system

Abstract

The system according to this embodiment aims to summarize the provided content, score the relationships, and provide automatically generated information. [Solution] The system according to the embodiment comprises a provisioning unit, a summarizing unit, a scoring unit, a generation unit, an information provisioning unit, and a satisfaction improvement unit. The provisioning unit provides content. The summarizing unit summarizes the content provided by the provisioning unit. The scoring unit scores the relationships based on the information summarized by the summarizing unit. The generation unit automatically generates and indexes the content provided by the provisioning unit. The information provisioning unit provides the information generated by the generation unit. The satisfaction improvement unit improves user satisfaction based on the information provided by the information provisioning unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, summarization of provided content, scoring of relevance, provision of automatically generated information, etc. are not sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to summarize provided content, score relevance, and provide automatically generated information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a provisioning unit, a summarizing unit, a scoring unit, a generation unit, an information provisioning unit, and a satisfaction improvement unit. The provisioning unit provides content. The summarizing unit summarizes the content provided by the provisioning unit. The scoring unit scores relationships based on the information summarized by the summarizing unit. The generation unit automatically generates and indexes the content provided by the provisioning unit. The information provisioning unit provides the information generated by the generation unit. The satisfaction improvement unit improves user satisfaction based on the information provided by the information provisioning unit. [Effects of the Invention]

[0007] The system according to this embodiment can summarize the provided content, score the relationships, and provide automatically generated information. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The e-commerce platform according to an embodiment of the present invention is a system that provides content owned by official account operators on the platform, summarizes the official account overview using data owned by the group, scores the relationship between the official account and the user, and automatically generates and indexes the provided content using a generation AI. This evolves the platform into a media where reliable information can be easily found, satisfying both users and all business partners. For example, content provided by official account operators is automatically generated by the generation AI and indexed under the official account. This allows users to easily find reliable information, and the scoring of the relationship between the official account and the user provides more personalized services. Furthermore, by utilizing data owned by the group, the official account overview is summarized, providing information that is easy for users to understand. This mechanism allows the platform's services to evolve into a media where reliable information can be easily found, satisfying both users and all business partners. For example, users can easily find content provided through official accounts, and official account operators can utilize content automatically generated by the generation AI to conduct more effective promotional activities. Furthermore, by utilizing data owned by the group, the official account overview is summarized, providing information that is easy for users to understand. This allows e-commerce platforms to efficiently provide content owned by official account operators and deliver information that is easy for users to understand.

[0029] The e-commerce platform according to this embodiment comprises a provision unit, a summarization unit, a scoring unit, a generation unit, an information provision unit, and a satisfaction improvement unit. The provision unit provides content owned by the official account operator. For example, the provision unit can provide content such as text, images, and videos owned by the official account operator on the platform. The summarization unit summarizes data owned by the group. For example, the summarization unit can analyze a large amount of data owned by the group, extract important information, and summarize it. The scoring unit scores the relationship between the official account and the user based on the summarized information. For example, the scoring unit can evaluate the frequency of interaction and common interests between the official account and the user and assign a score. The generation unit automatically generates the provided content and indexes it under the official account. For example, the generation unit can automatically generate the provided content using a generation AI and index it under the official account. The information provision unit provides the generated information. For example, the information provision unit can provide the generated information to the user and make it easily accessible to the user. The satisfaction improvement unit improves user satisfaction based on the provided information. The satisfaction improvement unit can, for example, collect user feedback and use it to improve the service. As a result, the e-commerce platform according to this embodiment can efficiently provide content owned by official account operators and provide information that is easy for users to understand.

[0030] The content provider provides content owned by official account operators. For example, the content provider can provide content such as text, images, and videos owned by official account operators onto the platform. Specifically, the content provider has a function to automatically classify content uploaded by official account operators and place it in the appropriate category. This allows users to easily find content from categories that interest them. The content provider also has an algorithm that analyzes the metadata of the content and recommends highly relevant content. For example, if a user shows interest in a particular brand or product, the content provider will automatically display other content related to that brand or product. Furthermore, the content provider has a function to notify users of content updates in real time, so that users do not miss the latest information. In this way, the content provider can efficiently manage the diverse content owned by official account operators and provide it to users in the most optimal way.

[0031] The summarization unit summarizes the data held by the group. For example, the summarization unit can analyze large amounts of data held by the group, extract important information, and summarize it. Specifically, the summarization unit uses natural language processing technology to analyze text data and extract important keywords and phrases. This allows users to quickly grasp the main points without having to read long texts. The summarization unit also has the function of analyzing the content of images and videos and generating visual summaries. For example, it can automatically extract important scenes from a video and provide them as short clips. Furthermore, the summarization unit can provide individually customized summaries considering the user's browsing history and interests. This allows the summarization unit to efficiently extract and provide the most important information to the user from large amounts of data.

[0032] The scoring unit scores the relationship between the official account and the user based on the summarized information. For example, the scoring unit can evaluate and score the frequency of interaction and shared interests between the official account and the user. Specifically, the scoring unit analyzes user behavior data and evaluates the frequency and quality of interaction with the official account. For example, it measures how many "likes" or comments a user leaves on the official account's posts, or how much time they spend on that content. Furthermore, the scoring unit analyzes the user's interests and concerns and evaluates the degree of match with the content provided by the official account. This allows the scoring unit to quantitatively evaluate the relationship between the user and the official account and recommend the most suitable content to individual users.

[0033] The generation unit automatically generates the provided content and indexes it under the official account. For example, the generation unit can automatically generate the provided content using generation AI and index it under the official account. Specifically, the generation unit uses natural language generation technology to automatically generate new articles and posts based on the text data provided by the official account operator. For example, it can generate product reviews and usage articles based on the product information provided by the official account. In addition, the generation unit uses image generation technology to create new visual content based on the image data provided by the official account. This allows the official account operator to provide diverse content without extra effort. Furthermore, the generation unit automatically indexes the generated content, making it easily accessible to users. This allows the generation unit to efficiently manage the content provided by the official account operator and deliver attractive content to users.

[0034] The Information Provider provides the generated information. For example, the Information Provider can provide the generated information to users and make it easily accessible to them. Specifically, the Information Provider can display the generated content on the user's dashboard or feed, allowing users to quickly find information that interests them. The Information Provider also has a notification function, allowing users to receive real-time notifications when new content is generated. Furthermore, the Information Provider enhances the search function, making it easy for users to find specific information. For example, it provides keyword search and filtering functions, allowing users to quickly find the information they need. In this way, the Information Provider can efficiently provide generated information to users and improve user convenience.

