system

Abstract

The system according to this embodiment aims to visualize the user's expertise and provide opportunities for monetization based on that expertise. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a display unit, and a monetization unit. The collection unit collects information related to the user's expertise. The analysis unit analyzes the information collected by the collection unit. The display unit visually displays the expertise based on the analysis results obtained by the analysis unit. The monetization unit provides monetization opportunities based on the expertise visually displayed by the display unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to effectively visualize users' expertise and provide them with monetization opportunities based on that expertise.

[0005] The system according to this embodiment aims to visualize the user's expertise and provide opportunities for monetization based on that expertise. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a display unit, and a monetization unit. The collection unit collects information on a user's expertise. The analysis unit analyzes the information collected by the collection unit. The display unit visually displays the expertise based on the analysis results obtained by the analysis unit. The monetization unit provides monetization opportunities based on the expertise visually displayed by the display unit. [Effects of the Invention]

[0007] The system according to this embodiment can visualize the user's expertise and provide opportunities for monetization based on that expertise. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The social networking platform according to an embodiment of the present invention is a system that allows professionals, scholars, creators, and equipment engineers to share information and pursue side jobs in specialized fields that are an extension of their expertise or hobbies. This social networking platform learns the content posted by each user, visualizes their expertise, and provides a service for monetization. First, users post information related to their area of ​​expertise on the social networking platform. For example, lawyers post legal advice, scholars share their research results, and creators publish their works. This information is collected and analyzed by a generative AI. Next, the generative AI uses the collected information to learn and visualize each user's expertise. For example, it can identify a lawyer's area of ​​expertise or specialization from his or her posts and reflect this in the user's profile. It can also identify a scholar's area of ​​expertise from his or her research results, allowing other users to promote that expertise. Furthermore, the generative AI provides monetization opportunities based on the user's expertise. For example, lawyers can offer legal consultations for a fee, scholars can offer online courses, and creators can sell their works. In this way, users can earn revenue by leveraging their expertise. This SNS platform will help professionals, academics, creators, equipment engineers, and others to disseminate information or take on side jobs in their own areas of expertise by making their expertise visible and providing opportunities for monetization.

[0029] An SNS platform according to an embodiment includes a collection unit, an analysis unit, a visualization unit, and a monetization unit. The collection unit collects information about users' expertise. For example, the collection unit collects information posted by users on the SNS platform. For example, lawyers post legal advice, scholars share research results, and creators publish their works. This information is collected by a generation AI. The generation AI can collect user posts using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information using the generation AI to identify the user's expertise. For example, the generation AI can identify the user's field of expertise or area of ​​expertise from the user's posted content and reflect it in the user profile. The generation AI can analyze the user's posted content using, for example, natural language processing technology to identify the user's expertise. The visualization unit visualizes the expertise based on the analysis results obtained by the analysis unit. For example, the visualization unit visually displays the user's expertise using the generation AI. For example, the generation AI can display the user's expertise in the form of graphs, charts, dashboards, etc. The monetization unit provides monetization opportunities based on the expertise visualized by the visualization unit. The monetization unit provides monetization opportunities based on the user's expertise, for example, using the generation AI. For example, a lawyer can provide legal consultations for a fee, a scholar can offer an online course, or a creator can sell their work. In this way, the SNS platform according to the embodiment can visualize the user's expertise and provide monetization opportunities.

[0030] The collection unit can collect user posted content. The collection unit, for example, collects information posted by users on social media platforms. For example, lawyers post legal advice, scholars share research results, and creators publish their works. This information is collected by the generative AI. The generative AI can collect user posted content using, for example, text generation AI (e.g., LLM) or multimodal generation AI. In this way, by collecting user posted content, information on expertise can be efficiently collected.

[0031] The analysis unit can analyze the collected information and identify the user's expertise. The analysis unit can analyze the collected information and identify the user's expertise, for example, using a generation AI. For example, the generation AI can identify the user's field of expertise or area of ​​expertise from the content posted by the user and reflect this in the user profile. The generation AI can analyze the content posted by the user and identify the expertise, for example, using natural language processing technology. In this way, the user's expertise can be identified by analyzing the collected information.

[0032] The evaluation collection unit can collect user evaluations. The evaluation collection unit, for example, collects user feedback and reviews. For example, users comment on and rate posts by other users. This information is collected by the generation AI. The generation AI can collect user evaluations using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. In this way, expertise can be evaluated by collecting user evaluations.

[0033] The evaluation analysis unit can analyze the collected evaluation results. The evaluation analysis unit analyzes the collected evaluation results, for example, using a generation AI. For example, the generation AI analyzes user feedback and reviews to evaluate expertise. The generation AI can analyze user evaluations and identify expertise, for example, using natural language processing technology. This allows for a detailed evaluation of user expertise by analyzing the collected evaluation results.

[0034] The collaboration unit can promote collaboration between users. For example, the collaboration unit provides a function that enables users to work together on a project. For example, users can create content together or exchange opinions in a discussion forum. This information is collected by the generation AI. The generation AI can promote collaboration between users by using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. This can promote collaboration between users, thereby supporting the sharing and development of expertise.

[0035] The collection unit can analyze the user's past posting history and select the optimal collection method. The collection unit can analyze the user's past posting history, for example, using a generation AI. For example, the generation AI can analyze the user's posting content and posting frequency and select the optimal collection method. The generation AI can analyze the user's past posting history, for example, using natural language processing technology or data mining technology. The collection unit selects the optimal collection method based on the analysis results. For example, it can analyze the time periods during which the user frequently posted in the past and collect information during those time periods. It can also analyze the user's past posting content and prioritize the collection of related information. It can also analyze the user's posting frequency and set the optimal collection interval. In this way, it is possible to select the optimal collection method by analyzing the user's past posting history.

