Information processing apparatus, information processing method, and information processing program

Abstract

To provide an information processing apparatus, an information processing method, and an information processing program capable of proposing even improvements that cannot be obtained based on a comparison with another commodity.SOLUTION: An information processing apparatus includes an acquisition unit, a generation unit, and a providing unit. The acquisition unit acquires post information which is information on a post for a commodity. The generation unit inputs input information to a generative AI or an AI agent configured to generate and output information according to input information, the input information including the post information acquired by the acquisition unit and information indicating an instruction to output improvement information which is information indicating improvements of the commodity based on the post information, and causes the generative AI or the AI agent to generate the improvement information. The providing unit provides the improvement information generated by the generation unit.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] Traditionally, many products have been developed to improve functionality and design to meet consumer needs, but understanding consumer needs is not easy, and the number of improvements that can be identified is limited.

[0003] The technology described in Patent Document 1 is known to use sales information of other products to make improvement suggestions for adding one or more attribute values ​​to products that are predicted to have good sales or improve in sales. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2019-53684 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, with the above-mentioned conventional technology, it is difficult to suggest improvements that cannot be obtained based on a comparison with other products.

[0006] The present application has been made in consideration of the above, and aims to provide an information processing device, an information processing method, and an information processing program that can suggest improvements that cannot be obtained based on comparison with other products. [Means for solving the problem]

[0007] The information processing device according to the present application includes an acquisition unit, a generation unit, and a provision unit. The acquisition unit acquires posted information, which is information posted about a product. The generation unit inputs input information including the posted information acquired by the acquisition unit and information indicating instructions for outputting improvement information, which is information indicating improvements to the product based on the posted information, to a generation AI or AI agent that generates and outputs information according to the input information, and causes the generation AI or AI agent to generate improvement information. The provision unit provides the improvement information generated by the generation unit. Effect of the Invention

[0008] According to one aspect of the embodiment, it is possible to provide an effect of suggesting improvements that cannot be obtained based on a comparison with other products. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating an example of information processing according to an embodiment. [Diagram 2] FIG. 2 is a diagram illustrating an example of the configuration of an information processing system according to the embodiment. [Diagram 3] FIG. 3 is a diagram illustrating an example of the configuration of the information processing device according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of a user information table stored in the user information storage unit of the information processing device according to the embodiment. [Diagram 5] FIG. 5 is a diagram illustrating an example of a product information table stored in the product information storage unit of the information processing device according to the embodiment. [Figure 6] FIG. 6 is a diagram illustrating an example of a posted information table stored in the posted information storage unit of the information processing device according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of improvement information displayed on the terminal device according to the embodiment. [Figure 8] FIG. 8 is a flowchart illustrating an example of information processing by the processing unit of the information processing device according to the embodiment. [Figure 9]FIG. 9 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device according to the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, the information processing device, the information processing method, and the information processing program according to the present application (hereinafter, referred to as "embodiments") will be described in detail with reference to the drawings. Note that the information processing device, the information processing method, and the information processing program according to the present application are not limited to these embodiments. In addition, each embodiment can be appropriately combined as long as the processing contents are not contradictory. In addition, the same parts in each of the following embodiments are given the same reference numerals, and duplicated explanations will be omitted.

[0011] [1. An example of information processing] First, an example of information processing according to the embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing an example of information processing according to the embodiment.

[0012] The information processing device 1 shown in FIG. 1 is an information processing device that works in cooperation with a terminal device 2 and a terminal device 3 of a user U, and provides various information online to the user U of the terminal device 2 and a business operator O who is a user of the terminal device 3, and is realized, for example, by one or more servers or a cloud system.

[0013] The information processing device 1 receives an improvement proposal request from a terminal device 3 of a business operator O that designs, develops, or sells a product (step S1). The improvement proposal request includes information about the business operator O and information about the product for which the business operator O is requesting an improvement proposal. The product information includes, for example, information indicating the name and model of the product, and information indicating the product category. In the following, the product for which the business operator O is requesting an improvement proposal may be referred to as a target product.

[0014] Next, the information processing device 1 acquires one or more pieces of posted information, which are information posted about the target product specified in the improvement proposal request, from an internal storage unit or an external information providing device (step S2).

[0015] The posted information is information posted on various online sites provided by the information processing device 1 or other information providing devices, and is text, but may be information including text and images, etc. Examples of online sites include, but are not limited to, social media, news sites, bulletin board sites, Q&A sites, shopping sites, price comparison sites, and word-of-mouth sites.

[0016] Examples of social media include, but are not limited to, Instagram (registered trademark), TikTok (registered trademark), YouTube (registered trademark), X (formerly Twitter: registered trademark) (registered trademark), and Facebook (registered trademark).

[0017] News sites include, but are not limited to, aggregation sites such as Yahoo (registered trademark), news sites provided by various news organizations, etc. Information posted to news sites includes, for example, information indicating news content, information indicating comments and ratings on the news content, etc., but may also include comments on comments on the news content, etc.

[0018] Bulletin board sites include 2chan and 5chan, where people post and exchange information on a wide range of topics. Q&A sites include Yahoo! Answers, where people post and exchange information on a wide range of topics, from everyday questions to specialized questions.

[0019] A shopping site is a site that sells multiple products. A price comparison site is a site that shows the prices of each store selling a product and the results of comparing prices of each store. Information posted on shopping sites and price comparison sites includes, for example, evaluations of products by purchasers, information showing evaluations of each function and design, and information showing reviews and comments on products by purchasers (for example, purchasers' critiques and impressions of products).

[0020] A word-of-mouth site is a site where product ratings and reviews are posted and viewed. Information posted on word-of-mouth sites includes ratings of products, ratings of each function and design, and reviews and comments on products (e.g., critiques and impressions of products).

[0021] The information processing device 1 can acquire various types of posted information by using, for example, APIs (Application Programming Interfaces) of various sites.

[0022] The information processing device 1 can also acquire, as the posted information, limited information on the target product posted by a poster who has received high ratings for the posted content. The rating for the posted content is the rating by others for the posted content among the posted information posted by the poster on the site, and a high rating indicates that the rating by others is equal to or higher than a threshold (for example, 4 or higher in a 5-point scale).

[0023] A poster who has received high ratings for posted content is a poster whose average rating for each piece of posted information is equal to or greater than a threshold, but is not limited to this example. For example, whether or not a poster has received high ratings for posted content may be determined for each piece of posted information.

[0024] In addition, the information processing device 1 can input information including information indicating comments by others on the posted information and information indicating instructions to output an evaluation of the posted information into a language model as input information, and output information indicating whether the posted information has been highly rated from the language model.

[0025] For example, the information processing device 1 inputs to the generation AI information including the string "There are comments from others on the posted information as follows. Please consider whether the posted information is rated highly or not and output the comments." and one or more comments as input information.