[0035] The Satisfaction Improvement Department improves user satisfaction based on the information provided. For example, the Satisfaction Improvement Department can collect user feedback and use it to improve the service. Specifically, the Satisfaction Improvement Department collects ratings and comments from users and uses this to identify areas for service improvement. For example, if a user is dissatisfied with a particular piece of content, the department will use that feedback to improve the quality of that content. The Satisfaction Improvement Department also analyzes user behavior data to understand what kind of content users are satisfied with. This allows the Satisfaction Improvement Department to provide content that meets user needs and improve user satisfaction. Furthermore, the Satisfaction Improvement Department can conduct regular user surveys to directly collect user opinions. This allows the Satisfaction Improvement Department to make service improvements that reflect user feedback and continuously improve user satisfaction.

[0036] The content provider can provide content owned by official account operators. For example, the provider can provide content such as text, images, and videos owned by official account operators to the platform. The provider can use AI to classify and organize content in order to efficiently provide content owned by official account operators. For example, the provider can use AI to automatically generate metadata for content and improve searchability. This enables the efficient provision of content from official account operators.

[0037] The summarization unit can summarize the data held by the group. For example, the summarization unit can analyze the large amount of data held by the group, extract important information, and summarize it. The summarization unit can use AI to summarize the data. For example, the summarization unit can use AI to evaluate the importance of the data, extract important information, and summarize it. By using AI to summarize the data, the summarization unit can perform summarization efficiently. This allows for efficient summarization of the data held by the group.

[0038] The scoring unit can score the relationship between an official account and a user based on summarized information. For example, the scoring unit evaluates the frequency of interaction and shared interests between the official account and the user and assigns a score. The scoring unit can use AI to score the relationship. For example, the scoring unit can use AI to analyze the relationship between the official account and the user and assign a score. By using AI to score the relationship, the scoring unit can perform the scoring efficiently. This allows for efficient scoring of the relationship between an official account and a user.

[0039] The generation unit can automatically generate the provided content and index it under the official account. For example, the generation unit can use a generation AI to automatically generate the provided content and index it under the official account. The generation unit can perform automatic content generation using a generation AI. For example, the generation unit can use a generation AI to analyze the provided content and generate new content. By using a generation AI to automatically generate content, the generation unit can efficiently generate and index content. This allows for the efficient automatic generation and indexing of provided content.

[0040] The information provision department can provide generated information. For example, the information provision department can provide generated information to users and make it easily accessible to them. The information provision department can provide information using AI. For example, the information provision department can provide information based on the user's interests and preferences using AI. By providing information using AI, the information provision department can provide information efficiently. This allows for the efficient provision of generated information.

[0041] The Satisfaction Improvement Department can improve user satisfaction based on the information provided. For example, the Satisfaction Improvement Department can collect user feedback and use it to improve services. The Satisfaction Improvement Department can use AI to improve user satisfaction. For example, the Satisfaction Improvement Department can use AI to analyze user feedback and identify areas for service improvement. By using AI to improve user satisfaction, the Satisfaction Improvement Department can efficiently improve satisfaction. This allows for efficient improvement of user satisfaction.

[0042] The content delivery department can analyze the past content delivery history of the official account operator and select the optimal delivery method. For example, the content delivery department can deliver content during times when user response was good, based on past delivery history. The content delivery department can select the type of content to deliver on specific days of the week, based on past delivery history. The content delivery department can deliver content in formats (video, text, etc.) that have received good user response, based on past delivery history. This allows for the analysis of past content delivery history and the selection of the optimal delivery method. Some or all of the above processes in the content delivery department may be performed using AI, for example, or not using AI.

[0043] The content provider can filter content based on the user's current areas of interest when providing it. For example, the provider can provide relevant content based on keywords the user has recently searched for. The provider can provide relevant content based on pages the user has recently viewed. The provider can provide relevant content based on events the user has recently attended. This allows content to be filtered based on the user's areas of interest. Some or all of the above processing in the provider may be performed using AI, for example, or not using AI.

[0044] The content provider can prioritize providing highly relevant content by considering the user's geographical location when delivering content. For example, if the user is in a specific region, the provider can provide event information related to that region. If the user is traveling, the provider can provide tourist information for their travel destination. If the user is at home, the provider can provide information about nearby shops. This allows the provider to deliver highly relevant content by considering the user's geographical location. Some or all of the above processing in the content provider may be performed using AI, for example, or without using AI.

[0045] The content provider can analyze a user's social media activity and provide relevant content when delivering content. For example, the provider can provide relevant content based on articles the user has shared on social media. The provider can provide relevant content based on accounts the user follows on social media. The provider can provide relevant content based on events the user has participated in on social media. This allows the provider to analyze the user's social media activity and provide relevant content. Some or all of the above processing in the content provider may be performed using AI, for example, or not using AI.

[0046] The summarization unit can adjust the level of detail of the summary based on the importance of the data during summary generation. For example, the summarization unit can provide a detailed summary for highly important data, a concise summary for less important data, and a summary with a moderate level of detail for moderately important data. This allows the level of detail of the summary to be adjusted based on the importance of the data. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI.

[0047] The summarization unit can apply different summarization algorithms depending on the data category when generating summaries. For example, the summarization unit can apply a news-specific summarization algorithm to news data. For product data, it can apply a product-specific summarization algorithm. For review data, it can apply a review-specific summarization algorithm. This allows for the application of different summarization algorithms depending on the data category. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without using AI.

[0048] The summarization unit can determine the priority of summaries based on when the data was acquired during the summarization process. For example, the summarization unit may prioritize summarizing the most recent data. For older data, the summarization unit may lower the priority of summarizing. For data of moderate recency, the summarization unit may give it a moderate priority. This allows the summarization priority to be determined based on when the data was acquired. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI.

[0049] The summarization unit can adjust the order of summaries based on the relevance of the data during summary generation. For example, the summarization unit prioritizes summarizing highly relevant data. For less relevant data, the summarization unit can postpone the order of summarization. For moderately relevant data, the summarization unit can summarize in an appropriate order. This allows the order of summaries to be adjusted based on the relevance of the data. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI.