[0036] The collection unit can filter information based on the user's current areas of interest when collecting information. The collection unit, for example, uses a generation AI to identify the user's current areas of interest. For example, the generation AI can analyze the user's browsing history and search keywords to identify the areas of interest. The generation AI can identify the user's areas of interest using natural language processing technology or data mining technology, for example. The collection unit filters information based on the identified areas of interest. For example, the collection unit prioritizes collecting information related to topics in which the user is currently interested. In addition, the collection unit can filter unnecessary information based on the user's areas of interest. Furthermore, if the user's areas of interest change, the filtering criteria for the information to be collected can be automatically updated. This makes it possible to collect highly relevant information by filtering information based on the user's current areas of interest.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, uses a generation AI to acquire the user's geographical location information. For example, the generation AI can analyze GPS data or an IP address to identify the user's location information. The generation AI can acquire the user's geographical location information by using a location information service, for example. The collection unit prioritizes collecting highly relevant information based on the acquired geographical location information. For example, if the user is in a specific area, information related to that area can be prioritized. Local news and event information can also be collected based on the user's location information. Furthermore, if the user is traveling, information related to the travel destination can be prioritized. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0038] The data collection unit analyzes the user's social media activity during information gathering and collects relevant information. For example, the data collection unit uses generative AI to analyze the user's social media activity. For example, the generative AI can analyze the user's posts, follower count, and likes, and collect relevant information. The generative AI can also analyze the user's social media activity using, for example, natural language processing or data mining techniques. Based on the analysis results, the data collection unit collects relevant information. For example, it collects the content of posts from accounts that the user follows on social media. It also analyzes the user's social media activity history and collects relevant information. Furthermore, it can collect information related to topics that the user has shown interest in on social media. This allows for the efficient collection of relevant information by analyzing the user's social media activity.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can use generative AI to evaluate the importance of information. For example, generative AI can analyze the impact and relevance of information and evaluate its importance. For example, generative AI can use natural language processing technology or data mining technology to evaluate the importance of information. The analysis unit adjusts the level of detail of the analysis based on the evaluated importance. For example, it can perform a detailed analysis on information with high importance, and a concise analysis on information with low importance. Furthermore, it can determine the priority of the analysis according to the importance of the information. This makes efficient analysis possible by adjusting the level of detail of the analysis based on the importance of the information.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. The analysis unit, for example, uses a generation AI to identify the category of information. For example, the generation AI can analyze the content and format of the information to identify the category. The generation AI can identify the category of information using natural language processing technology or data mining technology, for example. The analysis unit applies different analysis algorithms depending on the identified category. For example, a specialized analysis algorithm can be applied to academic information. A visual analysis algorithm can be applied to creative information. A technical analysis algorithm can be applied to technical information. In this way, by applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. The analysis unit, for example, uses the generation AI to obtain the time of information submission. For example, the generation AI can analyze the submission date and time or timestamp to identify the time of information submission. The generation AI can obtain the time of information submission using, for example, data mining technology. The analysis unit determines the priority of analysis based on the obtained time of submission. For example, the latest information is analyzed with priority. Also, the analysis priority of older information can be lowered. Furthermore, the analysis schedule can be adjusted depending on the time of submission. In this way, efficient analysis is possible by determining the priority of analysis based on the time of information submission.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, uses a generation AI to evaluate the relevance of the information. For example, the generation AI can analyze common keywords and related topics of the information and evaluate the relevance. The generation AI can evaluate the relevance of the information using, for example, natural language processing technology or data mining technology. The analysis unit adjusts the order of analysis based on the evaluated relevance. For example, highly relevant information is analyzed first. Furthermore, less relevant information can be analyzed later. Furthermore, the analysis schedule can be adjusted according to the relevance of the information. As a result, efficient analysis is possible by adjusting the order of analysis based on the relevance of the information.

[0043] The visualization unit can adjust the level of detail of the visualization based on the importance of the analysis results during visualization. The visualization unit, for example, uses a generation AI to evaluate the importance of the analysis results. For example, the generation AI can analyze the impact and relevance of the analysis results and evaluate the importance. The generation AI can evaluate the importance of the analysis results using, for example, natural language processing technology or data mining technology. The visualization unit adjusts the level of detail of the visualization based on the evaluated importance. For example, a detailed visualization can be provided for analysis results with high importance. Furthermore, a concise visualization can be provided for analysis results with low importance. Furthermore, the priority of the visualization can be determined according to the importance of the analysis results. As a result, efficient visualization is possible by adjusting the level of detail of the visualization based on the importance of the analysis results.

[0044] The visualization unit can apply different visualization methods depending on the category of the analysis results when visualizing them. The visualization unit, for example, uses a generation AI to identify the category of the analysis results. For example, the generation AI can analyze the content and format of the analysis results to identify the category. The generation AI can identify the category of the analysis results using, for example, natural language processing technology or data mining technology. The visualization unit applies different visualization methods depending on the identified category. For example, a specialized visualization method can be applied to academic analysis results. A visual visualization method can be applied to creative analysis results. Furthermore, a technical visualization method can be applied to technical analysis results. In this way, by applying different visualization methods depending on the category of the analysis results, more appropriate visualization results can be provided.

[0045] During visualization, the visualization unit can determine the priority of visualization based on the submission time of the analysis results. The visualization unit, for example, uses a generation AI to obtain the submission time of the analysis results. For example, the generation AI can analyze the submission date and time or a timestamp to identify the submission time of the analysis results. The generation AI can obtain the submission time of the analysis results using, for example, data mining technology. The visualization unit determines the priority of visualization based on the obtained submission time. For example, the latest analysis results are visualized with priority. Also, the visualization priority of older analysis results can be lowered. Furthermore, the visualization schedule can be adjusted depending on the submission time. As a result, efficient visualization is possible by determining the priority of visualization based on the submission time of the analysis results.

[0046] The visualization unit can adjust the visualization order based on the relevance of the analysis results during visualization. For example, the visualization unit uses generative AI to evaluate the relevance of the analysis results. For instance, the generative AI can analyze common keywords and related topics in the analysis results and evaluate their relevance. The generative AI can also use natural language processing or data mining techniques to evaluate the relevance of the analysis results. Based on the evaluated relevance, the visualization unit adjusts the visualization order. For example, highly relevant analysis results are visualized preferentially. Furthermore, less relevant analysis results can be visualized later. In addition, the visualization schedule can be adjusted according to the relevance of the analysis results. This allows for efficient visualization by adjusting the visualization order based on the relevance of the analysis results.

[0047] The monetization department can analyze a user's past revenue history to select the optimal monetization method. For example, the monetization department can use generative AI to analyze a user's past revenue history. For example, the generative AI can analyze a user's revenue history and select the optimal monetization method. The generative AI can also use data mining techniques to analyze a user's revenue history. Based on the analysis results, the monetization department selects the optimal monetization method. For example, it analyzes a user's past revenue history and proposes the most effective monetization method. Furthermore, it can identify time slots where revenue is likely to increase based on the user's revenue history and monetize during those times. In addition, it can propose content that is likely to generate revenue based on the user's revenue history. In this way, by analyzing a user's past revenue history, the optimal monetization method can be selected.

[0048] The monetization department can customize the monetization method based on the user's current living situation when monetizing. The monetization department, for example, uses a generation AI to obtain the user's current living situation. For example, the generation AI can analyze the user's income, family composition, and lifestyle to identify the user's current living situation. The generation AI can obtain the user's living situation using data mining technology, for example. The monetization department customizes the monetization method based on the obtained living situation. For example, if the user is busy, the monetization department can suggest a monetization method that can generate revenue in a short period of time. Also, if the user has time, the monetization department can suggest a monetization method that can generate long-term revenue. Furthermore, if the user has a specific skill, the monetization department can suggest a monetization method that makes use of that skill. This allows the monetization method to be customized based on the user's current living situation, thereby providing a more appropriate monetization method.

[0049] The monetization department can select the optimal monetization method during monetization by taking into account the user's geographic location information. The monetization department, for example, uses a generation AI to acquire the user's geographic location information. For example, the generation AI can analyze GPS data or an IP address to identify the user's location information. The generation AI can acquire the user's geographic location information by using a location information service, for example. The monetization department selects the optimal monetization method based on the acquired geographic location information. For example, if the user is in a specific area, the monetization department can suggest monetization methods related to that area. Also, based on the user's location information, the monetization department can suggest monetization methods related to local markets or events. Furthermore, if the user is traveling, the monetization department can suggest monetization methods for the user's travel destination. This allows the optimal monetization method to be selected by taking into account the user's geographic location information.