[0026] The language model is, for example, a trained model trained to estimate and output the next token from an input token string, and is, for example, a language model such as an attenuation model or a transfer model. The transfer model is, for example, a Generative Pre-trained Transformer (GPT) or a Bidirectional Encoder Representations from Transformers (BERT), but is not limited to such examples.

[0027] Furthermore, the information processing device 1 can determine whether or not the posted information is information obtained through stealth marketing, and acquire posted information that is determined not to be information obtained through stealth marketing as posted information.

[0028] For example, the information processing device 1 determines whether or not the information is a post made through stealth marketing by using a trained model generated by training using training information including, for each post, the post information and information indicating whether or not the information is a post made through stealth marketing. The information indicating whether or not the information is a post made through stealth marketing is used, for example, as a label in training.

[0029] In addition, the information processing device 1 can determine that the information is not a stealth marketing post if, for example, the number of words that appear infrequently in the text of a stealth marketing post or the number of typos is above a threshold, and can determine that the information is a stealth marketing post if this is not the case.

[0030] Furthermore, the information processing device 1 can also determine whether or not information has been generated by a generation AI, and acquire posted information determined not to have been generated by a generation AI as posted information.

[0031] For example, the information processing device 1 determines whether or not the information on a post is generated by a generation AI by using a trained model generated by training using training information including, for each post, information on the post and information indicating whether the information on the post is generated by a generation AI. The information indicating whether the information on the post is generated by a generation AI is used, for example, as a label in training.

[0032] In addition, the information processing device 1 can determine that the information is not a post generated by a generation AI if, for example, the number of words that appear infrequently in a sentence output by the generation AI or the number of typos is above a threshold, and can also determine that the information is a post generated by a generation AI if this is not the case.

[0033] The above-mentioned trained model is, for example, a language model trained to estimate and output the next token from an input token string, and is, for example, a language model such as an attenuation model or a transfer model, but is not limited to such examples. Note that the above-mentioned training of the language model is performed by fine tuning, but is not limited to such examples.

[0034] Next, the information processing device 1 uses one or more pieces of posted information acquired in step S2 to cause a generating AI (Artificial Intelligence) or AI agent to generate and output information corresponding to the input information and generate improvement information (step S3).

[0035] The improvement information is information indicating areas for improvement of the target product identified in the improvement proposal request, and includes, for example, information indicating areas for improvement in terms of functionality, information indicating areas for improvement in terms of design, and information indicating areas for improvement in terms of quality.

[0036] The information processing device 1 inputs input information to the generation AI or AI agent, the input information including one or more posted information acquired in step S2 and information indicating instructions for outputting improvement information, which is information indicating improvements to the target product based on the posted information, and causes the generation AI or AI agent to generate the improvement information.

[0037] First, generation of improvement information using a generative AI will be described. The generative AI is, for example, a text generation AI, an image generation AI, or a multimodal generation AI. The text generation AI is, for example, a language model trained to estimate and output the next token from an input token string, such as an attenuation model or a transfer model.

[0038] The transfer model is, for example, GPT or BERT, but is not limited to such examples. It is desirable to conceal information such as input personal information by learning the information input to the above-mentioned language model so that it is not used as a new answer. In addition, the generation AI may be one that is specially trained (for example, fine-tuned) for generating improvement information.

[0039] The image generation AI is a generation AI that generates images from text, such as StackGAN (Generative Adversarial Networks), AttnGAN, T2I (Text-to-Image) with Transformers, DALL-E, etc., but is not limited to such examples.

[0040] A multimodal generative AI is, for example, a generative AI that generates images from text or generates text from images, and examples of such AI include GPT-4 and CM3Leon (Chameleon Multimodal Model), but are not limited to such examples.

[0041] The AI ​​agent may be, for example, an autonomous AI agent such as AutoGPT, etc. The AI ​​agent may also be an autonomous AI agent created specifically for generating improvement information.

[0042] For example, when a goal is given to an autonomous AI agent, the autonomous AI agent makes a generating AI generate a task for achieving the goal, collects information for making the generating AI execute the generated task, and outputs information for achieving the goal by repeating the process of making the generating AI execute the task. Note that the AI ​​agent may be a non-autonomous AI agent that makes the generating AI generate improvement information by using the generating AI multiple times according to a predetermined procedure.

[0043] The information processing device 1, for example, inputs input information to the generation AI using an API provided by a business providing the generation AI and acquires information generated by the generation AI, but the information processing device 1 may be configured to include the generation AI. Also, the information processing device 1 has an AI agent, but may be configured to use the AI ​​agent using an API provided by a business providing the AI ​​agent.

[0044] First, the generation of improvement information by the generation AI will be described. The information processing device 1 inputs, as input information, information including the character string "The following posted information is about the product. Please consider improvements to the product from the posted information and output them" and one or more pieces of posted information to the generation AI. The input information input to the generation AI is instruction information in natural language, and is also called a prompt.

[0045] This allows the information processing device 1 to acquire improvement information output from the generation AI. The improvement information output from the generation AI is, for example, information indicating functions with low average or median ratings by each contributor, information indicating design parts with low average or median ratings by each contributor, information indicating functions or design parts criticized by each contributor, and the like, but is not limited to such examples.

[0046] The information processing device 1 can also input input information further including product information, which is information on a target product specified in an improvement proposal request, to the generation AI. The product information is, for example, information indicating the name of the target product, information indicating the category of the target product, etc. Hereinafter, the information indicating the name of the target product or the information indicating the category of the target product may be referred to as product specification information.

[0047] For example, the information processing device 1 inputs to the generation AI, as input information, information including the character string "The following posted information exists for the product indicated by the following product identification information. Please consider and output improvements to the product from the posted information," the target product identification information, and one or more pieces of posted information. This allows the information processing device 1 to make the generation AI output improvements to the target product with greater accuracy.

[0048] The product information included in the input information may include, for example, information indicating each function of the target product (for example, information indicating catalog information or a list of specifications), information indicating the design of each part of the target product, information indicating the manual for the target product, etc. This allows the generation AI to output improvement information including information indicating that, for example, a function or design that is already provided in the target product but that the poster considers to be an issue needs improvement (for example, a point of insufficient emphasis in the product introduction).

[0049] The information processing device 1 can also cause the generation AI to generate improvement information using as input information information further including information indicating weighting according to the posting destination of the posted information. The weighting according to the posting destination of the posted information is, for example, weighting according to the type of posting destination of the posted information. The types of posting destination of the posted information are, for example, shopping sites, price comparison sites, review sites, news sites, Q&A sites, social media, bulletin board sites, etc.

