System

Abstract

An object of a system according to an embodiment is to effectively distribute know-how information of a company and to perform appropriate evaluation and pricing.SOLUTION: A system according to an embodiment includes a posting unit, an evaluation unit, a pricing unit, and a recommendation unit. The posting unit posts know-how information of a company. The evaluation unit evaluates the know-how information posted by the posting unit. The pricing unit sets a price of the know-how information evaluated by the evaluation unit. The recommendation unit recommends the related information based on the price set by the pricing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of making it difficult to effectively distribute corporate know-how information and to appropriately evaluate and price it.

[0005] The system according to the embodiment aims to effectively distribute corporate know-how information and to perform appropriate evaluation and pricing. [Means for solving the problem]

[0006] The system according to the embodiment includes a posting unit, an evaluation unit, a pricing unit, and a recommendation unit. The posting unit posts know-how information of a company. The evaluation unit evaluates the know-how information posted by the posting unit. The pricing unit sets a price for the know-how information evaluated by the evaluation unit. The recommendation unit recommends related information based on the price set by the pricing unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively distribute corporate know-how information and perform appropriate evaluation and pricing. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A know-how sharing platform according to an embodiment of the present invention is a system for sharing best practices in marketing, management, finance, accounting, sales, and other know-how possessed by companies. This system has a mechanism in which companies post know-how information, and other companies that want to view that information pay a fee, generating rewards for the posting company. This allows the know-how sharing platform to effectively share companies' know-how information, enabling appropriate pricing and recommendations for related information.

[0029] A know-how sharing platform according to an embodiment includes a posting unit, an evaluation unit, a pricing unit, and a recommendation unit. The posting unit posts know-how information of companies. For example, companies can post their own marketing strategies and management know-how. The posting unit also stores the posted information in a database so that other companies can access it. The evaluation unit evaluates the know-how information posted by the posting unit. For example, a purchasing company evaluates the content and usability of the know-how information. The evaluation unit also stores the evaluation results in a database and provides them to the pricing unit. The pricing unit sets a price for the know-how information evaluated by the evaluation unit. For example, know-how information with a high rating increases in price, and know-how information with a low rating decreases in price. The pricing unit also dynamically adjusts the price using a dynamic pricing algorithm. The recommendation unit recommends related information based on the price set by the pricing unit. For example, the recommendation unit analyzes a user's search history and browsing history to recommend highly relevant know-how information. The recommendation unit also provides optimal information to the user using a generation AI. As a result, the know-how sharing platform according to the embodiment can effectively distribute companies' know-how information and recommend appropriate pricing and related information. For example, the output unit displays the evaluation results and pricing of the know-how information to companies via a web application or a mobile application. If companies wish to receive feedback in paper form, they can print the results using a printer. Sending the results via email allows companies to receive quick feedback by sending the results directly to them.

[0030] The posting department can use the generation AI to automatically evaluate the value of the information and set an initial price. For example, when a posting company posts know-how information, the generation AI automatically evaluates the value of the information and sets an initial price. For example, the value of the posted information is calculated based on the prices and evaluations of similar information in the past. The posting department also uses the generation AI to evaluate the novelty and practicality of the information and set an initial price. For example, the generation AI analyzes the content of the information and evaluates its novelty and practicality. This allows the value of the posted information to be automatically evaluated and an initial price to be set.

[0031] The posting department can vary the rate of reward depending on the level of detail or specificity of the information. For example, when a posting company posts know-how information, the posting department introduces a system that varies the rate of reward depending on the level of detail and specificity of the information. For example, a higher reward is set for information that includes detailed procedures and specific data. The posting department also evaluates the specificity of the information and varies the rate of reward. For example, a higher reward is set for information that includes specific examples and detailed explanations. This makes it possible to vary the rate of reward depending on the level of detail and specificity of the information.

[0032] The posting unit can accept posts of know-how information not only in text format but also in video or audio format, thereby enhancing multimedia capabilities. The posting unit can, for example, accept posts of know-how information not only in text format but also in video or audio format, thereby enhancing multimedia capabilities. For example, it can be made possible to post video presentations or audio explanations. The posting unit can also store posts in video or audio format in a database, making them accessible to other companies. For example, it can store know-how information in video or audio format in a database and make it searchable. This can enhance the posting of know-how information to be multimedia capable.

[0033] The posting unit can add a joint posting function to promote collaboration with other companies. For example, when a posting company posts know-how information, the posting unit adds a joint posting function to promote collaboration with other companies. For example, it enables multiple companies to jointly post know-how information. The posting unit can also use the joint posting function to allow multiple companies to jointly edit and post know-how information. For example, it provides a joint editing function to enable multiple companies to edit information simultaneously. This allows the posting unit to add a joint posting function to promote collaboration with other companies.

[0034] When a purchasing company evaluates know-how information, the evaluation unit can use the generation AI to automatically analyze the reliability of the evaluation and correct any bias in the evaluation. For example, when a purchasing company evaluates know-how information, the evaluation unit uses the generation AI to automatically analyze the reliability of the evaluation and correct any bias in the evaluation. For example, the evaluation unit corrects any bias in the evaluation based on past evaluation history and the reliability of the evaluator. The evaluation unit also uses the generation AI to analyze the reliability of the evaluation and develops an algorithm to correct any bias in the evaluation. For example, the evaluation bias is corrected using an evaluation bias correction algorithm. This makes it possible to automatically analyze the reliability of the evaluation and correct any bias in the evaluation.

[0035] The evaluation unit can dynamically change not only the price but also the viewing restrictions or access permissions based on the evaluation of the know-how information. The evaluation unit, for example, introduces a mechanism for dynamically changing not only the price but also the viewing restrictions and access permissions based on the evaluation of the know-how information. For example, highly rated information increases in price and expands in access permissions. The evaluation unit also develops an algorithm for dynamically adjusting the viewing restrictions and access permissions based on the evaluation. For example, the evaluation unit sets viewing restrictions and adjusts access permissions based on the evaluation score. This makes it possible to dynamically change not only the price but also the viewing restrictions and access permissions based on the evaluation of the know-how information.

[0036] The evaluation unit can accept the evaluation of the know-how information in various formats, such as not only a text evaluation but also a star evaluation or a comment evaluation. For example, the evaluation unit accepts the evaluation of the know-how information in various formats, such as not only a text evaluation but also a star evaluation or a comment evaluation. For example, the quality of the information can be easily evaluated using a star evaluation. The evaluation unit also accepts a comment evaluation and stores the evaluation result in a database. For example, the quality of the information is evaluated based on the evaluator's comments. This allows the evaluation of the know-how information to be accepted in various formats.

