System

Abstract

An object of the system according to the embodiment is to allow a user to efficiently collect and provide an opinion of a specific talent or influencer and a recommended product.SOLUTION: A system includes a reception unit, a collection unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a question from a user. The collection unit collects information from the social media platform on the basis of the question received by the reception unit. The analysis unit analyzes the information collected by the collection unit and specifies a specific product or opinion. The generation unit generates an answer to the question of the user based on the information analyzed by the analysis unit. The providing unit provides the response generated by the generating unit to the user.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 for users to efficiently collect opinions and recommended products from specific celebrities or influencers.

[0005] The system according to the embodiment aims to enable users to efficiently collect and provide opinions and recommended products of specific talents or influencers. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a user's question. The collection unit collects information from social media platforms based on the question received by the reception unit. The analysis unit analyzes the information collected by the collection unit and identifies specific products or opinions. The generation unit generates an answer to the user's question based on the information analyzed by the analysis unit. The provision unit provides the user with the response generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to efficiently collect and provide opinions and recommended products of specific talents or influencers. [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 AI assistant system according to an embodiment of the present invention fulfills users' desire to know the recommended products and opinions of their favorite celebrities and influencers. This system collects and analyzes information from social media, blogs, and video platforms, and provides products and opinions recommended by the user's favorite celebrities and influencers in response to user questions. For example, if a user asks, "What eyeshadow is trending these days?", the AI ​​can provide details, feel, and price of the eyeshadow recommended by the user's favorite influencer. This experience is as if the user is receiving advice directly from their idol. This allows the AI ​​assistant system to incorporate the wisdom of their favorite celebrities and influencers into their daily lives. For example, a user can input a question they have written. For example, a user can input a question such as, "What eyeshadow is trending these days?" This question is input into the AI. The AI ​​then collects related information from social media, blogs, and video platforms. The AI ​​analyzes posts from the celebrities and influencers specified by the user to identify the recommended products and opinions. For example, the AI ​​can collect information about eyeshadows recently recommended by influencers. The collected information is analyzed by AI to generate answers to the user's questions. For example, details of the eyeshadow recommended by the influencer, its feel, and price can be included. Finally, the generated answers are provided to the user. This allows users to experience the same experience as if they were receiving advice directly from their idol.

[0029] An AI assistant system according to an embodiment includes a reception unit, a collection unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a user's question. The user's question may be in text format, audio format, or the like, but is not limited to these examples. For example, the reception unit receives a user's question in text format. The reception unit can also receive audio format questions. The collection unit collects information from social media platforms based on the question received by the reception unit. The collection unit collects information from social media platforms such as Twitter (registered trademark), Facebook (registered trademark), and Instagram (registered trademark). The collection unit can collect information using an API. The collection unit can also collect information using scraping technology. The analysis unit analyzes the information collected by the collection unit and identifies specific products or opinions. The analysis unit analyzes the information using, for example, natural language processing technology. The analysis unit can also analyze the information using a machine learning algorithm. The generation unit generates an answer to the user's question based on the information analyzed by the analysis unit. The generation unit generates the answer using, for example, a template-based generation method. The generation unit can also generate an answer using an AI generation method. The provision unit provides the answer generated by the generation unit to the user. The provision unit provides the answer as, for example, a text message. The provision unit can also provide the answer as a voice message. This allows the AI ​​assistant system according to the embodiment to provide recommended products and opinions of celebrities and influencers in response to a user's question.

[0030] The collection unit can collect posts from influential people designated by the user. Examples of influential people include, but are not limited to, people with a large number of followers and people with a high engagement rate. The collection unit, for example, preferentially collects the latest posts from celebrities designated by the user. The collection unit can also collect past posts from influencers designated by the user. Furthermore, the collection unit can also collect a wide range of related posts from celebrities and influencers designated by the user. This allows posts from celebrities and influencers designated by the user to be preferentially collected. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect posts using an AI model for collecting posts from celebrities and influencers designated by the user.

[0031] The analysis unit can analyze the collected posts and identify specific products or opinions. Examples of the products or opinions to be identified include, but are not limited to, product reviews and user impressions. For example, the analysis unit extracts reviews of specific products from the collected posts. The analysis unit can also extract user impressions from the collected posts. Furthermore, the analysis unit can extract specific opinions from the collected posts. This makes it possible to analyze the collected posts and identify recommended products or opinions. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the posts using an AI model for analyzing the collected posts.

[0032] The generation unit can generate an answer to the user's question based on the analyzed information. Methods for generating an answer include, but are not limited to, a template-based generation method and an AI-based generation method. For example, the generation unit can generate an answer to the user's question using the template-based generation method. The generation unit can also generate an answer to the user's question using an AI-based generation method. Furthermore, the generation unit can apply an algorithm for generating an answer to the user's question based on the analyzed information. This allows the answer to the user's question to be generated based on the identified information. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can generate an answer using an AI model for generating an answer based on the analyzed information.

[0033] The providing unit can provide the generated response to the user. Methods for providing the response include, but are not limited to, for example, a text message, a voice message, and the like. The providing unit can provide the generated response to the user, for example, as a text message. The providing unit can also provide the generated response to the user as a voice message. Furthermore, the providing unit can apply an algorithm for providing the generated response to the user. In this way, the generated answer can be provided to the user. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without using AI. For example, the providing unit can provide the response using an AI model for providing the generated response to the user.

[0034] The reception unit can analyze the user's past question history and select an appropriate question reception method. Examples of question reception methods include, but are not limited to, chatbots and telephone reception. The reception unit, for example, preferentially suggests question formats (text, voice, etc.) that the user has frequently used in the past. The reception unit can also analyze the content of the user's past questions and automatically suggest related questions. Furthermore, the reception unit can identify, from the user's past question history, a specific time period during which questions are most frequently asked and accept questions during that time period. This allows the selection of an optimal question reception method based on the user's past question history. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can analyze the user's past question history using an AI model for analyzing the user's past question history and select an optimal question reception method.

[0035] When receiving a question, the reception unit may filter the questions based on the user's current areas of interest. Examples of the user's areas of interest include, but are not limited to, past search history and social media activity. For example, the reception unit may preferentially receive questions related to topics that the user has recently been interested in. The reception unit may also analyze the user's social media activity and filter questions related to areas of high interest. Furthermore, the reception unit may filter and receive related questions based on keywords the user has previously searched for. This allows questions to be filtered based on the user's current areas of interest. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may identify the user's areas of interest using an AI model for identifying the user's areas of interest and filter questions.

[0036] When receiving a question, the reception unit can select an appropriate reception means depending on the user's input method. Examples of the user's input method include, but are not limited to, keyboard input, voice input, and image input. For example, if the user selects voice input, the reception unit can use voice recognition technology to receive the question. Furthermore, if the user selects text input, the reception unit can also use text analysis technology to receive the question. Furthermore, if the user uploads an image, the reception unit can also use image analysis technology to identify and accept the question. This makes it possible to select the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can identify the input method using an AI model for identifying the user's input method and select the appropriate reception means.

