a specific task, the distribution unit provides the optimal distribution method for that task. The distribution unit can also prioritize displaying related distribution methods according to the user's purpose. Furthermore, the distribution unit can customize the optimal distribution method based on the user's current task. This improves user convenience by providing a distribution method customized according to the user's current task or purpose.

[0107] The distribution unit can select an appropriate distribution means according to the user's input method at the time of distribution. The distribution unit, for example, uses a data analysis algorithm to identify the user's input method. The input method includes, but is not limited to, for example, voice input, text input, and image input. For example, when the user uses voice input, the distribution unit performs distribution using voice recognition technology. Furthermore, when the user uses text input, the distribution unit can also perform distribution using text analysis technology. Furthermore, when the user uses image input, the distribution unit can also perform distribution using image recognition technology. This improves user convenience by selecting the optimal distribution means according to the user's input method.

[0108] The distribution unit can estimate the user's emotions and determine the priority of models to be distributed based on the estimated user emotions. The distribution unit uses, for example, an emotion estimation algorithm to estimate the user's emotions. Emotion estimation includes, for example, facial expression recognition, voice analysis, text analysis, and other technologies, but is not limited to these examples. For example, if the user is feeling stressed, the distribution unit can prioritize distributing models that can help the user relax. Furthermore, if the user is in a hurry, the distribution unit can prioritize distributing models that can be distributed quickly. Furthermore, if the user is enjoying themselves, the distribution unit can prioritize distributing entertainment-related models. This enables more appropriate distribution by determining the priority of models to be distributed according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0109] During distribution, the distribution unit can prioritize distribution of highly relevant models taking into account the user's geographical location information. The distribution unit, for example, uses a location information service to acquire the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the distribution unit prioritizes distribution of models related to that area. The distribution unit can also distribute models related to nearby stores and facilities based on the user's current location. Furthermore, the distribution unit can suggest an optimal model based on the user's geographical location information. This improves user convenience by distributing highly relevant models taking into account the user's geographical location information.

[0110] During distribution, the distribution unit can analyze the user's social media activity and distribute a related model. The distribution unit, for example, uses a data analysis algorithm to analyze the user's social media activity. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The distribution unit, for example, distributes a model related to a location where the user checked in on social media. The distribution unit can also analyze the content of the user's social media posts and distribute a related model. Furthermore, the distribution unit can distribute a related model based on the activity of the user's friends on social media. In this way, analyzing the user's social media activity and distributing a related model improves user convenience.

[0111] The distribution unit can customize the distribution method by reflecting the user's past feedback during distribution. The distribution unit, for example, uses a data analysis algorithm to reflect the user's past feedback. The feedback includes, for example, but is not limited to, the user's evaluation comments and survey results. The distribution unit customizes the distribution method based on, for example, feedback provided by the user in the past. The distribution unit can also suggest a distribution method that enhances specific functions based on the user's past feedback. Furthermore, the distribution unit can also provide an optimal distribution method by reflecting the user's feedback. In this way, customizing the distribution method by reflecting the user's past feedback improves user convenience.

[0112] The fee management unit can estimate the user's emotions and adjust the fee management method based on the estimated user emotions. The fee management unit uses, for example, an emotion estimation algorithm to estimate the user's emotions. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the fee management unit provides a simple interface to minimize fee management procedures. Furthermore, when the user is relaxed, the fee management unit can provide detailed fee management options and suggest customizable methods. Furthermore, when the user is in a hurry, the fee management unit can prioritize voice input to enable quick fee management. This enables more appropriate fee management by adjusting the fee management method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0113] The fee management unit can analyze the user's past usage history and propose an appropriate fee management method during fee management. The fee management unit, for example, uses a data analysis algorithm to analyze the user's past usage history. The usage history includes, for example, but is not limited to, the number of times and content of past usage. The fee management unit, for example, automatically displays as candidates fee management methods that the user has frequently used in the past. The fee management unit can also prioritize and propose fee management methods (such as voice and text) that the user has used in the past. Furthermore, the fee management unit can predict and propose the optimal fee management method for a specific time period based on the user's past usage history. This improves user convenience by proposing the optimal fee management method based on the user's past usage history.

[0114] The fee management unit can provide a fee management method customized based on the user's current task or purpose during fee management. The fee management unit, for example, uses a data analysis algorithm to identify the user's current task or purpose. Examples of tasks and purposes include, but are not limited to, user input and a pre-questionnaire. For example, when the user is performing a specific task, the fee management unit provides the fee management method optimal for that task. The fee management unit can also prioritize displaying related fee management methods according to the user's purpose. Furthermore, the fee management unit can customize the optimal fee management method based on the user's current task. This improves user convenience by providing a fee management method customized according to the user's current task or purpose.

[0115] The fee management unit can select an appropriate fee management means depending on the user's input method during fee management. The fee management unit, for example, uses a data analysis algorithm to identify the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the fee management unit performs fee management using voice recognition technology. Furthermore, when the user uses text input, the fee management unit can also perform fee management using text analysis technology. Furthermore, when the user uses image input, the fee management unit can also perform fee management using image recognition technology. This improves user convenience by selecting the optimal fee management means depending on the user's input method.