[0050] The information indicating the weighting according to the posting destination of the posted information includes, for example, the following character string: "When considering improvements to a product, the weight (importance) of the posted information should be, from highest to lowest, shopping site > price comparison site > review site > news site > Q&A site > social media > bulletin board site." Note that the weighting according to the posting destination of the posted information may be indicated in n levels (n is an integer equal to or greater than 2), for example.

[0051] The information processing device 1 can also cause the generation AI to generate improvement information using as input information information further including information indicating weighting according to the type of posted information. The type of posted information is the type of posted content indicated by the posted information, such as reviews, evaluations, comments, questions, answers, questionnaires, tweets, etc., but is not limited to such examples.

[0052] The information indicating the weighting according to the type of posted information includes, for example, a character string "When considering improvements to a product, the weight (importance) of posted information should be, from highest to lowest, reviews > tweets > ratings > surveys > answers > comments > questions." Note that the weighting according to the type of posted information may be indicated, for example, in m stages (m is an integer equal to or greater than 2).

[0053] The information processing device 1 can also cause the generation AI to generate improvement information using as input information information further including information indicating weighting according to a combination of a posting destination of posted information and a type of posted information. The combination of a posting destination of posted information and a type of posted information is, for example, a combination of a shopping site and a review, a combination of a Q&A site and an answer, etc.

[0054] Next, the generation of improvement information by the AI ​​agent will be described. In this case, the information processing device 1 inputs, as input information to the AI ​​agent, information including, for example, a character string "The following posted information exists for the product indicated by the following product identification information. Please consider and output improvements to the product from the posted information," the product identification information, and one or more pieces of posted information.

[0055] As a result, the AI ​​agent sets the input information as a goal, generates and executes a task according to the goal, and generates and outputs improvement information. The input information input to the AI ​​agent is instruction information in natural language, and is also called a prompt.

[0056] The AI ​​agent has functions such as having the generation AI generate tasks based on input information, collecting information from an internal memory unit or an external information providing device in response to requests from the generation AI, and having the generation AI analyze the collected information.

[0057] For example, an AI agent has a generation AI generate a task based on input information, collects the information necessary to execute the task from an internal memory unit or an external information providing device, etc., in response to a request from the generation AI, and executes the task by having the generation AI analyze the collected information.

[0058] The input information input to the AI ​​agent includes, for example, one or more of information indicating each function of the target product specified by the product specification information (for example, information indicating catalog information or a list of specifications), information indicating the design of each part of the target product, and information indicating the manual for the target product, etc. This information is collected by the AI ​​agent from an internal memory unit or an external information providing device, etc.

[0059] This allows the AI ​​agent to output improvement information that includes, for example, features or design that the target product already has but that the poster believes to be problematic, and information that identifies areas for improvement that are not emphasized enough in the product introduction.

[0060] The input information input to the AI ​​agent may include, for example, other company's product posting information, which is information posted about other company's products in the same category as the target product specified by the product specification information. The other company's product posting information includes, for example, information indicating functions and design features that are highly rated by the poster. This information is collected by the AI ​​agent, but may also be acquired by the acquisition unit 30.

[0061] This allows the AI ​​agent to, for example, compare information posted about the target product with information posted about other companies' products, and output improvement information including information on areas for improving the product introduction.

[0062] The AI ​​agent can also collect only information posted about other companies' products in the same category as the product identified by the product-specific information, and only information indicating features or design features that are highly rated by the poster.

[0063] If the target product specified by the product specifying information is men's sneakers, the category is men's sneakers, but it may also be sneakers or shoes, etc., and if the target product specified by the product specifying information is beer, the category is beer, but it may also be beer-type products, beer-type products, alcoholic beverages, etc. Men's sneakers and beer are lower-level categories, sneakers and beer-type products are middle-level categories, and shoes and alcoholic beverages are higher-level categories.

[0064] In addition, the AI ​​agent can collect only information from posts made by posters who have given high ratings to other companies' products in the same category as the target product identified by the product identification information, and that indicates functions or design features that have been highly rated by the posters.

[0065] A high rating indicates, for example, that the value indicating the rating (for example, a value from 0 to 5) is equal to or greater than a threshold, but is not limited to such an example. For example, a high rating indicates that the number of high ratings (for example, likes) is equal to or greater than a threshold, and the ratio of the number of high ratings to the total number of ratings is equal to or greater than a threshold. For example, there are two types of ratings: high ratings and low ratings (for example, bad ratings), and the total number of ratings is the sum of the number of high ratings and the number of low ratings.

[0066] The AI ​​agent can also generate improvement information that includes improvement methods for the improvement points in addition to the improvement points. The information processing device 1 inputs to the AI ​​agent, as input information, information including the product specification information and one or more pieces of posted information, such as a character string "The following posted information exists for the product indicated by the product specification information below. Please consider and output the improvement points and improvement methods for the product from the posted information."

[0067] This allows the AI ​​agent to collect information about improvement methods from an internal memory unit or an external information providing device, and by using the collected information as part of the input information for the generation AI, it can generate improvement information that includes not only improvement points but also improvement methods for the improvement points.

[0068] The improvement information described above is text indicating the improvement points, but may be text and image information indicating the improvement points. The images are, for example, images of the design parts of the target product, including images before and after the improvement. Such images are generated by an image generation AI or a multimodal generation AI.

[0069] In addition, the generation AI that generates the improvement information may be generated, for example, by learning (e.g., fine tuning) using learning information that includes posted information and improvement information for each product, or may be a generation AI that has been trained specifically for generating improvement information.

[0070] Next, the information processing device 1 provides the improvement information generated in step S3 to the business operator O (step S4). For example, the information processing device 1 provides the improvement information generated by the generation AI or AI agent to the business operator O by transmitting the improvement information generated in step S3 to the terminal device 3 of the business operator O.

[0071] The terminal device 3 of the business operator O receives the improvement information transmitted from the information processing device 1 and displays the received improvement information. This allows the business operator O to grasp improvements and the like of the target product.

[0072] In this way, the information processing device 1 obtains posted information, which is information posted about the target product, and inputs input information including the posted information and information indicating instructions for outputting improvement information, which is information indicating improvements to the target product based on the posted information, to a generation AI or AI agent that generates and outputs information according to the input information, causes the generation AI or AI agent to generate improvement information, and provides the improvement information. In this way, the information processing device 1 can, for example, suggest improvements that cannot be obtained based on a comparison with other products.

[0073] 2. Configuration of the Information Processing System 2 is a diagram showing an example of a configuration of an information processing system according to an embodiment. As shown in FIG. 2, the information processing system 100 according to the embodiment includes an information processing device 1, a plurality of terminal devices 2, and a plurality of terminal devices 3.