[0037] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, when the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving questions related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving questions related to daily life. This allows highly relevant questions to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can identify the user's geographical location information using an AI model for identifying the user's geographical location information, and prioritize receiving highly relevant questions.

[0038] When receiving a question, the reception unit may analyze the user's social media activity and receive related questions. Social media activity includes, but is not limited to, for example, post content and engagement. For example, the reception unit may preferentially receive questions related to topics to which the user frequently posts on social media. The reception unit may also receive related questions by referring to the activities of the user's friends on social media. Furthermore, the reception unit may analyze the user's social media posts and filter and receive related questions. This allows reception of related questions based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may analyze the social media activity using an AI model for analyzing the user's social media activity and receive related questions.

[0039] When accepting a question, the acceptance unit can customize the acceptance method by reflecting the user's past feedback. Past feedback includes, but is not limited to, for example, survey results and reviews. The acceptance unit can, for example, suggest an optimal question acceptance method based on feedback previously provided by the user. The acceptance unit can also preferentially accept specific question formats based on the user's past feedback. Furthermore, the acceptance unit can analyze the user's past feedback and customize and provide the acceptance method. This allows the acceptance method to be customized based on the user's past feedback. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can analyze the feedback and customize the acceptance method using an AI model for analyzing the user's past feedback.

[0040] When collecting information, the collection unit can prioritize collecting posts from talents or influencers designated by the user. Examples of talents and influencers include, but are not limited to, people with a large number of followers and people with a high engagement rate. The collection unit, for example, prioritizes collecting the latest posts from talents designated by the user. The collection unit can also collect past posts from influencers designated by the user. Furthermore, the collection unit can collect a wide range of related posts from talents or influencers designated by the user. This allows posts from talents or influencers designated by the user to be prioritized. Some or all of the above-described processing by the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect posts using an AI model for collecting posts from talents or influencers designated by the user.

[0041] When collecting information, the collection unit can improve the accuracy of the collection by referring to the user's past collection history. The past collection history includes, for example, the type and frequency of collected information, but is not limited to such examples. For example, the collection unit preferentially collects related information based on information previously collected by the user. The collection unit can also collect information related to a specific topic from the user's past collection history. Furthermore, the collection unit can analyze the user's past collection history and apply an algorithm to improve the accuracy of the collection. This allows the accuracy of the collection to be improved based on the user's past collection history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can analyze the collection history using an AI model for analyzing the user's past collection history and improve the accuracy of the collection.

[0042] When collecting information, the collection unit can apply different collection algorithms to each platform from which information is collected. Examples of collection algorithms include, but are not limited to, scraping and API usage. For example, the collection unit can apply an algorithm based on specific hashtags or keywords to collect information from social media platforms. Furthermore, the collection unit can apply an algorithm based on article content or tags to collect information from blog platforms. Furthermore, the collection unit can apply an algorithm based on video titles and descriptions to collect information from video platforms. This allows different collection algorithms to be applied to each platform from which information is collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can apply a collection algorithm using an AI model for applying a different collection algorithm to each platform from which information is collected.

[0043] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, when the user is in a specific area, the collection unit can prioritize collecting information related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting information related to the user's travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting information related to the user's daily life. This allows highly relevant information to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can identify the user's geographical location information using an AI model for identifying the user's geographical location information and prioritize collecting highly relevant information.

[0044] When collecting information, the collection unit may analyze the user's social media activity and collect related information. Social media activity includes, but is not limited to, for example, posting content and engagement. For example, the collection unit may preferentially collect information related to topics on which the user frequently posts on social media. The collection unit may also collect related information by referring to the activities of the user's friends on social media. Furthermore, the collection unit may analyze the user's social media posting content and filter and collect related information. This makes it possible to collect related information based on the user's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may analyze the social media activity and collect related information using an AI model for analyzing the user's social media activity.

[0045] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. Past feedback includes, but is not limited to, for example, survey results and reviews. The collection unit can, for example, suggest an optimal information collection method based on feedback provided by the user in the past. The collection unit can also preferentially apply a specific information collection method based on the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and customize and provide the collection method. This allows the collection method to be customized based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can analyze the feedback and customize the collection method using an AI model for analyzing the user's past feedback.

[0046] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the collected information during analysis. Examples of interrelationships include, but are not limited to, co-occurrence networks and relevance scores. For example, the analysis unit analyzes the interrelationships of the collected information and prioritizes analysis of highly related information. The analysis unit can also apply an algorithm to improve the accuracy of the analysis by taking into account the interrelationships of the collected information. Furthermore, the analysis unit can improve the reliability of the analysis results based on the interrelationships of the collected information. This can improve the accuracy of the analysis by taking into account the interrelationships of the collected information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can analyze the interrelationships using an AI model for analyzing the interrelationships of the collected information and improve the accuracy of the analysis.

[0047] The analysis unit may perform the analysis while taking into consideration the attribute information of the person who submitted the collected information. The attribute information may include, but is not limited to, for example, age, gender, and occupation. The analysis unit may perform the analysis while taking into consideration, for example, the attribute information (age, gender, occupation, etc.) of the person who submitted the collected information. The analysis unit may also improve the reliability of the analysis results based on the attribute information of the person who submitted the collected information. Furthermore, the analysis unit may analyze the attribute information of the person who submitted the collected information and apply an algorithm to improve the accuracy of the analysis. This allows the analysis to be performed while taking into consideration the attribute information of the person who submitted the collected information. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may analyze the attribute information using an AI model for analyzing the attribute information of the person who submitted the collected information.

[0048] During analysis, the analysis unit can weight the analysis based on the submission frequency of the collected information. The submission frequency includes, but is not limited to, for example, the number of posts, the time interval, etc. For example, if the submission frequency of the collected information is high, the analysis unit can weight the information highly. Furthermore, if the submission frequency of the collected information is low, the analysis unit can weight the information less. Furthermore, the analysis unit can apply an algorithm to improve the reliability of the analysis results based on the submission frequency of the collected information. This allows the analysis to be weighted based on the submission frequency of the collected information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the submission frequency and weight the analysis using an AI model for analyzing the submission frequency of the collected information.

[0049] The analysis unit may perform the analysis while taking into account the geographical distribution of the collected information. Examples of the geographical distribution include, but are not limited to, regional data and geographical trends. For example, the analysis unit may analyze the geographical distribution of the collected information and prioritize the analysis of highly relevant information. The analysis unit may also improve the reliability of the analysis results based on the geographical distribution of the collected information. Furthermore, the analysis unit may apply an algorithm to improve the accuracy of the analysis while taking into account the geographical distribution of the collected information. This allows the analysis to be performed while taking into account the geographical distribution of the collected information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may analyze the geographical distribution using an AI model for analyzing the geographical distribution of the collected information.