[0037] The evaluation unit can add a function that allows a purchasing company to refer to the evaluation history and feedback of other purchasers when evaluating know-how information. The evaluation unit can add a function that allows a purchasing company to refer to the evaluation history and feedback of other purchasers when evaluating know-how information. For example, it can display the evaluation comments of other purchasers. The evaluation unit can also store the evaluation history of other purchasers in a database so that the purchasing company can refer to it. For example, it can display past evaluation scores and comments. This can add a function that allows a purchasing company to refer to the evaluation history and feedback of other purchasers.

[0038] The recommendation unit can use generation AI to analyze a user's search history and browsing history and provide individually optimized recommendations. The recommendation unit can, for example, use generation AI to analyze a user's search history and browsing history and provide individually optimized recommendations. For example, it can recommend related know-how information based on previously searched keywords and viewed information. The recommendation unit can also use generation AI to analyze a user's interests and develop an algorithm to provide optimal information. For example, it can recommend highly relevant information based on the user's behavioral history. This makes it possible to analyze a user's search history and browsing history and provide individually optimized recommendations.

[0039] The recommendation unit customizes search results according to the user's industry and occupation, and can provide more relevant information. For example, the recommendation unit customizes search results according to the user's industry and occupation, and can provide more relevant information. For example, marketing-related know-how information is preferentially displayed to a user in the marketing industry. The recommendation unit also analyzes the user's industry and occupation and develops an algorithm that provides optimal information. For example, it recommends information based on industry-specific keywords and topics. This allows the search results to be customized according to the user's industry and occupation, and can provide more relevant information.

[0040] The recommendation unit can make the search function compatible not only with text search but also with voice search or image search. The recommendation unit, for example, can make the search function compatible not only with text search but also with voice search or image search. For example, the recommendation unit can enable the user to input search keywords by voice. The recommendation unit can also provide an image search function, allowing the user to search for information based on images. For example, the recommendation unit can search for related know-how information using image recognition technology. This allows the search function to support not only text search but also voice search or image search.

[0041] The recommendation unit can link the recommendation function with the user's social network or professional network to provide a wider range of information. For example, the recommendation unit can link the recommendation function with the user's social network or professional network to provide a wider range of information. For example, the recommendation unit can recommend information based on data from LinkedIn or Facebook. The recommendation unit can also analyze data from social networks or professional networks to develop algorithms that provide optimal information. For example, the recommendation unit can recommend information based on trends and interests within the user's network. This allows the recommendation function to link with the user's social network or professional network to provide a wider range of information.

[0042] The security countermeasures department can use generation AI to automatically analyze the security risks of posted know-how information and take measures according to the risk level. For example, the security countermeasures department can use generation AI to automatically analyze the security risks of posted know-how information and take measures according to the risk level. For example, it can evaluate the confidentiality and importance of the information and implement appropriate security measures. The security countermeasures department can also use generation AI to analyze security risks and develop algorithms that take measures according to the risk level. For example, it can determine measures based on the severity and probability of occurrence of the risk. This makes it possible to automatically analyze the security risks of posted know-how information and take measures according to the risk level.

[0043] The security countermeasures department can dynamically change information masking depending on the user's access authority. For example, the security countermeasures department introduces a mechanism for dynamically changing information masking depending on the user's access authority. For example, confidential information is masked for users with low access authority. The security countermeasures department also develops an algorithm for dynamically changing information masking based on access authority. For example, the algorithm makes changes in real time or based on conditions. This makes it possible to dynamically change information masking depending on the user's access authority.

[0044] The security countermeasures unit can apply security countermeasures not only to text information but also to image or video information. For example, the security countermeasures unit applies security countermeasures not only to text information but also to image and video information. For example, confidential information in images and videos is masked. The security countermeasures unit also develops algorithms to implement security countermeasures for image and video information. For example, confidential information is detected and masked using image recognition technology. This allows security countermeasures to be applied not only to text information but also to image and video information.

[0045] The security countermeasures department can dynamically change information masking based on the user's geographical location information. For example, the security countermeasures department introduces a mechanism for dynamically changing information masking based on the user's geographical location information. For example, confidential information is masked for users in a specific area. The security countermeasures department also develops an algorithm for dynamically changing information masking based on geographical location information. For example, the masking is controlled based on location information such as GPS data or IP address. This makes it possible to dynamically change information masking based on the user's geographical location information.

[0046] The legal resolution department can use generation AI to automatically analyze whether posted know-how information infringes on other companies' patents. For example, the legal resolution department uses generation AI to automatically analyze whether posted know-how information infringes on other companies' patents. For example, it compares the information with a patent database to evaluate the possibility of infringement. The legal resolution department also uses generation AI to analyze the risk of patent infringement and develops algorithms to evaluate the possibility of infringement. For example, it evaluates based on the scope of the patent right and the degree of infringement. This makes it possible to automatically analyze whether posted know-how information infringes on other companies' patents using generation AI.

[0047] The legal settlement unit can automatically assess the legal risk when a posting company posts know-how information and provide guidelines according to the risk level. For example, when a posting company posts know-how information, the legal settlement unit can automatically assess the legal risk and provide guidelines according to the risk level. For example, a warning is displayed if there is a high risk of patent infringement. The legal settlement unit also develops an algorithm that assesses legal risk and provides guidelines according to the risk level. For example, guidelines are created based on the risk of litigation or the risk of regulatory violation. This makes it possible to automatically assess the legal risk when a posting company posts know-how information and provide guidelines according to the risk level.

[0048] The Legal Resolution Department organizes legal matters from not only a domestic but also an international perspective, making it possible to evaluate global legal risks. For example, the Legal Resolution Department organizes legal matters from not only a domestic but also an international perspective, making it possible to evaluate global legal risks. For example, it compares patent databases from each country to evaluate the possibility of infringement. The Legal Resolution Department also develops algorithms to evaluate legal risks based on international legal standards. For example, it makes evaluations based on the risk of international litigation or the risk of regulatory violations. This makes it possible to organize legal matters from not only a domestic but also an international perspective, making it possible to evaluate global legal risks.

[0049] The legal reorganization department can add a function for promoting legal collaboration with other companies when a posting company posts know-how information. The legal reorganization department, for example, adds a function for promoting legal collaboration with other companies when a posting company posts know-how information. For example, it provides a support function for joint patent applications. The legal reorganization department also develops algorithms for promoting legal collaboration with other companies. For example, it provides legal advice for joint projects. This allows a posting company to add a function for promoting legal collaboration with other companies when posting know-how information.