[0074] The multiple terminal devices 2 are used by different users U. The multiple terminal devices 3 are used by different businesses O. The terminal devices 2 and 3 are, for example, notebook PCs (Personal Computers), desktop PCs, smartphones, tablet PCs, and wearable devices. The wearable device is, for example, smart glasses or a smart watch, but is not limited to such examples.

[0075] The information processing device 1, the terminal device 2, and the terminal device 3 are connected to each other so as to be able to communicate with each other by wire or wirelessly via a network N. Note that the information processing system 100 shown in FIG. 2 may include a plurality of information processing devices 1 and the like.

[0076] The network N includes, for example, a Wide Area Network (WAN) such as the Internet and mobile communication networks such as Long Term Evolution (LTE), 4th Generation (4G), and 5th Generation (5G: fifth generation mobile communication system).

[0077] The terminal devices 2 and 3 can connect to the network N via short-range wireless communication such as a mobile communication network, Bluetooth (registered trademark), or a wireless LAN (Local Area Network), and communicate with the information processing device 1.

[0078] 3. Configuration of information processing device 1 3 is a diagram showing an example of the configuration of the information processing device 1 according to the embodiment. As shown in FIG. 3, the information processing device 1 includes a communication unit 10, a storage unit 11, and a processing unit 12.

[0079] [3.1. Communication unit 10] The communication unit 10 is realized by, for example, a communication module or a NIC (Network Interface Card). The communication unit 10 is connected to a network N by wire or wirelessly, and transmits and receives information to and from various other devices. For example, the communication unit 10 transmits and receives information to and from the terminal device 2 and the terminal device 3 via the network N.

[0080] [3.2. Storage section 11] The storage unit 11 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 11 stores a user information storage unit 20, a product information storage unit 21, and a posted information storage unit 22.

[0081] 3.2.1. User information storage unit 20 The user information storage unit 20 stores information about the user U. Fig. 4 is a diagram showing an example of a user information table stored in the user information storage unit 20 of the information processing device 1 according to the embodiment. As shown in Fig. 4, the user information table stored in the user information storage unit 20 includes items such as "User ID (Identifier)", "Attribute Information", "History Information", and "Evaluation Information".

[0082] The "user ID" is identification information for identifying the user U. The "attribute information" is information on the user attributes of the user U corresponding to the "user ID", and includes information on psychographic attributes and information on demographic attributes. The demographic attributes are, for example, gender, age, place of residence, and occupation, while the psychographic attributes are interests such as travel, clothes, cars, and religion, lifestyle, thoughts, and thought trends.

[0083] The "history information" includes information on the behavior history of the user U associated with the "user ID." The behavior history of the user U includes, for example, information on the movement history of the user U and information on the behavior history of the user U in online services. The information on the movement history of the user U includes, for example, information on the route traveled by the user U and information on the places visited by the user U.

[0084] The information on the user U's behavioral history in the online service includes, for example, search history information, browsing history information, and transaction history information in the online service of the user U. The search history information of the user U includes, for example, information on the search history of content by the user U in a web search service. The browsing history information of the user U includes, for example, information on the browsing history of content by the user U in the online service. The transaction history information includes, for example, information on the transaction history of products by the user U in the online service.

[0085] The "rating information" is information indicating an evaluation of the user U associated with the "user ID" as a contributor, for example, information indicating an evaluation of the user U as a contributor for each posting destination (for example, for each site).

[0086] [3.2.2. Product information storage unit 21] The product information storage unit 21 stores various information related to products. Fig. 5 is a diagram showing an example of a product information table stored in the product information storage unit 21 of the information processing device 1 according to the embodiment.

[0087] 5, the product information table stored in the product information storage unit 21 includes information items such as “product ID,” “product name,” “product category,” and “product information.” The “product ID” is an identifier that identifies a product.

[0088] "Product name" is information indicating the name of the product associated with the "product ID", and includes, for example, information indicating the product model number. "Product category" is information indicating the product category associated with the "product ID". "Product information" is information indicating each function of the product associated with the "product ID" (for example, information indicating catalog information or a list of specifications), information indicating the design of each part of the product, information indicating the product manual, etc.

[0089] 3.2.3. Posted information storage unit 22 The posted information storage unit 22 stores various types of posted information. Fig. 6 is a diagram showing an example of a posted information table stored in the posted information storage unit 22 of the information processing device 1 according to the embodiment.

[0090] 6, the posted information table stored in the posted information storage unit 22 includes information items such as "posting ID," "user ID," "posting destination," "posting type," and "posted information." The "posting ID" is an identifier that identifies the posted information.

[0091] "User ID" is the user ID of the user U who posted the posted information corresponding to the "posting ID". "Posting destination" is information indicating the type of posting destination of the posted information corresponding to the "posting ID". The types of posting destinations include, for example, social media, news sites, bulletin board sites, Q&A sites, shopping sites, price comparison sites, and review sites.

[0092] "Post type" is information indicating the type of posted information corresponding to "Post ID". The type of posted information is the type of posted content indicated by the posted information, such as reviews, ratings, comments, questions, answers, surveys, tweets, etc., but is not limited to such examples.

[0093] The "posted information" is posted information corresponding to the "posted ID." The posted information is information posted to various online sites provided by the information processing device 1 or other information providing devices, and is text, but may also be information including text and images.

[0094] [3.3. Processing section 12] The processing unit 12 is a controller, and is realized by a processor such as a CPU (Central Processing Unit) or MPU (Micro Processing Unit) by executing various programs (corresponding to an example of an information processing program) stored in a storage device inside the information processing device 1 using a RAM or the like as a working area.

[0095] Furthermore, the processing unit 12 is a controller, and may be realized by an integrated circuit such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or a general purpose graphic processing unit (GPGPU).

[0096] 3, the processing unit 12 has an acquisition unit 30, a reception unit 31, a determination unit 32, a generation unit 33, and a provision unit 34, and realizes or executes the functions and actions of information processing described below. Note that the internal configuration of the processing unit 12 is not limited to the configuration shown in FIG. 3, and may be other configurations as long as they perform the information processing described below.

[0097] [3.3.1. Acquisition part 30] The acquisition unit 30 acquires various pieces of information from an external information processing device, the terminal device 2, the terminal device 3, or the like via the communication unit 10. The acquisition unit 30 acquires various pieces of information from the storage unit 11. For example, the acquisition unit 30 acquires user information from the external information processing device or the terminal device 2, and updates the user information table stored in the user information storage unit 20 based on the acquired user information.

[0098] Furthermore, the acquiring unit 30 acquires various types of information included in the request accepted by the accepting unit 31. For example, when the request accepted by the accepting unit 31 is an improvement proposal request, the acquiring unit 30 acquires information included in the improvement proposal request.