[0050] The monetization department can analyze users' social media activity and propose monetization methods during the monetization process. For example, the monetization department can use generative AI to analyze users' social media activity. For instance, the generative AI can analyze users' posts, follower count, and likes to propose the most suitable monetization method. The generative AI can also use natural language processing and data mining techniques to analyze users' social media activity. Based on the analysis results, the monetization department proposes the most suitable monetization method. For example, it can analyze the activity of accounts that users follow on social media and propose relevant monetization methods. It can also analyze the user's social media activity history and propose the most suitable monetization method. Furthermore, it can propose monetization methods related to topics that users have shown interest in on social media. In this way, by analyzing users' social media activity, the department can propose the most suitable monetization method.

[0051] The evaluation collection unit can analyze a user's past evaluation history and select the optimal collection method. For example, the evaluation collection unit uses generative AI to analyze a user's past evaluation history. For example, the generative AI can analyze a user's evaluation history and select the optimal collection method. The generative AI can analyze a user's evaluation history using data mining techniques. Based on the analysis results, the evaluation collection unit selects the optimal collection method. For example, it can analyze the time periods when a user previously received high ratings and collect evaluations during those times. It can also analyze the content of a user's past evaluations and prioritize the collection of relevant evaluations. Furthermore, it can analyze the user's evaluation history and set the optimal collection interval. This allows the optimal collection method to be selected by analyzing a user's past evaluation history.

[0052] When collecting reviews, the review collection unit can prioritize collecting highly relevant reviews by taking into account the user's geographical location information. The review collection unit, for example, uses a generation AI to acquire the user's geographical location information. For example, the generation AI can analyze GPS data or an IP address to identify the user's location information. The generation AI can acquire the user's geographical location information by using a location information service, for example. The review collection unit prioritizes collecting highly relevant reviews based on the acquired geographical location information. For example, if the user is in a specific area, reviews related to that area can be prioritized. Local reviews can also be collected based on the user's location information. Furthermore, if the user is traveling, reviews from the travel destination can be prioritized. In this way, highly relevant reviews can be prioritized by taking into account the user's geographical location information.

[0053] The evaluation analysis unit can adjust the level of detail of the analysis based on the importance of the evaluation during evaluation analysis. The evaluation analysis unit, for example, uses a generation AI to evaluate the importance of the evaluation. For example, the generation AI can analyze the impact and relevance of the evaluation and evaluate the importance. The generation AI can evaluate the importance of the evaluation using, for example, natural language processing technology or data mining technology. The evaluation analysis unit adjusts the level of detail of the analysis based on the evaluated importance. For example, a detailed analysis can be performed for evaluations with high importance. Furthermore, a concise analysis can be performed for evaluations with low importance. Furthermore, the priority of the analysis can be determined according to the importance of the evaluation. As a result, efficient evaluation analysis is possible by adjusting the level of detail of the analysis based on the importance of the evaluation.

[0054] The evaluation analysis unit can adjust the order of analysis based on the submission date of the evaluations. For example, the evaluation analysis unit can obtain the submission date of the evaluations using a generative AI. For example, the generative AI can analyze the submission date and time or timestamp to identify the submission date of the evaluation. The generative AI can obtain the submission date of the evaluation using data mining techniques, for example. The evaluation analysis unit adjusts the order of analysis based on the obtained submission date. For example, the most recent evaluations are analyzed preferentially. Older evaluations can be postponed in the analysis order. Furthermore, the analysis schedule can be adjusted according to the submission date. This allows for efficient evaluation analysis by adjusting the order of analysis based on the evaluation submission date.

[0055] The Collaboration Department can analyze a user's past collaboration history to select the optimal collaboration method during a collaboration. For example, the Collaboration Department uses generative AI to analyze a user's past collaboration history. For example, generative AI can analyze a user's collaboration history and select the optimal collaboration method. For example, generative AI can use data mining techniques to analyze a user's collaboration history. Based on the analysis results, the Collaboration Department selects the optimal collaboration method. For example, it analyzes a user's past collaboration history and proposes the most effective collaboration method. It can also identify collaboration methods with a high success rate from the user's collaboration history and propose those methods. Furthermore, it can propose the optimal collaboration partner based on the user's collaboration history. In this way, the optimal collaboration method can be selected by analyzing a user's past collaboration history.

[0056] The collaboration unit can select the optimal collaboration method during collaboration by taking into account the user's geographic location information. The collaboration unit, for example, uses a generation AI to acquire the user's geographic location information. For example, the generation AI can analyze GPS data or an IP address to identify the user's location information. The generation AI can acquire the user's geographic location information by using a location information service, for example. The collaboration unit selects the optimal collaboration method based on the acquired geographic location information. For example, if the user is in a specific area, the collaboration unit can suggest collaboration methods related to that area. Furthermore, the collaboration unit can suggest collaboration methods related to local markets or events based on the user's location information. Furthermore, if the user is traveling, the collaboration unit can suggest collaboration methods at the user's travel destination. In this way, the optimal collaboration method can be selected by taking into account the user's geographic location information.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] Social media platforms can also feature project matching functions that utilize users' expertise. For example, if a lawyer is looking for a project related to a specific legal matter, the platform can suggest appropriate projects based on that expertise. Similarly, if an academic is seeking research funding, the platform can introduce them to funders related to that research field. Furthermore, if a creator is looking for collaborators to create a new work, the platform can match them with other users who are knowledgeable in that field. This allows users to make the most of their own expertise and efficiently advance their projects.

[0059] The collection unit can also collect the latest trend information related to the user's expertise. For example, a lawyer can collect relevant news and papers to stay up to date on legal precedents and legal reforms. A scholar can also collect relevant academic papers and conference information to stay up to date on the latest discoveries and technologies in their field of research. Furthermore, a creator can collect relevant blogs and social media posts to stay up to date on the latest trends in design and art. This allows users to always have the latest information and enhance their expertise.

[0060] The analysis unit can also provide a mutual evaluation system with other users based on the user's expertise. For example, a lawyer can evaluate the posts of other lawyers, and the expertise can be further identified based on the evaluation results. Also, a scholar can evaluate the research results of other scholars, and the expertise can be identified based on the evaluation results. Furthermore, a creator can evaluate the work of other creators, and the expertise can be identified based on the evaluation results. In this way, users can evaluate each other, thereby improving the accuracy of the expertise.

[0061] The evaluation collection unit can also evaluate the reliability of evaluations based on the user's expertise. For example, if a lawyer receives evaluations from other lawyers, the reliability of the evaluation can be determined by taking into account the evaluator's expertise. Similarly, if a scholar receives evaluations from other scholars, the reliability of the evaluation can be determined by taking into account the evaluator's research field and track record. Furthermore, if a creator receives evaluations from other creators, the reliability of the evaluation can be determined by taking into account the evaluator's work and evaluation history. This can increase the reliability of the evaluations.