[0116] The fee management unit can estimate the user's emotions and determine fee management priorities based on the estimated user emotions. The fee management unit uses, for example, an emotion estimation algorithm to estimate the user's emotions. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the fee management unit can prioritize providing a fee management method that allows the user to relax. Furthermore, if the user is in a hurry, the fee management unit can prioritize providing a method that allows for quick fee management. Furthermore, if the user is enjoying themselves, the fee management unit can prioritize providing an entertainment-related fee management method. This enables more appropriate fee management by determining fee management priorities according to the user's emotions. 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.

[0117] During fee management, the fee management unit can prioritize providing a highly relevant fee management method by taking into account the user's geographical location information. The fee management unit, for example, uses a location information service to acquire the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. For example, if the user is in a specific area, the fee management unit prioritizes providing a fee management method related to that area. The fee management unit can also provide a fee management method related to nearby stores or facilities based on the user's current location. Furthermore, the fee management unit can suggest an optimal fee management method based on the user's geographical location information. This improves user convenience by providing a highly relevant fee management method by taking into account the user's geographical location information.

[0118] The fee management unit may analyze the user's social media activity during fee management and provide a related fee management method. The fee management unit may, for example, use a data analysis algorithm to analyze the user's social media activity. Social media activity may include, but is not limited to, the content of posts and the number of likes. The fee management unit may, for example, provide a fee management method related to the locations where the user has checked in on social media. The fee management unit may also analyze the content of the user's social media posts and provide a related fee management method. Furthermore, the fee management unit may refer to the activities of the user's friends on social media to provide a related fee management method. This improves user convenience by analyzing the user's social media activity and providing a related fee management method.

[0119] The fee management unit can customize the fee management method by reflecting the user's past feedback during fee management. The fee management unit, for example, uses a data analysis algorithm to reflect the user's past feedback. Feedback includes, for example, but is not limited to, the user's evaluation comments and survey results. The fee management unit customizes the fee management method based on, for example, feedback provided by the user in the past. The fee management unit can also propose a fee management method with enhanced specific functions based on the user's past feedback. Furthermore, the fee management unit can also reflect the user's feedback and provide an optimal fee management method. In this way, customizing the fee management method by reflecting the user's past feedback improves user convenience. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned utilization unit, provision unit, fine-tuning unit, distribution unit, and fee management unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the utilization unit is realized by the control unit 46A of the smart device 14, allowing a user to use the AI ​​model service. The provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, allowing a user to provide correct answer data. The fine-tuning unit is realized by the specific processing unit 290 of the data processing device 12, retraining the AI ​​model based on the provided correct answer data. The distribution unit is realized by the specific processing unit 290 of the data processing device 12, distributing the fine-tuned model to other users. The fee management unit is realized by the specific processing unit 290 of the data processing device 12, managing the usage fees for the distributed model. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned utilization unit, provision unit, fine-tuning unit, distribution unit, and fee management unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the utilization unit is realized by the control unit 46A of the smart glasses 214, allowing a user to use the service of the AI ​​model. The provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, allowing a user to provide correct answer data. The fine-tuning unit is realized by the specific processing unit 290 of the data processing device 12, retraining the AI ​​model based on the provided correct answer data. The distribution unit is realized by the specific processing unit 290 of the data processing device 12, distributing the fine-tuned model to other users. The fee management unit is realized by the specific processing unit 290 of the data processing device 12, managing the usage fees for the distributed model. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned utilization unit, provision unit, fine-tuning unit, distribution unit, and fee management unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the utilization unit is realized by the control unit 46A of the headset-type terminal 314, allowing a user to use the service of the AI ​​model. The provision unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12, allowing a user to provide correct answer data. The fine-tuning unit is realized by the specific processing unit 290 of the data processing device 12, retraining the AI ​​model based on the provided correct answer data. The distribution unit is realized by the specific processing unit 290 of the data processing device 12, distributing the fine-tuned model to other users. The fee management unit is realized by the specific processing unit 290 of the data processing device 12, managing the usage fees for the distributed model. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned utilization unit, provision unit, fine-tuning unit, distribution unit, and fee management unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the utilization unit is realized by the control unit 46A of the robot 414, allowing a user to use the service of the AI ​​model. The provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, allowing a user to provide correct answer data. The fine-tuning unit is realized by the specific processing unit 290 of the data processing device 12, retraining the AI ​​model based on the provided correct answer data. The distribution unit is realized by the specific processing unit 290 of the data processing device 12, distributing the fine-tuned model to other users. The fee management unit is realized by the specific processing unit 290 of the data processing device 12, managing the usage fees for the distributed model.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The utilization unit can estimate the user's emotions and adjust the service usage method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize usage steps. Alternatively, if the user is relaxed, detailed usage options can be provided and customizable usage methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable quick use of the service. This allows for more appropriate service to be provided by adjusting the service usage method according to the user's emotions.