[0099] The improvement proposal request includes information about the business operator O and information about a target product for which the business operator O requests an improvement proposal. The information about the target product includes, for example, information indicating the name and model of the target product, information indicating the category of the target product, and the like.

[0100] Furthermore, the acquisition unit 30 acquires a plurality of pieces of posted information from an external information providing device or the like, and stores the acquired plurality of pieces of posted information in the posted information storage unit 22. The acquisition unit 30 can acquire various pieces of posted information by using, for example, APIs of various sites provided by the external information providing device or the like.

[0101] Furthermore, the acquisition unit 30 acquires various pieces of information from the storage unit 11. For example, the acquisition unit 30 acquires a plurality of pieces of posted information, each of which is information posted on a target product, from the storage unit 11 as a plurality of pieces of posted information to be used as input information. The posted information is information posted on various online sites provided by the information processing device 1 or other information processing devices.

[0102] Furthermore, the acquisition unit 30 acquires user information, product information, and the like from the storage unit 11. For example, when improvement information is generated by the generation unit 33 using a generation AI or an AI agent, the acquisition unit 30 acquires information used as input information, which is information to be input to the generation AI or AI agent, from the storage unit 11, an external information providing device, or the like.

[0103] Furthermore, the acquisition unit 30 can acquire, as posted information to be used as input information, information on a post determined by the determination unit 32 to be information on a target product posted by a contributor who has received high ratings for the posted content.

[0104] Furthermore, the acquisition unit 30 can acquire posted information determined by the determination unit 32 to not be information generated by stealth marketing as posted information to be used as input information. Furthermore, the acquisition unit 30 can acquire posted information determined by the determination unit 32 to not be information generated by a generation AI as posted information to be used as input information.

[0105] Information used as input information for the generation AI or AI agent is, for example, posted information that is information posted about the target product, information showing details of the target product, the above-mentioned other company's product posted information, etc. Information showing details of the target product is, for example, information showing each function of the target product (for example, information showing catalog information or a list of specifications), information showing the design of each part of the target product, information showing the manual for the target product, etc., but is not limited to such examples.

[0106] [3.3.2. Reception Unit 31] The receiving unit 31 receives various requests and various information from the terminal device 2, the terminal device 3, an external information processing device, etc. via the communication unit 10. For example, the receiving unit 31 receives an improvement proposal request from the terminal device 3.

[0107] Furthermore, the reception unit 31 receives posting information, which is information posted to an online service using an online site. For example, when the provision unit 34 provides an online service that provides news content using a news site, the reception unit 31 receives posting information, which is information posted on the news content.

[0108] [3.3.3. Judgment unit 32] The determination unit 32 performs various determinations. For example, the determination unit 32 determines whether the posted information is about a target product posted by a contributor who has received high ratings for the posted content.

[0109] An evaluation of a posted content is an evaluation by others of the posted content among the posted information posted by the poster on the site, and a high evaluation indicates that the evaluation by others is above a threshold (for example, 4 or above on a 5-point scale).

[0110] A poster who has received high ratings for posted content is a poster whose average rating for each piece of posted information is equal to or greater than a threshold, but is not limited to this example. For example, whether or not a poster has received high ratings for posted content may be determined for each piece of posted information.

[0111] Furthermore, the determination unit 32 determines whether or not the posted information is information generated by stealth marketing. For example, the determination unit 32 determines whether or not the posted information is information generated by stealth marketing by using a trained model generated by learning using training information including, for each post, the post information and information indicating whether or not the posted information is information generated by stealth marketing. The information indicating whether or not the posted information is information generated by stealth marketing is used, for example, as a label in the learning.

[0112] In addition, the judgment unit 32 can, for example, determine that the information is not a post made through stealth marketing if the number of words that appear infrequently in the text of a post made through stealth marketing or the number of typos is equal to or greater than a threshold value, and can also determine that the information is a post made through stealth marketing if this is not the case.

[0113] In addition, the determination unit 32 determines whether the posted information is information generated by a generation AI. For example, the determination unit 32 determines whether the posted information is information generated by a generation AI by using a trained model generated by learning using learning information including, for each post, the post information and information indicating whether the post information is information generated by a generation AI. The information indicating whether the post information is information generated by a generation AI is used, for example, as a label in learning.

[0114] In addition, the judgment unit 32 can, for example, determine that the information is not a post generated by a generation AI if the number of words that appear infrequently in a sentence output by the generation AI or the number of typos is equal to or greater than a threshold value, and can also determine that the information is a post generated by a generation AI if this is not the case.

[0115] The trained model used in the determination unit 32 is, for example, a language model trained to estimate and output the next token from an input token string, such as an attenuation model or a transfer model, but is not limited to such examples. The above-mentioned training of the language model is performed by fine tuning, but is not limited to such examples.

[0116] [3.3.4. Generation unit 33] The generating unit 33 generates various information. For example, the generating unit 33 causes a generating AI or an AI agent that generates and outputs information according to input information to generate improvement information.

[0117] The improvement information is information indicating areas for improvement of the target product identified in the improvement proposal request, and includes, for example, information indicating areas for improvement in terms of functionality, information indicating areas for improvement in terms of design, and information indicating areas for improvement in terms of quality.

[0118] The generation unit 33 inputs input information to the generation AI or AI agent, the input information including one or more pieces of posted information acquired by the acquisition unit 30 and information indicating instructions for outputting improvement information, which is information indicating improvements to the target product based on the posted information, and causes the generation AI or AI agent to generate the improvement information.

[0119] First, generation of improvement information using a generative AI will be described. The generative AI is, for example, a text generation AI, an image generation AI, or a multimodal generation AI. The text generation AI is, for example, a language model trained to estimate and output the next token from an input token string, such as an attenuation model or a transfer model.

[0120] The transfer model is, for example, GPT or BERT, but is not limited to such examples. It is desirable to conceal information such as input personal information by learning the information input to the above-mentioned language model so that it is not used as a new answer. In addition, the generation AI may be one that is specially trained (for example, fine-tuned) for generating improvement information.

[0121] The image generation AI is a generation AI that generates images from text, such as, but not limited to, StackGAN, AttnGAN, T2I with Transformers, DALL-E, etc.

[0122] A multimodal generative AI is, for example, a generative AI that generates images from text or generates text from images, such as GPT-4 and CM3Leo, but is not limited to such examples.

[0123] The AI ​​agent may be, for example, an autonomous AI agent such as AutoGPT, etc. The AI ​​agent may also be an autonomous AI agent created specifically for generating improvement information.

[0124] For example, when a goal is given to an autonomous AI agent, the autonomous AI agent makes a generating AI generate a task for achieving the goal, collects information for making the generating AI execute the generated task, and outputs information for achieving the goal by repeating the process of making the generating AI execute the task. Note that the AI ​​agent may be a non-autonomous AI agent that makes the generating AI generate improvement information by using the generating AI multiple times according to a predetermined procedure.