[0050] The purchaser's benefits department can use generation AI to automatically provide related additional information when a purchasing company views know-how information. For example, when a purchasing company views know-how information, the purchaser's benefits department can use generation AI to automatically provide related additional information. For example, related success stories and reference materials can be displayed. The purchaser's benefits department can also use generation AI to analyze related information and develop algorithms that provide optimal additional information. For example, related information can be recommended based on the purchasing company's browsing history. This allows the purchaser's benefits department to automatically provide related additional information when a purchasing company views know-how information.

[0051] The purchaser's benefit section can display other purchasers' feedback and ratings in real time when a purchasing company views know-how information. The purchaser's benefit section, for example, displays other purchasers' feedback and ratings in real time when a purchasing company views know-how information. For example, it displays other purchasers' evaluation comments and star ratings. The purchaser's benefit section also stores other purchasers' feedback in a database so that purchasing companies can refer to it. For example, it displays past evaluation scores and comments. This allows other purchasers' feedback and ratings to be displayed in real time when a purchasing company views know-how information.

[0052] The purchaser's benefit section can provide information on related webinars and online seminars when a purchasing company views know-how information. The purchaser's benefit section, for example, provides information on related webinars and online seminars when a purchasing company views know-how information. For example, it displays links to webinars on related topics. The purchaser's benefit section also stores information on webinars and online seminars in a database so that purchasing companies can access it. For example, it displays the webinar schedule and how to participate. This allows information on related webinars and online seminars to be provided when a purchasing company views know-how information.

[0053] The buyer's benefits department can provide a discussion forum with other buyers when a purchasing company views know-how information. The buyer's benefits department, for example, provides a discussion forum with other buyers when a purchasing company views know-how information. For example, it sets up a forum where purchasing companies can exchange opinions with other companies that have purchased the same information. The buyer's benefits department also stores the discussion forum in a database so that purchasing companies can access it. For example, it displays the forum's topic and how to participate. This allows a discussion forum with other buyers to be provided when a purchasing company views know-how information.

[0054] The Technological Breakthrough Department uses generative AI to analyze and evaluate know-how information in real time, thereby reducing operational costs. The Technological Breakthrough Department, for example, uses generative AI to analyze and evaluate know-how information in real time, thereby reducing operational costs. For example, the value and reliability of information is automatically evaluated. The Technological Breakthrough Department also develops algorithms that use generative AI to analyze and evaluate know-how information. For example, real-time analysis and evaluation reduces operational costs. This makes it possible to analyze and evaluate know-how information in real time using generative AI, thereby reducing operational costs.

[0055] The technological breakthrough department can improve the dynamic pricing algorithm to achieve more accurate pricing. The technological breakthrough department can, for example, improve the dynamic pricing algorithm to achieve more accurate pricing. For example, the price can be dynamically adjusted based on evaluation data and market trends. The technological breakthrough department can also conduct research to improve the dynamic pricing algorithm. For example, the accuracy of pricing can be improved using a machine learning algorithm. This can improve the dynamic pricing algorithm to achieve more accurate pricing.

[0056] The Technological Breakthrough Department can integrate other datasets when analyzing and evaluating know-how information using generative AI. For example, the Technological Breakthrough Department integrates other datasets (e.g., patent data or market data) when analyzing and evaluating know-how information using generative AI. For example, it compares the information with a patent database and evaluates the value of the information. The Technological Breakthrough Department also develops algorithms for integrating other datasets. For example, it evaluates the value of the information based on patent data or market data. This allows it to integrate other datasets when analyzing and evaluating know-how information using generative AI.

[0057] The Technical Breakthrough Department can customize the dynamic pricing algorithm for different industries and applications. The Technical Breakthrough Department, for example, customizes the dynamic pricing algorithm for different industries and applications. For example, different pricing is set for the manufacturing industry and the service industry. The Technical Breakthrough Department also develops customized algorithms for different industries and applications. For example, pricing is set according to product application and service application. This makes it possible to customize the dynamic pricing algorithm for different industries and applications.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The know-how sharing platform can further include a behavioral analysis unit that analyzes the user's behavioral history. The behavioral analysis unit, for example, analyzes what information the user frequently views and what search keywords they use to understand the user's interests. The behavioral analysis unit can also predict information that the user may need in the future based on the user's behavioral patterns and recommend it in advance. This makes it possible to analyze the user's behavioral history and provide more personalized information.

[0060] The know-how sharing platform may further include a feedback collection unit that collects user feedback. For example, the feedback collection unit displays a simple questionnaire after the user has viewed the information, and collects opinions on the usefulness of the information and areas for improvement. The feedback collection unit may also store the collected feedback in a database and provide it to the posting company. This allows for the collection of user feedback and the improvement of the information quality.

[0061] The know-how sharing platform may further include a behavior adjustment unit that analyzes the user's behavior history and adjusts the display order of information based on the user's behavior pattern. The behavior adjustment unit, for example, prioritizes displaying information that the user views frequently and postpones information that the user views less frequently. The behavior adjustment unit may also analyze the user's behavior pattern in real time and dynamically adjust the display order. This allows the display order of information to be adjusted based on the user's behavior history, improving the quality of the viewing experience.

[0062] The know-how sharing platform may further include a behavior recommendation unit that analyzes a user's behavior history and recommends information based on the user's behavioral patterns. For example, the behavior recommendation unit may preferentially recommend information related to keywords that the user frequently searches for, and postpone information related to keywords that the user searches less frequently. The behavior recommendation unit may also analyze the user's behavioral patterns in real time and dynamically adjust the recommended information. This allows information to be recommended based on the user's behavioral history, improving the quality of the browsing experience.

[0063] The know-how sharing platform may further include a behavior correction unit that analyzes the user's behavior history and corrects the evaluation of information based on the behavior pattern. The behavior correction unit, for example, may increase the evaluation of information that the user frequently views and decrease the evaluation of information that the user views infrequently. The behavior correction unit may also analyze the user's behavior pattern in real time to improve the reliability of the evaluation. This allows the information evaluation to be corrected based on the user's behavior history, thereby improving the reliability of the evaluation.

