system

Abstract

The system according to this embodiment aims to analyze users' purchase history and behavioral data to recommend optimal products and services, as well as to develop new products and improve existing products based on user feedback. [Solution] The system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, a feedback collection unit, an improvement unit, and a provision unit. The collection unit collects the user's purchase history and behavioral data. The analysis unit analyzes the data collected by the collection unit to identify the user's preferences and behavioral patterns. The recommendation unit recommends products and services based on the preferences and behavioral patterns identified by the analysis unit. The feedback collection unit collects user reviews and feedback. The improvement unit analyzes the reviews and feedback collected by the feedback collection unit to develop new products or improve existing products. The provision unit provides special products and services that are only available in specific communities.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that products and services are not sufficiently recommended by effectively utilizing the purchase history and behavior data of users, and new product development and improvement of existing products are not sufficiently carried out based on user feedback.

[0005] The system according to the embodiment aims to analyze the purchase history and behavior data of users, recommend optimal products and services, and perform new product development and improvement of existing products based on user feedback.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, a feedback collection unit, an improvement unit, and a provision unit. The collection unit collects user purchase history and behavioral data. The analysis unit analyzes the data collected by the collection unit to identify user preferences and behavioral patterns. The recommendation unit recommends products and services based on the preferences and behavioral patterns identified by the analysis unit. The feedback collection unit collects user reviews and feedback. The improvement unit analyzes the reviews and feedback collected by the feedback collection unit to develop new products or improve existing products. The provision unit provides special products and services that are only available within specific communities. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's purchase history and behavioral data to recommend the most suitable products and services, and can also develop new products and improve existing products based on user feedback. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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 ecosystem according to an embodiment of the present invention is an ecosystem that not only uses generative AI to analyze users' purchase history and behavioral data and recommend optimal products and services, but also promotes cooperation among users. This ecosystem can develop new products and improve existing products based on reviews and feedback provided by users, and further provides special products and services that are only available in specific communities. For example, the ecosystem collects users' purchase history and behavioral data. For example, it collects data such as products that users have purchased in the past, products that they have viewed, and advertisements that they have clicked. This data is input into the generative AI. Next, the generative AI analyzes the collected data to identify users' preferences and behavioral patterns. For example, it analyzes product categories that users frequently purchase and their level of interest in specific brands. This makes it possible to recommend optimal products and services to users. Furthermore, the ecosystem promotes cooperation among users. Users can provide reviews and feedback on products they have purchased. These reviews and feedback are analyzed by the generative AI and used for the development of new products and the improvement of existing products. For example, if a user provides feedback such as "This product is difficult to use," the generative AI analyzes that feedback and identifies areas for improvement in the product. Furthermore, the ecosystem provides special products and services that are only available within specific communities. For example, it might offer products limited to a particular region or group, or services only available at special events. This allows users to acquire special products and services in cooperation with other users. As a result, the ecosystem makes it easy for users to find products that are best suited to them and to acquire products that reflect their opinions. Moreover, cooperation among users is promoted through special products and services, revitalizing the entire ecosystem. In this way, the ecosystem can not only analyze users' purchase history and behavioral data to recommend the best products and services, but also promote cooperation among users.

[0029] The ecosystem according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, a feedback collection unit, an improvement unit, and a provision unit. The collection unit collects user purchase history and behavioral data. For example, the collection unit collects data such as products the user has previously purchased, products they have viewed, and advertisements they have clicked. For example, the collection unit can collect data such as the name of the product the user has previously purchased, the date and time of purchase, and the purchase amount. The collection unit can also collect data on the history of products the user has viewed and advertisements they have clicked. The analysis unit analyzes the data collected by the collection unit to identify user preferences and behavioral patterns. For example, based on the collected data, the analysis unit analyzes the product categories that the user frequently purchases and their level of interest in specific brands. For example, the analysis unit can analyze the user's purchase history to identify products and services that the user prefers. The analysis unit can also analyze user behavioral data to identify user behavioral patterns. The recommendation unit recommends the most suitable products and services to the user based on the preferences and behavioral patterns identified by the analysis unit. The recommendation unit, for example, lists and presents the most suitable products and services to the user based on the user's preferences and behavioral patterns. The recommendation unit can recommend related products based on products the user has purchased in the past. It can also recommend products and services that the user is likely to be interested in based on the user's behavioral data. The feedback collection unit collects reviews and feedback provided by users. For example, the feedback collection unit collects reviews and feedback on products that users have purchased. For example, the feedback collection unit can provide a form for users to rate products and collect the reviews and feedback entered by users. It can also provide an interface for users to rate products with stars and collect the rating data entered by users. The improvement unit analyzes the reviews and feedback collected by the feedback collection unit and develops new products or improves existing products. For example, the improvement unit identifies areas for improvement in products based on the collected reviews and feedback and makes improvements.The improvement department can, for example, analyze user feedback and make suggestions to improve the usability and functionality of products. The improvement department can also develop new products based on user feedback. The supply department provides special products and services that are only available within specific communities. For example, the supply department can provide products limited to specific regions or groups, or services only available at special events. For example, the supply department can provide limited-edition products to specific membership communities. Furthermore, the supply department can provide products limited to specific regions or services only available at special events. As a result, the ecosystem according to this embodiment can not only analyze users' purchase history and behavioral data to recommend optimal products and services, but also promote cooperation among users.

[0030] The data collection unit collects user purchase history and behavioral data. For example, it collects data on products users have previously purchased, products they have viewed, and advertisements they have clicked. Specifically, it can collect detailed data such as the name of the product a user has previously purchased, the date and time of purchase, and the purchase amount. This makes it possible to understand users' purchasing trends and consumption patterns in detail. The data collection unit can also collect data on products users have viewed and advertisements they have clicked. This provides basic data for analyzing what products users are interested in and which advertisements are effective. Furthermore, the data collection unit can collect data from users' devices. For example, it can collect users' browsing history and app usage history from devices such as smartphones, tablets, and PCs. This allows for a comprehensive understanding of users' online behavior and enables more accurate data analysis. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and recommendation departments. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the performance of the entire ecosystem.

[0031] The analytics department analyzes data collected by the data collection department to identify user preferences and behavioral patterns. For example, based on the collected data, the analytics department can analyze the product categories that users frequently purchase and their level of interest in specific brands. Specifically, it can analyze users' purchase history to identify products and services that users prefer. For example, it can analyze trends in products that users have purchased in the past to reveal their preferences for specific brands and categories. The analytics department can also analyze user behavior data to identify user behavioral patterns. For example, it can analyze what kinds of products users tend to view at what times of day and which advertisements they respond well to. Furthermore, the analytics department can process data in real time using AI to identify user preferences and behavioral patterns with greater accuracy. The AI ​​uses machine learning algorithms to extract patterns and trends from the collected data and predict user preferences and behavior. For example, based on data on products that users have purchased or viewed in the past, it can predict which products they are most likely to purchase next. As a result, the analytics department can analyze the collected data quickly and accurately, and understand user preferences and behavioral patterns in real time. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term trend analysis and risk assessment. This allows the analysis unit to not only grasp the situation in real time but also to formulate long-term strategies and manage risks, thereby improving the reliability and safety of the entire ecosystem.