[0050] The scoring unit can improve the accuracy of scoring by considering the relationship between the official account and the user during the scoring process. For example, the scoring unit can score based on past interactions between the official account and the user. The scoring unit can score by considering the number of mutual followers between the official account and the user. The scoring unit can score by considering the common interests of the official account and the user. This allows for improved accuracy of scoring by considering the relationship between the official account and the user. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without using AI.

[0051] The scoring unit can perform scoring while considering the attribute information of the official account operator. For example, the scoring unit can perform scoring while considering the industry of the official account operator. The scoring unit can perform scoring while considering the size of the official account operator. The scoring unit can perform scoring while considering the region of the official account operator. By doing so, the accuracy of scoring can be improved by considering the attribute information of the official account operator. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without using AI.

[0052] The scoring unit can perform scoring while considering the geographical distribution of official accounts. For example, the scoring unit can perform scoring while considering the location of the official accounts. The scoring unit can perform scoring while considering the service area of ​​the official accounts. The scoring unit can perform scoring while considering the popularity of the official accounts in each region. By performing scoring while considering the geographical distribution of official accounts, the accuracy of the scoring can be improved. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without using AI.

[0053] The scoring unit can improve the accuracy of its scoring by referring to relevant literature related to the official account during the scoring process. For example, the scoring unit can score by referring to academic papers related to the official account. The scoring unit can score by referring to news articles related to the official account. The scoring unit can score by referring to reviews related to the official account. This allows for improved accuracy of scoring by referring to relevant literature related to the official account. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without using AI.

[0054] The generation unit can improve the accuracy of content generation by referring to the official account's past content during content generation. For example, the generation unit can generate relevant content based on the official account's past posts. The generation unit can generate content that will attract user interest based on the official account's past popular posts. The generation unit can analyze the official account's past posting patterns and generate optimal content. This allows the generation unit to improve accuracy by referring to the official account's past content. Some or all of the above processes in the generation unit may be performed using AI, for example, or without using AI.

[0055] The generation unit can generate content while considering the attribute information of the official account operator. For example, the generation unit can generate content according to the industry of the official account operator. The generation unit can generate content according to the scale of the official account operator. The generation unit can generate content according to the region of the official account operator. By considering the attribute information of the official account operator during generation, the accuracy of generation can be improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0056] The generation unit can generate content while considering the geographical distribution of official accounts. For example, the generation unit can generate content related to the location of official accounts. The generation unit can generate content related to the service area of ​​official accounts. The generation unit can generate content based on the popularity of official accounts in each region. By considering the geographical distribution of official accounts during generation, the accuracy of generation can be improved. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI.

[0057] The generation unit can improve the accuracy of content generation by referring to relevant literature related to the official account. For example, the generation unit can generate content by referring to academic papers related to the official account. The generation unit can generate content by referring to news articles related to the official account. The generation unit can generate content by referring to reviews related to the official account. This allows for improved accuracy of content generation by referring to relevant literature related to the official account. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0058] The information provision unit can select the optimal method of providing information by referring to the user's past information acquisition history when providing information. For example, the information provision unit can provide relevant information based on information the user has previously viewed. The information provision unit can provide relevant information based on keywords the user has previously searched for. The information provision unit can provide relevant information based on events the user has previously participated in. This allows the system to select the optimal method of providing information by referring to the user's past information acquisition history. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without using AI.

[0059] The information provision unit can provide optimal information by taking into account the user's geographical location when providing information. For example, if the user is in a specific region, the information provision unit can provide event information related to that region. If the user is traveling, the information provision unit can provide tourist information for the travel destination. If the user is at home, the information provision unit can provide information on nearby shops. In this way, optimal information can be provided by taking into account the user's geographical location. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without using AI.

[0060] The satisfaction improvement unit can select the optimal improvement method by referring to the user's past feedback when improving satisfaction. For example, the satisfaction improvement unit adjusts the service based on the feedback the user has provided in the past. The satisfaction improvement unit can provide relevant services based on the services the user has evaluated in the past. The satisfaction improvement unit can provide relevant services based on the events the user has participated in in the past. This allows the unit to select the optimal improvement method by referring to the user's past feedback. Some or all of the above processes in the satisfaction improvement unit may be performed using AI, for example, or without using AI.

[0061] The satisfaction improvement unit can select the optimal method of improving satisfaction by considering the user's geographical location. For example, if the user is in a specific region, the satisfaction improvement unit can provide services related to that region. If the user is traveling, the satisfaction improvement unit can provide services that can be used at the travel destination. If the user is at home, the satisfaction improvement unit can provide services that can be used at nearby stores. This allows the unit to select the optimal method of improvement by considering the user's geographical location. Some or all of the above processing in the satisfaction improvement unit may be performed using AI, for example, or without using AI.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The content delivery department can analyze the past content delivery history of official account operators and select the optimal delivery method. For example, it can deliver content during times when user response was good based on past delivery history. It can select the type of content to deliver on specific days of the week. It can deliver content in formats (video, text, etc.) that have received good user response based on past delivery history. In this way, it can analyze past content delivery history and select the optimal delivery method.

[0064] The content provider can filter content based on the user's current areas of interest when providing it. For example, it can provide relevant content based on keywords the user has recently searched for, relevant content based on pages the user has recently viewed, and relevant content based on events the user has recently attended. This allows content to be filtered based on the user's areas of interest.

[0065] The content provider can prioritize providing highly relevant content by considering the user's geographical location when delivering content. For example, if a user is in a specific region, it can provide event information related to that region. If a user is traveling, it can provide tourist information for their travel destination. If a user is at home, it can provide information about nearby shops. This allows for the provision of highly relevant content that takes the user's geographical location into consideration.

[0066] The summarization section can adjust the level of detail in the summary based on the importance of the data during summary generation. For example, it can provide a detailed summary for highly important data, a concise summary for less important data, and a summary with a moderate level of detail for moderately important data. This allows the level of detail in the summary to be adjusted based on the importance of the data.

[0067] The summarization section can apply different summarization algorithms depending on the data category during summary generation. For example, a news-specific summarization algorithm can be applied to news data, a product-specific summarization algorithm to product data, and a review-specific summarization algorithm to review data. This allows for the application of different summarization algorithms depending on the data category.