[0050] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the collected information. Examples of related literature include, but are not limited to, academic papers and industry reports. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the collected information. The analysis unit can also improve the reliability of the analysis results based on the literature related to the collected information. Furthermore, the analysis unit can analyze the literature related to the collected information and apply an algorithm to improve the accuracy of the analysis. This can improve the accuracy of the analysis by referring to the literature related to the collected information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by referring to the literature related to the collected information using an AI model.

[0051] The analysis unit may perform the analysis while taking into account the market value of the collected information. Market value includes, but is not limited to, sales data, market trends, and the like. For example, the analysis unit may analyze the market value of the collected information and prioritize the analysis of highly relevant information. The analysis unit may also improve the reliability of the analysis results based on the market value of the collected information. Furthermore, the analysis unit may apply an algorithm to improve the accuracy of the analysis while taking into account the market value of the collected information. This allows the analysis to be performed while taking into account the market value of the collected information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may analyze the market value using an AI model for analyzing the market value of the collected information.

[0052] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the collected information. Examples of importance include, but are not limited to, engagement rate and influence. For example, the generation unit generates a detailed answer when the importance of the collected information is high. Furthermore, the generation unit can generate a concise answer when the importance of the collected information is low. Furthermore, the generation unit can apply an algorithm for adjusting the level of detail of the answer based on the importance of the collected information. This allows the level of detail of the answer to be adjusted based on the importance of the collected information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can analyze the importance using an AI model for analyzing the importance of the collected information and adjust the level of detail of the answer.

[0053] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the collected information. Examples of categories include, but are not limited to, beauty-related and technology-related information. For example, the generation unit can apply a beauty-specific generation algorithm to beauty-related information. Furthermore, the generation unit can apply a technology-specific generation algorithm to technology-related information. Furthermore, the generation unit can select and apply an optimal generation algorithm depending on the category of the collected information. This allows different generation algorithms to be applied depending on the category of the collected information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can classify the categories of the collected information using an AI model for classifying the categories and apply a generation algorithm.

[0054] When generating an answer, the generation unit can improve the accuracy of the generation by referring to the user's past answer results. Past answer results include, but are not limited to, past feedback, answer accuracy, etc. For example, the generation unit preferentially generates related information based on answers the user has received in the past. The generation unit can also analyze the user's past answer results and apply an algorithm to improve the accuracy of the generation. Furthermore, the generation unit can generate an optimal answer by referring to the user's past answer results. This can improve the accuracy of the generation based on the user's past answer results. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can analyze the user's past answer results using an AI model for analyzing the user's past answer results and improve the accuracy of the generation.

[0055] When generating an answer, the generation unit can determine the priority of answers based on the submission date of the collected information. The submission date includes, but is not limited to, the latest information, past information, etc. For example, if the collected information was submitted recently, the generation unit can preferentially reflect that information in the answer. Furthermore, if the collected information was submitted older, the generation unit can reflect that information in the answer for reference only. Furthermore, the generation unit can apply an algorithm for determining the priority of answers based on the submission date of the collected information. This allows the priority of answers to be determined based on the submission date of the collected information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can analyze the submission date using an AI model for analyzing the submission date of the collected information and determine the priority of answers.

[0056] When generating an answer, the generation unit can adjust the order of answers based on the relevance of the collected information. Relevance includes, but is not limited to, co-occurrence networks, relevance scores, and the like. For example, if the collected information is highly relevant, the generation unit preferentially reflects that information in the answer. Furthermore, if the collected information is low in relevance, the generation unit can reflect that information in the answer only for reference. Furthermore, the generation unit can apply an algorithm for adjusting the order of answers based on the relevance of the collected information. This allows the order of answers to be adjusted based on the relevance of the collected information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can analyze the relevance of the collected information using an AI model for analyzing the relevance of the collected information and adjust the order of answers.

[0057] When generating an answer, the generation unit can adjust the use of technical terms in the answer depending on the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, if the user has technical expertise, the generation unit can generate an answer that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the generation unit can generate an answer that explains things in easy-to-understand language. Furthermore, the generation unit can apply an algorithm to adjust the use of technical terms in the answer based on the user's level of expertise. This allows the use of technical terms in the answer to be adjusted depending on the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can evaluate the user's level of expertise using an AI model for evaluating the user's level of expertise and adjust the use of technical terms in the answer.

[0058] When providing an answer, the providing unit can select an optimal display method by referring to the user's past operation history. The operation history includes, but is not limited to, for example, a click history and a browsing history. For example, the providing unit can preferentially provide a display method that the user has used favorably in the past. The providing unit can also suggest an optimal display method based on the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and customize and provide a display method. This makes it possible to select an optimal display method based on the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can analyze the user's operation history using an AI model for analyzing the user's operation history and select an optimal display method.

[0059] When providing an answer, the providing unit can customize the display content according to the user's current task. Examples of current tasks include, but are not limited to, a project in progress and a current activity. For example, when the user is working, the providing unit can prioritize displaying work-related information. Furthermore, when the user is taking a break, the providing unit can prioritize displaying relaxing information. Furthermore, when the user is performing a specific task, the providing unit can also prioritize displaying information related to the task. This allows the display content to be customized according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can identify the user's current task using an AI model for identifying the user's current task and customize the display content.

[0060] When providing an answer, the providing unit can select the optimal display method by taking into account the user's device information. Device information includes, but is not limited to, the device type, screen size, and OS. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple, highly visible display method. This allows the optimal display method to be selected based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can identify the device information and select the optimal display method using an AI model for identifying the user's device information.

[0061] When providing an answer, the providing unit can select the optimal display method by taking into account the user's device information. Device information includes, but is not limited to, the device type, screen size, and OS. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple, highly visible display method. This allows the optimal display method to be selected based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can identify the device information and select the optimal display method using an AI model for identifying the user's device information.

[0062] When providing an answer, the providing unit can make the display content multilingual according to the user's language setting. Language settings include, but are not limited to, for example, the user's preferred language and region setting. For example, the providing unit automatically sets the language of the answer based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the providing unit can also provide the answer in that language. This makes it possible to make the display content multilingual based on the user's language setting. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can identify the language setting using an AI model for identifying the user's language setting and make the display content multilingual.

[0063] When providing a response, the providing unit may analyze the user's social media activity and provide related information. Social media activity includes, but is not limited to, for example, posted content and engagement. For example, the providing unit may provide information about places where the user has checked in on social media. The providing unit may also analyze the user's social media posts and provide information about related tourist spots and stores. Furthermore, the providing unit may provide information about related places and events by referring to the activities of the user's friends on social media. This allows related information to be provided based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may analyze the user's social media activity using an AI model for analyzing the user's social media activity and provide related information.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] When accepting a user's question, the reception unit can analyze the user's past question history and automatically suggest related questions. For example, if a user has previously asked, "What eyeshadow is trending these days?", the reception unit can suggest a related question such as, "What lipstick is trending these days?" based on that history. Also, if the user has previously asked many questions about a particular brand, the reception unit can suggest new questions related to that brand. Furthermore, if the user has previously asked many questions about a particular category (e.g., beauty, fashion, etc.), the reception unit can preferentially suggest questions related to that category. This makes it possible to suggest more relevant questions based on the user's past question history.