[0032] The recommendation unit recommends the most suitable products and services to users based on preferences and behavioral patterns identified by the analysis unit. For example, the recommendation unit lists and presents the most suitable products and services to the user based on their preferences and behavioral patterns. Specifically, it can recommend related products based on products the user has purchased in the past. For example, it can recommend new products in the same category or brand as products the user has previously purchased. The recommendation unit can also recommend products and services that the user is likely to be interested in based on their behavioral data. For example, it can identify and recommend products that the user is likely to be interested in based on data on products the user frequently views and advertisements they click on. Furthermore, the recommendation unit can improve the accuracy of its recommendations using AI. The AI ​​uses machine learning algorithms to learn user preferences and behavioral patterns, enabling more accurate recommendations. For example, it can predict and recommend products that the user is likely to purchase next based on data on products they have previously purchased or viewed. This allows the recommendation department to provide users with the most suitable products and services, thereby improving user satisfaction. Furthermore, the recommendation department can collect user feedback and continuously improve the accuracy and effectiveness of its recommendations. As a result, the recommendation department can always provide highly accurate recommendations based on the latest information, and respond quickly and accurately to user needs.

[0033] The feedback collection unit collects reviews and feedback provided by users. For example, it collects reviews and feedback on products purchased by users. Specifically, it can provide a form for users to rate products and collect the reviews and feedback they enter. For example, it can provide a form where users can fill in details about their experience using the product, their satisfaction level, and areas for improvement. The feedback collection unit can also provide an interface for users to rate products with stars and collect the rating data they enter. For example, it can collect rating data from users who rate products with 1 to 5 stars. Furthermore, the feedback collection unit collects user feedback in real time, making it readily accessible to the analysis and improvement units. This allows the feedback collection unit to efficiently collect user opinions and evaluations and use them to improve the entire ecosystem. In addition, the feedback collection unit can analyze user feedback and identify trends and patterns. For example, if a particular product receives a low rating, it can identify the cause and make specific suggestions for improvement. This allows the feedback collection unit to promote improvements to products and services that reflect user opinions and increase user satisfaction.

[0034] The Improvement Department analyzes reviews and feedback collected by the Feedback Collection Department to develop new products and improve existing ones. For example, based on the collected reviews and feedback, the Improvement Department can identify areas for improvement in products and implement improvements. Specifically, it can analyze user feedback and make suggestions to improve the usability and functionality of products. For example, if a user points out that a particular product is difficult to use, the Improvement Department can propose specific solutions to resolve that problem. The Improvement Department can also develop new products based on user feedback. For example, it can develop new products that reflect the functions and features that users desire, thereby meeting user needs. Furthermore, the Improvement Department can use AI to analyze feedback and make more accurate improvement suggestions. The AI ​​uses natural language processing technology to analyze user reviews and feedback and extract important keywords and trends. This allows the Improvement Department to quickly and accurately grasp user opinions and propose specific improvement measures. In addition, based on the collected feedback, the Improvement Department can formulate long-term strategies and improve the entire ecosystem. This allows the improvement department to promote product and service improvements that reflect user feedback, thereby enhancing the overall reliability and satisfaction of the ecosystem.

[0035] The service provider offers special products and services that are only available within specific communities. For example, they might offer products limited to a particular region or group, or services only available at special events. Specifically, they can offer exclusive products to specific membership communities. For instance, they could set up an online store accessible only to members and sell exclusive products. The service provider can also offer products limited to specific regions or services only available at special events. For example, they could offer regional specialties or exclusive services only available at specific events. Furthermore, the service provider can plan the provision of special products and services based on user feedback. For example, they could identify products and services that users want and provide exclusive products and services accordingly. This allows the service provider to provide special products and services that meet user needs and improve user satisfaction. In addition, through the provision of special products and services, the service provider can promote cooperation among users and revitalize the community. For example, by sharing information and cooperating with each other through exclusive products and services, users can strengthen the overall cohesion of the community. This allows the service provider to not only offer special products and services, but also to promote cooperation among users and revitalize the entire ecosystem.

[0036] The data collection unit can collect data such as products that the user has previously purchased, products that have been viewed, and advertisements that have been clicked. For example, the data collection unit can collect data such as the name of the product the user has previously purchased, the date and time of purchase, and the purchase amount. The data collection unit can also collect data on the history of products the user has viewed and advertisements that have been clicked. This allows for more accurate analysis by collecting data on the user's past behavior. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history and behavioral data into AI and have the AI ​​perform the data collection.

[0037] The analysis unit can analyze collected data to identify user preferences and behavioral patterns. For example, based on the collected data, the analysis unit can analyze the product categories that users frequently purchase and their level of interest in specific brands. For example, the analysis unit can analyze a user's purchase history to identify products and services that users prefer. The analysis unit can also analyze user behavioral data to identify user behavioral patterns. This allows for more appropriate recommendations by identifying user preferences and behavioral patterns. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input collected data into the generative AI and have the generative AI identify user preferences and behavioral patterns. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0038] The recommendation unit can recommend the most suitable products and services to a user based on identified preferences and behavioral patterns. For example, the recommendation unit can list and present the most suitable products and services to the user based on their preferences and behavioral patterns. For example, the recommendation unit can recommend related products based on products the user has purchased in the past. Furthermore, the recommendation unit can recommend products and services that the user is likely to be interested in based on their behavioral data. This improves user satisfaction by recommending the most suitable products and services to the user. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input identified preferences and behavioral patterns into the generative AI and have the generative AI perform the recommendation of the most suitable products and services. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0039] The feedback collection unit can collect reviews and feedback provided by users. For example, the feedback collection unit can collect reviews and feedback on products purchased by users. For example, the feedback collection unit can provide a form for users to rate products and collect the reviews and feedback entered by users. The feedback collection unit can also provide an interface for users to rate products with stars and collect the rating data entered by users. By collecting user reviews and feedback, it is possible to use this information to improve products and develop new products. Some or all of the above-described processes in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input user reviews and feedback into an AI and have the AI ​​perform the data collection.

[0040] The Improvement Department can analyze collected reviews and feedback to develop new products or improve existing ones. For example, the Improvement Department can identify areas for improvement in a product based on collected reviews and feedback and make improvements. For example, the Improvement Department can analyze user feedback and make suggestions to improve the usability and functionality of a product. The Improvement Department can also develop new products based on user feedback. In this way, by improving products and developing new products based on user feedback, it is possible to meet user needs. Some or all of the above processes in the Improvement Department are performed using generative AI. For example, the Improvement Department can input collected reviews and feedback into the generative AI and have the generative AI identify areas for improvement in products or develop new products. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0041] The service provider can offer special products and services that are only available to specific communities. For example, the service provider can offer products limited to specific regions or groups, or services only available at special events. For example, the service provider can offer exclusive products to specific membership communities. The service provider can also offer products limited to specific regions, or services only available at special events. By offering special products and services that are only available to specific communities, the service provider can attract user interest and revitalize the ecosystem. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can have AI select the products and services to offer to specific communities.

[0042] The data collection unit can analyze a user's past purchase history and select the optimal data collection method. For example, if a user frequently shops online, the data collection unit will focus on collecting website browsing history. It can also collect receipt and loyalty card data if the user prefers in-store purchases. Furthermore, if a user prefers a particular brand, the data collection unit can prioritize collecting data related to that brand. This allows for more effective data collection by analyzing the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past purchase history into an AI and have the AI ​​select the optimal data collection method.

[0043] The data collection unit can filter purchase history and behavioral data based on the user's current lifestyle and areas of interest. For example, if a user starts a new hobby, the data collection unit will prioritize collecting data related to that hobby. It can also collect data related to a new address if a user moves. Furthermore, if a user plans to attend a specific event, the data collection unit can collect data related to that event. This allows for the collection of more relevant data by filtering it based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the user's lifestyle and areas of interest into an AI and have the AI ​​perform the filtering.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting purchase history and behavioral data. For example, if the user is in a specific region, the data collection unit can collect data related to products and services in that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of data related to their travel destination. Additionally, if the user is participating in a specific event, the data collection unit can collect data related to that event. This allows for the collection of more relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's geographical location information into the AI ​​and have the AI ​​perform the collection of highly relevant data.