[0062] The evaluation analysis unit can also analyze the impact of evaluations based on the user's expertise. For example, it can analyze the extent to which an evaluation of a lawyer influences other users. It can also analyze the extent to which an evaluation of a scholar influences other researchers. It can also analyze the extent to which an evaluation of a creator influences other creators and fans. This allows the impact of evaluations to be understood, further enhancing the user's expertise.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The collection unit collects information about the user's expertise. For example, it collects information posted by users on social media platforms. Specifically, lawyers post legal advice, scholars share research results, and creators publish their work. This information is collected by generative AI (e.g., text generation AI or multimodal generation AI). Step 2: The analysis unit analyzes the information collected by the collection unit. The generation AI is used to analyze the collected information and identify the user's expertise. For example, the generation AI can identify the user's field of expertise or specialization from the content of the user's posts and reflect this in the user's profile. The generation AI can use natural language processing technology to analyze the content of the user's posts and identify the user's expertise. Step 3: The display unit visually displays the expertise based on the analysis results obtained by the analysis unit. Using the generation AI, the user's expertise is visually displayed in the form of graphs, charts, dashboards, etc. Step 4: The monetization unit provides monetization opportunities based on the expertise visually displayed by the display unit. Generative AI is used to provide monetization opportunities based on the user's expertise. For example, lawyers can offer legal consultations for a fee, scholars can offer online courses, and creators can sell their work.

[0065] (Example 2) The social networking platform according to an embodiment of the present invention is a system that allows professionals, scholars, creators, and equipment engineers to share information and pursue side jobs in specialized fields that are an extension of their expertise or hobbies. This social networking platform learns the content posted by each user, visualizes their expertise, and provides a service for monetization. First, users post information related to their area of ​​expertise on the social networking platform. For example, lawyers post legal advice, scholars share their research results, and creators publish their works. This information is collected and analyzed by a generative AI. Next, the generative AI uses the collected information to learn and visualize each user's expertise. For example, it can identify a lawyer's area of ​​expertise or specialization from his or her posts and reflect this in the user's profile. It can also identify a scholar's area of ​​expertise from his or her research results, allowing other users to promote that expertise. Furthermore, the generative AI provides monetization opportunities based on the user's expertise. For example, lawyers can offer legal consultations for a fee, scholars can offer online courses, and creators can sell their works. In this way, users can earn revenue by leveraging their expertise. This SNS platform will help professionals, academics, creators, equipment engineers, and others to disseminate information or take on side jobs in their own areas of expertise by making their expertise visible and providing opportunities for monetization.

[0066] An SNS platform according to an embodiment includes a collection unit, an analysis unit, a visualization unit, and a monetization unit. The collection unit collects information about users' expertise. For example, the collection unit collects information posted by users on the SNS platform. For example, lawyers post legal advice, scholars share research results, and creators publish their works. This information is collected by a generation AI. The generation AI can collect user posts using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information using the generation AI to identify the user's expertise. For example, the generation AI can identify the user's field of expertise or area of ​​expertise from the user's posted content and reflect it in the user profile. The generation AI can analyze the user's posted content using, for example, natural language processing technology to identify the user's expertise. The visualization unit visualizes the expertise based on the analysis results obtained by the analysis unit. For example, the visualization unit visually displays the user's expertise using the generation AI. For example, the generation AI can display the user's expertise in the form of graphs, charts, dashboards, etc. The monetization unit provides monetization opportunities based on the expertise visualized by the visualization unit. The monetization unit provides monetization opportunities based on the user's expertise, for example, using the generation AI. For example, a lawyer can provide legal consultations for a fee, a scholar can offer an online course, or a creator can sell their work. In this way, the SNS platform according to the embodiment can visualize the user's expertise and provide monetization opportunities.

[0067] The collection unit can collect user posted content. The collection unit, for example, collects information posted by users on social media platforms. For example, lawyers post legal advice, scholars share research results, and creators publish their works. This information is collected by the generative AI. The generative AI can collect user posted content using, for example, text generation AI (e.g., LLM) or multimodal generation AI. In this way, by collecting user posted content, information on expertise can be efficiently collected.

[0068] The analysis unit can analyze the collected information and identify the user's expertise. The analysis unit can analyze the collected information and identify the user's expertise, for example, using a generation AI. For example, the generation AI can identify the user's field of expertise or area of ​​expertise from the content posted by the user and reflect this in the user profile. The generation AI can analyze the content posted by the user and identify the expertise, for example, using natural language processing technology. In this way, the user's expertise can be identified by analyzing the collected information.

[0069] The evaluation collection unit can collect user evaluations. The evaluation collection unit, for example, collects user feedback and reviews. For example, users comment on and rate posts by other users. This information is collected by the generation AI. The generation AI can collect user evaluations using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. In this way, expertise can be evaluated by collecting user evaluations.

[0070] The evaluation analysis unit can analyze the collected evaluation results. The evaluation analysis unit analyzes the collected evaluation results, for example, using a generation AI. For example, the generation AI analyzes user feedback and reviews to evaluate expertise. The generation AI can analyze user evaluations and identify expertise, for example, using natural language processing technology. This allows for a detailed evaluation of user expertise by analyzing the collected evaluation results.

[0071] The collaboration unit can promote collaboration between users. For example, the collaboration unit provides a function that enables users to work together on a project. For example, users can create content together or exchange opinions in a discussion forum. This information is collected by the generation AI. The generation AI can promote collaboration between users by using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. This can promote collaboration between users, thereby supporting the sharing and development of expertise.

[0072] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the emotions. The collection unit, for example, uses a generation AI to estimate the user's emotions. For example, the generation AI can analyze the user's posted content and behavioral history to estimate emotions. The generation AI can estimate the user's emotions using, for example, natural language processing technology or an emotion analysis algorithm. The collection unit adjusts the timing of information collection based on the estimated emotions. For example, if the user is feeling stressed, the frequency of information collection can be reduced to reduce the user's burden. Also, if the user is relaxed, the frequency of information collection can be increased to collect more information. Furthermore, if the user is excited, information can be collected in real time and reflected immediately. As a result, by adjusting the timing of information collection according to the user's emotions, the burden on the user can be reduced and efficient information collection becomes possible.

[0073] The collection unit can analyze the user's past posting history and select the optimal collection method. The collection unit can analyze the user's past posting history, for example, using a generation AI. For example, the generation AI can analyze the user's posting content and posting frequency and select the optimal collection method. The generation AI can analyze the user's past posting history, for example, using natural language processing technology or data mining technology. The collection unit selects the optimal collection method based on the analysis results. For example, it can analyze the time periods during which the user frequently posted in the past and collect information during those time periods. It can also analyze the user's past posting content and prioritize the collection of related information. It can also analyze the user's posting frequency and set the optimal collection interval. In this way, it is possible to select the optimal collection method by analyzing the user's past posting history.

[0074] The collection unit can filter information based on the user's current areas of interest when collecting information. The collection unit, for example, uses a generation AI to identify the user's current areas of interest. For example, the generation AI can analyze the user's browsing history and search keywords to identify the areas of interest. The generation AI can identify the user's areas of interest using natural language processing technology or data mining technology, for example. The collection unit filters information based on the identified areas of interest. For example, the collection unit prioritizes collecting information related to topics in which the user is currently interested. In addition, the collection unit can filter unnecessary information based on the user's areas of interest. Furthermore, if the user's areas of interest change, the filtering criteria for the information to be collected can be automatically updated. This makes it possible to collect highly relevant information by filtering information based on the user's current areas of interest.