[0122] The providing unit can estimate the user's emotions and adjust the method of providing correct answer data based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the steps for providing correct answer data. Alternatively, if the user is relaxed, detailed provision options can be provided and a customizable provision method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to quickly provide correct answer data. This makes it possible to provide more appropriate data by adjusting the method of providing correct answer data according to the user's emotions.

[0123] The fine tuning unit can estimate the user's emotions and adjust the fine tuning method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the fine tuning procedure. Alternatively, if the user is relaxed, detailed fine tuning options can be provided and a customizable method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable quick fine tuning. This allows for more appropriate fine tuning by adjusting the fine tuning method according to the user's emotions.

[0124] The distribution unit can estimate the user's emotions and adjust the distribution method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the distribution procedure. Alternatively, if the user is relaxed, detailed distribution options can be provided and a customizable distribution method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable quick distribution. This allows for more appropriate distribution by adjusting the distribution method according to the user's emotions.

[0125] The fee management unit can estimate the user's emotions and adjust the fee management method based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize fee management procedures. Alternatively, if the user is relaxed, detailed fee management options can be provided and a customizable method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable quick fee management. This allows for more appropriate fee management by adjusting the fee management method according to the user's emotions.

[0126] The usage unit can analyze the user's past usage history and suggest appropriate usage methods. For example, it can automatically display services that the user has frequently used in the past as candidates. It can also prioritize and suggest usage methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest services that will be used during specific time periods based on the user's past usage history. This improves user convenience by suggesting optimal usage methods based on the user's past usage history.

[0127] When providing correct answer data, the providing unit can analyze the user's past data provision history and suggest an appropriate provision method. For example, correct answer data that the user has frequently provided in the past can be automatically displayed as a candidate. It can also preferentially suggest a provision method (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest correct answer data to be provided in a specific time period based on the user's past data provision history. This improves user convenience by suggesting the optimal provision method based on the user's past data provision history.

[0128] During fine tuning, the fine tuning unit can analyze the user's past data provision history and suggest an appropriate fine tuning method. For example, the unit can suggest the optimal fine tuning method based on the correct answer data that the user has frequently provided in the past. It can also preferentially suggest fine tuning methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the optimal fine tuning method for a specific time period based on the user's past data provision history. This improves user convenience by suggesting the optimal fine tuning method based on the user's past data provision history.

[0129] The distribution unit can analyze the user's past usage history and suggest an appropriate distribution method at the time of distribution. For example, distribution methods that the user has frequently used in the past can be automatically displayed as candidates. It can also prioritize and suggest distribution methods (audio, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the optimal distribution method for a specific time period based on the user's past usage history. This improves user convenience by suggesting the optimal distribution method based on the user's past usage history.

[0130] The fee management unit can analyze the user's past usage history and suggest an appropriate fee management method when managing fees. For example, fee management methods that the user has frequently used in the past can be automatically displayed as candidates. It can also prioritize and suggest fee management methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the optimal fee management method for a specific time period based on the user's past usage history. This improves user convenience by suggesting the optimal fee management method based on the user's past usage history.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: In the user unit, the user uses the AI ​​model's services. For example, the user uses an AI model for image recognition or natural language processing to perform a specific task. Step 2: The provider provides correct answer data if the user is not satisfied with the accuracy of the service. For example, in the case of image recognition, the user provides image data with the correct labels. Step 3: The fine-tuning unit fine-tunes the AI ​​model based on the correct answer data provided by the providing unit. For example, the AI ​​model is retrained based on the correct answer data provided by the user, thereby improving its accuracy. Step 4: The distribution unit distributes the fine-tuned model to other users, so that other users can use the fine-tuned model when performing the same task. Step 5: The fee management unit manages the fees for using the model distributed by the distribution unit. For example, when other users use the fine-tuned model, the fee is paid to the service provider and the user who provided the correct answer data.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 utilization unit where a user utilizes the services of the AI ​​model; a providing unit that provides correct answer data when the accuracy of the service used by the using unit is not satisfactory; a fine tuning unit that fine-tunes an AI model based on the correct answer data provided by the providing unit; a distribution unit that distributes the model fine-tuned by the fine-tuning unit to other users; a fee management unit that manages fees for using the model distributed by the distribution unit; Equipped with A system characterized by:

2. The providing unit Provides an interface for users to upload correct data 2. The system of claim 1.

3. The fine tuning unit Equipped with a re-learning unit that re-trains the AI ​​model 2. The system of claim 1.

4. The fine tuning unit Equipped with an evaluation unit that evaluates the results of fine tuning 2. The system of claim 1.

5. The fee management unit Equipped with a calculation unit that calculates fees 2. The system of claim 1.

6. The fee management unit Equipped with a payment department that pays rewards 2. The system of claim 1.

7. The utilization unit includes: Estimate user emotions and adjust service usage based on the estimated user emotions 2. The system of claim 1.

8. The utilization unit includes: Analyze users' past usage history and suggest appropriate usage methods 2. The system of claim 1.

9. The utilization unit includes: When using the service, provide a customized experience based on the user's current task or goal.

2. The system of claim 1.

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