[0045] The data collection unit can analyze a user's social media activity and collect relevant data when collecting purchase history and behavioral data. For example, the data collection unit can collect data related to products and services that a user has shared on social media. It can also collect data related to brands and influencers that a user follows. Furthermore, the data collection unit can collect data related to online communities that a user participates in. This allows for the collection of more relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on a user's social media activity into an AI and have the AI ​​collect relevant data.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on important data. It can also perform a concise analysis on general data. Furthermore, the analysis unit can perform a particularly detailed analysis on data of high user interest. This allows for more effective analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI adjust the level of detail of the analysis. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0047] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history data. It can also apply a behavior pattern analysis algorithm to behavior data. Furthermore, it can apply a social network analysis algorithm to social media data. This allows for more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI apply the appropriate analysis algorithm. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0048] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit can prioritize the analysis of the most recent data. It can also analyze the latest data while referring to past data. Furthermore, the analysis unit can focus its analysis on data collected during a specific period. This allows for more effective analysis by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input the data collection period into the generative AI and have the generative AI determine the priority of analysis. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This allows for more effective analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit can input the relevance of the data into the generative AI and have the generative AI adjust the order of analysis. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0050] The recommendation unit can adjust the level of detail in recommendations based on the importance of the products. For example, the recommendation unit can provide detailed recommendations for important products, and concise recommendations for general products. Furthermore, the recommendation unit can provide particularly detailed recommendations for products of high user interest. By adjusting the level of detail in recommendations based on the importance of the products, more effective recommendations become possible. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input the importance of the products into the generative AI and have the generative AI perform the adjustment of the level of detail in the recommendations. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0051] The recommendation unit can apply different recommendation algorithms depending on the product category when making recommendations. For example, for electronic devices, the recommendation unit can provide recommendations that emphasize technical features. For fashion items, the recommendation unit can provide recommendations that emphasize trends. Furthermore, for food products, the recommendation unit can provide recommendations that emphasize health aspects. By applying different recommendation algorithms depending on the product category, more accurate recommendations become possible. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input the product category into the generative AI and have the generative AI execute the application of an appropriate recommendation algorithm. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0052] The recommendation unit can determine the priority of recommendations based on the timing of product submission. For example, the recommendation unit can prioritize recommending new products. It can also recommend seasonal products in a timely manner. Furthermore, the recommendation unit can prioritize recommending products that are of high interest to the user. By determining the priority of recommendations based on the timing of product submission, more effective recommendations become possible. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input the timing of product submission into the generative AI and have the generative AI determine the priority of recommendations. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0053] The recommendation unit can adjust the order of recommendations based on the relevance of the products. For example, the recommendation unit can prioritize recommending highly relevant products. It can also postpone recommending less relevant products. Furthermore, the recommendation unit can dynamically adjust the order of recommendations based on the relevance of the products. This allows for more effective recommendations by adjusting the order of recommendations based on product relevance. Some or all of the above processing in the recommendation unit is performed using a generative AI. For example, the recommendation unit can input the relevance of products into the generative AI and have the generative AI perform the adjustment of the recommendation order. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0054] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, if the user has provided detailed feedback in the past, the feedback collection unit will request similar detailed feedback. Alternatively, if the user has provided concise feedback in the past, the feedback collection unit can provide a concise feedback form. Furthermore, the feedback collection unit can focus on collecting specific questions from the user's past feedback history. This allows for more effective feedback collection by referring to the user's past feedback history. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's past feedback history into AI and have the AI ​​select the optimal collection method.

[0055] The feedback collection unit can filter feedback based on the user's current lifestyle and areas of interest. For example, if a user starts a new hobby, the feedback collection unit can request feedback related to that hobby. Similarly, if a user moves, the feedback collection unit can request feedback related to their new address. Furthermore, if a user plans to attend a specific event, the feedback collection unit can request feedback related to that event. This allows for the collection of more relevant feedback by filtering it based on the user's current lifestyle and areas of interest. Some or all of the above processing in the feedback collection unit may be performed using AI, or not. For example, the feedback collection unit can input data on the user's lifestyle and areas of interest into an AI and have the AI ​​perform the filtering.

[0056] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, if the user is in a specific region, the feedback collection unit can request feedback related to that region. Furthermore, if the user is traveling, the feedback collection unit can request feedback related to their travel destination. Additionally, if the user is participating in a specific event, the feedback collection unit can request feedback related to that event. This allows for the collection of more relevant feedback by considering the user's geographical location information. Some or all of the above processing in the feedback collection unit may be performed using AI, or not. For example, the feedback collection unit can input the user's geographical location information into the AI ​​and have the AI ​​select the optimal collection method.

[0057] The improvement unit can analyze past user feedback to select the optimal improvement method during the improvement process. For example, the improvement unit can make detailed improvement suggestions based on feedback previously provided by the user. Furthermore, the improvement unit can focus on suggesting specific areas for improvement based on past user feedback. In addition, the improvement unit can analyze the user's feedback history to select the most effective improvement method. This allows for more effective improvement suggestions by analyzing past user feedback. Some or all of the above processes in the improvement unit are performed using generative AI. For example, the improvement unit can input past user feedback into the generative AI and have the generative AI select the optimal improvement method. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0058] The improvement unit can customize the means of improvement based on the user's current living situation when making improvements. For example, if the user starts a new hobby, the improvement unit can make improvement suggestions related to that hobby. Also, if the user moves, the improvement unit can make improvement suggestions related to the new address. Furthermore, if the user plans to attend a specific event, the improvement unit can make improvement suggestions related to that event. By customizing the means of improvement based on the user's current living situation, more relevant improvement suggestions become possible. Some or all of the above processing in the improvement unit is performed using a generative AI. For example, the improvement unit can input data on the user's living situation into the generative AI and have the generative AI perform the customization of the means of improvement. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0059] The improvement unit can select the optimal improvement method by considering the user's geographical location information during the improvement process. For example, if the user is in a specific region, the improvement unit can make improvement suggestions related to that region. Furthermore, if the user is traveling, the improvement unit can make improvement suggestions related to the travel destination. In addition, if the user is participating in a specific event, the improvement unit can make improvement suggestions related to that event. This allows for more relevant improvement suggestions by considering the user's geographical location information. Some or all of the above processing in the improvement unit is performed using a generative AI. For example, the improvement unit can input the user's geographical location information into the generative AI and have the generative AI select the optimal improvement method. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0060] The improvement unit can analyze users' social media activity and propose improvement measures during the improvement process. For example, the improvement unit can make improvement suggestions related to products or services that users have shared on social media. It can also make improvement suggestions related to brands and influencers that users follow. Furthermore, it can make improvement suggestions related to online communities that users participate in. This allows for more relevant improvement suggestions by analyzing users' social media activity. Some or all of the above processing in the improvement unit is performed using generative AI. For example, the improvement unit can input data on users' social media activity into the generative AI and have the generative AI execute suggestions for improvement measures. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0061] The service provider can select the optimal service delivery method by referring to the user's past purchase history at the time of delivery. For example, the service provider can offer special products or services related to products the user has previously purchased. The service provider can also offer special products or services related to a specific brand based on the user's past purchase history. Furthermore, the service provider can analyze the user's past purchase history and offer the products or services that are of the user's greatest interest. This allows for more effective service delivery by referring to the user's past purchase history. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the user's past purchase history into AI and have the AI ​​select the optimal service delivery method.