[0075] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the emotions. The collection unit estimates the user's emotions using, for example, a generation AI. For example, the generation AI can analyze the user's posted content and behavioral history to estimate emotions. The generation AI can estimate the user's emotions using, for example, natural language processing technology or an emotion analysis algorithm. The collection unit determines the priority of information to be collected based on the estimated emotions. For example, if the user is feeling stressed, relaxing information can be preferentially collected. Also, if the user is excited, stimulating information can be preferentially collected. Furthermore, if the user is calm, academic information can be preferentially collected. In this way, more appropriate information can be collected by determining the priority of information to be collected according to the user's emotions.

[0076] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, uses a generation AI to acquire the user's geographical location information. For example, the generation AI can analyze GPS data or an IP address to identify the user's location information. The generation AI can acquire the user's geographical location information by using a location information service, for example. The collection unit prioritizes collecting highly relevant information based on the acquired geographical location information. For example, if the user is in a specific area, information related to that area can be prioritized. Local news and event information can also be collected based on the user's location information. Furthermore, if the user is traveling, information related to the travel destination can be prioritized. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0077] The data collection unit analyzes the user's social media activity during information gathering and collects relevant information. For example, the data collection unit uses generative AI to analyze the user's social media activity. For example, the generative AI can analyze the user's posts, follower count, and likes, and collect relevant information. The generative AI can also analyze the user's social media activity using, for example, natural language processing or data mining techniques. Based on the analysis results, the data collection unit collects relevant information. For example, it collects the content of posts from accounts that the user follows on social media. It also analyzes the user's social media activity history and collects relevant information. Furthermore, it can collect information related to topics that the user has shown interest in on social media. This allows for the efficient collection of relevant information by analyzing the user's social media activity.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, the analysis unit can estimate the user's emotions using generative AI. For example, the generative AI can estimate emotions by analyzing the user's posts and behavioral history. For example, the generative AI can estimate the user's emotions using natural language processing technology or emotion analysis algorithms. The analysis unit adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, it can provide visually stimulating analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can use generative AI to evaluate the importance of information. For example, generative AI can analyze the impact and relevance of information and evaluate its importance. For example, generative AI can use natural language processing technology or data mining technology to evaluate the importance of information. The analysis unit adjusts the level of detail of the analysis based on the evaluated importance. For example, it can perform a detailed analysis on information with high importance, and a concise analysis on information with low importance. Furthermore, it can determine the priority of the analysis according to the importance of the information. This makes efficient analysis possible by adjusting the level of detail of the analysis based on the importance of the information.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. The analysis unit, for example, uses a generation AI to identify the category of information. For example, the generation AI can analyze the content and format of the information to identify the category. The generation AI can identify the category of information using natural language processing technology or data mining technology, for example. The analysis unit applies different analysis algorithms depending on the identified category. For example, a specialized analysis algorithm can be applied to academic information. A visual analysis algorithm can be applied to creative information. A technical analysis algorithm can be applied to technical information. In this way, by applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on those emotions. The analysis unit estimates the user's emotions, for example, using a generation AI. For example, the generation AI can analyze the user's posted content and behavioral history to estimate emotions. The generation AI can estimate the user's emotions, for example, using natural language processing technology or an emotion analysis algorithm. The analysis unit adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, a short and to-the-point analysis result can be provided. Alternatively, if the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is excited, a visually stimulating analysis result can be provided. In this way, by adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0082] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. The analysis unit, for example, uses the generation AI to obtain the time of information submission. For example, the generation AI can analyze the submission date and time or timestamp to identify the time of information submission. The generation AI can obtain the time of information submission using, for example, data mining technology. The analysis unit determines the priority of analysis based on the obtained time of submission. For example, the latest information is analyzed with priority. Also, the analysis priority of older information can be lowered. Furthermore, the analysis schedule can be adjusted depending on the time of submission. In this way, efficient analysis is possible by determining the priority of analysis based on the time of information submission.

[0083] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, uses a generation AI to evaluate the relevance of the information. For example, the generation AI can analyze common keywords and related topics of the information and evaluate the relevance. The generation AI can evaluate the relevance of the information using, for example, natural language processing technology or data mining technology. The analysis unit adjusts the order of analysis based on the evaluated relevance. For example, highly relevant information is analyzed first. Furthermore, less relevant information can be analyzed later. Furthermore, the analysis schedule can be adjusted according to the relevance of the information. As a result, efficient analysis is possible by adjusting the order of analysis based on the relevance of the information.

[0084] The visualization unit can estimate the user's emotions and adjust the visualization method based on those emotions. For example, the visualization unit uses generative AI to estimate the user's emotions. For instance, the generative AI can analyze the user's posts and behavioral history to estimate emotions. The generative AI can also use natural language processing techniques and emotion analysis algorithms to estimate the user's emotions. The visualization unit adjusts the visualization method based on the estimated emotions. For example, if the user is relaxed, it can provide a detailed visualization. If the user is in a hurry, it can provide a concise visualization. Furthermore, if the user is excited, it can provide a visually stimulating visualization. By adjusting the visualization method according to the user's emotions, more appropriate visualization results can be provided.

[0085] The visualization unit can adjust the level of detail of the visualization based on the importance of the analysis results during visualization. The visualization unit, for example, uses a generation AI to evaluate the importance of the analysis results. For example, the generation AI can analyze the impact and relevance of the analysis results and evaluate the importance. The generation AI can evaluate the importance of the analysis results using, for example, natural language processing technology or data mining technology. The visualization unit adjusts the level of detail of the visualization based on the evaluated importance. For example, a detailed visualization can be provided for analysis results with high importance. Furthermore, a concise visualization can be provided for analysis results with low importance. Furthermore, the priority of the visualization can be determined according to the importance of the analysis results. As a result, efficient visualization is possible by adjusting the level of detail of the visualization based on the importance of the analysis results.

[0086] The visualization unit can apply different visualization methods depending on the category of the analysis results when visualizing them. The visualization unit, for example, uses a generation AI to identify the category of the analysis results. For example, the generation AI can analyze the content and format of the analysis results to identify the category. The generation AI can identify the category of the analysis results using, for example, natural language processing technology or data mining technology. The visualization unit applies different visualization methods depending on the identified category. For example, a specialized visualization method can be applied to academic analysis results. A visual visualization method can be applied to creative analysis results. Furthermore, a technical visualization method can be applied to technical analysis results. In this way, by applying different visualization methods depending on the category of the analysis results, more appropriate visualization results can be provided.

[0087] The visualization unit can estimate the user's emotions and adjust the length of the visualization based on the emotions. The visualization unit estimates the user's emotions using, for example, a generation AI. For example, the generation AI can analyze the user's posted content and behavioral history to estimate emotions. The generation AI can estimate the user's emotions using, for example, natural language processing technology or an emotion analysis algorithm. The visualization unit adjusts the length of the visualization based on the estimated emotions. For example, if the user is in a hurry, a short and to-the-point visualization can be provided. If the user is relaxed, a detailed visualization can be provided. Furthermore, if the user is excited, a visually stimulating visualization can be provided. In this way, by adjusting the length of the visualization according to the user's emotions, more appropriate visualization results can be provided.