[0125] The generation unit 33, for example, inputs input information to the generation AI using an API provided by a business providing the generation AI and acquires information generated by the generation AI, but the generation unit 33 may be configured to include the generation AI. Also, the generation unit 33 has an AI agent, but may be configured to use the AI ​​agent using an API provided by a business providing the AI ​​agent.

[0126] First, the generation of improvement information by the generation AI will be described. The generation unit 33 inputs, as input information, information including the character string "The following posted information is available for the product. Please consider improvements to the product from the posted information and output them" and one or more pieces of posted information to the generation AI. The input information input to the generation AI is instruction information in natural language, and is also called a prompt.

[0127] This allows the generation unit 33 to acquire improvement information output from the generation AI. The improvement information output from the generation AI is, for example, information indicating functions with low average or median ratings by each contributor, information indicating design parts with low average or median ratings by each contributor, information indicating functions or design parts criticized by each contributor, and the like, but is not limited to such examples.

[0128] The generation unit 33 can also input input information further including product information, which is information on the target product specified in the improvement proposal request, to the generation AI. The product information is product specification information such as information indicating the name of the target product, information indicating the category of the target product, etc.

[0129] For example, the generation unit 33 inputs to the generation AI, as input information, information including the character string "The following posted information is for the product indicated by the following product identification information. Please consider and output improvements to the product from the posted information," the product identification information, and one or more pieces of posted information. This allows the generation unit 33 to make the generation AI output improvements to the target product with greater accuracy.

[0130] The product information included in the input information may include, for example, information indicating each function of the target product (for example, information indicating catalog information or a list of specifications), information indicating the design of each part of the target product, information indicating the manual for the target product, etc. This allows the generation AI to output improvement information including information indicating that, for example, a function or design that is already provided in the target product but that the poster considers to be an issue needs improvement (for example, a point of insufficient emphasis in the product introduction).

[0131] The generating unit 33 can also cause the generating AI to generate improvement information using as input information information further including information indicating weighting according to the posting destination of the posted information. The weighting according to the posting destination of the posted information is, for example, weighting according to the type of posting destination of the posted information. The types of posting destination of the posted information are, for example, shopping sites, price comparison sites, review sites, news sites, Q&A sites, social media, bulletin board sites, etc.

[0132] The information indicating the weighting according to the posting destination of the posted information includes, for example, the following character string: "When considering improvements to a product, the weight (importance) of the posted information should be, from highest to lowest, shopping site > price comparison site > review site > news site > Q&A site > social media > bulletin board site." Note that the weighting according to the posting destination of the posted information may be indicated in n levels (n is an integer equal to or greater than 2), for example.

[0133] The information indicating the weighting according to the posting destination of the posted information may be preset information or may be information specified in the improvement proposal request. When the weighting according to the posting destination of the posted information is specified in the improvement proposal request, the generation unit 33 can cause the generation AI to generate improvement information using as input information information further including the information indicating the weighting specified in the improvement proposal request.

[0134] The generating unit 33 can also cause the generating AI to generate improvement information using as input information information further including information indicating weighting according to the type of posted information. The type of posted information is the type of posted content indicated by the posted information, such as reviews, evaluations, comments, questions, answers, questionnaires, tweets, etc., but is not limited to such examples.

[0135] The information indicating the weighting according to the type of posted information includes, for example, a character string "When considering improvements to a product, the weight (importance) of posted information should be, from highest to lowest, reviews > tweets > ratings > surveys > answers > comments > questions." Note that the weighting according to the type of posted information may be indicated, for example, in m stages (m is an integer equal to or greater than 2).

[0136] The information indicating the weighting according to the type of posted information may be preset information or may be information specified in the improvement proposal request. When the weighting according to the type of posted information is specified in the improvement proposal request, the generation unit 33 can cause the generation AI to generate improvement information using as input information information further including the information indicating the weighting specified in the improvement proposal request.

[0137] The generating unit 33 can also cause the generating AI to generate improvement information using as input information information further including information indicating weighting according to a combination of a posting destination of posted information and a type of posted information. The combination of a posting destination of posted information and a type of posted information is, for example, a combination of a shopping site and a review, a combination of a Q&A site and an answer, etc.

[0138] Next, the generation of improvement information by the AI ​​agent will be described. In this case, the generation unit 33 inputs, as input information to the AI ​​agent, information including, for example, a character string "The following posted information is for the product indicated by the following product identification information. Please consider and output improvements to the product from the posted information," the product identification information, and one or more posted information.

[0139] As a result, the AI ​​agent sets the input information as a goal, generates and executes a task according to the goal, and generates and outputs improvement information. The input information input to the AI ​​agent is instruction information in natural language, and is also called a prompt.

[0140] The AI ​​agent has functions such as having the generation AI generate tasks based on input information, collecting information from an internal memory unit or an external information providing device in response to requests from the generation AI, and having the generation AI analyze the collected information.

[0141] For example, an AI agent has a generation AI generate a task based on input information, collects the information necessary to execute the task from an internal memory unit or an external information providing device, etc., in response to a request from the generation AI, and executes the task by having the generation AI analyze the collected information.

[0142] The input information input to the AI ​​agent includes, for example, one or more of information indicating each function of the target product specified by the product specification information (for example, information indicating catalog information or a list of specifications), information indicating the design of each part of the target product, and information indicating the manual for the target product, etc. This information is collected by the AI ​​agent from an internal memory unit or an external information providing device, etc.

[0143] This allows the AI ​​agent to output improvement information that includes, for example, features or design that the target product already has but that the poster believes to be problematic, and information that identifies areas for improvement that are not emphasized enough in the product introduction.

[0144] The input information input to the AI ​​agent may include, for example, other company's product posting information, which is information posted about other company's products in the same category as the target product specified by the product specification information. The other company's product posting information includes, for example, information indicating functions and design features that are highly rated by the poster. This information is collected by the AI ​​agent, but may also be acquired by the acquisition unit 30.

[0145] This allows the AI ​​agent to, for example, compare information posted about the target product with information posted about other companies' products, and output improvement information including information on areas for improving the product introduction.

[0146] The AI ​​agent can also collect only information posted about other companies' products in the same category as the target product identified by the product-specific information, and only information indicating features or design features that are highly rated by posters.

[0147] If the target product specified by the product specifying information is men's sneakers, the category is men's sneakers, but it may also be sneakers or shoes, etc., and if the target product specified by the product specifying information is beer, the category is beer, but it may also be beer-type products, beer-type products, alcoholic beverages, etc. Men's sneakers and beer are lower-level categories, sneakers and beer-type products are middle-level categories, and shoes and alcoholic beverages are higher-level categories.

