System

Abstract

An object of a system according to an embodiment is to introduce an appropriate product or service in a company group based on a post content of a user.SOLUTION: A system includes a reception part, an analysis part, a retrieval part, and a provision part. The reception unit receives a post content of a user. The analysis part analyzes the contribution contents received by the reception part. The search unit searches the database in the company group based on the content analyzed by the analysis unit. The provision part provides the article or service specified by the retrieval part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently introduce the wide range of products and services within a corporate group.

[0005] The system according to the embodiment aims to introduce appropriate products and services within a corporate group based on the content posted by users. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a search unit, and a provision unit. The reception unit receives content posted by users. The analysis unit analyzes the content posted by the reception unit. The search unit searches a database within the corporate group based on the content analyzed by the analysis unit. The provision unit provides the product or service identified by the search unit. [Effects of the Invention]

[0007] The system according to the embodiment can introduce appropriate products and services within a corporate group based on the content posted by users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An introduction system according to an embodiment of the present invention is a system that accepts user posts, analyzes them with a generation AI, identifies appropriate products and services within a corporate group, and provides them to the user. The introduction system accepts user posts, analyzes them with a generation AI, and searches a database within the corporate group to identify appropriate products and services and introduce them to the user. For example, if a user posts, "I'm looking for a new smartphone," the introduction system searches smartphone-related product information within the corporate group to identify the latest smartphone models, related accessories, service plans, and so on. The introduction system then introduces the identified products and services to the user. For example, if a user posts, "I want to know about cloud services," the generation AI provides information about cloud services within the corporate group. This information is presented to the user in an appropriate format. This allows the introduction system to effectively introduce products and services that consumers and employees are not familiar with. This allows the introduction system to effectively introduce products and services that consumers and employees are not familiar with. For example, by quickly providing information about new products and services, consumers' purchasing desire can be increased. Furthermore, by effectively introducing a wide range of products and services within the corporate group to employees, work efficiency and knowledge can be improved.

[0029] The introduction system according to the embodiment includes a reception unit, an analysis unit, a search unit, and a providing unit. The reception unit receives user posts. The user posts may include, but are not limited to, text, images, and videos. For example, if a user posts "I'm looking for a new smartphone," the reception unit receives the posts. The reception unit may also receive, for example, a user posts "I want to know about cloud services." The analysis unit uses a generation AI to analyze the posts received by the reception unit. The analysis may be performed using, for example, natural language processing, image analysis, or voice analysis, but is not limited to these examples. For example, the analysis unit uses the generation AI to understand the user's posts and extract information for searching databases within the corporate group. The search unit searches databases within the corporate group based on the content analyzed by the analysis unit. The search may be performed using, for example, keyword search, filtering, ranking, or other methods, but is not limited to these examples. For example, if a user posts "I'm looking for a new smartphone," the search unit searches for smartphone-related product information within the corporate group. The providing unit provides the product or service identified by the search unit to the user. The provision may be performed, for example, by email notification, dashboard display, push notification, or other methods, but is not limited to these examples. For example, if a user posts "I want to know about cloud services," the providing unit provides information about cloud services within the corporate group. This allows the introduction system according to the embodiment to efficiently analyze the content posted by the user and provide appropriate products and services.

[0030] The reception unit can receive specific needs or requests from the user. Specific needs or requests include, but are not limited to, product improvements, service requests, and the like. For example, if a user posts, "I'm looking for a new smartphone," the reception unit can receive the specific need. Furthermore, if a user posts, "I want to know about cloud services," the reception unit can also receive the specific request. This allows the specific needs and requests of the user to be accurately received.

[0031] The analysis unit can analyze the content posted by the user and search the database within the corporate group. The analysis can be performed using methods such as, but not limited to, natural language processing, image analysis, and voice analysis. For example, the analysis unit allows the generation AI to understand the content posted by the user and extract information for searching the database within the corporate group. The analysis unit can also analyze the content posted by the user and generate keywords for searching the database within the corporate group. Furthermore, the analysis unit can analyze the content posted by the user and extract related information. This allows the content posted by the user to be analyzed and an appropriate database to be searched.

[0032] The search unit can identify appropriate products or services within the corporate group. Suitable products or services include, but are not limited to, recommendation algorithms based on user needs. For example, if a user posts, "I'm looking for a new smartphone," the search unit searches for smartphone-related product information within the corporate group and identifies the latest smartphone models, related accessories, service plans, etc. Furthermore, if a user posts, "I want to know about cloud services," the search unit can also identify information about cloud services within the corporate group. Furthermore, the search unit can identify related products and services based on the user's needs. This makes it possible to identify appropriate products and services within the corporate group.

[0033] The providing unit can introduce the identified product or service to the user. The introduction can be made, for example, by email notification, dashboard display, push notification, or other methods, but is not limited to these examples. For example, if a user posts, "I want to know about cloud services," the providing unit can provide information about cloud services within the corporate group. Also, if a user posts, "I'm looking for a new smartphone," the providing unit can introduce the latest smartphone models, related accessories, service plans, and the like. Furthermore, the providing unit can introduce related products and services based on the user's needs. This allows the identified product or service to be introduced to the user effectively.

[0034] The providing unit can collect user feedback. Examples of feedback include, but are not limited to, questionnaires, reviews, and comments. For example, when a user provides feedback on provided information, the providing unit collects the feedback. Furthermore, when a user posts a review on a provided product or service, the providing unit can also collect the review. Furthermore, when a user posts a comment on provided information, the providing unit can also collect the comment. In this way, user feedback can be collected and used to improve the system.