[0088] During visualization, the visualization unit can determine the priority of visualization based on the submission time of the analysis results. The visualization unit, for example, uses a generation AI to obtain the submission time of the analysis results. For example, the generation AI can analyze the submission date and time or a timestamp to identify the submission time of the analysis results. The generation AI can obtain the submission time of the analysis results using, for example, data mining technology. The visualization unit determines the priority of visualization based on the obtained submission time. For example, the latest analysis results are visualized with priority. Also, the visualization priority of older analysis results can be lowered. Furthermore, the visualization schedule can be adjusted depending on the submission time. As a result, efficient visualization is possible by determining the priority of visualization based on the submission time of the analysis results.

[0089] The visualization unit can adjust the visualization order based on the relevance of the analysis results during visualization. For example, the visualization unit uses generative AI to evaluate the relevance of the analysis results. For instance, the generative AI can analyze common keywords and related topics in the analysis results and evaluate their relevance. The generative AI can also use natural language processing or data mining techniques to evaluate the relevance of the analysis results. Based on the evaluated relevance, the visualization unit adjusts the visualization order. For example, highly relevant analysis results are visualized preferentially. Furthermore, less relevant analysis results can be visualized later. In addition, the visualization schedule can be adjusted according to the relevance of the analysis results. This allows for efficient visualization by adjusting the visualization order based on the relevance of the analysis results.

[0090] The monetization department can estimate user emotions and adjust monetization methods based on those emotions. For example, the monetization department can estimate user emotions using generative AI. For example, generative AI can estimate emotions by analyzing the user's posts and behavioral history. For example, generative AI can estimate user emotions using natural language processing technology and emotion analysis algorithms. The monetization department adjusts monetization methods based on the estimated emotions. For example, if the user is relaxed, it can suggest detailed monetization methods. If the user is in a hurry, it can suggest concise monetization methods. Furthermore, if the user is excited, it can suggest visually stimulating monetization methods. In this way, by adjusting monetization methods according to user emotions, more appropriate monetization methods can be provided.

[0091] The monetization department can analyze a user's past revenue history to select the optimal monetization method. For example, the monetization department can use generative AI to analyze a user's past revenue history. For example, the generative AI can analyze a user's revenue history and select the optimal monetization method. The generative AI can also use data mining techniques to analyze a user's revenue history. Based on the analysis results, the monetization department selects the optimal monetization method. For example, it analyzes a user's past revenue history and proposes the most effective monetization method. Furthermore, it can identify time slots where revenue is likely to increase based on the user's revenue history and monetize during those times. In addition, it can propose content that is likely to generate revenue based on the user's revenue history. In this way, by analyzing a user's past revenue history, the optimal monetization method can be selected.

[0092] The monetization department can customize the monetization method based on the user's current living situation when monetizing. The monetization department, for example, uses a generation AI to obtain the user's current living situation. For example, the generation AI can analyze the user's income, family composition, and lifestyle to identify the user's current living situation. The generation AI can obtain the user's living situation using data mining technology, for example. The monetization department customizes the monetization method based on the obtained living situation. For example, if the user is busy, the monetization department can suggest a monetization method that can generate revenue in a short period of time. Also, if the user has time, the monetization department can suggest a monetization method that can generate long-term revenue. Furthermore, if the user has a specific skill, the monetization department can suggest a monetization method that makes use of that skill. This allows the monetization method to be customized based on the user's current living situation, thereby providing a more appropriate monetization method.

[0093] The monetization unit can estimate a user's emotions and determine monetization priorities based on those emotions. The monetization unit estimates a user's emotions, for example, using a generation AI. For example, the generation AI can analyze the user's posted content and behavioral history to estimate emotions. The generation AI can estimate a user's emotions using, for example, natural language processing technology or an emotion analysis algorithm. The monetization unit determines monetization priorities based on the estimated emotions. For example, if a user is relaxed, detailed monetization methods can be prioritized. Also, if a user is in a hurry, simple monetization methods can be prioritized. Furthermore, if a user is excited, visually stimulating monetization methods can be prioritized. This allows for more appropriate monetization methods to be provided by determining monetization priorities according to the user's emotions.

[0094] The monetization department can select the optimal monetization method during monetization by taking into account the user's geographic location information. The monetization department, for example, uses a generation AI to acquire the user's geographic location information. For example, the generation AI can analyze GPS data or an IP address to identify the user's location information. The generation AI can acquire the user's geographic location information by using a location information service, for example. The monetization department selects the optimal monetization method based on the acquired geographic location information. For example, if the user is in a specific area, the monetization department can suggest monetization methods related to that area. Also, based on the user's location information, the monetization department can suggest monetization methods related to local markets or events. Furthermore, if the user is traveling, the monetization department can suggest monetization methods for the user's travel destination. This allows the optimal monetization method to be selected by taking into account the user's geographic location information.

[0095] The monetization department can analyze users' social media activity and propose monetization methods during the monetization process. For example, the monetization department can use generative AI to analyze users' social media activity. For instance, the generative AI can analyze users' posts, follower count, and likes to propose the most suitable monetization method. The generative AI can also use natural language processing and data mining techniques to analyze users' social media activity. Based on the analysis results, the monetization department proposes the most suitable monetization method. For example, it can analyze the activity of accounts that users follow on social media and propose relevant monetization methods. It can also analyze the user's social media activity history and propose the most suitable monetization method. Furthermore, it can propose monetization methods related to topics that users have shown interest in on social media. In this way, by analyzing users' social media activity, the department can propose the most suitable monetization method.

[0096] The evaluation collection unit can estimate the user's emotions and adjust the timing of evaluation collection based on the emotions. The evaluation collection unit estimates the user's emotions using, for example, a generation AI. For example, the generation AI can analyze the user's posted content and behavioral history to estimate the emotions. The generation AI can estimate the user's emotions using, for example, natural language processing technology or an emotion analysis algorithm. The evaluation collection unit adjusts the timing of evaluation collection based on the estimated emotions. For example, if the user is relaxed, the frequency of evaluation collection can be increased. Also, if the user is stressed, the frequency of evaluation collection can be reduced. Furthermore, if the user is excited, evaluations can be collected in real time. This enables more appropriate evaluation collection by adjusting the timing of evaluation collection according to the user's emotions.

[0097] The evaluation collection unit can analyze a user's past evaluation history and select the optimal collection method. For example, the evaluation collection unit uses generative AI to analyze a user's past evaluation history. For example, the generative AI can analyze a user's evaluation history and select the optimal collection method. The generative AI can analyze a user's evaluation history using data mining techniques. Based on the analysis results, the evaluation collection unit selects the optimal collection method. For example, it can analyze the time periods when a user previously received high ratings and collect evaluations during those times. It can also analyze the content of a user's past evaluations and prioritize the collection of relevant evaluations. Furthermore, it can analyze the user's evaluation history and set the optimal collection interval. This allows the optimal collection method to be selected by analyzing a user's past evaluation history.