[0062] The service provider can offer special products and services based on the user's current lifestyle and areas of interest at the time of delivery. For example, if the user starts a new hobby, the service provider can offer special products and services related to that hobby. Also, if the user moves, the service provider can offer special products and services related to the new address. Furthermore, if the user plans to attend a specific event, the service provider can offer special products and services related to that event. This allows for more relevant offerings by providing special products and services based on the user's current lifestyle and areas of interest. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input data on the user's lifestyle and areas of interest into an AI and have the AI ​​perform the provision of special products and services.

[0063] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can provide special products or services related to that region. If the user is traveling, the service provider can provide special products or services related to the travel destination. Furthermore, if the user is participating in a specific event, the service provider can provide special products or services related to that event. This allows for more relevant deliveries by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location information into AI and have the AI ​​select the optimal delivery method.

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

[0065] The data collection unit can analyze a user's past purchase history and select the optimal data collection method. For example, if a user frequently shops online, the unit can focus on collecting their website browsing history. If a user prefers in-store purchases, it can also collect receipt and loyalty card data. Furthermore, if a user prefers a particular brand, the unit can prioritize collecting data related to that brand. This allows for more effective data collection by analyzing the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past purchase history into an AI and have the AI ​​select the optimal data collection method.

[0066] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on important data, and a concise analysis on general data. Furthermore, it can perform a particularly detailed analysis on data of high user interest. By adjusting the level of detail of the analysis based on the importance of the data, more effective analysis becomes possible. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI adjust the level of detail of the analysis. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0067] The recommendation unit can apply different recommendation algorithms depending on the product category during the recommendation process. For example, it can provide recommendations that emphasize technical features for electronic devices, recommendations that emphasize trends for fashion items, and recommendations that emphasize health aspects for food products. By applying different recommendation algorithms depending on the product category, more accurate recommendations become possible. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input the product category into the generative AI and have the generative AI apply the appropriate recommendation algorithm. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0068] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, if the user has provided detailed feedback in the past, it will request similar detailed feedback. If the user has provided concise feedback in the past, it can provide a concise feedback form. Furthermore, it can focus on collecting specific questions based on the user's past feedback history. This makes it possible to collect feedback more effectively by referring to the user's past feedback history. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's past feedback history into AI and have the AI ​​select the optimal collection method.

[0069] The service delivery unit can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, it can provide special products or services related to that region. If the user is traveling, it can provide special products or services related to the travel destination. Furthermore, if the user is participating in a specific event, it can provide special products or services related to that event. This allows for more relevant deliveries by considering the user's geographical location information. Some or all of the above processing in the service delivery unit may be performed using AI or not. For example, the service delivery unit can input the user's geographical location information into the AI ​​and have the AI ​​select the optimal delivery method.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The data collection unit collects user purchase history and behavioral data. For example, it collects data such as the names of products the user has purchased in the past, the date and time of purchase, the purchase amount, products viewed, and advertisements clicked. Step 2: The analysis unit analyzes the data collected by the data collection unit to identify user preferences and behavioral patterns. For example, it analyzes the product categories that users frequently purchase and their level of interest in specific brands to identify products and services that users prefer. Step 3: The recommendation unit recommends the most suitable products and services to the user based on the preferences and behavioral patterns identified by the analysis unit. For example, it lists relevant products based on the user's preferences and behavioral patterns and presents them to the user. Step 4: The feedback collection unit collects reviews and feedback provided by users. For example, it collects reviews and feedback on products purchased by users and provides forms and star rating interfaces for them to rate the products. Step 5: The Improvement Department analyzes the reviews and feedback collected by the Feedback Collection Department to develop new products or improve existing ones. For example, based on the collected reviews and feedback, they identify areas for improvement in products and make those improvements. Step 6: The provider offers exclusive products or services that are only available within a specific community. For example, products limited to a particular region or group, or services only available at special events.

[0072] (Example of form 2) The ecosystem according to an embodiment of the present invention is an ecosystem that not only uses generative AI to analyze users' purchase history and behavioral data and recommend optimal products and services, but also promotes cooperation among users. This ecosystem can develop new products and improve existing products based on reviews and feedback provided by users, and further provides special products and services that are only available in specific communities. For example, the ecosystem collects users' purchase history and behavioral data. For example, it collects data such as products that users have purchased in the past, products that they have viewed, and advertisements that they have clicked. This data is input into the generative AI. Next, the generative AI analyzes the collected data to identify users' preferences and behavioral patterns. For example, it analyzes product categories that users frequently purchase and their level of interest in specific brands. This makes it possible to recommend optimal products and services to users. Furthermore, the ecosystem promotes cooperation among users. Users can provide reviews and feedback on products they have purchased. These reviews and feedback are analyzed by the generative AI and used for the development of new products and the improvement of existing products. For example, if a user provides feedback such as "This product is difficult to use," the generative AI analyzes that feedback and identifies areas for improvement in the product. Furthermore, the ecosystem provides special products and services that are only available within specific communities. For example, it might offer products limited to a particular region or group, or services only available at special events. This allows users to acquire special products and services in cooperation with other users. As a result, the ecosystem makes it easy for users to find products that are best suited to them and to acquire products that reflect their opinions. Moreover, cooperation among users is promoted through special products and services, revitalizing the entire ecosystem. In this way, the ecosystem can not only analyze users' purchase history and behavioral data to recommend the best products and services, but also promote cooperation among users.

[0073] The ecosystem according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, a feedback collection unit, an improvement unit, and a provision unit. The collection unit collects user purchase history and behavioral data. For example, the collection unit collects data such as products the user has previously purchased, products they have viewed, and advertisements they have clicked. For example, the collection unit can collect data such as the name of the product the user has previously purchased, the date and time of purchase, and the purchase amount. The collection unit can also collect data on the history of products the user has viewed and advertisements they have clicked. The analysis unit analyzes the data collected by the collection unit to identify user preferences and behavioral patterns. For example, based on the collected data, the analysis unit analyzes the product categories that the user frequently purchases and their level of interest in specific brands. For example, the analysis unit can analyze the user's purchase history to identify products and services that the user prefers. The analysis unit can also analyze user behavioral data to identify user behavioral patterns. The recommendation unit recommends the most suitable products and services to the user based on the preferences and behavioral patterns identified by the analysis unit. The recommendation unit, for example, lists and presents the most suitable products and services to the user based on the user's preferences and behavioral patterns. The recommendation unit can recommend related products based on products the user has purchased in the past. It can also recommend products and services that the user is likely to be interested in based on the user's behavioral data. The feedback collection unit collects reviews and feedback provided by users. For example, the feedback collection unit collects reviews and feedback on products that users have purchased. For example, the feedback collection unit can provide a form for users to rate products and collect the reviews and feedback entered by users. It can also provide an interface for users to rate products with stars and collect the rating data entered by users. The improvement unit analyzes the reviews and feedback collected by the feedback collection unit and develops new products or improves existing products. For example, the improvement unit identifies areas for improvement in products based on the collected reviews and feedback and makes improvements.The improvement department can, for example, analyze user feedback and make suggestions to improve the usability and functionality of products. The improvement department can also develop new products based on user feedback. The supply department provides special products and services that are only available within specific communities. For example, the supply department can provide products limited to specific regions or groups, or services only available at special events. For example, the supply department can provide limited-edition products to specific membership communities. Furthermore, the supply department can provide products limited to specific regions or services only available at special events. As a result, the ecosystem according to this embodiment can not only analyze users' purchase history and behavioral data to recommend optimal products and services, but also promote cooperation among users.