[0035] The providing unit can evaluate the provided information. Examples of the evaluation include, but are not limited to, user satisfaction, frequency of use, and feedback content. For example, when a user evaluates his or her satisfaction with the provided information, the providing unit collects the evaluation. The providing unit can also evaluate the extent to which the user used the provided information. Furthermore, the providing unit can evaluate the effectiveness of the provided information based on the user's feedback content. This allows the effectiveness of the provided information to be evaluated and the accuracy of the system to be improved.

[0036] The reception unit can analyze the user's past posting history and select the optimal reception method. The past posting history includes, for example, the frequency of posting, the type of content, and ratings, but is not limited to these examples. The reception unit can, for example, automatically display content that the user has frequently posted in the past as candidates. The reception unit can also preferentially suggest posting methods (audio, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the posting method that will be used in a specific time period based on the user's past posting history. In this way, the optimal reception method can be selected by analyzing the user's past posting history.

[0037] When receiving the posted content, the reception unit can filter the posted content based on the user's current areas of interest. Areas of interest include, but are not limited to, past posted content, browsing history, and survey results. For example, the reception unit preferentially receives posted content related to areas in which the user is currently interested. The reception unit can also automatically extract related keywords based on the user's areas of interest and filter the posted content. Furthermore, the reception unit can preferentially receive posted content related to related products and services based on the user's areas of interest. By filtering the posted content based on the user's current areas of interest, highly relevant information can be preferentially received.

[0038] When accepting a post, the acceptance unit can select an appropriate acceptance means depending on the user's input method. Examples of input methods include, but are not limited to, text input, voice input, and image input. For example, when a user inputs a post by voice, the acceptance unit accepts the post using voice recognition technology. Furthermore, when a user inputs a post by text, the acceptance unit can also accept the post using text analysis technology. Furthermore, when a user inputs a post by image, the acceptance unit can also accept the post using image recognition technology. This allows for efficient acceptance by selecting the optimal acceptance means depending on the user's input method.

[0039] When accepting posted content, the reception unit can prioritize accepting highly relevant content by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, and user input. For example, if the user is in a specific area, the reception unit can prioritize accepting posted content related to products and services related to that area. Furthermore, the reception unit can prioritize accepting posted content related to related events and stores based on the user's current location. Furthermore, the reception unit can prioritize accepting posted content related to area-specific campaigns and services based on the user's geographical location information. This allows for prioritized acceptance of information related to the area by taking into account the user's geographical location information.

[0040] The reception unit can analyze the user's social media activity when receiving the posted content and receive related content. Social media activity includes, but is not limited to, for example, the posted content, the number of likes, and the number of followers. For example, the reception unit can preferentially receive posted content related to the content shared by the user on social media. The reception unit can also analyze the user's social media activity history and preferentially receive posted content related to related products and services. Furthermore, the reception unit can also preferentially receive related posted content by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant information.

[0041] The reception unit can customize the reception method by reflecting the user's past feedback when receiving the content to be posted. Past feedback includes, but is not limited to, for example, survey results, review content, and comments. The reception unit can, for example, suggest the optimal reception method based on the user's past feedback. The reception unit can also preferentially suggest a specific posting method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the posted content. The importance includes, but is not limited to, for example, urgency, scope of impact, and user priority. For example, the analysis unit performs a detailed analysis of posted content with a high level of importance. The analysis unit can also perform a concise analysis of posted content with a low level of importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the posted content. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the posted content.

[0043] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the category of the posted content. Examples of categories include, but are not limited to, product categories, service categories, and topic categories. For example, the analysis unit can apply a product-specific analysis algorithm to posted content related to a product. The analysis unit can also apply a service-specific analysis algorithm to posted content related to a service. Furthermore, the analysis unit can select and apply the optimal analysis algorithm for each category. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the category of the posted content.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, for example, the accuracy of the analysis, user feedback, and areas for improvement. The analysis unit improves the accuracy of the analysis based on, for example, the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the analysis. Furthermore, the analysis unit can analyze the user's past analysis results and optimize the analysis algorithm. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the posted content. The submission time includes, but is not limited to, for example, the submission date and time, urgency, and scope of impact. For example, the analysis unit prioritizes analysis of recently submitted posted content. The analysis unit can also postpone analysis of older submitted posted content. Furthermore, the analysis unit can adjust the analysis schedule based on the submission time. This enables efficient analysis by determining the priority of analysis based on the submission time of the posted content.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the posted content. Relevance includes, for example, keyword matching, topic similarity, etc., but is not limited to these examples. For example, the analysis unit prioritizes analysis of highly relevant posted content. The analysis unit can also postpone analysis of less relevant posted content. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the posted content. In this way, by adjusting the order of analysis based on the relevance of the posted content, highly relevant information can be analyzed preferentially.

[0047] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of technical terms include, but are not limited to, the user's occupation, past postings, and survey results. For example, if the user's level of expertise is high, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user's level of expertise is low, the analysis unit can also provide analysis results that avoid technical terms. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0048] The search unit can improve search accuracy by taking into account the interrelationships between posted contents during a search. Examples of interrelationships include, but are not limited to, co-occurrence network analysis and relevance scores. For example, the search unit can analyze the interrelationships between posted contents and search for related products or services. The search unit can also filter search results based on the interrelationships between posted contents. Furthermore, the search unit can determine the priority of search results by taking into account the interrelationships between posted contents. In this way, highly relevant search results can be provided by taking into account the interrelationships between posted contents.