[0068] The summarization unit can determine the priority of summaries based on when the data was acquired during the summarization process. For example, the most recent data can be prioritized for summarization. Older data can be given a lower priority for summarization. Data of moderate recency can be given a moderate priority for summarization. This allows for determining the priority of summarization based on when the data was acquired.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The provider will provide content owned by the official account operator. For example, they will provide content such as text, images, and videos owned by the official account operator on the platform. Step 2: The summarization section summarizes the data held by the group. For example, it analyzes the large amount of data held by the group, extracts important information, and summarizes it. Step 3: The scoring unit scores the relationship between the official account and the user based on the summarized information. For example, it evaluates the frequency of interaction between the official account and the user and their shared interests, and assigns a score. Step 4: The generation unit automatically generates the provided content and indexes it under the official account. For example, it uses a generation AI to automatically generate the provided content and indexes it under the official account. Step 5: The information provision unit provides the generated information. For example, it provides the generated information to the user and makes it easily accessible to the user. Step 6: The Satisfaction Improvement Department improves user satisfaction based on the information provided. For example, they collect user feedback and use it to improve the service.

[0071] (Example of form 2) The e-commerce platform according to an embodiment of the present invention is a system that provides content owned by official account operators on the platform, summarizes the official account overview using data owned by the group, scores the relationship between the official account and the user, and automatically generates and indexes the provided content using a generation AI. This evolves the platform into a media where reliable information can be easily found, satisfying both users and all business partners. For example, content provided by official account operators is automatically generated by the generation AI and indexed under the official account. This allows users to easily find reliable information, and the scoring of the relationship between the official account and the user provides more personalized services. Furthermore, by utilizing data owned by the group, the official account overview is summarized, providing information that is easy for users to understand. This mechanism allows the platform's services to evolve into a media where reliable information can be easily found, satisfying both users and all business partners. For example, users can easily find content provided through official accounts, and official account operators can utilize content automatically generated by the generation AI to conduct more effective promotional activities. Furthermore, by utilizing data owned by the group, the official account overview is summarized, providing information that is easy for users to understand. This allows e-commerce platforms to efficiently provide content owned by official account operators and deliver information that is easy for users to understand.

[0072] The e-commerce platform according to this embodiment comprises a provision unit, a summarization unit, a scoring unit, a generation unit, an information provision unit, and a satisfaction improvement unit. The provision unit provides content owned by the official account operator. For example, the provision unit can provide content such as text, images, and videos owned by the official account operator on the platform. The summarization unit summarizes data owned by the group. For example, the summarization unit can analyze a large amount of data owned by the group, extract important information, and summarize it. The scoring unit scores the relationship between the official account and the user based on the summarized information. For example, the scoring unit can evaluate the frequency of interaction and common interests between the official account and the user and assign a score. The generation unit automatically generates the provided content and indexes it under the official account. For example, the generation unit can automatically generate the provided content using a generation AI and index it under the official account. The information provision unit provides the generated information. For example, the information provision unit can provide the generated information to the user and make it easily accessible to the user. The satisfaction improvement unit improves user satisfaction based on the provided information. The satisfaction improvement unit can, for example, collect user feedback and use it to improve the service. As a result, the e-commerce platform according to this embodiment can efficiently provide content owned by official account operators and provide information that is easy for users to understand.

[0073] The content provider provides content owned by official account operators. For example, the content provider can provide content such as text, images, and videos owned by official account operators onto the platform. Specifically, the content provider has a function to automatically classify content uploaded by official account operators and place it in the appropriate category. This allows users to easily find content from categories that interest them. The content provider also has an algorithm that analyzes the metadata of the content and recommends highly relevant content. For example, if a user shows interest in a particular brand or product, the content provider will automatically display other content related to that brand or product. Furthermore, the content provider has a function to notify users of content updates in real time, so that users do not miss the latest information. In this way, the content provider can efficiently manage the diverse content owned by official account operators and provide it to users in the most optimal way.

[0074] The summarization unit summarizes the data held by the group. For example, the summarization unit can analyze large amounts of data held by the group, extract important information, and summarize it. Specifically, the summarization unit uses natural language processing technology to analyze text data and extract important keywords and phrases. This allows users to quickly grasp the main points without having to read long texts. The summarization unit also has the function of analyzing the content of images and videos and generating visual summaries. For example, it can automatically extract important scenes from a video and provide them as short clips. Furthermore, the summarization unit can provide individually customized summaries considering the user's browsing history and interests. This allows the summarization unit to efficiently extract and provide the most important information to the user from large amounts of data.

[0075] The scoring unit scores the relationship between the official account and the user based on the summarized information. For example, the scoring unit can evaluate and score the frequency of interaction and shared interests between the official account and the user. Specifically, the scoring unit analyzes user behavior data and evaluates the frequency and quality of interaction with the official account. For example, it measures how many "likes" or comments a user leaves on the official account's posts, or how much time they spend on that content. Furthermore, the scoring unit analyzes the user's interests and concerns and evaluates the degree of match with the content provided by the official account. This allows the scoring unit to quantitatively evaluate the relationship between the user and the official account and recommend the most suitable content to individual users.

[0076] The generation unit automatically generates the provided content and indexes it under the official account. For example, the generation unit can automatically generate the provided content using generation AI and index it under the official account. Specifically, the generation unit uses natural language generation technology to automatically generate new articles and posts based on the text data provided by the official account operator. For example, it can generate product reviews and usage articles based on the product information provided by the official account. In addition, the generation unit uses image generation technology to create new visual content based on the image data provided by the official account. This allows the official account operator to provide diverse content without extra effort. Furthermore, the generation unit automatically indexes the generated content, making it easily accessible to users. This allows the generation unit to efficiently manage the content provided by the official account operator and deliver attractive content to users.

[0077] The Information Provider provides the generated information. For example, the Information Provider can provide the generated information to users and make it easily accessible to them. Specifically, the Information Provider can display the generated content on the user's dashboard or feed, allowing users to quickly find information that interests them. The Information Provider also has a notification function, allowing users to receive real-time notifications when new content is generated. Furthermore, the Information Provider enhances the search function, making it easy for users to find specific information. For example, it provides keyword search and filtering functions, allowing users to quickly find the information they need. In this way, the Information Provider can efficiently provide generated information to users and improve user convenience.