[0074] The data collection unit collects user purchase history and behavioral data. For example, it collects data on products users have previously purchased, products they have viewed, and advertisements they have clicked. Specifically, it can collect detailed data such as the name of the product a user has previously purchased, the date and time of purchase, and the purchase amount. This makes it possible to understand users' purchasing trends and consumption patterns in detail. The data collection unit can also collect data on products users have viewed and advertisements they have clicked. This provides basic data for analyzing what products users are interested in and which advertisements are effective. Furthermore, the data collection unit can collect data from users' devices. For example, it can collect users' browsing history and app usage history from devices such as smartphones, tablets, and PCs. This allows for a comprehensive understanding of users' online behavior and enables more accurate data analysis. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and recommendation departments. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the performance of the entire ecosystem.

[0075] The analytics department analyzes data collected by the data collection department to identify user preferences and behavioral patterns. For example, based on the collected data, the analytics department can analyze the product categories that users frequently purchase and their level of interest in specific brands. Specifically, it can analyze users' purchase history to identify products and services that users prefer. For example, it can analyze trends in products that users have purchased in the past to reveal their preferences for specific brands and categories. The analytics department can also analyze user behavior data to identify user behavioral patterns. For example, it can analyze what kinds of products users tend to view at what times of day and which advertisements they respond well to. Furthermore, the analytics department can process data in real time using AI to identify user preferences and behavioral patterns with greater accuracy. The AI ​​uses machine learning algorithms to extract patterns and trends from the collected data and predict user preferences and behavior. For example, based on data on products that users have purchased or viewed in the past, it can predict which products they are most likely to purchase next. As a result, the analytics department can analyze the collected data quickly and accurately, and understand user preferences and behavioral patterns in real time. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term trend analysis and risk assessment. This allows the analysis unit to not only grasp the situation in real time but also to formulate long-term strategies and manage risks, thereby improving the reliability and safety of the entire ecosystem.

[0076] The recommendation unit recommends the most suitable products and services to users based on preferences and behavioral patterns identified by the analysis unit. For example, the recommendation unit lists and presents the most suitable products and services to the user based on their preferences and behavioral patterns. Specifically, it can recommend related products based on products the user has purchased in the past. For example, it can recommend new products in the same category or brand as products the user has previously purchased. The recommendation unit can also recommend products and services that the user is likely to be interested in based on their behavioral data. For example, it can identify and recommend products that the user is likely to be interested in based on data on products the user frequently views and advertisements they click on. Furthermore, the recommendation unit can improve the accuracy of its recommendations using AI. The AI ​​uses machine learning algorithms to learn user preferences and behavioral patterns, enabling more accurate recommendations. For example, it can predict and recommend products that the user is likely to purchase next based on data on products they have previously purchased or viewed. This allows the recommendation department to provide users with the most suitable products and services, thereby improving user satisfaction. Furthermore, the recommendation department can collect user feedback and continuously improve the accuracy and effectiveness of its recommendations. As a result, the recommendation department can always provide highly accurate recommendations based on the latest information, and respond quickly and accurately to user needs.

[0077] The feedback collection unit collects reviews and feedback provided by users. For example, it collects reviews and feedback on products purchased by users. Specifically, it can provide a form for users to rate products and collect the reviews and feedback they enter. For example, it can provide a form where users can fill in details about their experience using the product, their satisfaction level, and areas for improvement. The feedback collection unit can also provide an interface for users to rate products with stars and collect the rating data they enter. For example, it can collect rating data from users who rate products with 1 to 5 stars. Furthermore, the feedback collection unit collects user feedback in real time, making it readily accessible to the analysis and improvement units. This allows the feedback collection unit to efficiently collect user opinions and evaluations and use them to improve the entire ecosystem. In addition, the feedback collection unit can analyze user feedback and identify trends and patterns. For example, if a particular product receives a low rating, it can identify the cause and make specific suggestions for improvement. This allows the feedback collection unit to promote improvements to products and services that reflect user opinions and increase user satisfaction.

[0078] The Improvement Department analyzes reviews and feedback collected by the Feedback Collection Department to develop new products and improve existing ones. For example, based on the collected reviews and feedback, the Improvement Department can identify areas for improvement in products and implement improvements. Specifically, it can analyze user feedback and make suggestions to improve the usability and functionality of products. For example, if a user points out that a particular product is difficult to use, the Improvement Department can propose specific solutions to resolve that problem. The Improvement Department can also develop new products based on user feedback. For example, it can develop new products that reflect the functions and features that users desire, thereby meeting user needs. Furthermore, the Improvement Department can use AI to analyze feedback and make more accurate improvement suggestions. The AI ​​uses natural language processing technology to analyze user reviews and feedback and extract important keywords and trends. This allows the Improvement Department to quickly and accurately grasp user opinions and propose specific improvement measures. In addition, based on the collected feedback, the Improvement Department can formulate long-term strategies and improve the entire ecosystem. This allows the improvement department to promote product and service improvements that reflect user feedback, thereby enhancing the overall reliability and satisfaction of the ecosystem.

[0079] The service provider offers special products and services that are only available within specific communities. For example, they might offer products limited to a particular region or group, or services only available at special events. Specifically, they can offer exclusive products to specific membership communities. For instance, they could set up an online store accessible only to members and sell exclusive products. The service provider can also offer products limited to specific regions or services only available at special events. For example, they could offer regional specialties or exclusive services only available at specific events. Furthermore, the service provider can plan the provision of special products and services based on user feedback. For example, they could identify products and services that users want and provide exclusive products and services accordingly. This allows the service provider to provide special products and services that meet user needs and improve user satisfaction. In addition, through the provision of special products and services, the service provider can promote cooperation among users and revitalize the community. For example, by sharing information and cooperating with each other through exclusive products and services, users can strengthen the overall cohesion of the community. This allows the service provider to not only offer special products and services, but also to promote cooperation among users and revitalize the entire ecosystem.

[0080] The data collection unit can collect data such as products that the user has previously purchased, products that have been viewed, and advertisements that have been clicked. For example, the data collection unit can collect data such as the name of the product the user has previously purchased, the date and time of purchase, and the purchase amount. The data collection unit can also collect data on the history of products the user has viewed and advertisements that have been clicked. This allows for more accurate analysis by collecting data on the user's past behavior. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history and behavioral data into AI and have the AI ​​perform the data collection.

[0081] The analysis unit can analyze collected data to identify user preferences and behavioral patterns. For example, based on the collected data, the analysis unit can analyze the product categories that users frequently purchase and their level of interest in specific brands. For example, the analysis unit can analyze a user's purchase history to identify products and services that users prefer. The analysis unit can also analyze user behavioral data to identify user behavioral patterns. This allows for more appropriate recommendations by identifying user preferences and behavioral patterns. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input collected data into the generative AI and have the generative AI identify user preferences and behavioral patterns. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The recommendation unit can recommend the most suitable products and services to a user based on identified preferences and behavioral patterns. For example, the recommendation unit can list and present the most suitable products and services to the user based on their preferences and behavioral patterns. For example, the recommendation unit can recommend related products based on products the user has purchased in the past. Furthermore, the recommendation unit can recommend products and services that the user is likely to be interested in based on their behavioral data. This improves user satisfaction by recommending the most suitable products and services to the user. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input identified preferences and behavioral patterns into the generative AI and have the generative AI perform the recommendation of the most suitable products and services. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The feedback collection unit can collect reviews and feedback provided by users. For example, the feedback collection unit can collect reviews and feedback on products purchased by users. For example, the feedback collection unit can provide a form for users to rate products and collect the reviews and feedback entered by users. The feedback collection unit can also provide an interface for users to rate products with stars and collect the rating data entered by users. By collecting user reviews and feedback, it is possible to use this information to improve products and develop new products. Some or all of the above-described processes in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input user reviews and feedback into an AI and have the AI ​​perform the data collection.