[0049] The search unit can perform a search while taking into consideration attribute information of the person who submitted the posted content. Attribute information includes, but is not limited to, for example, age, gender, occupation, and region. The search unit can search for related products and services based on, for example, the occupation or job title of the person who submitted the posted content. The search unit can also search for related products and services based on the age and gender of the person who submitted the posted content. Furthermore, the search unit can search for related products and services based on the interests and concerns of the person who submitted the posted content. In this way, by taking into consideration the attribute information of the person who submitted the posted content, it is possible to provide highly relevant search results.

[0050] During a search, the search unit can weight the search based on the submission frequency of the posted content. The submission frequency includes, but is not limited to, for example, the number of submissions, the frequency within a period, and the user's activity level. For example, the search unit prioritizes searching for posted content that is submitted more frequently. The search unit can also postpone searching for posted content that is submitted less frequently. Furthermore, the search unit can adjust the weighting of search results based on the submission frequency. Thus, by weighting the search based on the submission frequency of the posted content, it is possible to prioritize searching for information that is frequently submitted.

[0051] The search unit can perform a search while taking into account the geographic distribution of posted content. Examples of geographic distribution include, but are not limited to, the number of posts by region and geographic trends. For example, the search unit can analyze the geographic distribution of posted content and search for related products and services. The search unit can also filter search results based on the geographic distribution. Furthermore, the search unit can determine the priority of search results by taking into account the geographic distribution. This allows for preferential searching of information related to a region by taking into account the geographic distribution of posted content.

[0052] During a search, the search unit can improve the accuracy of the search by referring to literature related to the posted content. Examples of related literature include, but are not limited to, academic papers, patent documents, and industry reports. For example, the search unit can refer to literature related to the posted content and examine the search results. The search unit can also filter the search results based on the related literature. Furthermore, the search unit can determine the priority of the search results by taking the related literature into consideration. Thus, by referring to literature related to the posted content, the accuracy of the search can be improved.

[0053] The search unit can perform a search while taking into consideration the market value of the posted content. Market value includes, but is not limited to, sales forecasts, demand forecasts, and competitive analysis, for example. The search unit, for example, searches for products and services with high market value with priority. The search unit can also search for products and services with low market value later. Furthermore, the search unit can adjust the weighting of search results based on market value. This allows for a priority search for high-value information by taking into consideration the market value of the posted content.

[0054] The providing unit can improve the accuracy of the information provided by taking into account the interrelationships between the posted contents. Examples of interrelationships include, but are not limited to, co-occurrence network analysis, relevance scores, and the like. For example, the providing unit can analyze the interrelationships between the posted contents and provide related products or services. The providing unit can also filter the information to be provided based on the interrelationships between the posted contents. Furthermore, the providing unit can determine the priority of the information to be provided by taking into account the interrelationships between the posted contents. This makes it possible to provide highly relevant information by taking into account the interrelationships between the posted contents.

[0055] The providing unit can provide the information while taking into consideration attribute information of the person who submitted the posted content. Attribute information includes, but is not limited to, for example, age, gender, occupation, and region. The providing unit can provide related products and services based on, for example, the occupation or job title of the person who submitted the posted content. The providing unit can also provide related products and services based on the age and gender of the person who submitted the posted content. Furthermore, the providing unit can also provide related products and services based on the interests and concerns of the person who submitted the posted content. In this way, highly relevant information can be provided by taking into consideration the attribute information of the person who submitted the posted content.

[0056] The providing unit can weight the information provided based on the frequency of submission of the posted content when providing the information. The frequency of submission includes, but is not limited to, for example, the number of submissions, the frequency within a period, and the user's activity level. For example, the providing unit can prioritize providing posted content that is submitted more frequently. The providing unit can also postpone providing posted content that is submitted less frequently. Furthermore, the providing unit can adjust the weight of the information to be provided based on the submission frequency. In this way, by weighting the information provided based on the frequency of submission of the posted content, it is possible to prioritize providing information that is frequently submitted.

[0057] The providing unit may provide the information taking into consideration the geographical distribution of the posted content. Examples of the geographical distribution include, but are not limited to, the number of posts by region and geographical trends. For example, the providing unit may analyze the geographical distribution of the posted content and provide related products and services. The providing unit may also filter the information to be provided based on the geographical distribution. Furthermore, the providing unit may determine the priority of the information to be provided taking into consideration the geographical distribution. In this way, by taking into consideration the geographical distribution of the posted content, it is possible to provide information related to a region with priority.

[0058] The providing unit can improve the accuracy of the information provided by referring to literature related to the posted content when providing the information. Examples of related literature include, but are not limited to, academic papers, patent documents, and industry reports. For example, the providing unit can refer to literature related to the posted content and scrutinize the information to be provided. The providing unit can also filter the information to be provided based on the related literature. Furthermore, the providing unit can determine the priority of the information to be provided by taking the related literature into consideration. In this way, the accuracy of the information provided can be improved by referring to literature related to the posted content.

[0059] The providing unit can provide the posted content while taking into consideration the market value of the posted content. Market value includes, but is not limited to, for example, sales forecasts, demand forecasts, and competitive analysis. For example, the providing unit can provide products and services with high market value preferentially. The providing unit can also provide products and services with low market value later. Furthermore, the providing unit can adjust the weighting of the information to be provided based on the market value. In this way, by taking into consideration the market value of the posted content, it is possible to provide information with high value preferentially.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] When accepting content posted by a user, the accepting unit can refer to the user's past purchasing history and prioritize accepting related products and services. For example, the accepting unit can suggest a new smartphone based on the smartphone model and accessories the user has previously purchased. The accepting unit can also provide information on related cloud services based on the types of cloud services the user has previously used. Furthermore, the accepting unit can prioritize accepting products of specific brands or categories based on the user's purchasing history. This makes it possible to make more personalized suggestions by utilizing the user's past purchasing history.