[0098] The evaluation collection unit can estimate the user's emotions and determine the priority of the evaluations to be collected based on the emotions. The evaluation collection unit estimates the user's emotions using, for example, a generation AI. For example, the generation AI can analyze the user's posted content and behavioral history to estimate the emotions. The generation AI can estimate the user's emotions using, for example, natural language processing technology or an emotion analysis algorithm. The evaluation collection unit determines the priority of the evaluations to be collected based on the estimated emotions. For example, if the user is relaxed, detailed evaluations can be collected preferentially. Also, if the user is in a hurry, brief evaluations can be collected preferentially. Furthermore, if the user is excited, visually stimulating evaluations can be collected preferentially. This enables more appropriate evaluation collection by determining the priority of the evaluations to be collected according to the user's emotions.

[0099] When collecting reviews, the review collection unit can prioritize collecting highly relevant reviews by taking into account the user's geographical location information. The review collection unit, for example, uses a generation AI to acquire the user's geographical location information. For example, the generation AI can analyze GPS data or an IP address to identify the user's location information. The generation AI can acquire the user's geographical location information by using a location information service, for example. The review collection unit prioritizes collecting highly relevant reviews based on the acquired geographical location information. For example, if the user is in a specific area, reviews related to that area can be prioritized. Local reviews can also be collected based on the user's location information. Furthermore, if the user is traveling, reviews from the travel destination can be prioritized. In this way, highly relevant reviews can be prioritized by taking into account the user's geographical location information.

[0100] The evaluation analysis unit can estimate the user's emotions and adjust the evaluation analysis method based on the emotions. The evaluation analysis unit estimates the user's emotions using, for example, a generation AI. For example, the generation AI can analyze the user's posted content and behavioral history to estimate emotions. The generation AI can estimate the user's emotions using, for example, natural language processing technology or an emotion analysis algorithm. The evaluation analysis unit adjusts the evaluation analysis method based on the estimated emotions. For example, if the user is relaxed, a detailed evaluation analysis can be performed. If the user is in a hurry, a concise evaluation analysis can be performed. Furthermore, if the user is excited, a visually stimulating evaluation analysis can be performed. This enables more appropriate evaluation analysis by adjusting the evaluation analysis method according to the user's emotions.

[0101] The evaluation analysis unit can adjust the level of detail of the analysis based on the importance of the evaluation during evaluation analysis. The evaluation analysis unit, for example, uses a generation AI to evaluate the importance of the evaluation. For example, the generation AI can analyze the impact and relevance of the evaluation and evaluate the importance. The generation AI can evaluate the importance of the evaluation using, for example, natural language processing technology or data mining technology. The evaluation analysis unit adjusts the level of detail of the analysis based on the evaluated importance. For example, a detailed analysis can be performed for evaluations with high importance. Furthermore, a concise analysis can be performed for evaluations with low importance. Furthermore, the priority of the analysis can be determined according to the importance of the evaluation. As a result, efficient evaluation analysis is possible by adjusting the level of detail of the analysis based on the importance of the evaluation.

[0102] The evaluation analysis unit can estimate the user's emotions and determine the priority of evaluation analysis based on the emotions. The evaluation analysis unit estimates the user's emotions using, for example, a generation AI. For example, the generation AI can analyze the user's posted content and behavioral history to estimate emotions. The generation AI can estimate the user's emotions using, for example, natural language processing technology or an emotion analysis algorithm. The evaluation analysis unit determines the priority of evaluation analysis based on the estimated emotions. For example, if the user is relaxed, detailed evaluation analysis can be prioritized. Also, if the user is in a hurry, brief evaluation analysis can be prioritized. Furthermore, if the user is excited, visually stimulating evaluation analysis can be prioritized. This enables more appropriate evaluation analysis by determining the priority of evaluation analysis according to the user's emotions.

[0103] The evaluation analysis unit can adjust the order of analysis based on the submission date of the evaluations. For example, the evaluation analysis unit can obtain the submission date of the evaluations using a generative AI. For example, the generative AI can analyze the submission date and time or timestamp to identify the submission date of the evaluation. The generative AI can obtain the submission date of the evaluation using data mining techniques, for example. The evaluation analysis unit adjusts the order of analysis based on the obtained submission date. For example, the most recent evaluations are analyzed preferentially. Older evaluations can be postponed in the analysis order. Furthermore, the analysis schedule can be adjusted according to the submission date. This allows for efficient evaluation analysis by adjusting the order of analysis based on the evaluation submission date.

[0104] The collaboration unit can estimate the user's emotions and adjust the collaboration method based on the emotions. The collaboration unit estimates the user's emotions using, for example, a generation AI. For example, the generation AI can analyze the user's posted content and behavioral history to estimate emotions. The generation AI can estimate the user's emotions using, for example, natural language processing technology or an emotion analysis algorithm. The collaboration unit adjusts the collaboration method based on the estimated emotions. For example, if the user is relaxed, a detailed collaboration method can be suggested. Also, if the user is in a hurry, a concise collaboration method can be suggested. Furthermore, if the user is excited, a visually stimulating collaboration method can be suggested. In this way, by adjusting the collaboration method according to the user's emotions, a more appropriate collaboration method can be provided.

[0105] The Collaboration Department can analyze a user's past collaboration history to select the optimal collaboration method during a collaboration. For example, the Collaboration Department uses generative AI to analyze a user's past collaboration history. For example, generative AI can analyze a user's collaboration history and select the optimal collaboration method. For example, generative AI can use data mining techniques to analyze a user's collaboration history. Based on the analysis results, the Collaboration Department selects the optimal collaboration method. For example, it analyzes a user's past collaboration history and proposes the most effective collaboration method. It can also identify collaboration methods with a high success rate from the user's collaboration history and propose those methods. Furthermore, it can propose the optimal collaboration partner based on the user's collaboration history. In this way, the optimal collaboration method can be selected by analyzing a user's past collaboration history.

[0106] The collaboration unit can estimate a user's emotions and prioritize collaborations based on those emotions. For example, the collaboration unit uses generative AI to estimate user emotions. For instance, generative AI can analyze a user's posts and behavioral history to estimate emotions. For example, generative AI can use natural language processing techniques and emotion analysis algorithms to estimate user emotions. Based on the estimated emotions, the collaboration unit prioritizes collaborations. For example, if a user is relaxed, it will prioritize suggesting detailed collaboration methods. If a user is in a hurry, it will prioritize suggesting concise collaboration methods. Furthermore, if a user is excited, it will prioritize suggesting visually stimulating collaboration methods. This allows for more appropriate collaboration methods to be provided by prioritizing collaborations according to the user's emotions.