[0078] The Satisfaction Improvement Department improves user satisfaction based on the information provided. For example, the Satisfaction Improvement Department can collect user feedback and use it to improve the service. Specifically, the Satisfaction Improvement Department collects ratings and comments from users and uses this to identify areas for service improvement. For example, if a user is dissatisfied with a particular piece of content, the department will use that feedback to improve the quality of that content. The Satisfaction Improvement Department also analyzes user behavior data to understand what kind of content users are satisfied with. This allows the Satisfaction Improvement Department to provide content that meets user needs and improve user satisfaction. Furthermore, the Satisfaction Improvement Department can conduct regular user surveys to directly collect user opinions. This allows the Satisfaction Improvement Department to make service improvements that reflect user feedback and continuously improve user satisfaction.

[0079] The content provider can provide content owned by official account operators. For example, the provider can provide content such as text, images, and videos owned by official account operators to the platform. The provider can use AI to classify and organize content in order to efficiently provide content owned by official account operators. For example, the provider can use AI to automatically generate metadata for content and improve searchability. This enables the efficient provision of content from official account operators.

[0080] The summarization unit can summarize the data held by the group. For example, the summarization unit can analyze the large amount of data held by the group, extract important information, and summarize it. The summarization unit can use AI to summarize the data. For example, the summarization unit can use AI to evaluate the importance of the data, extract important information, and summarize it. By using AI to summarize the data, the summarization unit can perform summarization efficiently. This allows for efficient summarization of the data held by the group.

[0081] The scoring unit can score the relationship between an official account and a user based on summarized information. For example, the scoring unit evaluates the frequency of interaction and shared interests between the official account and the user and assigns a score. The scoring unit can use AI to score the relationship. For example, the scoring unit can use AI to analyze the relationship between the official account and the user and assign a score. By using AI to score the relationship, the scoring unit can perform the scoring efficiently. This allows for efficient scoring of the relationship between an official account and a user.

[0082] The generation unit can automatically generate the provided content and index it under the official account. For example, the generation unit can use a generation AI to automatically generate the provided content and index it under the official account. The generation unit can perform automatic content generation using a generation AI. For example, the generation unit can use a generation AI to analyze the provided content and generate new content. By using a generation AI to automatically generate content, the generation unit can efficiently generate and index content. This allows for the efficient automatic generation and indexing of provided content.

[0083] The information provision department can provide generated information. For example, the information provision department can provide generated information to users and make it easily accessible to them. The information provision department can provide information using AI. For example, the information provision department can provide information based on the user's interests and preferences using AI. By providing information using AI, the information provision department can provide information efficiently. This allows for the efficient provision of generated information.

[0084] The Satisfaction Improvement Department can improve user satisfaction based on the information provided. For example, the Satisfaction Improvement Department can collect user feedback and use it to improve services. The Satisfaction Improvement Department can use AI to improve user satisfaction. For example, the Satisfaction Improvement Department can use AI to analyze user feedback and identify areas for service improvement. By using AI to improve user satisfaction, the Satisfaction Improvement Department can efficiently improve satisfaction. This allows for efficient improvement of user satisfaction.

[0085] The content delivery unit can estimate the user's emotions and adjust the timing of content delivery based on the estimated emotions. For example, if the user is stressed, the delivery unit can deliver content during a time when the user can relax. If the user is excited, the delivery unit can deliver content immediately to keep the user interested. If the user is tired, the delivery unit can deliver content to coincide with a break time. This allows the timing of content delivery to be adjusted based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The content delivery department can analyze the past content delivery history of the official account operator and select the optimal delivery method. For example, the content delivery department can deliver content during times when user response was good, based on past delivery history. The content delivery department can select the type of content to deliver on specific days of the week, based on past delivery history. The content delivery department can deliver content in formats (video, text, etc.) that have received good user response, based on past delivery history. This allows for the analysis of past content delivery history and the selection of the optimal delivery method. Some or all of the above processes in the content delivery department may be performed using AI, for example, or not using AI.

[0087] The content provider can filter content based on the user's current areas of interest when providing it. For example, the provider can provide relevant content based on keywords the user has recently searched for. The provider can provide relevant content based on pages the user has recently viewed. The provider can provide relevant content based on events the user has recently attended. This allows content to be filtered based on the user's areas of interest. Some or all of the above processing in the provider may be performed using AI, for example, or not using AI.

[0088] The service provider can estimate the user's emotions and prioritize the content to be delivered based on those emotions. For example, if the user is relaxed, the service provider can prioritize entertainment content. If the user is focused, the service provider can prioritize educational content. If the user is tired, the service provider can prioritize relaxing content. This allows the service provider to prioritize content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The content provider can prioritize providing highly relevant content by considering the user's geographical location when delivering content. For example, if the user is in a specific region, the provider can provide event information related to that region. If the user is traveling, the provider can provide tourist information for their travel destination. If the user is at home, the provider can provide information about nearby shops. This allows the provider to deliver highly relevant content by considering the user's geographical location. Some or all of the above processing in the content provider may be performed using AI, for example, or without using AI.

[0090] The content provider can analyze a user's social media activity and provide relevant content when delivering content. For example, the provider can provide relevant content based on articles the user has shared on social media. The provider can provide relevant content based on accounts the user follows on social media. The provider can provide relevant content based on events the user has participated in on social media. This allows the provider to analyze the user's social media activity and provide relevant content. Some or all of the above processing in the content provider may be performed using AI, for example, or not using AI.

[0091] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on those emotions. For example, if the user is relaxed, the summarization unit can provide a detailed summary. If the user is in a hurry, the summarization unit can provide a concise summary. If the user is excited, the summarization unit can provide a visually appealing summary. This allows the summarization to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The summarization unit can adjust the level of detail of the summary based on the importance of the data during summary generation. For example, the summarization unit can provide a detailed summary for highly important data, a concise summary for less important data, and a summary with a moderate level of detail for moderately important data. This allows the level of detail of the summary to be adjusted based on the importance of the data. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI.

[0093] The summarization unit can apply different summarization algorithms depending on the data category when generating summaries. For example, the summarization unit can apply a news-specific summarization algorithm to news data. For product data, it can apply a product-specific summarization algorithm. For review data, it can apply a review-specific summarization algorithm. This allows for the application of different summarization algorithms depending on the data category. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without using AI.

[0094] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is relaxed, the summarization unit can provide a longer summary. If the user is in a hurry, the summarization unit can provide a shorter summary. If the user is excited, the summarization unit can provide a visually appealing summary. This allows the length of the summary to be adjusted based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The summarization unit can determine the priority of summaries based on when the data was acquired during the summarization process. For example, the summarization unit may prioritize summarizing the most recent data. For older data, the summarization unit may lower the priority of summarizing. For data of moderate recency, the summarization unit may give it a moderate priority. This allows the summarization priority to be determined based on when the data was acquired. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI.