[0066] When collecting posts from influential people designated by the user, the collection unit can determine priorities by taking into consideration the engagement rate of the post (e.g., number of likes, number of comments, number of shares, etc.). For example, it is possible to preferentially collect posts that not only have a large number of followers but also have a high engagement rate. It is also possible to preferentially collect posts whose engagement rate has risen sharply within a specific period. Furthermore, it is also possible to preferentially collect posts that contain specific keywords (e.g., "recommended," "review," etc.) from the posts from influential people designated by the user. This makes it possible to collect more influential information.

[0067] When generating an answer to a user's question based on the analyzed information, the generation unit can improve the quality of the answer by reflecting the user's past feedback. For example, a more appropriate answer can be generated based on feedback (e.g., satisfaction level, comments, etc.) provided by the user in the past. The generation unit can also analyze patterns of answers that the user has previously given high ratings to and generate an answer based on those patterns. Furthermore, the quality of the answer can be improved by avoiding patterns of answers that the user has previously given low ratings to. This makes it possible to generate a more satisfying answer based on the user's past feedback.

[0068] When providing the generated response to the user, the providing unit can select the optimal display method by taking into consideration the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a desktop, a display method that includes detailed information can be provided. This makes it possible to select the optimal display method based on the user's device information.

[0069] The reception unit can analyze the user's past question history and select an appropriate question reception method. For example, it can prioritize and suggest question formats (text, voice, etc.) that the user has frequently used in the past. It can also analyze the content of the user's past questions and automatically suggest related questions. Furthermore, it can identify, from the user's past question history, the time period during which questions are most frequently asked and accept questions during that time period. This makes it possible to select the optimal question reception method based on the user's past question history.

[0070] When accepting questions, the accepting unit can filter the questions based on the user's current areas of interest. For example, questions related to topics that the user has recently been interested in can be preferentially accepted. The accepting unit can also analyze the user's social media activity and filter questions related to areas of high interest. Furthermore, the accepting unit can filter and accept related questions based on keywords that the user has searched in the past. This makes it possible to filter questions based on the user's current areas of interest.

[0071] When accepting a question, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, if the user selects voice input, the question can be accepted using voice recognition technology. If the user selects text input, the question can be accepted using text analysis technology. Furthermore, if the user uploads an image, the question content can be identified and accepted using image analysis technology. This makes it possible to select the optimal acceptance means depending on the user's input method.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The reception unit receives a question from a user. The question from the user may be in text format or voice format. For example, the user may input a question in text format or ask a question in voice format. Step 2: The collection unit collects information from social media platforms based on the inquiries received by the reception unit. The collection unit collects information from social media platforms such as Twitter, Facebook, and Instagram, and can use APIs or scraping techniques. Step 3: The analysis unit analyzes the information collected by the collection unit and identifies specific products or opinions. The analysis unit can analyze the information using natural language processing technology or machine learning algorithms. Step 4: The generator generates an answer to the user's question based on the information analyzed by the analyzer. The generator can generate the answer using a template-based generation method or an AI generation method. Step 5: The providing unit provides the answer generated by the generating unit to the user. The providing unit can provide the answer as a text message or a voice message.

[0074] (Example 2) An AI assistant system according to an embodiment of the present invention fulfills users' desire to know the recommended products and opinions of their favorite celebrities and influencers. This system collects and analyzes information from social media, blogs, and video platforms, and provides products and opinions recommended by the user's favorite celebrities and influencers in response to user questions. For example, if a user asks, "What eyeshadow is trending these days?", the AI ​​can provide details, feel, and price of the eyeshadow recommended by the user's favorite influencer. This experience is as if the user is receiving advice directly from their idol. This allows the AI ​​assistant system to incorporate the wisdom of their favorite celebrities and influencers into their daily lives. For example, a user can input a question they have written. For example, a user can input a question such as, "What eyeshadow is trending these days?" This question is input into the AI. The AI ​​then collects related information from social media, blogs, and video platforms. The AI ​​analyzes posts from the celebrities and influencers specified by the user to identify the recommended products and opinions. For example, the AI ​​can collect information about eyeshadows recently recommended by influencers. The collected information is analyzed by AI to generate answers to the user's questions. For example, details of the eyeshadow recommended by the influencer, its feel, and price can be included. Finally, the generated answers are provided to the user. This allows users to experience the same experience as if they were receiving advice directly from their idol.

[0075] An AI assistant system according to an embodiment includes a reception unit, a collection unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a user's question. The user's question may be in, but is not limited to, a text format or a voice format. For example, the reception unit receives a user's question in text format. The reception unit can also receive a voice format question. The collection unit collects information from a social media platform based on the question received by the reception unit. The collection unit collects information from social media platforms such as Twitter, Facebook, and Instagram. The collection unit can collect information using an API. The collection unit can also collect information using scraping technology. The analysis unit analyzes the information collected by the collection unit and identifies specific products or opinions. The analysis unit analyzes the information using, for example, natural language processing technology. The analysis unit can also analyze the information using a machine learning algorithm. The generation unit generates an answer to the user's question based on the information analyzed by the analysis unit. The generation unit generates the answer using, for example, a template-based generation method. The generation unit can also generate an answer using an AI generation method. The provision unit provides the answer generated by the generation unit to the user. The provision unit provides the answer as, for example, a text message. The provision unit can also provide the answer as a voice message. This allows the AI ​​assistant system according to the embodiment to provide recommended products and opinions of celebrities and influencers in response to a user's question.

[0076] The collection unit can collect posts from influential people designated by the user. Examples of influential people include, but are not limited to, people with a large number of followers and people with a high engagement rate. The collection unit, for example, preferentially collects the latest posts from celebrities designated by the user. The collection unit can also collect past posts from influencers designated by the user. Furthermore, the collection unit can also collect a wide range of related posts from celebrities and influencers designated by the user. This allows posts from celebrities and influencers designated by the user to be preferentially collected. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect posts using an AI model for collecting posts from celebrities and influencers designated by the user.

[0077] The analysis unit can analyze the collected posts and identify specific products or opinions. Examples of the products or opinions to be identified include, but are not limited to, product reviews and user impressions. For example, the analysis unit extracts reviews of specific products from the collected posts. The analysis unit can also extract user impressions from the collected posts. Furthermore, the analysis unit can extract specific opinions from the collected posts. This makes it possible to analyze the collected posts and identify recommended products or opinions. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the posts using an AI model for analyzing the collected posts.