[0084] The Improvement Department can analyze collected reviews and feedback to develop new products or improve existing ones. For example, the Improvement Department can identify areas for improvement in a product based on collected reviews and feedback and make improvements. For example, the Improvement Department can analyze user feedback and make suggestions to improve the usability and functionality of a product. The Improvement Department can also develop new products based on user feedback. In this way, by improving products and developing new products based on user feedback, it is possible to meet user needs. Some or all of the above processes in the Improvement Department are performed using generative AI. For example, the Improvement Department can input collected reviews and feedback into the generative AI and have the generative AI identify areas for improvement in products or develop new products. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] The service provider can offer special products and services that are only available to specific communities. For example, the service provider can offer products limited to specific regions or groups, or services only available at special events. For example, the service provider can offer exclusive products to specific membership communities. The service provider can also offer products limited to specific regions, or services only available at special events. By offering special products and services that are only available to specific communities, the service provider can attract user interest and revitalize the ecosystem. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can have AI select the products and services to offer to specific communities.

[0086] The data collection unit can estimate the user's emotions and adjust the timing of data collection for purchase history and behavioral data based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect data when the user is relaxed. Alternatively, if the user is excited, the data collection unit can collect data immediately and perform real-time analysis. Furthermore, if the user is tired, the data collection unit can adjust the collection timing to collect data after the user has rested. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing based on the emotions.

[0087] The data collection unit can analyze a user's past purchase history and select the optimal data collection method. For example, if a user frequently shops online, the data collection unit will focus on collecting website browsing history. It can also collect receipt and loyalty card data if the user prefers in-store purchases. Furthermore, if a user prefers a particular brand, the data collection unit can prioritize collecting data related to that brand. This allows for more effective data collection by analyzing the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past purchase history into an AI and have the AI ​​select the optimal data collection method.

[0088] The data collection unit can filter purchase history and behavioral data based on the user's current lifestyle and areas of interest. For example, if a user starts a new hobby, the data collection unit will prioritize collecting data related to that hobby. It can also collect data related to a new address if a user moves. Furthermore, if a user plans to attend a specific event, the data collection unit can collect data related to that event. This allows for the collection of more relevant data by filtering it based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the user's lifestyle and areas of interest into an AI and have the AI ​​perform the filtering.

[0089] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting real-time behavioral data. If the user is relaxed, the data collection unit may also focus on collecting past purchase history. Furthermore, if the user is stressed, the data collection unit may prioritize collecting data related to stress reduction. This allows for more effective data collection by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data based on emotions.

[0090] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting purchase history and behavioral data. For example, if the user is in a specific region, the data collection unit can collect data related to products and services in that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of data related to their travel destination. Additionally, if the user is participating in a specific event, the data collection unit can collect data related to that event. This allows for the collection of more relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's geographical location information into the AI ​​and have the AI ​​perform the collection of highly relevant data.

[0091] The data collection unit can analyze a user's social media activity and collect relevant data when collecting purchase history and behavioral data. For example, the data collection unit can collect data related to products and services that a user has shared on social media. It can also collect data related to brands and influencers that a user follows. Furthermore, the data collection unit can collect data related to online communities that a user participates in. This allows for the collection of more relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on a user's social media activity into an AI and have the AI ​​collect relevant data.

[0092] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis based on emotions.

[0093] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on important data. It can also perform a concise analysis on general data. Furthermore, the analysis unit can perform a particularly detailed analysis on data of high user interest. This allows for more effective analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI adjust the level of detail of the analysis. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history data. It can also apply a behavior pattern analysis algorithm to behavior data. Furthermore, it can apply a social network analysis algorithm to social media data. This allows for more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI apply the appropriate analysis algorithm. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis based on the emotions.

[0096] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit can prioritize the analysis of the most recent data. It can also analyze the latest data while referring to past data. Furthermore, the analysis unit can focus its analysis on data collected during a specific period. This allows for more effective analysis by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input the data collection period into the generative AI and have the generative AI determine the priority of analysis. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This allows for more effective analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit can input the relevance of the data into the generative AI and have the generative AI adjust the order of analysis. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, the recommendation unit can provide detailed recommendations. If the user is in a hurry, it can provide concise recommendations. Furthermore, if the user is excited, it can provide visually appealing recommendations. By adjusting the way recommendations are presented based on the user's emotions, more appropriate recommendations become possible. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input user emotion data into the generative AI and have the generative AI adjust the way recommendations are presented based on those emotions.

[0099] The recommendation unit can adjust the level of detail in recommendations based on the importance of the products. For example, the recommendation unit can provide detailed recommendations for important products, and concise recommendations for general products. Furthermore, the recommendation unit can provide particularly detailed recommendations for products of high user interest. By adjusting the level of detail in recommendations based on the importance of the products, more effective recommendations become possible. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input the importance of the products into the generative AI and have the generative AI perform the adjustment of the level of detail in the recommendations. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The recommendation unit can apply different recommendation algorithms depending on the product category when making recommendations. For example, for electronic devices, the recommendation unit can provide recommendations that emphasize technical features. For fashion items, the recommendation unit can provide recommendations that emphasize trends. Furthermore, for food products, the recommendation unit can provide recommendations that emphasize health aspects. By applying different recommendation algorithms depending on the product category, more accurate recommendations become possible. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input the product category into the generative AI and have the generative AI execute the application of an appropriate recommendation algorithm. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The recommendation unit can estimate the user's emotions and adjust the length of recommendations based on those emotions. For example, if the user is in a hurry, the recommendation unit can provide short, concise recommendations. If the user is relaxed, it can provide detailed recommendations. Furthermore, if the user is excited, it can provide visually appealing recommendations. By adjusting the length of recommendations based on the user's emotions, more appropriate recommendations can be made. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input user emotion data into the generative AI and have the generative AI adjust the length of recommendations based on those emotions.

[0102] The recommendation unit can determine the priority of recommendations based on the timing of product submission. For example, the recommendation unit can prioritize recommending new products. It can also recommend seasonal products in a timely manner. Furthermore, the recommendation unit can prioritize recommending products that are of high interest to the user. By determining the priority of recommendations based on the timing of product submission, more effective recommendations become possible. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input the timing of product submission into the generative AI and have the generative AI determine the priority of recommendations. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The recommendation unit can adjust the order of recommendations based on the relevance of the products. For example, the recommendation unit can prioritize recommending highly relevant products. It can also postpone recommending less relevant products. Furthermore, the recommendation unit can dynamically adjust the order of recommendations based on the relevance of the products. This allows for more effective recommendations by adjusting the order of recommendations based on product relevance. Some or all of the above processing in the recommendation unit is performed using a generative AI. For example, the recommendation unit can input the relevance of products into the generative AI and have the generative AI perform the adjustment of the recommendation order. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, the feedback collection unit may request detailed feedback. If the user is in a hurry, the feedback collection unit may request concise feedback. Furthermore, if the user is excited, the feedback collection unit may provide a visually appealing feedback form. This allows for more appropriate feedback to be obtained by adjusting the feedback collection method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input user emotion data into a generative AI and have the generative AI adjust the feedback collection method based on emotions.

[0105] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, if the user has provided detailed feedback in the past, the feedback collection unit will request similar detailed feedback. Alternatively, if the user has provided concise feedback in the past, the feedback collection unit can provide a concise feedback form. Furthermore, the feedback collection unit can focus on collecting specific questions from the user's past feedback history. This allows for more effective feedback collection by referring to the user's past feedback history. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's past feedback history into AI and have the AI ​​select the optimal collection method.