[0064] The know-how sharing platform may further include a behavioral feedback unit that analyzes the user's behavioral history and provides information feedback based on the user's behavioral patterns. For example, the behavioral feedback unit may request detailed feedback for information that the user views frequently and simple feedback for information that the user views infrequently. The behavioral feedback unit may also analyze the user's behavioral patterns in real time and dynamically adjust the feedback content. This allows for information feedback based on the user's behavioral history, improving the quality of the browsing experience.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The posting department posts corporate know-how information. For example, a company can post its own marketing strategy or management know-how. The posting department also stores the posted information in a database so that other companies can access it. Step 2: The evaluation department evaluates the know-how information posted by the posting department. For example, a purchasing company evaluates the content and usability of the know-how information. The evaluation department also saves the evaluation results in a database and provides them to the pricing department. Step 3: The pricing unit sets the price of the know-how information evaluated by the evaluation unit. For example, the price of highly rated know-how information increases, and the price of low-rated know-how information decreases. The pricing unit also dynamically adjusts the price using a dynamic pricing algorithm. Step 4: The recommendation department recommends related information based on the price set by the pricing department. For example, it analyzes the user's search history and browsing history to recommend highly relevant know-how information. The recommendation department also uses generation AI to provide the user with the most suitable information.

[0067] (Example 2) A know-how sharing platform according to an embodiment of the present invention is a system for sharing best practices in marketing, management, finance, accounting, sales, and other know-how possessed by companies. This system has a mechanism in which companies post know-how information, and other companies that want to view that information pay a fee, generating rewards for the posting company. This allows the know-how sharing platform to effectively share companies' know-how information, enabling appropriate pricing and recommendations for related information.

[0068] A know-how sharing platform according to an embodiment includes a posting unit, an evaluation unit, a pricing unit, and a recommendation unit. The posting unit posts know-how information of companies. For example, companies can post their own marketing strategies and management know-how. The posting unit also stores the posted information in a database so that other companies can access it. The evaluation unit evaluates the know-how information posted by the posting unit. For example, a purchasing company evaluates the content and usability of the know-how information. The evaluation unit also stores the evaluation results in a database and provides them to the pricing unit. The pricing unit sets a price for the know-how information evaluated by the evaluation unit. For example, know-how information with a high rating increases in price, and know-how information with a low rating decreases in price. The pricing unit also dynamically adjusts the price using a dynamic pricing algorithm. The recommendation unit recommends related information based on the price set by the pricing unit. For example, the recommendation unit analyzes a user's search history and browsing history to recommend highly relevant know-how information. The recommendation unit also provides optimal information to the user using a generation AI. As a result, the know-how sharing platform according to the embodiment can effectively distribute companies' know-how information and recommend appropriate pricing and related information. For example, the output unit displays the evaluation results and pricing of the know-how information to companies via a web application or a mobile application. If companies wish to receive feedback in paper form, they can print the results using a printer. Sending the results via email allows companies to receive quick feedback by sending the results directly to them.

[0069] The posting department can use the generation AI to automatically evaluate the value of the information and set an initial price. For example, when a posting company posts know-how information, the generation AI automatically evaluates the value of the information and sets an initial price. For example, the value of the posted information is calculated based on the prices and evaluations of similar information in the past. The posting department also uses the generation AI to evaluate the novelty and practicality of the information and set an initial price. For example, the generation AI analyzes the content of the information and evaluates its novelty and practicality. This allows the value of the posted information to be automatically evaluated and an initial price to be set.

[0070] The posting department can vary the rate of reward depending on the level of detail or specificity of the information. For example, when a posting company posts know-how information, the posting department introduces a system that varies the rate of reward depending on the level of detail and specificity of the information. For example, a higher reward is set for information that includes detailed procedures and specific data. The posting department also evaluates the specificity of the information and varies the rate of reward. For example, a higher reward is set for information that includes specific examples and detailed explanations. This makes it possible to vary the rate of reward depending on the level of detail and specificity of the information.

[0071] The posting unit can use the emotion estimation function to analyze the emotions of employees and encourage them to post information that conveys positive emotions. For example, the posting unit can use the emotion estimation function to analyze the emotions of employees of the posting company when they post know-how information and encourage them to post information that conveys positive emotions. For example, information that conveys strong positive emotions is displayed preferentially. The posting unit can also use the emotion estimation function to provide feedback on employees' emotions in real time and encourage them to post information that conveys positive emotions. For example, if positive emotions are strong, an encouraging message can be displayed. This can encourage them to post information that conveys positive emotions.

[0072] The posting unit can accept posts of know-how information not only in text format but also in video or audio format, thereby enhancing multimedia capabilities. The posting unit can, for example, accept posts of know-how information not only in text format but also in video or audio format, thereby enhancing multimedia capabilities. For example, it can be made possible to post video presentations or audio explanations. The posting unit can also store posts in video or audio format in a database, making them accessible to other companies. For example, it can store know-how information in video or audio format in a database and make it searchable. This can enhance the posting of know-how information to be multimedia capable.

[0073] The posting unit can add a joint posting function to promote collaboration with other companies. For example, when a posting company posts know-how information, the posting unit adds a joint posting function to promote collaboration with other companies. For example, it enables multiple companies to jointly post know-how information. The posting unit can also use the joint posting function to allow multiple companies to jointly edit and post know-how information. For example, it provides a joint editing function to enable multiple companies to edit information simultaneously. This allows the posting unit to add a joint posting function to promote collaboration with other companies.

[0074] The posting unit uses the emotion estimation function to provide real-time feedback on the emotions of employees when they post know-how information, thereby improving the quality of their posts. For example, the posting unit uses the emotion estimation function to provide real-time feedback on the emotions of employees of the posting company when they post know-how information, thereby improving the quality of their posts. For example, if positive emotions are strong, an encouraging message is displayed. The posting unit also uses the emotion estimation function to analyze employees' emotions in real-time, thereby improving the quality of their posts. For example, if negative emotions are strong, the posting is paused. This allows real-time feedback on the emotions of employees of the posting company when they post know-how information, thereby improving the quality of their posts.

[0075] When a purchasing company evaluates know-how information, the evaluation unit can use the generation AI to automatically analyze the reliability of the evaluation and correct any bias in the evaluation. For example, when a purchasing company evaluates know-how information, the evaluation unit uses the generation AI to automatically analyze the reliability of the evaluation and correct any bias in the evaluation. For example, the evaluation unit corrects any bias in the evaluation based on past evaluation history and the reliability of the evaluator. The evaluation unit also uses the generation AI to analyze the reliability of the evaluation and develops an algorithm to correct any bias in the evaluation. For example, the evaluation bias is corrected using an evaluation bias correction algorithm. This makes it possible to automatically analyze the reliability of the evaluation and correct any bias in the evaluation.