[0096] The summarization unit can adjust the order of summaries based on the relevance of the data during summary generation. For example, the summarization unit prioritizes summarizing highly relevant data. For less relevant data, the summarization unit can postpone the order of summarization. For moderately relevant data, the summarization unit can summarize in an appropriate order. This allows the order of summaries to be adjusted based on the relevance of the data. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI.

[0097] The scoring unit can estimate the user's emotions and adjust the scoring criteria based on the estimated emotions. For example, if the user is relaxed, the scoring unit can apply detailed scoring criteria. If the user is in a hurry, the scoring unit can apply concise scoring criteria. If the user is excited, the scoring unit can apply visually appealing scoring criteria. This allows the scoring criteria to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The scoring unit can improve the accuracy of scoring by considering the relationship between the official account and the user during the scoring process. For example, the scoring unit can score based on past interactions between the official account and the user. The scoring unit can score by considering the number of mutual followers between the official account and the user. The scoring unit can score by considering the common interests of the official account and the user. This allows for improved accuracy of scoring by considering the relationship between the official account and the user. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without using AI.

[0099] The scoring unit can perform scoring while considering the attribute information of the official account operator. For example, the scoring unit can perform scoring while considering the industry of the official account operator. The scoring unit can perform scoring while considering the size of the official account operator. The scoring unit can perform scoring while considering the region of the official account operator. By doing so, the accuracy of scoring can be improved by considering the attribute information of the official account operator. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without using AI.

[0100] The scoring unit can estimate the user's emotions and adjust the order in which the scoring results are displayed based on the estimated emotions. For example, if the user is relaxed, the scoring unit may prioritize displaying detailed scoring results. If the user is in a hurry, the scoring unit may prioritize displaying concise scoring results. If the user is excited, the scoring unit may prioritize displaying visually appealing scoring results. This allows the order in which the scoring results are displayed to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The scoring unit can perform scoring while considering the geographical distribution of official accounts. For example, the scoring unit can perform scoring while considering the location of the official accounts. The scoring unit can perform scoring while considering the service area of ​​the official accounts. The scoring unit can perform scoring while considering the popularity of the official accounts in each region. By performing scoring while considering the geographical distribution of official accounts, the accuracy of the scoring can be improved. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without using AI.

[0102] The scoring unit can improve the accuracy of its scoring by referring to relevant literature related to the official account during the scoring process. For example, the scoring unit can score by referring to academic papers related to the official account. The scoring unit can score by referring to news articles related to the official account. The scoring unit can score by referring to reviews related to the official account. This allows for improved accuracy of scoring by referring to relevant literature related to the official account. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without using AI.

[0103] The generation unit can estimate the user's emotions and determine the priority of content to generate based on the estimated emotions. For example, if the user is relaxed, the generation unit can prioritize generating entertainment content. If the user is focused, the generation unit can prioritize generating educational content. If the user is tired, the generation unit can prioritize generating relaxing content. This allows the generation unit to determine the priority of content to generate based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The generation unit can improve the accuracy of content generation by referring to the official account's past content during content generation. For example, the generation unit can generate relevant content based on the official account's past posts. The generation unit can generate content that will attract user interest based on the official account's past popular posts. The generation unit can analyze the official account's past posting patterns and generate optimal content. This allows the generation unit to improve accuracy by referring to the official account's past content. Some or all of the above processes in the generation unit may be performed using AI, for example, or without using AI.

[0105] The generation unit can generate content while considering the attribute information of the official account operator. For example, the generation unit can generate content according to the industry of the official account operator. The generation unit can generate content according to the scale of the official account operator. The generation unit can generate content according to the region of the official account operator. By considering the attribute information of the official account operator during generation, the accuracy of generation can be improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0106] The generation unit can estimate the user's emotions and adjust how the generated content is displayed based on those emotions. For example, if the user is relaxed, the generation unit can provide a visually appealing display method. If the user is in a hurry, the generation unit can provide a concise and easily readable display method. If the user is excited, the generation unit can provide a display method with dynamic effects. This allows the generation unit to adjust how the generated content is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The generation unit can generate content while considering the geographical distribution of official accounts. For example, the generation unit can generate content related to the location of official accounts. The generation unit can generate content related to the service area of ​​official accounts. The generation unit can generate content based on the popularity of official accounts in each region. By considering the geographical distribution of official accounts during generation, the accuracy of generation can be improved. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI.

[0108] The generation unit can improve the accuracy of content generation by referring to relevant literature related to the official account. For example, the generation unit can generate content by referring to academic papers related to the official account. The generation unit can generate content by referring to news articles related to the official account. The generation unit can generate content by referring to reviews related to the official account. This allows for improved accuracy of content generation by referring to relevant literature related to the official account. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0109] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is relaxed, the information provider can provide detailed information. If the user is in a hurry, the information provider can provide concise information. If the user is excited, the information provider can provide visually appealing information. This allows the information provider to adjust the method of information delivery based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The information provision unit can select the optimal method of providing information by referring to the user's past information acquisition history when providing information. For example, the information provision unit can provide relevant information based on information the user has previously viewed. The information provision unit can provide relevant information based on keywords the user has previously searched for. The information provision unit can provide relevant information based on events the user has previously participated in. This allows the system to select the optimal method of providing information by referring to the user's past information acquisition history. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without using AI.

[0111] The information provider can estimate the user's emotions and determine the priority of information provision based on the estimated emotions. For example, if the user is relaxed, the information provider can prioritize providing entertainment-related information. If the user is focused, the information provider can prioritize providing learning-related information. If the user is tired, the information provider can prioritize providing relaxing information. In this way, the priority of information provision can be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The information provision unit can provide optimal information by taking into account the user's geographical location when providing information. For example, if the user is in a specific region, the information provision unit can provide event information related to that region. If the user is traveling, the information provision unit can provide tourist information for the travel destination. If the user is at home, the information provision unit can provide information on nearby shops. In this way, optimal information can be provided by taking into account the user's geographical location. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without using AI.