[0062] When searching the database within the corporate group, the search unit can determine search priorities based on the importance of the user's posted content. For example, it can quickly search for highly urgent posted content to identify related products and services. It can also search for less important posted content at a later date. It can also adjust search priorities based on the scope of influence of the user's posted content. This enables efficient searches by determining search priorities based on the importance of the posted content.

[0063] When providing a specified product or service to a user, the providing unit can refer to the user's past feedback and customize the content of the information to be provided. For example, detailed information about a specific product can be provided based on feedback provided by the user in the past. Information about related services can also be provided based on services that the user has previously rated. Furthermore, the providing unit can analyze the user's past feedback and provide products or services in a specific category with priority. This makes it possible to provide more personalized information by utilizing the user's past feedback.

[0064] When accepting content posted by a user, the acceptance unit can prioritize accepting related products and services by taking into account the user's current geographical location information. For example, if the user is in a specific area, information about products and services related to that area can be accepted with priority. Information about related events and stores can also be provided based on the user's current location. Furthermore, information about area-specific campaigns and services can also be accepted with priority based on the user's geographical location information. In this way, by taking into account the user's geographical location information, information related to the area can be accepted with priority.

[0065] When analyzing the content posted by a user, the analysis unit can refer to the user's past posting history to improve the accuracy of the analysis. For example, specific keywords can be extracted based on the content posted by the user in the past and reflected in the analysis. Specific patterns can also be extracted from the user's past posting history to optimize the analysis algorithm. Furthermore, the analysis unit can analyze the user's past posting history and prioritize the analysis of related information. This makes it possible to utilize the user's past posting history to improve the accuracy of the analysis.

[0066] When providing identified products or services to a user, the providing unit can analyze the user's social media activity and provide related information preferentially. For example, the providing unit can provide information about products or services related to content shared by the user on social media. The providing unit can also provide information about related products or services based on the user's social media activity history. Furthermore, the providing unit can provide related information by taking into account the activities of the user's friends on social media. In this way, highly relevant information can be provided by analyzing the user's social media activity.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit receives the user's postings. The user's postings include text, images, videos, etc. For example, if a user posts "I'm looking for a new smartphone," the reception unit receives the postings. Also, if a user posts "I want to know about cloud services," the reception unit receives the postings. Step 2: The analysis unit uses the generation AI to analyze the content of the posts received by the reception unit. The analysis is performed using methods such as natural language processing, image analysis, and voice analysis. For example, the analysis unit allows the generation AI to understand the content of the user's post and extract information that can be used to search the corporate group's database. Step 3: The search unit searches the database within the corporate group based on the content analyzed by the analysis unit. The search is performed using methods such as keyword search, filtering, and ranking. For example, if a user posts that they are "looking for a new smartphone," the search unit searches for smartphone-related product information within the corporate group. Step 4: The provision unit provides the products and services identified by the search unit to the user. Provision is performed by methods such as email notification, dashboard display, and push notification. For example, if a user posts, "I want to know about cloud services," information about cloud services within the corporate group is provided.

[0069] (Example 2) An introduction system according to an embodiment of the present invention is a system that accepts user posts, analyzes them with a generation AI, identifies appropriate products and services within a corporate group, and provides them to the user. The introduction system accepts user posts, analyzes them with a generation AI, and searches a database within the corporate group to identify appropriate products and services and introduce them to the user. For example, if a user posts, "I'm looking for a new smartphone," the introduction system searches smartphone-related product information within the corporate group to identify the latest smartphone models, related accessories, service plans, and so on. The introduction system then introduces the identified products and services to the user. For example, if a user posts, "I want to know about cloud services," the generation AI provides information about cloud services within the corporate group. This information is presented to the user in an appropriate format. This allows the introduction system to effectively introduce products and services that consumers and employees are not familiar with. This allows the introduction system to effectively introduce products and services that consumers and employees are not familiar with. For example, by quickly providing information about new products and services, consumers' purchasing desire can be increased. Furthermore, by effectively introducing a wide range of products and services within the corporate group to employees, work efficiency and knowledge can be improved.

[0070] The introduction system according to the embodiment includes a reception unit, an analysis unit, a search unit, and a providing unit. The reception unit receives user posts. The user posts may include, but are not limited to, text, images, and videos. For example, if a user posts "I'm looking for a new smartphone," the reception unit receives the posts. The reception unit may also receive, for example, a user posts "I want to know about cloud services." The analysis unit uses a generation AI to analyze the posts received by the reception unit. The analysis may be performed using, for example, natural language processing, image analysis, or voice analysis, but is not limited to these examples. For example, the analysis unit uses the generation AI to understand the user's posts and extract information for searching databases within the corporate group. The search unit searches databases within the corporate group based on the content analyzed by the analysis unit. The search may be performed using, for example, keyword search, filtering, ranking, or other methods, but is not limited to these examples. For example, if a user posts "I'm looking for a new smartphone," the search unit searches for smartphone-related product information within the corporate group. The providing unit provides the product or service identified by the search unit to the user. The provision may be performed, for example, by email notification, dashboard display, push notification, or other methods, but is not limited to these examples. For example, if a user posts "I want to know about cloud services," the providing unit provides information about cloud services within the corporate group. This allows the introduction system according to the embodiment to efficiently analyze the content posted by the user and provide appropriate products and services.