[0148] In addition, the AI ​​agent can collect only information from posts made by posters who have given high ratings to other companies' products in the same category as the target product identified by the product identification information, and that indicates functions or design features that have been highly rated by the posters.

[0149] A high rating indicates, for example, that the value indicating the rating (for example, a value from 0 to 5) is equal to or greater than a threshold, but is not limited to such an example. For example, a high rating indicates that the number of high ratings (for example, likes) is equal to or greater than a threshold, and the ratio of the number of high ratings to the total number of ratings is equal to or greater than a threshold. For example, there are two types of ratings: high ratings and low ratings (for example, bad ratings), and the total number of ratings is the sum of the number of high ratings and the number of low ratings.

[0150] The AI ​​agent can also generate improvement information including improvement methods for the improvement points in addition to the improvement points. The generation unit 33 inputs, as input information to the AI ​​agent, information including, for example, a character string "The following posted information exists for the product indicated by the following product identification information. Please consider and output the improvement points and improvement methods for the product from the posted information," the product identification information, and one or more pieces of posted information.

[0151] This allows the AI ​​agent to collect information about improvement methods from an internal memory unit or an external information providing device, and by using the collected information as part of the input information for the generation AI, it can generate improvement information that includes not only improvement points but also improvement methods for the improvement points.

[0152] The improvement information described above is text indicating the improvement points, but may be text and image information indicating the improvement points. The images are, for example, images of the design parts of the target product, including images before and after the improvement. Such images are generated by an image generation AI or a multimodal generation AI.

[0153] In addition, the generation AI that generates the improvement information may be generated, for example, by learning (e.g., fine tuning) using learning information that includes posted information and improvement information for each product, or may be a generation AI that has been trained specifically for generating improvement information.

[0154] [3.3.5.Providing Department 34] The providing unit 34 provides various pieces of information via the communication unit 10. The providing unit 34 provides various pieces of information to the terminal device 2 or the terminal device 3 via the communication unit 10, for example.

[0155] For example, the providing unit 34 provides an online service to the user U of the terminal device 2 using an online site or the like. For example, the providing unit 34 provides news content to the user U by transmitting news content using a news site to the terminal device 2 of the user U.

[0156] Furthermore, the providing unit 34 provides the improvement information generated by the generation AI or AI agent in the generation unit 33 to the user U who is the recipient. For example, the providing unit 34 provides the improvement information generated by the generation AI or AI agent to the user U by transmitting the improvement information generated by the generation AI or AI agent in the generation unit 33 to the terminal device 2 of the user U who is the recipient.

[0157] Fig. 7 is a diagram showing an example of improvement information displayed on the terminal device 2 according to the embodiment. Improvement information 50 shown in Fig. 7 is information indicating improvements to be made to the refrigerator F. This allows a business operator O who designs, develops, or sells products to know the improvements to be made to the refrigerator F.

[0158] The improvement information 50 shown in FIG. 7 includes, for example, the information of the character string "Improvement point 1: Uniformity of cool air: Some areas are harder to cool than others", the information of the character string "Improvement point 2: Door opening and closing sound: An unusual noise can be heard when opening the door", the information of the character string "Improvement point 3: Shelf position adjustment: The range for adjusting the shelf height is narrow", and the information of the character string "Improvement point 4: Temperature setting: Although it is mentioned in the manual, there are many complaints about the temperature setting".

[0159] 4. Processing Procedure Next, a procedure of information processing by the processing unit 12 of the information processing device 1 according to the embodiment will be described. Fig. 8 is a flowchart showing an example of information processing by the processing unit 12 of the information processing device 1 according to the embodiment.

[0160] 8, the processing unit 12 of the information processing device 1 determines whether or not an improvement proposal request transmitted from the terminal device 3 has been accepted (step S10). When the processing unit 12 determines that the improvement proposal request has been accepted (step S10: Yes), the processing unit 12 stores information included in the improvement proposal request (step S11).

[0161] Next, the processing unit 12 acquires posted information on the target product, which is the product specified in the improvement proposal request, and which satisfies a predetermined condition (step S12). The predetermined condition is, for example, one or more of the following: the posted information is on the target product by a poster who has received high evaluations for the posted content; the posted information is not posted by stealth marketing; and the posted information is not generated by a generation AI.

[0162] Next, the processing unit 12 inputs input information including the posting information acquired in step S12 and information indicating an instruction to output improvement information to the generation AI or AI agent, and causes the generation AI or AI agent to generate improvement information (step S13). Then, the processing unit 12 provides the improvement information to the business operator O by transmitting the improvement information generated by the generation AI or AI agent in step S13, for example, to the terminal device 2 that transmitted the improvement proposal request (step S14).

[0163] When the process of step S14 is completed or when it is determined that the improvement proposal request has not been accepted (step S10: No), the processing unit 12 determines whether or not the operation end timing has arrived (step S15). The processing unit 12 determines that the operation end timing has arrived when, for example, the power supply of the information processing device 1 is turned off.

[0164] If the processing unit 12 determines that the operation end time has not arrived (step S15: No), it transitions to step S15, and if the processing unit 12 determines that the operation end time has arrived (step S15: Yes), it terminates the processing shown in FIG. 8.

[0165] 5. Modifications A part or all of the processes of the information processing device 1 described above may be performed by the terminal device 2. In this case, the terminal device 2 has a part or all of the functions of the acquisition unit 30, the reception unit 31, the determination unit 32, the generation unit 33, and the provision unit 34. In this case, the information processing device 1 and the terminal device 2 function as an information processing device, or the terminal device 2 functions as an information processing device instead of the information processing device 1.

[0166] [6. Hardware Configuration] The information processing device 1 according to the embodiment described above is realized by a computer 200 having a configuration as shown in Fig. 9. Fig. 9 is a hardware configuration diagram showing an example of the computer 200 that realizes the functions of the information processing device 1 according to the embodiment. The computer 200 has a CPU 201, a RAM 202, a ROM (Read Only Memory) 203, a HDD (Hard Disk Drive) 204, a communication interface (I / F) 205, an input / output interface (I / F) 206, and a media interface (I / F) 207.

[0167] The CPU 201 operates and controls each unit based on a program stored in the ROM 203 or the HDD 204. The ROM 203 stores a boot program executed by the CPU 201 when the computer 200 is started up, and programs that depend on the hardware of the computer 200.

[0168] The HDD 204 stores programs executed by the CPU 201, data used by such programs, etc. The communication interface 205 receives data from other devices via a network N (see FIG. 2) and sends it to the CPU 201, and transmits data generated by the CPU 201 to other devices via the network N.