[0078] The generation unit can generate an answer to the user's question based on the analyzed information. Methods for generating an answer include, but are not limited to, a template-based generation method and an AI-based generation method. For example, the generation unit can generate an answer to the user's question using the template-based generation method. The generation unit can also generate an answer to the user's question using an AI-based generation method. Furthermore, the generation unit can apply an algorithm for generating an answer to the user's question based on the analyzed information. This allows the answer to the user's question to be generated based on the identified information. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can generate an answer using an AI model for generating an answer based on the analyzed information.

[0079] The providing unit can provide the generated response to the user. Methods for providing the response include, but are not limited to, for example, a text message, a voice message, and the like. The providing unit can provide the generated response to the user, for example, as a text message. The providing unit can also provide the generated response to the user as a voice message. Furthermore, the providing unit can apply an algorithm for providing the generated response to the user. In this way, the generated answer can be provided to the user. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without using AI. For example, the providing unit can provide the response using an AI model for providing the generated response to the user.

[0080] The reception unit can analyze the user's emotions and adjust the timing of accepting questions based on the analyzed user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and excitement. For example, if the user is feeling stressed, the reception unit temporarily delays accepting questions and resumes accepting questions when the user is able to relax. Furthermore, if the user is relaxed, the reception unit can immediately accept questions and respond quickly. Furthermore, if the user is excited, the reception unit can quickly accept questions and provide answers before the user's excitement cools down. This makes it possible to adjust the timing of accepting questions according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can estimate the user's emotions using an AI model for estimating the user's emotions and adjust the timing of accepting questions.

[0081] The reception unit can analyze the user's past question history and select an appropriate question reception method. Examples of question reception methods include, but are not limited to, chatbots and telephone reception. The reception unit, for example, preferentially suggests question formats (text, voice, etc.) that the user has frequently used in the past. The reception unit can also analyze the content of the user's past questions and automatically suggest related questions. Furthermore, the reception unit can identify, from the user's past question history, a specific time period during which questions are most frequently asked and accept questions during that time period. This allows the selection of an optimal question reception method based on the user's past question history. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can analyze the user's past question history using an AI model for analyzing the user's past question history and select an optimal question reception method.

[0082] When receiving a question, the reception unit may filter the questions based on the user's current areas of interest. Examples of the user's areas of interest include, but are not limited to, past search history and social media activity. For example, the reception unit may preferentially receive questions related to topics that the user has recently been interested in. The reception unit may also analyze the user's social media activity and filter questions related to areas of high interest. Furthermore, the reception unit may filter and receive related questions based on keywords the user has previously searched for. This allows questions to be filtered based on the user's current areas of interest. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may identify the user's areas of interest using an AI model for identifying the user's areas of interest and filter questions.

[0083] When receiving a question, the reception unit can select an appropriate reception means depending on the user's input method. Examples of the user's input method include, but are not limited to, keyboard input, voice input, and image input. For example, if the user selects voice input, the reception unit can use voice recognition technology to receive the question. Furthermore, if the user selects text input, the reception unit can also use text analysis technology to receive the question. Furthermore, if the user uploads an image, the reception unit can also use image analysis technology to identify and accept the question. This makes it possible to select the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can identify the input method using an AI model for identifying the user's input method and select the appropriate reception means.

[0084] The reception unit can analyze the user's emotions and determine the priority of questions to be received based on the analyzed user's emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and excitement. For example, when the user is stressed, the reception unit can prioritize urgent questions. Furthermore, when the user is relaxed, the reception unit can also prioritize questions with normal priority. Furthermore, when the user is excited, the reception unit can prioritize relevant questions before the user's excitement cools down. This allows the priority of questions to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as 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. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can estimate the user's emotions and determine the priority of questions using an AI model for estimating the user's emotions.

[0085] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, when the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving questions related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving questions related to daily life. This allows highly relevant questions to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can identify the user's geographical location information using an AI model for identifying the user's geographical location information, and prioritize receiving highly relevant questions.

[0086] When receiving a question, the reception unit may analyze the user's social media activity and receive related questions. Social media activity includes, but is not limited to, for example, post content and engagement. For example, the reception unit may preferentially receive questions related to topics to which the user frequently posts on social media. The reception unit may also receive related questions by referring to the activities of the user's friends on social media. Furthermore, the reception unit may analyze the user's social media posts and filter and receive related questions. This allows reception of related questions based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may analyze the social media activity using an AI model for analyzing the user's social media activity and receive related questions.

[0087] When accepting a question, the acceptance unit can customize the acceptance method by reflecting the user's past feedback. Past feedback includes, but is not limited to, for example, survey results and reviews. The acceptance unit can, for example, suggest an optimal question acceptance method based on feedback previously provided by the user. The acceptance unit can also preferentially accept specific question formats based on the user's past feedback. Furthermore, the acceptance unit can analyze the user's past feedback and customize and provide the acceptance method. This allows the acceptance method to be customized based on the user's past feedback. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can analyze the feedback and customize the acceptance method using an AI model for analyzing the user's past feedback.

[0088] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and excitement. For example, if the user is feeling stressed, the collection unit temporarily delays information collection and resumes it when the user is able to relax. Furthermore, if the user is relaxed, the collection unit can immediately start information collection. Furthermore, if the user is excited, the collection unit can quickly collect information and provide the information before the user's excitement cools down. This allows the timing of information collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can estimate the user's emotions using an AI model for estimating the user's emotions and adjust the timing of information collection.

[0089] When collecting information, the collection unit can prioritize collecting posts from talents or influencers designated by the user. Examples of talents and influencers include, but are not limited to, people with a large number of followers and people with a high engagement rate. The collection unit, for example, prioritizes collecting the latest posts from talents designated by the user. The collection unit can also collect past posts from influencers designated by the user. Furthermore, the collection unit can collect a wide range of related posts from talents or influencers designated by the user. This allows posts from talents or influencers designated by the user to be prioritized. Some or all of the above-described processing by the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect posts using an AI model for collecting posts from talents or influencers designated by the user.

[0090] When collecting information, the collection unit can improve the accuracy of the collection by referring to the user's past collection history. The past collection history includes, for example, the type and frequency of collected information, but is not limited to such examples. For example, the collection unit preferentially collects related information based on information previously collected by the user. The collection unit can also collect information related to a specific topic from the user's past collection history. Furthermore, the collection unit can analyze the user's past collection history and apply an algorithm to improve the accuracy of the collection. This allows the accuracy of the collection to be improved based on the user's past collection history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can analyze the collection history using an AI model for analyzing the user's past collection history and improve the accuracy of the collection.

[0091] When collecting information, the collection unit can apply different collection algorithms to each platform from which information is collected. Examples of collection algorithms include, but are not limited to, scraping and API usage. For example, the collection unit can apply an algorithm based on specific hashtags or keywords to collect information from social media platforms. Furthermore, the collection unit can apply an algorithm based on article content or tags to collect information from blog platforms. Furthermore, the collection unit can apply an algorithm based on video titles and descriptions to collect information from video platforms. This allows different collection algorithms to be applied to each platform from which information is collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can apply a collection algorithm using an AI model for applying a different collection algorithm to each platform from which information is collected.