[0071] The reception unit can receive specific needs or requests from the user. Specific needs or requests include, but are not limited to, product improvements, service requests, and the like. For example, if a user posts, "I'm looking for a new smartphone," the reception unit can receive the specific need. Furthermore, if a user posts, "I want to know about cloud services," the reception unit can also receive the specific request. This allows the specific needs and requests of the user to be accurately received.

[0072] The analysis unit can analyze the content posted by the user and search the database within the corporate group. The analysis can be performed using methods such as, but not limited to, natural language processing, image analysis, and voice analysis. For example, the analysis unit allows the generation AI to understand the content posted by the user and extract information for searching the database within the corporate group. The analysis unit can also analyze the content posted by the user and generate keywords for searching the database within the corporate group. Furthermore, the analysis unit can analyze the content posted by the user and extract related information. This allows the content posted by the user to be analyzed and an appropriate database to be searched.

[0073] The search unit can identify appropriate products or services within the corporate group. Suitable products or services include, but are not limited to, recommendation algorithms based on user needs. For example, if a user posts, "I'm looking for a new smartphone," the search unit searches for smartphone-related product information within the corporate group and identifies the latest smartphone models, related accessories, service plans, etc. Furthermore, if a user posts, "I want to know about cloud services," the search unit can also identify information about cloud services within the corporate group. Furthermore, the search unit can identify related products and services based on the user's needs. This makes it possible to identify appropriate products and services within the corporate group.

[0074] The providing unit can introduce the identified product or service to the user. The introduction can be made, for example, by email notification, dashboard display, push notification, or other methods, but is not limited to these examples. For example, if a user posts, "I want to know about cloud services," the providing unit can provide information about cloud services within the corporate group. Also, if a user posts, "I'm looking for a new smartphone," the providing unit can introduce the latest smartphone models, related accessories, service plans, and the like. Furthermore, the providing unit can introduce related products and services based on the user's needs. This allows the identified product or service to be introduced to the user effectively.

[0075] The providing unit can collect user feedback. Examples of feedback include, but are not limited to, questionnaires, reviews, and comments. For example, when a user provides feedback on provided information, the providing unit collects the feedback. Furthermore, when a user posts a review on a provided product or service, the providing unit can also collect the review. Furthermore, when a user posts a comment on provided information, the providing unit can also collect the comment. In this way, user feedback can be collected and used to improve the system.

[0076] The providing unit can evaluate the provided information. Examples of the evaluation include, but are not limited to, user satisfaction, frequency of use, and feedback content. For example, when a user evaluates his or her satisfaction with the provided information, the providing unit collects the evaluation. The providing unit can also evaluate the extent to which the user used the provided information. Furthermore, the providing unit can evaluate the effectiveness of the provided information based on the user's feedback content. This allows the effectiveness of the provided information to be evaluated and the accuracy of the system to be improved.

[0077] The reception unit can estimate the user's emotions and adjust the timing of receiving posted content based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can quickly receive posted content, thereby reducing the user's burden. Furthermore, when the user is relaxed, the reception unit can receive posted content at a slower pace to receive detailed posted content. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving concise posted content. This reduces the user's burden by adjusting the timing of receiving posted content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] The reception unit can analyze the user's past posting history and select the optimal reception method. The past posting history includes, for example, the frequency of posting, the type of content, and ratings, but is not limited to these examples. The reception unit can, for example, automatically display content that the user has frequently posted in the past as candidates. The reception unit can also preferentially suggest posting methods (audio, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the posting method that will be used in a specific time period based on the user's past posting history. In this way, the optimal reception method can be selected by analyzing the user's past posting history.

[0079] When receiving the posted content, the reception unit can filter the posted content based on the user's current areas of interest. Areas of interest include, but are not limited to, past posted content, browsing history, and survey results. For example, the reception unit preferentially receives posted content related to areas in which the user is currently interested. The reception unit can also automatically extract related keywords based on the user's areas of interest and filter the posted content. Furthermore, the reception unit can preferentially receive posted content related to related products and services based on the user's areas of interest. By filtering the posted content based on the user's current areas of interest, highly relevant information can be preferentially received.

[0080] When accepting a post, the acceptance unit can select an appropriate acceptance means depending on the user's input method. Examples of input methods include, but are not limited to, text input, voice input, and image input. For example, when a user inputs a post by voice, the acceptance unit accepts the post using voice recognition technology. Furthermore, when a user inputs a post by text, the acceptance unit can also accept the post using text analysis technology. Furthermore, when a user inputs a post by image, the acceptance unit can also accept the post using image recognition technology. This allows for efficient acceptance by selecting the optimal acceptance means depending on the user's input method.

[0081] The reception unit can estimate the user's emotions and determine the priority of the posted content to be received based on the estimated user's emotions. For example, if the user is excited, the reception unit can prioritize receiving posted content with high urgency. Furthermore, if the user is relaxed, the reception unit can also prioritize receiving detailed posted content. Furthermore, if the user is stressed, the reception unit can also prioritize receiving concise posted content. In this way, by determining the priority of posted content based on the user's emotions, it is possible to prioritize receiving content with high urgency. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] When accepting posted content, the reception unit can prioritize accepting highly relevant content by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, and user input. For example, if the user is in a specific area, the reception unit can prioritize accepting posted content related to products and services related to that area. Furthermore, the reception unit can prioritize accepting posted content related to related events and stores based on the user's current location. Furthermore, the reception unit can prioritize accepting posted content related to area-specific campaigns and services based on the user's geographical location information. This allows for prioritized acceptance of information related to the area by taking into account the user's geographical location information.