[0076] The evaluation unit can dynamically change not only the price but also the viewing restrictions or access permissions based on the evaluation of the know-how information. The evaluation unit, for example, introduces a mechanism for dynamically changing not only the price but also the viewing restrictions and access permissions based on the evaluation of the know-how information. For example, highly rated information increases in price and expands in access permissions. The evaluation unit also develops an algorithm for dynamically adjusting the viewing restrictions and access permissions based on the evaluation. For example, the evaluation unit sets viewing restrictions and adjusts access permissions based on the evaluation score. This makes it possible to dynamically change not only the price but also the viewing restrictions and access permissions based on the evaluation of the know-how information.

[0077] The evaluation unit can use the emotion estimation function to analyze the emotions of employees of the purchasing company and prioritize reflect evaluations with positive emotions. The evaluation unit, for example, uses the emotion estimation function to analyze the emotions of employees of the purchasing company when evaluating know-how information and prioritize reflect evaluations with positive emotions. For example, evaluations with strong positive emotions are weighted highly. The evaluation unit also uses the emotion estimation function to analyze employee emotions in real time and develop an algorithm that prioritizes reflecting evaluations with positive emotions. For example, if positive emotions are strong, the evaluation score is increased. This makes it possible to prioritize reflect evaluations with positive emotions.

[0078] The evaluation unit can accept the evaluation of the know-how information in various formats, such as not only a text evaluation but also a star evaluation or a comment evaluation. For example, the evaluation unit accepts the evaluation of the know-how information in various formats, such as not only a text evaluation but also a star evaluation or a comment evaluation. For example, the quality of the information can be easily evaluated using a star evaluation. The evaluation unit also accepts a comment evaluation and stores the evaluation result in a database. For example, the quality of the information is evaluated based on the evaluator's comments. This allows the evaluation of the know-how information to be accepted in various formats.

[0079] The evaluation unit can add a function that allows a purchasing company to refer to the evaluation history and feedback of other purchasers when evaluating know-how information. The evaluation unit can add a function that allows a purchasing company to refer to the evaluation history and feedback of other purchasers when evaluating know-how information. For example, it can display the evaluation comments of other purchasers. The evaluation unit can also store the evaluation history of other purchasers in a database so that the purchasing company can refer to it. For example, it can display past evaluation scores and comments. This can add a function that allows a purchasing company to refer to the evaluation history and feedback of other purchasers.

[0080] The evaluation unit can use the emotion estimation function to provide real-time feedback on the emotions of employees of the purchasing company when they evaluate know-how information, thereby improving the quality of the evaluation. For example, the evaluation unit can use the emotion estimation function to provide real-time feedback on the emotions of employees of the purchasing company when they evaluate know-how information, thereby improving the quality of the evaluation. For example, an encouraging message can be displayed when positive emotions are strong. The evaluation unit can also use the emotion estimation function to analyze employees' emotions in real time and develop an algorithm to improve the quality of the evaluation. For example, the evaluation can be paused when negative emotions are strong. This allows real-time feedback on the emotions of employees of the purchasing company when they evaluate know-how information, thereby improving the quality of the evaluation.

[0081] The recommendation unit can use generation AI to analyze a user's search history and browsing history and provide individually optimized recommendations. The recommendation unit can, for example, use generation AI to analyze a user's search history and browsing history and provide individually optimized recommendations. For example, it can recommend related know-how information based on previously searched keywords and viewed information. The recommendation unit can also use generation AI to analyze a user's interests and develop an algorithm to provide optimal information. For example, it can recommend highly relevant information based on the user's behavioral history. This makes it possible to analyze a user's search history and browsing history and provide individually optimized recommendations.

[0082] The recommendation unit customizes search results according to the user's industry and occupation, and can provide more relevant information. For example, the recommendation unit customizes search results according to the user's industry and occupation, and can provide more relevant information. For example, marketing-related know-how information is preferentially displayed to a user in the marketing industry. The recommendation unit also analyzes the user's industry and occupation and develops an algorithm that provides optimal information. For example, it recommends information based on industry-specific keywords and topics. This allows the search results to be customized according to the user's industry and occupation, and can provide more relevant information.

[0083] The recommendation unit can use the emotion estimation function to analyze the user's emotions and make recommendations that elicit positive emotions. The recommendation unit, for example, uses the emotion estimation function to analyze the user's emotions and make recommendations that elicit positive emotions. For example, it preferentially recommends information that the user has positive emotions about. The recommendation unit also uses the emotion estimation function to analyze the user's emotions in real time and develops an algorithm that elicits positive emotions. For example, when positive emotions are strong, related information is preferentially displayed. This makes it possible to analyze the user's emotions and make recommendations that elicit positive emotions.

[0084] The recommendation unit can make the search function compatible not only with text search but also with voice search or image search. The recommendation unit, for example, can make the search function compatible not only with text search but also with voice search or image search. For example, the recommendation unit can enable the user to input search keywords by voice. The recommendation unit can also provide an image search function, allowing the user to search for information based on images. For example, the recommendation unit can search for related know-how information using image recognition technology. This allows the search function to support not only text search but also voice search or image search.

[0085] The recommendation unit can link the recommendation function with the user's social network or professional network to provide a wider range of information. For example, the recommendation unit can link the recommendation function with the user's social network or professional network to provide a wider range of information. For example, the recommendation unit can recommend information based on data from LinkedIn or Facebook. The recommendation unit can also analyze data from social networks or professional networks to develop algorithms that provide optimal information. For example, the recommendation unit can recommend information based on trends and interests within the user's network. This allows the recommendation function to link with the user's social network or professional network to provide a wider range of information.

[0086] The recommendation unit uses the emotion estimation function to provide real-time feedback on the emotions felt by users when they search, thereby improving the quality of search results. The recommendation unit, for example, uses the emotion estimation function to provide real-time feedback on the emotions felt by users when they search, thereby improving the quality of search results. For example, when positive emotions are strong, highly relevant information is preferentially displayed. The recommendation unit also uses the emotion estimation function to analyze the user's emotions in real time and develop an algorithm that improves the quality of search results. For example, when negative emotions are strong, the search results are adjusted. This allows real-time feedback on the emotions felt by users when they search, thereby improving the quality of search results.

[0087] The security countermeasures department can use generation AI to automatically analyze the security risks of posted know-how information and take measures according to the risk level. For example, the security countermeasures department can use generation AI to automatically analyze the security risks of posted know-how information and take measures according to the risk level. For example, it can evaluate the confidentiality and importance of the information and implement appropriate security measures. The security countermeasures department can also use generation AI to analyze security risks and develop algorithms that take measures according to the risk level. For example, it can determine measures based on the severity and probability of occurrence of the risk. This makes it possible to automatically analyze the security risks of posted know-how information and take measures according to the risk level.