[0092] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and excitement. For example, when the user is feeling stressed, the collection unit prioritizes collecting information that helps the user relax. Furthermore, when the user is relaxed, the collection unit can collect information with normal priority. Furthermore, when the user is excited, the collection unit can prioritize collecting related information before the user cools down. This allows the priority of information to be collected to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can estimate the user's emotions using an AI model for estimating the user's emotions and determine the priority of information to be collected.

[0093] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, when the user is in a specific area, the collection unit can prioritize collecting information related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting information related to the user's travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting information related to the user's daily life. This allows highly relevant information to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can identify the user's geographical location information using an AI model for identifying the user's geographical location information and prioritize collecting highly relevant information.

[0094] When collecting information, the collection unit may analyze the user's social media activity and collect related information. Social media activity includes, but is not limited to, for example, posting content and engagement. For example, the collection unit may preferentially collect information related to topics on which the user frequently posts on social media. The collection unit may also collect related information by referring to the activities of the user's friends on social media. Furthermore, the collection unit may analyze the user's social media posting content and filter and collect related information. This makes it possible to collect related information based on the user's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may analyze the social media activity and collect related information using an AI model for analyzing the user's social media activity.

[0095] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. Past feedback includes, but is not limited to, for example, survey results and reviews. The collection unit can, for example, suggest an optimal information collection method based on feedback provided by the user in the past. The collection unit can also preferentially apply a specific information collection method based on the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and customize and provide the collection method. This allows the collection method to be customized based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can analyze the feedback and customize the collection method using an AI model for analyzing the user's past feedback.

[0096] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and excitement. For example, when the user is feeling stressed, the analysis unit relaxes the analysis criteria and prioritizes analyzing information that helps the user relax. Furthermore, when the user is relaxed, the analysis unit can perform analysis using normal criteria. Furthermore, when the user is excited, the analysis unit can prioritize analyzing related information before the user cools down. This allows the analysis criteria to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can estimate the user's emotions and adjust the analysis criteria using an AI model for estimating the user's emotions.

[0097] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the collected information during analysis. Examples of interrelationships include, but are not limited to, co-occurrence networks and relevance scores. For example, the analysis unit analyzes the interrelationships of the collected information and prioritizes analysis of highly related information. The analysis unit can also apply an algorithm to improve the accuracy of the analysis by taking into account the interrelationships of the collected information. Furthermore, the analysis unit can improve the reliability of the analysis results based on the interrelationships of the collected information. This can improve the accuracy of the analysis by taking into account the interrelationships of the collected information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can analyze the interrelationships using an AI model for analyzing the interrelationships of the collected information and improve the accuracy of the analysis.

[0098] The analysis unit may perform the analysis while taking into consideration the attribute information of the person who submitted the collected information. The attribute information may include, but is not limited to, for example, age, gender, and occupation. The analysis unit may perform the analysis while taking into consideration, for example, the attribute information (age, gender, occupation, etc.) of the person who submitted the collected information. The analysis unit may also improve the reliability of the analysis results based on the attribute information of the person who submitted the collected information. Furthermore, the analysis unit may analyze the attribute information of the person who submitted the collected information and apply an algorithm to improve the accuracy of the analysis. This allows the analysis to be performed while taking into consideration the attribute information of the person who submitted the collected information. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may analyze the attribute information using an AI model for analyzing the attribute information of the person who submitted the collected information.

[0099] During analysis, the analysis unit can weight the analysis based on the submission frequency of the collected information. The submission frequency includes, but is not limited to, for example, the number of posts, the time interval, etc. For example, if the submission frequency of the collected information is high, the analysis unit can weight the information highly. Furthermore, if the submission frequency of the collected information is low, the analysis unit can weight the information less. Furthermore, the analysis unit can apply an algorithm to improve the reliability of the analysis results based on the submission frequency of the collected information. This allows the analysis to be weighted based on the submission frequency of the collected information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the submission frequency and weight the analysis using an AI model for analyzing the submission frequency of the collected information.

[0100] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and excitement. For example, when the user is feeling stressed, the analysis unit prioritizes displaying information that will help the user relax. Furthermore, when the user is relaxed, the analysis unit can display the analysis results in a normal order. Furthermore, when the user is excited, the analysis unit can prioritize displaying related information before the user cools down. This allows the display order of the analysis results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can estimate the user's emotions using an AI model for estimating the user's emotions and adjust the display order of the analysis results.

[0101] The analysis unit may perform the analysis while taking into account the geographical distribution of the collected information. Examples of the geographical distribution include, but are not limited to, regional data and geographical trends. For example, the analysis unit may analyze the geographical distribution of the collected information and prioritize the analysis of highly relevant information. The analysis unit may also improve the reliability of the analysis results based on the geographical distribution of the collected information. Furthermore, the analysis unit may apply an algorithm to improve the accuracy of the analysis while taking into account the geographical distribution of the collected information. This allows the analysis to be performed while taking into account the geographical distribution of the collected information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may analyze the geographical distribution using an AI model for analyzing the geographical distribution of the collected information.

[0102] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the collected information. Examples of related literature include, but are not limited to, academic papers and industry reports. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the collected information. The analysis unit can also improve the reliability of the analysis results based on the literature related to the collected information. Furthermore, the analysis unit can analyze the literature related to the collected information and apply an algorithm to improve the accuracy of the analysis. This can improve the accuracy of the analysis by referring to the literature related to the collected information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by referring to the literature related to the collected information using an AI model.

[0103] The analysis unit may perform the analysis while taking into account the market value of the collected information. Market value includes, but is not limited to, sales data, market trends, and the like. For example, the analysis unit may analyze the market value of the collected information and prioritize the analysis of highly relevant information. The analysis unit may also improve the reliability of the analysis results based on the market value of the collected information. Furthermore, the analysis unit may apply an algorithm to improve the accuracy of the analysis while taking into account the market value of the collected information. This allows the analysis to be performed while taking into account the market value of the collected information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may analyze the market value using an AI model for analyzing the market value of the collected information.

[0104] The generation unit can estimate the user's emotion and adjust the way the answer is expressed based on the estimated user's emotion. Examples of user emotions include, but are not limited to, stress, relaxation, and excitement. For example, if the user is stressed, the generation unit can use a simple and easy-to-understand expression. Furthermore, if the user is relaxed, the generation unit can use an expression that includes detailed information. Furthermore, if the user is excited, the generation unit can use a visually stimulating expression. This allows the way the answer is expressed to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can estimate the user's emotion using an AI model for estimating the user's emotion and adjust the way the answer is expressed.