[0083] The reception unit can analyze the user's social media activity when receiving the posted content and receive related content. Social media activity includes, but is not limited to, for example, the posted content, the number of likes, and the number of followers. For example, the reception unit can preferentially receive posted content related to the content shared by the user on social media. The reception unit can also analyze the user's social media activity history and preferentially receive posted content related to related products and services. Furthermore, the reception unit can also preferentially receive related posted content by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant information.

[0084] The reception unit can customize the reception method by reflecting the user's past feedback when receiving the content to be posted. Past feedback includes, but is not limited to, for example, survey results, review content, and comments. The reception unit can, for example, suggest the optimal reception method based on the user's past feedback. The reception unit can also preferentially suggest a specific posting method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0085] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is in a hurry. Furthermore, the analysis unit can provide visually stimulating analysis results when the user is excited. By adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0086] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the posted content. The importance includes, but is not limited to, for example, urgency, scope of impact, and user priority. For example, the analysis unit performs a detailed analysis of posted content with a high level of importance. The analysis unit can also perform a concise analysis of posted content with a low level of importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the posted content. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the posted content.

[0087] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the category of the posted content. Examples of categories include, but are not limited to, product categories, service categories, and topic categories. For example, the analysis unit can apply a product-specific analysis algorithm to posted content related to a product. The analysis unit can also apply a service-specific analysis algorithm to posted content related to a service. Furthermore, the analysis unit can select and apply the optimal analysis algorithm for each category. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the category of the posted content.

[0088] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, for example, the accuracy of the analysis, user feedback, and areas for improvement. The analysis unit improves the accuracy of the analysis based on, for example, the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the analysis. Furthermore, the analysis unit can analyze the user's past analysis results and optimize the analysis algorithm. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, it is possible to provide the optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the posted content. The submission time includes, but is not limited to, for example, the submission date and time, urgency, and scope of impact. For example, the analysis unit prioritizes analysis of recently submitted posted content. The analysis unit can also postpone analysis of older submitted posted content. Furthermore, the analysis unit can adjust the analysis schedule based on the submission time. This enables efficient analysis by determining the priority of analysis based on the submission time of the posted content.

[0091] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the posted content. Relevance includes, for example, keyword matching, topic similarity, etc., but is not limited to these examples. For example, the analysis unit prioritizes analysis of highly relevant posted content. The analysis unit can also postpone analysis of less relevant posted content. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the posted content. In this way, by adjusting the order of analysis based on the relevance of the posted content, highly relevant information can be analyzed preferentially.

[0092] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of technical terms include, but are not limited to, the user's occupation, past postings, and survey results. For example, if the user's level of expertise is high, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user's level of expertise is low, the analysis unit can also provide analysis results that avoid technical terms. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0093] The search unit can estimate the user's emotions and adjust search criteria based on the estimated user emotions. For example, the search unit can provide detailed search results when the user is relaxed. The search unit can also provide concise search results when the user is in a hurry. Furthermore, the search unit can provide visually stimulating search results when the user is excited. This allows the search criteria to be adjusted based on the user's emotions, thereby providing optimal search results for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] The search unit can improve search accuracy by taking into account the interrelationships between posted contents during a search. Examples of interrelationships include, but are not limited to, co-occurrence network analysis and relevance scores. For example, the search unit can analyze the interrelationships between posted contents and search for related products or services. The search unit can also filter search results based on the interrelationships between posted contents. Furthermore, the search unit can determine the priority of search results by taking into account the interrelationships between posted contents. In this way, highly relevant search results can be provided by taking into account the interrelationships between posted contents.

[0095] The search unit can perform a search while taking into consideration attribute information of the person who submitted the posted content. Attribute information includes, but is not limited to, for example, age, gender, occupation, and region. The search unit can search for related products and services based on, for example, the occupation or job title of the person who submitted the posted content. The search unit can also search for related products and services based on the age and gender of the person who submitted the posted content. Furthermore, the search unit can search for related products and services based on the interests and concerns of the person who submitted the posted content. In this way, by taking into consideration the attribute information of the person who submitted the posted content, it is possible to provide highly relevant search results.

[0096] During a search, the search unit can weight the search based on the submission frequency of the posted content. The submission frequency includes, but is not limited to, for example, the number of submissions, the frequency within a period, and the user's activity level. For example, the search unit prioritizes searching for posted content that is submitted more frequently. The search unit can also postpone searching for posted content that is submitted less frequently. Furthermore, the search unit can adjust the weighting of search results based on the submission frequency. Thus, by weighting the search based on the submission frequency of the posted content, it is possible to prioritize searching for information that is frequently submitted.

[0097] The search unit can estimate the user's emotions and adjust the order in which search results are displayed based on the estimated user emotions. For example, if the user is relaxed, the search unit can prioritize displaying detailed search results. Furthermore, if the user is in a hurry, the search unit can prioritize displaying concise search results. Furthermore, if the user is excited, the search unit can prioritize displaying visually stimulating search results. In this way, by adjusting the order in which search results are displayed based on the user's emotions, it is possible to provide optimal search results for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The search unit can perform a search while taking into account the geographic distribution of posted content. Examples of geographic distribution include, but are not limited to, the number of posts by region and geographic trends. For example, the search unit can analyze the geographic distribution of posted content and search for related products and services. The search unit can also filter search results based on the geographic distribution. Furthermore, the search unit can determine the priority of search results by taking into account the geographic distribution. This allows for preferential searching of information related to a region by taking into account the geographic distribution of posted content.