[0088] The security countermeasures department can dynamically change information masking depending on the user's access authority. For example, the security countermeasures department introduces a mechanism for dynamically changing information masking depending on the user's access authority. For example, confidential information is masked for users with low access authority. The security countermeasures department also develops an algorithm for dynamically changing information masking based on access authority. For example, the algorithm makes changes in real time or based on conditions. This makes it possible to dynamically change information masking depending on the user's access authority.

[0089] The security countermeasures department can use the emotion estimation function to analyze the emotions of employees of the posting company and prevent the posting of information that poses a high security risk. For example, the security countermeasures department can use the emotion estimation function to analyze the emotions of employees of the posting company when they post know-how information and prevent the posting of information that poses a high security risk. For example, the security countermeasures department can suspend posting if the employee's negative emotions are strong. The security countermeasures department can also use the emotion estimation function to analyze employee emotions in real time and develop an algorithm that prevents the posting of information that poses a high security risk. For example, the security countermeasures department can control posting based on the intensity and type of emotion. In this way, the emotion estimation function can be used to prevent the posting of information that poses a high security risk.

[0090] The security countermeasures unit can apply security countermeasures not only to text information but also to image or video information. For example, the security countermeasures unit applies security countermeasures not only to text information but also to image and video information. For example, confidential information in images and videos is masked. The security countermeasures unit also develops algorithms to implement security countermeasures for image and video information. For example, confidential information is detected and masked using image recognition technology. This allows security countermeasures to be applied not only to text information but also to image and video information.

[0091] The security countermeasures department can dynamically change information masking based on the user's geographical location information. For example, the security countermeasures department introduces a mechanism for dynamically changing information masking based on the user's geographical location information. For example, confidential information is masked for users in a specific area. The security countermeasures department also develops an algorithm for dynamically changing information masking based on geographical location information. For example, the masking is controlled based on location information such as GPS data or IP address. This makes it possible to dynamically change information masking based on the user's geographical location information.

[0092] The security countermeasures department can use the emotion estimation function to provide real-time feedback on the emotions expressed by employees of the posting company when they post information, thereby improving the quality of security countermeasures. For example, the security countermeasures department can use the emotion estimation function to provide real-time feedback on the emotions expressed by employees of the posting company when they post information, thereby improving the quality of security countermeasures. For example, if the employee's negative emotions are strong, the security countermeasures department can suspend the posting. The security countermeasures department can also use the emotion estimation function to analyze the employee's emotions in real time and develop an algorithm to improve the quality of security countermeasures. For example, the security countermeasures can be adjusted based on the intensity and type of emotion. In this way, the emotion estimation function can be used to improve the quality of security countermeasures.

[0093] The legal resolution department can use generation AI to automatically analyze whether posted know-how information infringes on other companies' patents. For example, the legal resolution department uses generation AI to automatically analyze whether posted know-how information infringes on other companies' patents. For example, it compares the information with a patent database to evaluate the possibility of infringement. The legal resolution department also uses generation AI to analyze the risk of patent infringement and develops algorithms to evaluate the possibility of infringement. For example, it evaluates based on the scope of the patent right and the degree of infringement. This makes it possible to automatically analyze whether posted know-how information infringes on other companies' patents using generation AI.

[0094] The legal settlement unit can automatically assess the legal risk when a posting company posts know-how information and provide guidelines according to the risk level. For example, when a posting company posts know-how information, the legal settlement unit can automatically assess the legal risk and provide guidelines according to the risk level. For example, a warning is displayed if there is a high risk of patent infringement. The legal settlement unit also develops an algorithm that assesses legal risk and provides guidelines according to the risk level. For example, guidelines are created based on the risk of litigation or the risk of regulatory violation. This makes it possible to automatically assess the legal risk when a posting company posts know-how information and provide guidelines according to the risk level.

[0095] The legal settlement unit can use the emotion estimation function to analyze the emotions of employees of the posting company and prevent the posting of information that poses a high legal risk. For example, the legal settlement unit can use the emotion estimation function to analyze the emotions of employees of the posting company when they post know-how information and prevent the posting of information that poses a high legal risk. For example, the legal settlement unit can suspend posting if the employee's negative emotions are strong. The legal settlement unit can also use the emotion estimation function to analyze the emotions of employees in real time and develop an algorithm that prevents the posting of information that poses a high legal risk. For example, the legal settlement unit can control posting based on the intensity and type of emotion. In this way, the legal settlement unit can prevent the posting of information that poses a high legal risk using the emotion estimation function.

[0096] The Legal Resolution Department organizes legal matters from not only a domestic but also an international perspective, making it possible to evaluate global legal risks. For example, the Legal Resolution Department organizes legal matters from not only a domestic but also an international perspective, making it possible to evaluate global legal risks. For example, it compares patent databases from each country to evaluate the possibility of infringement. The Legal Resolution Department also develops algorithms to evaluate legal risks based on international legal standards. For example, it makes evaluations based on the risk of international litigation or the risk of regulatory violations. This makes it possible to organize legal matters from not only a domestic but also an international perspective, making it possible to evaluate global legal risks.

[0097] The legal reorganization department can add a function for promoting legal collaboration with other companies when a posting company posts know-how information. The legal reorganization department, for example, adds a function for promoting legal collaboration with other companies when a posting company posts know-how information. For example, it provides a support function for joint patent applications. The legal reorganization department also develops algorithms for promoting legal collaboration with other companies. For example, it provides legal advice for joint projects. This allows a posting company to add a function for promoting legal collaboration with other companies when posting know-how information.

[0098] The legal resolution department can improve the quality of legal risk by using the emotion estimation function to provide real-time feedback on the emotions of employees of the posting company when they post information. The legal resolution department can improve the quality of legal risk by, for example, using the emotion estimation function to provide real-time feedback on the emotions of employees of the posting company when they post information. For example, the legal resolution department can suspend posting if the emotions are strong. The legal resolution department can also use the emotion estimation function to analyze employees' emotions in real time and develop an algorithm to improve the quality of legal risk. For example, the legal risk can be evaluated based on the intensity and type of emotions. In this way, the emotion estimation function can be used to improve the quality of legal risk.