[0106] The feedback collection unit can filter feedback based on the user's current lifestyle and areas of interest. For example, if a user starts a new hobby, the feedback collection unit can request feedback related to that hobby. Similarly, if a user moves, the feedback collection unit can request feedback related to their new address. Furthermore, if a user plans to attend a specific event, the feedback collection unit can request feedback related to that event. This allows for the collection of more relevant feedback by filtering it based on the user's current lifestyle and areas of interest. Some or all of the above processing in the feedback collection unit may be performed using AI, or not. For example, the feedback collection unit can input data on the user's lifestyle and areas of interest into an AI and have the AI ​​perform the filtering.

[0107] The feedback collection unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is excited, the feedback collection unit can immediately collect feedback. If the user is relaxed, the feedback collection unit can request more detailed feedback. Furthermore, if the user is stressed, the feedback collection unit can postpone collecting feedback. This allows for more effective feedback collection by prioritizing feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of feedback based on emotions.

[0108] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, if the user is in a specific region, the feedback collection unit can request feedback related to that region. Furthermore, if the user is traveling, the feedback collection unit can request feedback related to their travel destination. Additionally, if the user is participating in a specific event, the feedback collection unit can request feedback related to that event. This allows for the collection of more relevant feedback by considering the user's geographical location information. Some or all of the above processing in the feedback collection unit may be performed using AI, or not. For example, the feedback collection unit can input the user's geographical location information into the AI ​​and have the AI ​​select the optimal collection method.

[0109] The improvement unit can estimate the user's emotions and adjust the improvement method based on the estimated emotions. For example, if the user is relaxed, the improvement unit can provide detailed improvement suggestions. If the user is in a hurry, it can provide concise improvement suggestions. Furthermore, if the user is excited, the improvement unit can provide visually appealing improvement suggestions. By adjusting the improvement method based on the user's emotions, more appropriate improvement suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the improvement unit is performed using generative AI. For example, the improvement unit can input user emotion data into the generative AI and have the generative AI adjust the improvement method based on the emotion.

[0110] The improvement unit can analyze past user feedback to select the optimal improvement method during the improvement process. For example, the improvement unit can make detailed improvement suggestions based on feedback previously provided by the user. Furthermore, the improvement unit can focus on suggesting specific areas for improvement based on past user feedback. In addition, the improvement unit can analyze the user's feedback history to select the most effective improvement method. This allows for more effective improvement suggestions by analyzing past user feedback. Some or all of the above processes in the improvement unit are performed using generative AI. For example, the improvement unit can input past user feedback into the generative AI and have the generative AI select the optimal improvement method. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The improvement unit can customize the means of improvement based on the user's current living situation when making improvements. For example, if the user starts a new hobby, the improvement unit can make improvement suggestions related to that hobby. Also, if the user moves, the improvement unit can make improvement suggestions related to the new address. Furthermore, if the user plans to attend a specific event, the improvement unit can make improvement suggestions related to that event. By customizing the means of improvement based on the user's current living situation, more relevant improvement suggestions become possible. Some or all of the above processing in the improvement unit is performed using a generative AI. For example, the improvement unit can input data on the user's living situation into the generative AI and have the generative AI perform the customization of the means of improvement. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0112] The improvement unit can estimate the user's emotions and determine improvement priorities based on those emotions. For example, if the user is excited, the improvement unit can immediately suggest improvements. If the user is relaxed, the improvement unit can provide more detailed improvement suggestions. Furthermore, if the user is stressed, the improvement unit can postpone suggesting improvements. This allows for more effective improvement suggestions by determining improvement priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the improvement unit is performed using generative AI. For example, the improvement unit can input user emotion data into the generative AI and have the generative AI determine improvement priorities based on emotions.

[0113] The improvement unit can select the optimal improvement method by considering the user's geographical location information during the improvement process. For example, if the user is in a specific region, the improvement unit can make improvement suggestions related to that region. Furthermore, if the user is traveling, the improvement unit can make improvement suggestions related to the travel destination. In addition, if the user is participating in a specific event, the improvement unit can make improvement suggestions related to that event. This allows for more relevant improvement suggestions by considering the user's geographical location information. Some or all of the above processing in the improvement unit is performed using a generative AI. For example, the improvement unit can input the user's geographical location information into the generative AI and have the generative AI select the optimal improvement method. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0114] The improvement unit can analyze users' social media activity and propose improvement measures during the improvement process. For example, the improvement unit can make improvement suggestions related to products or services that users have shared on social media. It can also make improvement suggestions related to brands and influencers that users follow. Furthermore, it can make improvement suggestions related to online communities that users participate in. This allows for more relevant improvement suggestions by analyzing users' social media activity. Some or all of the above processing in the improvement unit is performed using generative AI. For example, the improvement unit can input data on users' social media activity into the generative AI and have the generative AI execute suggestions for improvement measures. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The service provider can estimate the user's emotions and adjust the delivery method of special products or services based on the estimated emotions. For example, if the user is relaxed, the service provider may provide a delivery method that includes detailed explanations. If the user is in a hurry, the service provider may provide a delivery method that includes concise explanations. Furthermore, if the user is excited, the service provider may provide a visually appealing delivery method. This allows for more appropriate delivery by adjusting the delivery method of special products or services based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the delivery method.

[0116] The service provider can select the optimal service delivery method by referring to the user's past purchase history at the time of delivery. For example, the service provider can offer special products or services related to products the user has previously purchased. The service provider can also offer special products or services related to a specific brand based on the user's past purchase history. Furthermore, the service provider can analyze the user's past purchase history and offer the products or services that are of the user's greatest interest. This allows for more effective service delivery by referring to the user's past purchase history. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the user's past purchase history into AI and have the AI ​​select the optimal service delivery method.

[0117] The service provider can offer special products and services based on the user's current lifestyle and areas of interest at the time of delivery. For example, if the user starts a new hobby, the service provider can offer special products and services related to that hobby. Also, if the user moves, the service provider can offer special products and services related to the new address. Furthermore, if the user plans to attend a specific event, the service provider can offer special products and services related to that event. This allows for more relevant offerings by providing special products and services based on the user's current lifestyle and areas of interest. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input data on the user's lifestyle and areas of interest into an AI and have the AI ​​perform the provision of special products and services.

[0118] The service provider can estimate the user's emotions and prioritize special products and services based on those emotions. For example, if the user is excited, the service provider can immediately offer special products or services. If the user is relaxed, the service provider can offer special products or services with detailed explanations. Furthermore, if the user is stressed, the service provider can offer special products or services related to stress reduction. This allows for more appropriate service provision by prioritizing special products and services based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the priority determination of special products and services based on emotions.

[0119] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can provide special products or services related to that region. If the user is traveling, the service provider can provide special products or services related to the travel destination. Furthermore, if the user is participating in a specific event, the service provider can provide special products or services related to that event. This allows for more relevant deliveries by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location information into AI and have the AI ​​select the optimal delivery method.

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

[0121] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is excited, real-time behavioral data can be prioritized for analysis. If the user is relaxed, past purchase history can be prioritized for analysis. Furthermore, if the user is stressed, data related to stress reduction can be prioritized for analysis. This allows for more effective analysis by determining the priority of analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI determine the priority of analysis based on emotions.

[0122] The recommendation unit can estimate the user's emotions and adjust the timing of recommendations based on those emotions. For example, if the user is relaxed, it can provide detailed recommendations. If the user is in a hurry, it can provide concise recommendations. Furthermore, if the user is excited, it can provide visually appealing recommendations. By adjusting the timing of recommendations based on the user's emotions, more appropriate recommendations become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input user emotion data into the generative AI and have the generative AI adjust the timing of recommendations based on emotions.