[0105] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the collected information. Examples of importance include, but are not limited to, engagement rate and influence. For example, the generation unit generates a detailed answer when the importance of the collected information is high. Furthermore, the generation unit can generate a concise answer when the importance of the collected information is low. Furthermore, the generation unit can apply an algorithm for adjusting the level of detail of the answer based on the importance of the collected information. This allows the level of detail of the answer to be adjusted based on the importance of the collected information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can analyze the importance using an AI model for analyzing the importance of the collected information and adjust the level of detail of the answer.

[0106] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the collected information. Examples of categories include, but are not limited to, beauty-related and technology-related information. For example, the generation unit can apply a beauty-specific generation algorithm to beauty-related information. Furthermore, the generation unit can apply a technology-specific generation algorithm to technology-related information. Furthermore, the generation unit can select and apply an optimal generation algorithm depending on the category of the collected information. This allows different generation algorithms to be applied depending on the category of the collected information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can classify the categories of the collected information using an AI model for classifying the categories and apply a generation algorithm.

[0107] When generating an answer, the generation unit can improve the accuracy of the generation by referring to the user's past answer results. Past answer results include, but are not limited to, past feedback, answer accuracy, etc. For example, the generation unit preferentially generates related information based on answers the user has received in the past. The generation unit can also analyze the user's past answer results and apply an algorithm to improve the accuracy of the generation. Furthermore, the generation unit can generate an optimal answer by referring to the user's past answer results. This can improve the accuracy of the generation based on the user's past answer results. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can analyze the user's past answer results using an AI model for analyzing the user's past answer results and improve the accuracy of the generation.

[0108] The generation unit can estimate the user's emotions and adjust the length of the answer based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and excitement. For example, if the user is stressed, the generation unit can generate a short and to-the-point answer. Furthermore, if the user is relaxed, the generation unit can generate a longer answer with detailed explanations. Furthermore, if the user is excited, the generation unit can generate an answer with visually stimulating effects. This allows the length of the answer to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can estimate the user's emotions and adjust the length of the answer using an AI model for estimating the user's emotions.

[0109] When generating an answer, the generation unit can determine the priority of answers based on the submission date of the collected information. The submission date includes, but is not limited to, the latest information, past information, etc. For example, if the collected information was submitted recently, the generation unit can preferentially reflect that information in the answer. Furthermore, if the collected information was submitted older, the generation unit can reflect that information in the answer for reference only. Furthermore, the generation unit can apply an algorithm for determining the priority of answers based on the submission date of the collected information. This allows the priority of answers to be determined based on the submission date of the collected information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can analyze the submission date using an AI model for analyzing the submission date of the collected information and determine the priority of answers.

[0110] When generating an answer, the generation unit can adjust the order of answers based on the relevance of the collected information. Relevance includes, but is not limited to, co-occurrence networks, relevance scores, and the like. For example, if the collected information is highly relevant, the generation unit preferentially reflects that information in the answer. Furthermore, if the collected information is low in relevance, the generation unit can reflect that information in the answer only for reference. Furthermore, the generation unit can apply an algorithm for adjusting the order of answers based on the relevance of the collected information. This allows the order of answers to be adjusted based on the relevance of the collected information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can analyze the relevance of the collected information using an AI model for analyzing the relevance of the collected information and adjust the order of answers.

[0111] When generating an answer, the generation unit can adjust the use of technical terms in the answer depending on the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, if the user has technical expertise, the generation unit can generate an answer that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the generation unit can generate an answer that explains things in easy-to-understand language. Furthermore, the generation unit can apply an algorithm to adjust the use of technical terms in the answer based on the user's level of expertise. This allows the use of technical terms in the answer to be adjusted depending on the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can evaluate the user's level of expertise using an AI model for evaluating the user's level of expertise and adjust the use of technical terms in the answer.

[0112] The providing unit can estimate the user's emotions and adjust the display method of the answer to be provided based on the estimated user's emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and excitement. For example, when the user is stressed, the providing unit can use a simple and easy-to-understand display method. Furthermore, when the user is relaxed, the providing unit can use a display method including detailed information. Furthermore, when the user is excited, the providing unit can use a visually stimulating display method. This makes it possible to adjust the display method of the answer to be provided depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can estimate the user's emotions using an AI model for estimating the user's emotions and adjust the display method of the answer to be provided.

[0113] When providing an answer, the providing unit can select an optimal display method by referring to the user's past operation history. The operation history includes, but is not limited to, for example, a click history and a browsing history. For example, the providing unit can preferentially provide a display method that the user has used favorably in the past. The providing unit can also suggest an optimal display method based on the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and customize and provide a display method. This makes it possible to select an optimal display method based on the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can analyze the user's operation history using an AI model for analyzing the user's operation history and select an optimal display method.

[0114] When providing an answer, the providing unit can customize the display content according to the user's current task. Examples of current tasks include, but are not limited to, a project in progress and a current activity. For example, when the user is working, the providing unit can prioritize displaying work-related information. Furthermore, when the user is taking a break, the providing unit can prioritize displaying relaxing information. Furthermore, when the user is performing a specific task, the providing unit can also prioritize displaying information related to the task. This allows the display content to be customized according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can identify the user's current task using an AI model for identifying the user's current task and customize the display content.

[0115] When providing an answer, the providing unit can select the optimal display method by taking into account the user's device information. Device information includes, but is not limited to, the device type, screen size, and OS. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple, highly visible display method. This allows the optimal display method to be selected based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can identify the device information and select the optimal display method using an AI model for identifying the user's device information.

[0116] The providing unit can estimate the user's emotions and adjust the operation procedure of the answer to be provided based on the estimated user's emotions. Examples of the user's emotions include, but are not limited to, stress, relaxation, and excitement. For example, when the user is stressed, the providing unit can provide simplified operation procedures. Furthermore, when the user is relaxed, the providing unit can provide detailed operation procedures. Furthermore, when the user is excited, the providing unit can provide visually stimulating operation procedures. This makes it possible to adjust the operation procedure of the answer to be provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can estimate the user's emotions using an AI model for estimating the user's emotions and adjust the operation procedure of the answer to be provided.

[0117] When providing an answer, the providing unit can select the optimal display method by taking into account the user's device information. Device information includes, but is not limited to, the device type, screen size, and OS. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple, highly visible display method. This allows the optimal display method to be selected based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can identify the device information and select the optimal display method using an AI model for identifying the user's device information.

[0118] When providing an answer, the providing unit can make the display content multilingual according to the user's language setting. Language settings include, but are not limited to, for example, the user's preferred language and region setting. For example, the providing unit automatically sets the language of the answer based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the providing unit can also provide the answer in that language. This makes it possible to make the display content multilingual based on the user's language setting. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can identify the language setting using an AI model for identifying the user's language setting and make the display content multilingual.