[0099] The purchaser's benefits department can use generation AI to automatically provide related additional information when a purchasing company views know-how information. For example, when a purchasing company views know-how information, the purchaser's benefits department can use generation AI to automatically provide related additional information. For example, related success stories and reference materials can be displayed. The purchaser's benefits department can also use generation AI to analyze related information and develop algorithms that provide optimal additional information. For example, related information can be recommended based on the purchasing company's browsing history. This allows the purchaser's benefits department to automatically provide related additional information when a purchasing company views know-how information.

[0100] The purchaser's benefit section can display other purchasers' feedback and ratings in real time when a purchasing company views know-how information. The purchaser's benefit section, for example, displays other purchasers' feedback and ratings in real time when a purchasing company views know-how information. For example, it displays other purchasers' evaluation comments and star ratings. The purchaser's benefit section also stores other purchasers' feedback in a database so that purchasing companies can refer to it. For example, it displays past evaluation scores and comments. This allows other purchasers' feedback and ratings to be displayed in real time when a purchasing company views know-how information.

[0101] The purchaser's benefit unit can use the emotion estimation function to analyze the emotions of employees of the purchasing company and provide information that elicits positive emotions preferentially. The purchaser's benefit unit, for example, uses the emotion estimation function to analyze the emotions of employees of the purchasing company when they view know-how information and provide information that elicits positive emotions preferentially. For example, it preferentially displays information that evokes strong positive emotions. The purchaser's benefit unit also uses the emotion estimation function to analyze employees' emotions in real time and develops an algorithm that provides information that elicits positive emotions. For example, it recommends information based on the intensity and type of emotion. This allows the emotion estimation function to provide information that elicits positive emotions preferentially.

[0102] The purchaser's benefit section can provide information on related webinars and online seminars when a purchasing company views know-how information. The purchaser's benefit section, for example, provides information on related webinars and online seminars when a purchasing company views know-how information. For example, it displays links to webinars on related topics. The purchaser's benefit section also stores information on webinars and online seminars in a database so that purchasing companies can access it. For example, it displays the webinar schedule and how to participate. This allows information on related webinars and online seminars to be provided when a purchasing company views know-how information.

[0103] The buyer's benefits department can provide a discussion forum with other buyers when a purchasing company views know-how information. The buyer's benefits department, for example, provides a discussion forum with other buyers when a purchasing company views know-how information. For example, it sets up a forum where purchasing companies can exchange opinions with other companies that have purchased the same information. The buyer's benefits department also stores the discussion forum in a database so that purchasing companies can access it. For example, it displays the forum's topic and how to participate. This allows a discussion forum with other buyers to be provided when a purchasing company views know-how information.

[0104] The purchase-side benefit unit uses the emotion estimation function to provide real-time feedback on the emotions of employees of the purchasing company when they view information, thereby improving the quality of the viewing experience. The purchase-side benefit unit, for example, uses the emotion estimation function to provide real-time feedback on the emotions of employees of the purchasing company when they view information, thereby improving the quality of the viewing experience. For example, when positive emotions are strong, related information is preferentially displayed. The purchase-side benefit unit also uses the emotion estimation function to analyze the emotions of employees in real time and develop an algorithm that improves the quality of the viewing experience. For example, information is recommended based on the intensity and type of emotion. In this way, the emotion estimation function can be used to improve the quality of the viewing experience.

[0105] The Technological Breakthrough Department uses generative AI to analyze and evaluate know-how information in real time, thereby reducing operational costs. The Technological Breakthrough Department, for example, uses generative AI to analyze and evaluate know-how information in real time, thereby reducing operational costs. For example, the value and reliability of information is automatically evaluated. The Technological Breakthrough Department also develops algorithms that use generative AI to analyze and evaluate know-how information. For example, real-time analysis and evaluation reduces operational costs. This makes it possible to analyze and evaluate know-how information in real time using generative AI, thereby reducing operational costs.

[0106] The technological breakthrough department can improve the dynamic pricing algorithm to achieve more accurate pricing. The technological breakthrough department can, for example, improve the dynamic pricing algorithm to achieve more accurate pricing. For example, the price can be dynamically adjusted based on evaluation data and market trends. The technological breakthrough department can also conduct research to improve the dynamic pricing algorithm. For example, the accuracy of pricing can be improved using a machine learning algorithm. This can improve the dynamic pricing algorithm to achieve more accurate pricing.

[0107] The technological breakthrough unit can use the emotion estimation function to analyze the user's emotions and prioritize the implementation of technological breakthroughs that elicit positive emotions. The technological breakthrough unit, for example, uses the emotion estimation function to analyze the user's emotions and prioritize the implementation of technological breakthroughs that elicit positive emotions. For example, it prioritizes the development of functions that elicit strong positive emotions. The technological breakthrough unit also uses the emotion estimation function to analyze the user's emotions in real time and develops algorithms to implement technological breakthroughs. For example, it selects technological breakthroughs based on the intensity and type of emotions. This allows the technological breakthroughs that elicit positive emotions to be prioritized for implementation using the emotion estimation function.

[0108] The Technological Breakthrough Department can integrate other datasets when analyzing and evaluating know-how information using generative AI. For example, the Technological Breakthrough Department integrates other datasets (e.g., patent data or market data) when analyzing and evaluating know-how information using generative AI. For example, it compares the information with a patent database and evaluates the value of the information. The Technological Breakthrough Department also develops algorithms for integrating other datasets. For example, it evaluates the value of the information based on patent data or market data. This allows it to integrate other datasets when analyzing and evaluating know-how information using generative AI.

[0109] The Technical Breakthrough Department can customize the dynamic pricing algorithm for different industries and applications. The Technical Breakthrough Department, for example, customizes the dynamic pricing algorithm for different industries and applications. For example, different pricing is set for the manufacturing industry and the service industry. The Technical Breakthrough Department also develops customized algorithms for different industries and applications. For example, pricing is set according to product application and service application. This makes it possible to customize the dynamic pricing algorithm for different industries and applications.

[0110] The technological breakthrough unit can use the emotion estimation function to provide real-time feedback on emotions felt by the user when experiencing a technological breakthrough, thereby improving the quality of the experience. The technological breakthrough unit, for example, can use the emotion estimation function to provide real-time feedback on emotions felt by the user when experiencing a technological breakthrough, thereby improving the quality of the experience. For example, when positive emotions are strong, related information is preferentially displayed. The technological breakthrough unit can also use the emotion estimation function to analyze the user's emotions in real time and develop an algorithm to improve the quality of the experience. For example, information is recommended based on the intensity and type of emotion. This allows the emotion estimation function to provide real-time feedback on emotions felt by the user when experiencing a technological breakthrough, thereby improving the quality of the experience.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The know-how sharing platform can further include a behavioral analysis unit that analyzes the user's behavioral history. The behavioral analysis unit, for example, analyzes what information the user frequently views and what search keywords they use to understand the user's interests. The behavioral analysis unit can also predict information that the user may need in the future based on the user's behavioral patterns and recommend it in advance. This makes it possible to analyze the user's behavioral history and provide more personalized information.