[0113] The satisfaction enhancement unit can estimate the user's emotions and adjust the methods of improving satisfaction based on the estimated emotions. For example, if the user is relaxed, the satisfaction enhancement unit can provide entertainment-related services. If the user is focused, the satisfaction enhancement unit can provide learning-related services. If the user is tired, the satisfaction enhancement unit can provide relaxing services. In this way, the methods of improving satisfaction can be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The satisfaction improvement unit can select the optimal improvement method by referring to the user's past feedback when improving satisfaction. For example, the satisfaction improvement unit adjusts the service based on the feedback the user has provided in the past. The satisfaction improvement unit can provide relevant services based on the services the user has evaluated in the past. The satisfaction improvement unit can provide relevant services based on the events the user has participated in in the past. This allows the unit to select the optimal improvement method by referring to the user's past feedback. Some or all of the above processes in the satisfaction improvement unit may be performed using AI, for example, or without using AI.

[0115] The satisfaction enhancement unit can estimate the user's emotions and determine the priority of satisfaction enhancement based on the estimated emotions. For example, if the user is relaxed, the satisfaction enhancement unit can prioritize providing entertainment-related services. If the user is focused, the satisfaction enhancement unit can prioritize providing learning-related services. If the user is tired, the satisfaction enhancement unit can prioritize providing relaxing services. In this way, the priority of satisfaction enhancement can be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The satisfaction improvement unit can select the optimal method of improving satisfaction by considering the user's geographical location. For example, if the user is in a specific region, the satisfaction improvement unit can provide services related to that region. If the user is traveling, the satisfaction improvement unit can provide services that can be used at the travel destination. If the user is at home, the satisfaction improvement unit can provide services that can be used at nearby stores. This allows the unit to select the optimal method of improvement by considering the user's geographical location. Some or all of the above processing in the satisfaction improvement unit may be performed using AI, for example, or without using AI.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The content delivery system can estimate the user's emotions and adjust the timing of content delivery based on those estimates. For example, if a user is stressed, content can be delivered during a time when they can relax. If a user is excited, content can be delivered immediately to maintain their interest. If a user is tired, content can be delivered to coincide with their break time. This allows for adjusting the timing of content delivery based on the user's emotions.

[0119] The content delivery department can analyze the past content delivery history of official account operators and select the optimal delivery method. For example, it can deliver content during times when user response was good based on past delivery history. It can select the type of content to deliver on specific days of the week. It can deliver content in formats (video, text, etc.) that have received good user response based on past delivery history. In this way, it can analyze past content delivery history and select the optimal delivery method.

[0120] The content provider can filter content based on the user's current areas of interest when providing it. For example, it can provide relevant content based on keywords the user has recently searched for, relevant content based on pages the user has recently viewed, and relevant content based on events the user has recently attended. This allows content to be filtered based on the user's areas of interest.

[0121] The content delivery system can estimate the user's emotions and prioritize the content delivered based on those emotions. For example, if the user is relaxed, entertainment content can be prioritized. If the user is focused, educational content can be prioritized. If the user is tired, relaxing content can be prioritized. This allows content prioritization to be determined based on the user's emotions.

[0122] The content provider can prioritize providing highly relevant content by considering the user's geographical location when delivering content. For example, if a user is in a specific region, it can provide event information related to that region. If a user is traveling, it can provide tourist information for their travel destination. If a user is at home, it can provide information about nearby shops. This allows for the provision of highly relevant content that takes the user's geographical location into consideration.

[0123] The summarization section can estimate the user's emotions and adjust the way the summary is presented based on those emotions. For example, if the user is relaxed, a detailed summary can be provided. If the user is in a hurry, a concise summary can be provided. If the user is excited, a visually appealing summary can be provided. This allows the summary's presentation to be adjusted based on the user's emotions.

[0124] The summarization section can adjust the level of detail in the summary based on the importance of the data during summary generation. For example, it can provide a detailed summary for highly important data, a concise summary for less important data, and a summary with a moderate level of detail for moderately important data. This allows the level of detail in the summary to be adjusted based on the importance of the data.

[0125] The summarization section can apply different summarization algorithms depending on the data category during summary generation. For example, a news-specific summarization algorithm can be applied to news data, a product-specific summarization algorithm to product data, and a review-specific summarization algorithm to review data. This allows for the application of different summarization algorithms depending on the data category.

[0126] The summary section can estimate the user's emotions and adjust the length of the summary based on those emotions. For example, if the user is relaxed, a longer summary can be provided. If the user is in a hurry, a shorter summary can be provided. If the user is excited, a visually appealing summary can be provided. This allows the summary length to be adjusted based on the user's emotions.

[0127] The summarization unit can determine the priority of summaries based on when the data was acquired during the summarization process. For example, the most recent data can be prioritized for summarization. Older data can be given a lower priority for summarization. Data of moderate recency can be given a moderate priority for summarization. This allows for determining the priority of summarization based on when the data was acquired.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The provider will provide content owned by the official account operator. For example, they will provide content such as text, images, and videos owned by the official account operator on the platform. Step 2: The summarization section summarizes the data held by the group. For example, it analyzes the large amount of data held by the group, extracts important information, and summarizes it. Step 3: The scoring unit scores the relationship between the official account and the user based on the summarized information. For example, it evaluates the frequency of interaction between the official account and the user and their shared interests, and assigns a score. Step 4: The generation unit automatically generates the provided content and indexes it under the official account. For example, it uses a generation AI to automatically generate the provided content and indexes it under the official account. Step 5: The information provision unit provides the generated information. For example, it provides the generated information to the user and makes it easily accessible to the user. Step 6: The Satisfaction Improvement Department improves user satisfaction based on the information provided. For example, they collect user feedback and use it to improve the service.