[0169] The CPU 201 controls output devices such as a display and a printer, and input devices such as a keyboard and a mouse, via the input / output interface 206. The CPU 201 acquires data from the input devices via the input / output interface 206. The CPU 201 also outputs generated data to the output devices via the input / output interface 206.

[0170] The media interface 207 reads a program or data stored in the recording medium 208 and provides it to the CPU 201 via the RAM 202. The CPU 201 loads the program from the recording medium 208 onto the RAM 202 via the media interface 207, and executes the loaded program. The recording medium 208 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase change rewritable Disc), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0171] For example, when the computer 200 functions as the information processing device 1 according to the embodiment, the CPU 201 of the computer 200 executes a program loaded on the RAM 202 to realize the function of the processing unit 12. In addition, the HDD 204 stores data in the storage unit 11. The CPU 201 of the computer 200 reads and executes these programs from the recording medium 208, but as another example, these programs may be obtained from another device via the network N.

[0172] [7. Other] In addition, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by a known method. In addition, the information including the processing procedures, specific names, various data and parameters shown in the above documents and drawings can be changed arbitrarily unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.

[0173] In addition, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads, usage conditions, etc.

[0174] For example, the above-mentioned information processing device 1 may be realized by a terminal device and a server computer, or may be realized by multiple server computers. Also, depending on the functions, the configuration can be flexibly changed, such as by calling an external platform using an API or network computing.

[0175] Furthermore, the above-described embodiments and modifications can be appropriately combined as long as the processing contents are not contradictory.

[0176] 8. Effects As described above, the information processing device 1 according to the embodiment includes an acquisition unit 30, a generation unit 33, and a provision unit 34. The acquisition unit 30 acquires posted information, which is information posted on a product. The generation unit 33 inputs input information including the posted information acquired by the acquisition unit 30 and information indicating an instruction to output improvement information, which is information indicating improvements of the product based on the posted information, to a generation AI or AI agent that generates and outputs information according to the input information, and causes the generation AI or AI agent to generate the improvement information. The provision unit 34 provides the improvement information generated by the generation unit 33. This allows the information processing device 1 to suggest improvements that cannot be obtained based on a comparison with other products, for example.

[0177] The generating unit 33 causes the generating AI or the AI ​​agent to generate the improvement information using information further including information indicating weighting according to the posting destination of the posted information as input information. This allows the information processing device 1 to, for example, more appropriately generate the improvement information.

[0178] The generation unit 33 causes the generation AI or AI agent to generate improvement information using information further including product information, which is information about a product, as input information. This allows the information processing device 1 to, for example, more appropriately generate improvement information.

[0179] The acquiring unit 30 acquires, as the posted information, information on a product posted by a contributor who has received a high evaluation for the posted content. This allows the information processing device 1 to generate, for example, improvement information more appropriately.

[0180] The acquiring unit 30 acquires information indicating evaluations of products as posted information, which enables the information processing device 1 to generate, for example, improvement information more appropriately.

[0181] The generation unit 33 causes the generation AI or the AI ​​agent to generate improvement information using information further including information indicating weighting according to the type of posted information as input information. This allows the information processing device 1 to, for example, more appropriately generate improvement information.

[0182] The information processing device 1 includes a determination unit 32 that determines whether or not the information is the result of stealth marketing, and the acquisition unit 30 acquires, as posted information, posted information that is determined not to be the result of stealth marketing by the determination unit 32. This enables the information processing device 1 to more appropriately generate improvement information, for example.

[0183] The information processing device 1 includes a determination unit 32 that determines whether or not the information is generated by a generation AI, and the acquisition unit 30 acquires, as posted information, posted information that is determined by the determination unit 32 not to be information generated by a generation AI. This enables the information processing device 1 to generate improvement information more appropriately, for example.

[0184] The above describes the embodiment of the present application in detail based on the drawings, but this is merely an example, and the present invention can be implemented in other forms that include various modifications and improvements based on the knowledge of those skilled in the art, including the forms described in the Disclosure of the Invention section.

[0185] Furthermore, the above-mentioned "section, module, unit" can be read as "means" or "circuit", etc. For example, an acquisition section can be read as an acquisition means or an acquisition circuit. [Explanation of symbols]

[0186] 1. Information processing device 2,3 Terminal Equipment 10. Communications Department 11 Storage section 12 Processing section 20 User information storage unit 21 Product information storage section 22 Posted information storage unit 30 Acquisition Department 31 Reception 32 Judgment section 33 Generation part 34 Providing Department 100 Information Processing Systems N Network

Claims

1. An acquisition unit that acquires posted information which is information on posts regarding a product; a generation unit that inputs input information including the posted information acquired by the acquisition unit and information indicating an instruction to output improvement information, which is information indicating improvements of the product based on the posted information, to a generation AI or AI agent that generates and outputs information according to the input information, and causes the generation AI or AI agent to generate the improvement information; a providing unit that provides the improvement information generated by the generating unit.

23. An information processing apparatus comprising:

2. The generation unit is The generating AI or the AI ​​agent generates the improvement information using information further including information indicating weighting according to a posting destination of the posted information as the input information.

2. The information processing apparatus according to claim 1,

3. The generation unit is The generating AI or the AI ​​agent generates the improvement information using information further including product information that is information about the product as the input information.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

4. The acquisition unit is Information on posts about the product by contributors who have received high ratings for the posted content is acquired as the posted information.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

5. The acquisition unit is Information indicating an evaluation of the product is acquired as the posted information.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

6. The generation unit is The generating AI or the AI ​​agent generates the improvement information using information further including information indicating weighting according to the type of the posted information as the input information.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

7. A determination unit is provided for determining whether or not the information is based on stealth marketing, The acquisition unit is The posted information that is determined by the determination unit not to be information based on stealth marketing is acquired as the posted information.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

8. A determination unit that determines whether the information is generated by a generation AI, The acquisition unit is The posted information determined by the determination unit to not be information generated by the generation AI is acquired as the posted information.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

9. 1. A computer-implemented information processing method, comprising: An acquisition step of acquiring posted information which is information on posts regarding a product; a generation process of inputting input information including the posted information acquired by the acquisition process and information indicating an instruction to output improvement information, which is information indicating improvements of the product based on the posted information, to a generation AI or AI agent that generates and outputs information according to the input information, and causing the generation AI or AI agent to generate the improvement information; and providing the improvement information generated by the generating step.

23. An information processing method comprising:

10. An acquisition step for acquiring post information which is information on posts regarding a product; a generation step of inputting input information including the posted information acquired by the acquisition step and information indicating an instruction to output improvement information, which is information indicating improvements of the product based on the posted information, to a generation AI or AI agent that generates and outputs information according to the input information, and causing the generation AI or AI agent to generate the improvement information; a providing step of providing the improvement information generated by the generating step, 2. An information processing program comprising:

Images