[0099] During a search, the search unit can improve the accuracy of the search by referring to literature related to the posted content. Examples of related literature include, but are not limited to, academic papers, patent documents, and industry reports. For example, the search unit can refer to literature related to the posted content and examine the search results. The search unit can also filter the search results based on the related literature. Furthermore, the search unit can determine the priority of the search results by taking the related literature into consideration. Thus, by referring to literature related to the posted content, the accuracy of the search can be improved.

[0100] The search unit can perform a search while taking into consideration the market value of the posted content. Market value includes, but is not limited to, sales forecasts, demand forecasts, and competitive analysis, for example. The search unit, for example, searches for products and services with high market value with priority. The search unit can also search for products and services with low market value later. Furthermore, the search unit can adjust the weighting of search results based on market value. This allows for a priority search for high-value information by taking into consideration the market value of the posted content.

[0101] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, when the user is in a hurry, the providing unit can prioritize providing concise information. Furthermore, when the user is excited, the providing unit can prioritize providing visually stimulating information. In this way, by determining the priority of information to be provided based on the user's emotions, it is possible to provide optimal information for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0102] The providing unit can improve the accuracy of the information provided by taking into account the interrelationships between the posted contents. Examples of interrelationships include, but are not limited to, co-occurrence network analysis, relevance scores, and the like. For example, the providing unit can analyze the interrelationships between the posted contents and provide related products or services. The providing unit can also filter the information to be provided based on the interrelationships between the posted contents. Furthermore, the providing unit can determine the priority of the information to be provided by taking into account the interrelationships between the posted contents. This makes it possible to provide highly relevant information by taking into account the interrelationships between the posted contents.

[0103] The providing unit can provide the information while taking into consideration attribute information of the person who submitted the posted content. Attribute information includes, but is not limited to, for example, age, gender, occupation, and region. The providing unit can provide related products and services based on, for example, the occupation or job title of the person who submitted the posted content. The providing unit can also provide related products and services based on the age and gender of the person who submitted the posted content. Furthermore, the providing unit can also provide related products and services based on the interests and concerns of the person who submitted the posted content. In this way, highly relevant information can be provided by taking into consideration the attribute information of the person who submitted the posted content.

[0104] The providing unit can weight the information provided based on the frequency of submission of the posted content when providing the information. The frequency of submission includes, but is not limited to, for example, the number of submissions, the frequency within a period, and the user's activity level. For example, the providing unit can prioritize providing posted content that is submitted more frequently. The providing unit can also postpone providing posted content that is submitted less frequently. Furthermore, the providing unit can adjust the weight of the information to be provided based on the submission frequency. In this way, by weighting the information provided based on the frequency of submission of the posted content, it is possible to prioritize providing information that is frequently submitted.

[0105] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide detailed information in a visually rich format. Furthermore, if the user is in a hurry, the providing unit can provide concise information in a simple format. Furthermore, if the user is excited, the providing unit can provide information in a visually stimulating format. In this way, by adjusting the display method of the information to be provided based on the user's emotions, it is possible to provide information in a format optimal for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0106] The providing unit may provide the information taking into consideration the geographical distribution of the posted content. Examples of the geographical distribution include, but are not limited to, the number of posts by region and geographical trends. For example, the providing unit may analyze the geographical distribution of the posted content and provide related products and services. The providing unit may also filter the information to be provided based on the geographical distribution. Furthermore, the providing unit may determine the priority of the information to be provided taking into consideration the geographical distribution. In this way, by taking into consideration the geographical distribution of the posted content, it is possible to provide information related to a region with priority.

[0107] The providing unit can improve the accuracy of the information provided by referring to literature related to the posted content when providing the information. Examples of related literature include, but are not limited to, academic papers, patent documents, and industry reports. For example, the providing unit can refer to literature related to the posted content and scrutinize the information to be provided. The providing unit can also filter the information to be provided based on the related literature. Furthermore, the providing unit can determine the priority of the information to be provided by taking the related literature into consideration. In this way, the accuracy of the information provided can be improved by referring to literature related to the posted content.

[0108] The providing unit can provide the posted content while taking into consideration the market value of the posted content. Market value includes, but is not limited to, for example, sales forecasts, demand forecasts, and competitive analysis. For example, the providing unit can provide products and services with high market value preferentially. The providing unit can also provide products and services with low market value later. Furthermore, the providing unit can adjust the weighting of the information to be provided based on the market value. In this way, by taking into consideration the market value of the posted content, it is possible to provide information with high value preferentially. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, search unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the search unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, search unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the search unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, search unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the search unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, search unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the search unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] When accepting content posted by a user, the accepting unit can refer to the user's past purchasing history and prioritize accepting related products and services. For example, the accepting unit can suggest a new smartphone based on the smartphone model and accessories the user has previously purchased. The accepting unit can also provide information on related cloud services based on the types of cloud services the user has previously used. Furthermore, the accepting unit can prioritize accepting products of specific brands or categories based on the user's purchasing history. This makes it possible to make more personalized suggestions by utilizing the user's past purchasing history.

[0111] When analyzing the content posted by a user, the analysis unit can estimate the user's current emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is excited, a detailed analysis can be performed to provide more information. Alternatively, if the user is relaxed, a concise analysis can be performed to provide the minimum necessary information. Furthermore, if the user is feeling stressed, the analysis results can be displayed visually in an easy-to-understand manner, reducing the burden on the user. In this way, the accuracy of the analysis can be adjusted according to the user's emotions, making it possible to provide the user with the most appropriate information.