[0130] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0131] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0132] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0133] Each of the multiple elements described above, including the provision unit, summarization unit, scoring unit, generation unit, information provision unit, and satisfaction improvement unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the provision unit is implemented by the control unit 46A of the smart device 14 and provides content such as text, images, and videos owned by the official account operator to the platform. The summarization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes a large amount of data owned by the group, extracts important information, and summarizes it. The scoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and evaluates the frequency of interaction and common interests between the official account and the user, and assigns a score. The generation unit is implemented, for example, by the control unit 46A of the smart device 14 and automatically generates the provided content using generation AI and indexes it under the official account. The information provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides the generated information to the user, making it easily accessible to the user. The satisfaction improvement unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which collects user feedback and uses it to improve the service. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the provision unit, summarization unit, scoring unit, generation unit, information provision unit, and satisfaction improvement unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the provision unit is implemented by the control unit 46A of the smart glasses 214 and provides content such as text, images, and videos owned by the official account operator to the platform. The summarization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes a large amount of data owned by the group, extracts important information, and summarizes it. The scoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and evaluates the frequency of interaction and common interests between the official account and the user, and assigns a score. The generation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and automatically generates the provided content using generation AI and indexes it under the official account. The information provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the generated information to the user, making it easily accessible to the user. The satisfaction improvement unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which collects user feedback and uses it to improve the service. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the provision unit, summarization unit, scoring unit, generation unit, information provision unit, and satisfaction improvement unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the provision unit is implemented by the control unit 46A of the headset terminal 314 and provides content such as text, images, and videos owned by the official account operator to the platform. The summarization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes a large amount of data owned by the group, extracts important information, and summarizes it. The scoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and evaluates the frequency of interaction and common interests between the official account and the user, and assigns a score. The generation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and automatically generates the provided content using generation AI and indexes it under the official account. The information provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides the generated information to the user, making it easily accessible to the user. The satisfaction improvement unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which collects user feedback and uses it to improve the service. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] As shown in Figure 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.

[0168] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0169] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0170] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0172] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0173] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0174] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0175] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0176] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0177] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0179] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0181] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] Each of the multiple elements described above, including the provision unit, summarization unit, scoring unit, generation unit, information provision unit, and satisfaction improvement unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the provision unit is implemented by the control unit 46A of the robot 414 and provides content such as text, images, and videos owned by the official account operator to the platform. The summarization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes a large amount of data owned by the group, extracts important information, and summarizes it. The scoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and evaluates the frequency of interaction and common interests between the official account and the user, and assigns a score. The generation unit is implemented by, for example, the control unit 46A of the robot 414 and automatically generates the provided content using generation AI and indexes it under the official account. The information provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the generated information to the user, making it easily accessible to the user. The satisfaction improvement unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which collects user feedback and uses it to improve the service. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0183] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0184] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0185] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0186] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0187] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0188] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0190] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0191] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0192] 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.

[0193] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0194] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0195] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0196] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0197] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0198] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0199] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0200] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0201] (Note 1) The content provider department, A summarization unit that summarizes the content provided by the aforementioned provisioning unit, A scoring unit that scores relationships based on the information summarized by the summarizing unit, A generation unit that automatically generates and indexes the content provided by the aforementioned provisioning unit, An information providing unit that provides the information generated by the generation unit, The system includes a satisfaction improvement unit that improves user satisfaction based on the information provided by the aforementioned information provision unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Provide content owned by the official account operator. The system described in Appendix 1, characterized by the features described herein. (Note 3) The summary section above is, Summarize the data held by the group. The system described in Appendix 1, characterized by the features described herein. (Note 4) The scoring unit, The relationship between the official account and the user is scored based on the summarized information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is The provided content is automatically generated and indexed under the official account. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned information provision unit, Provides the generated information The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned customer satisfaction improvement unit, Improve user satisfaction based on the information provided. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, It estimates user sentiment and adjusts the timing of content delivery based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned supply unit is, We analyze the past content delivery history of the official account operator and select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned supply unit is, When providing content, filter it based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the content to be delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned supply unit is, When providing content, we prioritize delivering highly relevant content by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, When providing content, we analyze users' social media activity and provide relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The summary section above is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The summary section above is, When generating a summary, adjust the level of detail in the summary based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The summary section above is, When generating summaries, different summarization algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The summary section above is, It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The summary section above is, When generating summaries, the priority of the summaries is determined based on when the data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 19) The summary section above is, When generating summaries, adjust the order of summaries based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The scoring unit, It estimates the user's emotions and adjusts the scoring criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The scoring unit, When scoring, the accuracy of the scoring will be improved by taking into account the interaction between the official account and the user. The system described in Appendix 1, characterized by the features described herein. (Note 22) The scoring unit, When calculating scores, the attribute information of the official account operator is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The scoring unit, It estimates the user's emotions and adjusts the order in which the scoring results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The scoring unit, When calculating scores, the geographical distribution of official accounts is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The scoring unit, When scoring, we refer to relevant literature from the official account to improve the accuracy of the scoring. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is It estimates user sentiment and determines the priority of content to generate based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is When generating content, we refer to past content from the official account to improve the accuracy of the generation process. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is When generating content, the attribute information of the official account operator is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is It estimates the user's emotions and adjusts how content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is When generating content, the geographical distribution of official accounts is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The generating unit is When generating content, we refer to relevant literature from official accounts to improve the accuracy of the generation process. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned information provision unit, It estimates the user's emotions and adjusts the way information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned information provision unit, When providing information, the system selects the most suitable method of delivery by referring to the user's past information acquisition history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned information provision unit, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned information provision unit, When providing information, we will consider the user's geographical location to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned customer satisfaction improvement unit, It estimates user emotions and adjusts methods for improving satisfaction based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned customer satisfaction improvement unit, When improving satisfaction, refer to past user feedback to select the most suitable improvement method. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned customer satisfaction improvement unit, It estimates user emotions and determines priorities for improving satisfaction based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned customer satisfaction improvement unit, When improving user satisfaction, the optimal improvement method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The content provider department, A summarization unit that summarizes the content provided by the aforementioned provisioning unit, A scoring unit that scores relationships based on the information summarized by the summarizing unit, A generation unit that automatically generates and indexes the content provided by the aforementioned provisioning unit, An information providing unit that provides the information generated by the generation unit, The system includes a satisfaction improvement unit that improves user satisfaction based on the information provided by the aforementioned information provision unit. A system characterized by the following features.

2. The aforementioned supply unit is, Provide content owned by the official account operator. The system according to feature 1.

3. The summary section above is, Summarize the data held by the group. The system according to feature 1.

4. The scoring unit, The relationship between the official account and the user is scored based on the summarized information. The system according to feature 1.

5. The generating unit is The provided content is automatically generated and indexed under the official account. The system according to feature 1.

6. The aforementioned information provision unit, Provides the generated information The system according to feature 1.

7. The aforementioned customer satisfaction improvement unit, Improve user satisfaction based on the information provided. The system according to feature 1.

8. The aforementioned supply unit is, It estimates user sentiment and adjusts the timing of content delivery based on the estimated user sentiment. The system according to feature 1.

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