[0107] The collaboration unit can select the optimal collaboration method during collaboration by taking into account the user's geographic location information. The collaboration unit, for example, uses a generation AI to acquire the user's geographic location information. For example, the generation AI can analyze GPS data or an IP address to identify the user's location information. The generation AI can acquire the user's geographic location information by using a location information service, for example. The collaboration unit selects the optimal collaboration method based on the acquired geographic location information. For example, if the user is in a specific area, the collaboration unit can suggest collaboration methods related to that area. Furthermore, the collaboration unit can suggest collaboration methods related to local markets or events based on the user's location information. Furthermore, if the user is traveling, the collaboration unit can suggest collaboration methods at the user's travel destination. In this way, the optimal collaboration method can be selected by taking into account the user's geographic location information. === Hard Collateral 1-1 === Each of the multiple elements described above, including the data collection unit, analysis unit, visualization unit, and monetization unit, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects information transmitted by the user on the SNS platform. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the collected information to identify the user's expertise. The visualization unit is implemented, for example, by the control unit 46A of the smart device 14 and visually displays the expertise based on the analysis results. The monetization unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and provides monetization opportunities based on the visualized expertise. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, visualization unit, and monetization unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects information posted by users on SNS platforms. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to identify the user's expertise. The visualization unit is realized, for example, by the control unit 46A of the smart glasses 214 and visually displays the expertise based on the analysis results. The monetization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides monetization opportunities based on the visualized expertise. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, visualization unit, and monetization unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 and collects information posted by users on the SNS platform. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to identify the user's expertise. The visualization unit is realized, for example, by the control unit 46A of the headset type terminal 314 and visually displays the expertise based on the analysis results. The monetization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides monetization opportunities based on the visualized expertise. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, visualization unit, and monetization unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects information posted by users on the SNS platform. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to identify the user's expertise. The visualization unit is realized, for example, by the control unit 46A of the robot 414 and visually displays the expertise based on the analysis results. The monetization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides monetization opportunities based on the visualized expertise.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] Social media platforms can also feature project matching functions that utilize users' expertise. For example, if a lawyer is looking for a project related to a specific legal matter, the platform can suggest appropriate projects based on that expertise. Similarly, if an academic is seeking research funding, the platform can introduce them to funders related to that research field. Furthermore, if a creator is looking for collaborators to create a new work, the platform can match them with other users who are knowledgeable in that field. This allows users to make the most of their own expertise and efficiently advance their projects.

[0110] The collection unit can also collect the latest trend information related to the user's expertise. For example, a lawyer can collect relevant news and papers to stay up to date on legal precedents and legal reforms. A scholar can also collect relevant academic papers and conference information to stay up to date on the latest discoveries and technologies in their field of research. Furthermore, a creator can collect relevant blogs and social media posts to stay up to date on the latest trends in design and art. This allows users to always have the latest information and enhance their expertise.

[0111] The analysis unit can also provide a mutual evaluation system with other users based on the user's expertise. For example, a lawyer can evaluate the posts of other lawyers, and the expertise can be further identified based on the evaluation results. Also, a scholar can evaluate the research results of other scholars, and the expertise can be identified based on the evaluation results. Furthermore, a creator can evaluate the work of other creators, and the expertise can be identified based on the evaluation results. In this way, users can evaluate each other, thereby improving the accuracy of the expertise.

[0112] The evaluation collection unit can also evaluate the reliability of evaluations based on the user's expertise. For example, if a lawyer receives evaluations from other lawyers, the reliability of the evaluation can be determined by taking into account the evaluator's expertise. Similarly, if a scholar receives evaluations from other scholars, the reliability of the evaluation can be determined by taking into account the evaluator's research field and track record. Furthermore, if a creator receives evaluations from other creators, the reliability of the evaluation can be determined by taking into account the evaluator's work and evaluation history. This can increase the reliability of the evaluations.

[0113] The evaluation analysis unit can also analyze the impact of evaluations based on the user's expertise. For example, it can analyze the extent to which an evaluation of a lawyer influences other users. It can also analyze the extent to which an evaluation of a scholar influences other researchers. It can also analyze the extent to which an evaluation of a creator influences other creators and fans. This allows the impact of evaluations to be understood, further enhancing the user's expertise.

[0114] The collaboration unit can also estimate the user's emotions and adjust the timing of collaboration based on those emotions. For example, if the user is feeling stressed, the collaboration unit will refrain from proposing collaboration. On the other hand, if the user is relaxed, the collaboration unit can proactively propose collaboration. Furthermore, if the user is excited, the collaboration unit can propose collaboration in real time and start the project immediately. This makes it possible to promote collaboration at the optimal timing according to the user's emotions.

[0115] The collection unit can also estimate the user's emotions and adjust the type of information to be collected based on the emotions. For example, if the user is feeling stressed, it can prioritize collecting relaxing information. Also, if the user is relaxed, it can prioritize collecting specialized information. Furthermore, if the user is excited, it can prioritize collecting stimulating information. In this way, it is possible to collect optimal information according to the user's emotions.

[0116] The analysis unit can also estimate the user's emotions and adjust the accuracy of the analysis based on those emotions. For example, if the user is feeling stressed, it can provide a concise and easy-to-understand analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is excited, it can provide a visually stimulating analysis result. This makes it possible to provide optimal analysis results according to the user's emotions.

[0117] The visualization unit can also estimate the user's emotions and adjust the visualization format based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-understand visualization. If the user is relaxed, it can provide a detailed visualization. Furthermore, if the user is excited, it can provide a visually stimulating visualization. This allows for the provision of optimal visualization according to the user's emotions.

[0118] The monetization department can also estimate the user's emotions and adjust monetization suggestions based on those emotions. For example, if a user is stressed, it can suggest easy and convenient ways to earn revenue. If the user is relaxed, it can suggest methods that offer long-term revenue potential. Furthermore, if the user is excited, it can suggest methods that offer immediate revenue. This allows the company to suggest the most suitable monetization method according to the user's emotions.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The collection unit collects information about the user's expertise. For example, it collects information posted by users on social media platforms. Specifically, lawyers post legal advice, scholars share research results, and creators publish their work. This information is collected by generative AI (e.g., text generation AI or multimodal generation AI). Step 2: The analysis unit analyzes the information collected by the collection unit. The generation AI is used to analyze the collected information and identify the user's expertise. For example, the generation AI can identify the user's field of expertise or specialization from the content of the user's posts and reflect this in the user's profile. The generation AI can use natural language processing technology to analyze the content of the user's posts and identify the user's expertise. Step 3: The display unit visually displays the expertise based on the analysis results obtained by the analysis unit. Using the generation AI, the user's expertise is visually displayed in the form of graphs, charts, dashboards, etc. Step 4: The monetization unit provides monetization opportunities based on the expertise visually displayed by the display unit. Generative AI is used to provide monetization opportunities based on the user's expertise. For example, lawyers can offer legal consultations for a fee, scholars can offer online courses, and creators can sell their work.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0123] 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.

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

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 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 collection unit for collecting information about the user's expertise; an analysis unit that analyzes the information collected by the collection unit; a display unit that visually displays expertise based on the analysis results obtained by the analysis unit; a monetization unit that provides monetization opportunities based on the expertise visually displayed by the display unit; Equipped with A system characterized by:

2. Equipped with a rating collection unit that collects user ratings 2. The system of claim 1.

3. an evaluation analysis unit that analyzes the evaluation results collected by the evaluation collection unit; 3. The system of claim 2.

4. Equipped with a collaboration section that promotes collaboration between users 2. The system of claim 1.

5. The collecting unit Collect user-submitted content.

2. The system of claim 1.

6. The analysis unit Analyze the collected information to identify the user's expertise 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on those emotions 2. The system of claim 1.

8. The collecting unit Analyze the user's past posting history and select the appropriate data collection method.

2. The system of claim 1.

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