[0112] When searching the database within the corporate group, the search unit can determine search priorities based on the importance of the user's posted content. For example, it can quickly search for highly urgent posted content to identify related products and services. It can also search for less important posted content at a later date. It can also adjust search priorities based on the scope of influence of the user's posted content. This enables efficient searches by determining search priorities based on the importance of the posted content.

[0113] When providing the identified product or service to the user, the providing unit can estimate the user's current emotion and adjust the format of the information to be provided based on the estimated emotion. For example, if the user is relaxed, detailed information can be provided in a visually rich format. If the user is in a hurry, concise information can be provided in a simple format. Furthermore, if the user is excited, information can be provided in a visually stimulating format. In this way, by adjusting the format of the information to be provided based on the user's emotion, it is possible to provide information in a format optimal for the user.

[0114] When providing a specified product or service to a user, the providing unit can refer to the user's past feedback and customize the content of the information to be provided. For example, detailed information about a specific product can be provided based on feedback provided by the user in the past. Information about related services can also be provided based on services that the user has previously rated. Furthermore, the providing unit can analyze the user's past feedback and provide products or services in a specific category with priority. This makes it possible to provide more personalized information by utilizing the user's past feedback.

[0115] When accepting content posted by a user, the acceptance unit can prioritize accepting related products and services by taking into account the user's current geographical location information. For example, if the user is in a specific area, information about products and services related to that area can be accepted with priority. Information about related events and stores can also be provided based on the user's current location. Furthermore, information about area-specific campaigns and services can also be accepted with priority based on the user's geographical location information. In this way, by taking into account the user's geographical location information, information related to the area can be accepted with priority.

[0116] When analyzing the content posted by a user, the analysis unit can refer to the user's past posting history to improve the accuracy of the analysis. For example, specific keywords can be extracted based on the content posted by the user in the past and reflected in the analysis. Specific patterns can also be extracted from the user's past posting history to optimize the analysis algorithm. Furthermore, the analysis unit can analyze the user's past posting history and prioritize the analysis of related information. This makes it possible to utilize the user's past posting history to improve the accuracy of the analysis.

[0117] When searching the database within the corporate group, the search unit can estimate the user's current emotion and adjust the display order of search results based on the estimated emotion. For example, if the user is relaxed, detailed search results can be displayed preferentially. If the user is in a hurry, concise search results can be displayed preferentially. Furthermore, if the user is excited, visually stimulating search results can be displayed preferentially. In this way, by adjusting the display order of search results based on the user's emotion, it is possible to provide the user with the most optimal search results.

[0118] When providing identified products or services to a user, the providing unit can analyze the user's social media activity and provide related information preferentially. For example, the providing unit can provide information about products or services related to content shared by the user on social media. The providing unit can also provide information about related products or services based on the user's social media activity history. Furthermore, the providing unit can provide related information by taking into account the activities of the user's friends on social media. In this way, highly relevant information can be provided by analyzing the user's social media activity.

[0119] When providing the identified product or service to the user, the providing unit can estimate the user's current emotion and determine the priority of information to be provided based on the estimated emotion. For example, if the user is relaxed, detailed information can be provided preferentially. Also, if the user is in a hurry, concise information can be provided preferentially. Furthermore, if the user is excited, visually stimulating information can be provided preferentially. In this way, by determining the priority of information to be provided based on the user's emotion, it is possible to provide optimal information for the user.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The reception unit receives the user's postings. The user's postings include text, images, videos, etc. For example, if a user posts "I'm looking for a new smartphone," the reception unit receives the postings. Also, if a user posts "I want to know about cloud services," the reception unit receives the postings. Step 2: The analysis unit uses the generation AI to analyze the content of the posts received by the reception unit. The analysis is performed using methods such as natural language processing, image analysis, and voice analysis. For example, the analysis unit allows the generation AI to understand the content of the user's post and extract information that can be used to search the corporate group's database. Step 3: The search unit searches the database within the corporate group based on the content analyzed by the analysis unit. The search is performed using methods such as keyword search, filtering, and ranking. For example, if a user posts that they are "looking for a new smartphone," the search unit searches for smartphone-related product information within the corporate group. Step 4: The provision unit provides the products and services identified by the search unit to the user. Provision is performed by methods such as email notification, dashboard display, and push notification. For example, if a user posts, "I want to know about cloud services," information about cloud services within the corporate group is provided.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 AI 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.

[0140] 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.

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

[0142] [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 type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0169] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0170] 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.

[0171] 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.

[0172] 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 AI 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives content posted by users; an analysis unit that analyzes the posted content received by the reception unit; a search unit that searches a database within the corporate group based on the content analyzed by the analysis unit; a providing unit that provides the product or service identified by the search unit; A system characterized by:

2. The reception unit Accepting specific needs or requests from users The system of claim 1 .

3. The analysis unit Analyze user posts and search the corporate group's database The system of claim 1 .

4. The search unit Identifying suitable products or services within a group of companies The system of claim 1 .

5. The providing unit Refer users to identified products or services The system of claim 1 .

6. The providing unit Gather user feedback The system of claim 1 .

7. The providing unit Evaluate the information provided The system of claim 1 .

8. The reception unit Estimates user emotions and adjusts the timing of accepting posts based on the estimated user emotions. The system of claim 1 .

9. The reception unit Analyze the user's past posting history and select the appropriate reception method The system of claim 1 .

10. The reception unit As posts are accepted, they are filtered based on the user's current interests. The system of claim 1 .

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