[0113] The know-how sharing platform may further include a feedback collection unit that collects user feedback. For example, the feedback collection unit displays a simple questionnaire after the user has viewed the information, and collects opinions on the usefulness of the information and areas for improvement. The feedback collection unit may also store the collected feedback in a database and provide it to the posting company. This allows for the collection of user feedback and the improvement of the information quality.

[0114] The know-how sharing platform may further include an emotion adjustment unit that estimates the user's emotions and adjusts the display order of information based on the estimated emotions. For example, the emotion adjustment unit may prioritize displaying information for which the user has positive emotions and postpone displaying information for which the user has negative emotions. The emotion adjustment unit may also analyze the user's emotions in real time and dynamically adjust the display order. This allows the display order of information to be adjusted based on the user's emotions, improving the quality of the browsing experience.

[0115] The know-how sharing platform may further include an emotion correction unit that estimates the user's emotions and corrects the rating of the information based on the estimated emotions. The emotion correction unit, for example, increases the rating when the user has positive emotions and decreases the rating when the user has negative emotions. The emotion correction unit can also analyze the user's emotions in real time to improve the reliability of the rating. This allows the rating of the information to be corrected based on the user's emotions, thereby improving the reliability of the rating.

[0116] The know-how sharing platform can further include an emotion recommendation unit that estimates the user's emotions and recommends information based on the estimated emotions. For example, the emotion recommendation unit preferentially recommends information that the user has positive emotions about and postpones information that the user has negative emotions about. The emotion recommendation unit can also analyze the user's emotions in real time and dynamically adjust the recommended information. This makes it possible to recommend information based on the user's emotions and improve the quality of the browsing experience.

[0117] The know-how sharing platform may further include an emotion feedback unit that estimates the user's emotions and provides information feedback based on the estimated emotions. For example, the emotion feedback unit displays an encouraging message when the user has positive emotions, and suggests areas for improvement when the user has negative emotions. The emotion feedback unit may also analyze the user's emotions in real time and dynamically adjust the feedback content. This allows for information feedback based on the user's emotions, improving the quality of the browsing experience.

[0118] The know-how sharing platform may further include a behavior adjustment unit that analyzes the user's behavior history and adjusts the display order of information based on the user's behavior pattern. The behavior adjustment unit, for example, prioritizes displaying information that the user views frequently and postpones information that the user views less frequently. The behavior adjustment unit may also analyze the user's behavior pattern in real time and dynamically adjust the display order. This allows the display order of information to be adjusted based on the user's behavior history, improving the quality of the viewing experience.

[0119] The know-how sharing platform may further include a behavior recommendation unit that analyzes a user's behavior history and recommends information based on the user's behavioral patterns. For example, the behavior recommendation unit may preferentially recommend information related to keywords that the user frequently searches for, and postpone information related to keywords that the user searches less frequently. The behavior recommendation unit may also analyze the user's behavioral patterns in real time and dynamically adjust the recommended information. This allows information to be recommended based on the user's behavioral history, improving the quality of the browsing experience.

[0120] The know-how sharing platform may further include a behavior correction unit that analyzes the user's behavior history and corrects the evaluation of information based on the behavior pattern. The behavior correction unit, for example, may increase the evaluation of information that the user frequently views and decrease the evaluation of information that the user views infrequently. The behavior correction unit may also analyze the user's behavior pattern in real time to improve the reliability of the evaluation. This allows the information evaluation to be corrected based on the user's behavior history, thereby improving the reliability of the evaluation.

[0121] The know-how sharing platform may further include a behavioral feedback unit that analyzes the user's behavioral history and provides information feedback based on the user's behavioral patterns. For example, the behavioral feedback unit may request detailed feedback for information that the user views frequently and simple feedback for information that the user views infrequently. The behavioral feedback unit may also analyze the user's behavioral patterns in real time and dynamically adjust the feedback content. This allows for information feedback based on the user's behavioral history, improving the quality of the browsing experience.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The posting department posts corporate know-how information. For example, a company can post its own marketing strategy or management know-how. The posting department also stores the posted information in a database so that other companies can access it. Step 2: The evaluation department evaluates the know-how information posted by the posting department. For example, a purchasing company evaluates the content and usability of the know-how information. The evaluation department also saves the evaluation results in a database and provides them to the pricing department. Step 3: The pricing unit sets the price of the know-how information evaluated by the evaluation unit. For example, the price of highly rated know-how information increases, and the price of low-rated know-how information decreases. The pricing unit also dynamically adjusts the price using a dynamic pricing algorithm. Step 4: The recommendation department recommends related information based on the price set by the pricing department. For example, it analyzes the user's search history and browsing history to recommend highly relevant know-how information. The recommendation department also uses generation AI to provide the user with the most suitable information.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0150] 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.

[0151] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0152] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] 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.

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

[0158] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0168] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[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 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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. [Explanation of symbols]

[0191] 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 posting section for posting corporate know-how information, an evaluation unit that evaluates the know-how information posted by the posting unit; a pricing unit that sets a price for the know-how information evaluated by the evaluation unit; a recommendation unit that recommends related information based on the price set by the pricing unit. A system characterized by:

2. The evaluation unit When a purchasing company evaluates the know-how information, the reliability of the evaluation is automatically analyzed using a generation AI to correct any bias in the evaluation.

2. The system of claim 1.

3. The recommendation unit Generative AI is used to analyze users' search and browsing history and provide individually optimized recommendations.

2. The system of claim 1.

4. The Security Measures Department Using a generation AI, the security risk of the posted know-how information is automatically analyzed, and measures are taken according to the risk level.

2. The system of claim 1.

5. The Legal Reorganization Department Using a generation AI, the posted know-how information is automatically analyzed to determine whether it infringes the patents of other companies.

2. The system of claim 1.

6. The buyer benefits section is When a purchasing company views the know-how information, it automatically provides additional related information using generative AI.

2. The system of claim 1.

7. The technical breakthrough section Using generative AI to analyze and evaluate the know-how information in real time, reducing operational costs.

2. The system of claim 1.

8. The posting unit: Analyze employee emotions using emotion estimation to encourage posting of information with positive sentiment 2. The system of claim 1.

Images