[0119] When providing a response, the providing unit may analyze the user's social media activity and provide related information. Social media activity includes, but is not limited to, for example, posted content and engagement. For example, the providing unit may provide information about places where the user has checked in on social media. The providing unit may also analyze the user's social media posts and provide information about related tourist spots and stores. Furthermore, the providing unit may provide information about related places and events by referring to the activities of the user's friends on social media. This allows related information to be provided based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may analyze the user's social media activity using an AI model for analyzing the user's social media activity and provide related information. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives questions from a user in text or voice format. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and collects information from social media platforms. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates an answer based on the analyzed information. The provision unit is implemented, for example, by the output device 40 of the smart device 14 and provides the generated answer to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, analysis unit, generation 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 microphone 238 of the smart glasses 214 and receives a user's text or voice questions. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information from social media platforms. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an answer based on the analyzed information. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated answer to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, generation 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 microphone 238 of the headset-type terminal 314 and receives questions in text or voice format from the user. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information from social media platforms. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an answer based on the analyzed information. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314 and provides the generated answer to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, generation 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 microphone 238 of the robot 414 and receives questions in text or voice format from a user. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information from social media platforms. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an answer based on the analyzed information. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated answer to the user.

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

[0121] When accepting a user's question, the reception unit can analyze the user's past question history and automatically suggest related questions. For example, if a user has previously asked, "What eyeshadow is trending these days?", the reception unit can suggest a related question such as, "What lipstick is trending these days?" based on that history. Also, if the user has previously asked many questions about a particular brand, the reception unit can suggest new questions related to that brand. Furthermore, if the user has previously asked many questions about a particular category (e.g., beauty, fashion, etc.), the reception unit can preferentially suggest questions related to that category. This makes it possible to suggest more relevant questions based on the user's past question history.

[0122] When collecting posts from influential people designated by the user, the collection unit can determine priorities by taking into consideration the engagement rate of the post (e.g., number of likes, number of comments, number of shares, etc.). For example, it is possible to preferentially collect posts that not only have a large number of followers but also have a high engagement rate. It is also possible to preferentially collect posts whose engagement rate has risen sharply within a specific period. Furthermore, it is also possible to preferentially collect posts that contain specific keywords (e.g., "recommended," "review," etc.) from the posts from influential people designated by the user. This makes it possible to collect more influential information.

[0123] When analyzing collected posts, the analysis unit can perform sentiment analysis of the posts and prioritize analysis of posts with positive sentiment. For example, posts with positive reviews and opinions can be prioritized for analysis, improving the quality of information provided to users. Posts with negative sentiment can also be filtered and excluded from the information provided to users. Furthermore, based on the results of the sentiment analysis, posts with specific sentiments (e.g., joy, excitement, etc.) can be prioritized for analysis. This can improve the emotional quality of information provided to users.

[0124] When generating an answer to a user's question based on the analyzed information, the generation unit can improve the quality of the answer by reflecting the user's past feedback. For example, a more appropriate answer can be generated based on feedback (e.g., satisfaction level, comments, etc.) provided by the user in the past. The generation unit can also analyze patterns of answers that the user has previously given high ratings to and generate an answer based on those patterns. Furthermore, the quality of the answer can be improved by avoiding patterns of answers that the user has previously given low ratings to. This makes it possible to generate a more satisfying answer based on the user's past feedback.

[0125] When providing the generated response to the user, the providing unit can select the optimal display method by taking into consideration the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a desktop, a display method that includes detailed information can be provided. This makes it possible to select the optimal display method based on the user's device information.

[0126] The reception unit can analyze the user's emotions and adjust the timing of receiving questions based on the analyzed user's emotions. For example, if the user is feeling stressed, the reception of questions can be temporarily delayed and resumed when the user is able to relax. Also, if the user is relaxed, questions can be received immediately and answered quickly. Furthermore, if the user is excited, questions can be received quickly and answers can be provided before the user's excitement cools down. In this way, the timing of receiving questions can be adjusted according to the user's emotions.

[0127] The reception unit can analyze the user's past question history and select an appropriate question reception method. For example, it can prioritize and suggest question formats (text, voice, etc.) that the user has frequently used in the past. It can also analyze the content of the user's past questions and automatically suggest related questions. Furthermore, it can identify, from the user's past question history, the time period during which questions are most frequently asked and accept questions during that time period. This makes it possible to select the optimal question reception method based on the user's past question history.

[0128] When accepting questions, the accepting unit can filter the questions based on the user's current areas of interest. For example, questions related to topics that the user has recently been interested in can be preferentially accepted. The accepting unit can also analyze the user's social media activity and filter questions related to areas of high interest. Furthermore, the accepting unit can filter and accept related questions based on keywords that the user has searched in the past. This makes it possible to filter questions based on the user's current areas of interest.

[0129] When accepting a question, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, if the user selects voice input, the question can be accepted using voice recognition technology. If the user selects text input, the question can be accepted using text analysis technology. Furthermore, if the user uploads an image, the question content can be identified and accepted using image analysis technology. This makes it possible to select the optimal acceptance means depending on the user's input method.

[0130] The reception unit can analyze the user's emotions and determine the priority of questions to be received based on the analyzed user's emotions. For example, if the user is feeling stressed, urgent questions can be given priority. If the user is relaxed, questions can be given normal priority. Furthermore, if the user is excited, related questions can be given priority before the user cools down. In this way, the priority of questions can be determined according to the user's emotions.

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

[0132] Step 1: The reception unit receives a question from a user. The question from the user may be in text format or voice format. For example, the user may input a question in text format or ask a question in voice format. Step 2: The collection unit collects information from social media platforms based on the inquiries received by the reception unit. The collection unit collects information from social media platforms such as Twitter, Facebook, and Instagram, and can use APIs or scraping techniques. Step 3: The analysis unit analyzes the information collected by the collection unit and identifies specific products or opinions. The analysis unit can analyze the information using natural language processing technology or machine learning algorithms. Step 4: The generator generates an answer to the user's question based on the information analyzed by the analyzer. The generator can generate the answer using a template-based generation method or an AI generation method. Step 5: The providing unit provides the answer generated by the generating unit to the user. The providing unit can provide the answer as a text message or a voice message.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0170] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0186] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0196] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives questions from users; a collection unit that collects information from social media platforms based on the questions received by the reception unit; an analysis unit that analyzes the information collected by the collection unit and identifies specific products or opinions; a generation unit that generates an answer to a user's question based on the information analyzed by the analysis unit; a providing unit that provides the response generated by the generating unit to a user. A system characterized by:

2. The collecting unit Collect posts from user-specified influencers 2. The system of claim 1.

3. The analysis unit Analyze collected posts to identify specific products or opinions 2. The system of claim 1.

4. The generation unit Generate answers to user questions based on the analyzed information 2. The system of claim 1.

5. The providing unit Providing the generated response to the user 2. The system of claim 1.

6. The reception unit Analyze user emotions and adjust the timing of question acceptance based on the analyzed user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past question history and select the appropriate method for accepting questions 2. The system of claim 1.

8. The reception unit When questions are submitted, they are filtered based on the user's areas of interest.

2. The system of claim 1.

Images