[0123] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, it may request detailed feedback. If the user is in a hurry, it may request concise feedback. Furthermore, if the user is excited, it may provide a visually appealing feedback form. By adjusting the feedback collection method based on the user's emotions, more appropriate feedback can be obtained. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input user emotion data into a generative AI and have the generative AI adjust the feedback collection method based on emotions.

[0124] The improvement unit can estimate the user's emotions and adjust the improvement methods based on those emotions. For example, if the user is relaxed, it can provide detailed improvement suggestions. If the user is in a hurry, it can provide concise suggestions. Furthermore, if the user is excited, it can provide visually appealing improvement suggestions. By adjusting the improvement methods based on the user's emotions, more appropriate improvement suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the improvement unit are performed using generative AI. For example, the improvement unit can input user emotion data into the generative AI and have the generative AI adjust the improvement methods based on those emotions.

[0125] The service delivery unit can estimate the user's emotions and adjust the delivery method of special products or services based on the estimated emotions. For example, if the user is relaxed, the delivery method can include detailed explanations. If the user is in a hurry, the delivery method can include concise explanations. Furthermore, if the user is excited, the delivery method can be visually appealing. This allows for more appropriate delivery by adjusting the delivery method of special products or services based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service delivery unit may be performed using AI or not. For example, the service delivery unit can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the delivery method.

[0126] The data collection unit can analyze a user's past purchase history and select the optimal data collection method. For example, if a user frequently shops online, the unit can focus on collecting their website browsing history. If a user prefers in-store purchases, it can also collect receipt and loyalty card data. Furthermore, if a user prefers a particular brand, the unit can prioritize collecting data related to that brand. This allows for more effective data collection by analyzing the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past purchase history into an AI and have the AI ​​select the optimal data collection method.

[0127] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on important data, and a concise analysis on general data. Furthermore, it can perform a particularly detailed analysis on data of high user interest. By adjusting the level of detail of the analysis based on the importance of the data, more effective analysis becomes possible. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI adjust the level of detail of the analysis. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0128] The recommendation unit can apply different recommendation algorithms depending on the product category during the recommendation process. For example, it can provide recommendations that emphasize technical features for electronic devices, recommendations that emphasize trends for fashion items, and recommendations that emphasize health aspects for food products. By applying different recommendation algorithms depending on the product category, more accurate recommendations become possible. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit can input the product category into the generative AI and have the generative AI apply the appropriate recommendation algorithm. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0129] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, if the user has provided detailed feedback in the past, it will request similar detailed feedback. If the user has provided concise feedback in the past, it can provide a concise feedback form. Furthermore, it can focus on collecting specific questions based on the user's past feedback history. This makes it possible to collect feedback more effectively by referring to the user's past feedback history. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's past feedback history into AI and have the AI ​​select the optimal collection method.

[0130] The service delivery unit can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, it can provide special products or services related to that region. If the user is traveling, it can provide special products or services related to the travel destination. Furthermore, if the user is participating in a specific event, it can provide special products or services related to that event. This allows for more relevant deliveries by considering the user's geographical location information. Some or all of the above processing in the service delivery unit may be performed using AI or not. For example, the service delivery unit can input the user's geographical location information into the AI ​​and have the AI ​​select the optimal delivery method.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The data collection unit collects user purchase history and behavioral data. For example, it collects data such as the names of products the user has purchased in the past, the date and time of purchase, the purchase amount, products viewed, and advertisements clicked. Step 2: The analysis unit analyzes the data collected by the data collection unit to identify user preferences and behavioral patterns. For example, it analyzes the product categories that users frequently purchase and their level of interest in specific brands to identify products and services that users prefer. Step 3: The recommendation unit recommends the most suitable products and services to the user based on the preferences and behavioral patterns identified by the analysis unit. For example, it lists relevant products based on the user's preferences and behavioral patterns and presents them to the user. Step 4: The feedback collection unit collects reviews and feedback provided by users. For example, it collects reviews and feedback on products purchased by users and provides forms and star rating interfaces for them to rate the products. Step 5: The Improvement Department analyzes the reviews and feedback collected by the Feedback Collection Department to develop new products or improve existing ones. For example, based on the collected reviews and feedback, they identify areas for improvement in the product and make those improvements. Step 6: The provider offers exclusive products or services that are only available within a specific community. For example, products limited to a particular region or group, or services only available at special events.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, feedback collection unit, improvement unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's purchase history and behavioral data using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to identify the user's preferences and behavioral patterns. The recommendation unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and recommends the optimal products and services to the user based on the analysis results. The feedback collection unit is implemented in the control unit 46A of the smart device 14, for example, and collects reviews and feedback provided by the user. The improvement unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected feedback to identify areas for improvement in the product. The provisioning unit is implemented, for example, by the control unit 46A of the smart device 14, and provides special products or services that are only available in a specific community. 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.

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

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0150] 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.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, feedback collection unit, improvement unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's purchase history and behavioral data using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to identify the user's preferences and behavioral patterns. The recommendation unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and recommends the optimal products and services to the user based on the analysis results. The feedback collection unit is implemented in the control unit 46A of the smart glasses 214, for example, and collects reviews and feedback provided by the user. The improvement unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected feedback to identify areas for improvement in the product. The service provider, for example, is implemented by the control unit 46A of the smart glasses 214, and provides special products or services that are only available in a specific community. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0168] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, feedback collection unit, improvement unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's purchase history and behavioral data using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to identify the user's preferences and behavioral patterns. The recommendation unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and recommends the most suitable products and services to the user based on the analysis results. The feedback collection unit is implemented in the control unit 46A of the headset terminal 314, for example, and collects reviews and feedback provided by the user. The improvement unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected feedback to identify areas for improvement in the product. The service provider, for example, is implemented by the control unit 46A of the headset terminal 314, and provides special products or services that are only available within a specific community. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0183] 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.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0185] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, feedback collection unit, improvement unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user purchase history and behavioral data using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to identify user preferences and behavioral patterns. The recommendation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which recommends the most suitable products and services to the user based on the analysis results. The feedback collection unit is implemented by, for example, the control unit 46A of the robot 414, which collects reviews and feedback provided by the user. The improvement unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the collected feedback to identify areas for improvement in the product. The supply unit, for example, is implemented by the control unit 46A of robot 414, and provides special goods or services that are only available to a specific community. 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

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

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) A data collection unit that collects user purchase history and behavioral data, An analysis unit analyzes the data collected by the aforementioned collection unit to identify user preferences and behavioral patterns, A recommendation unit that recommends products and services based on preferences and behavioral patterns identified by the aforementioned analysis unit, A feedback collection unit that collects user reviews and feedback, The Improvement Unit analyzes the reviews and feedback collected by the aforementioned Feedback Collection Unit and uses this information to develop new products and improve existing products. It includes a provision department that offers special products and services that are only available within a specific community. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects data such as products the user has previously purchased, products they have viewed, and advertisements they have clicked on. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to identify user preferences and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The recommendation unit is, Based on identified preferences and behavioral patterns, the system recommends the most suitable products and services to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback collection unit is Collect user reviews and feedback. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned improvement unit is, We analyze collected reviews and feedback to develop new products and improve existing ones. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, We offer special products and services that are only available within specific communities. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of collecting purchase history and behavioral data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past purchase history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting purchase history and behavioral data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting purchase history and behavioral data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting purchase history and behavioral data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The recommendation unit is, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The recommendation unit is, When making recommendations, adjust the level of detail based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 22) The recommendation unit is, When making recommendations, different recommendation algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The recommendation unit is, It estimates the user's emotions and adjusts the length of recommendations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The recommendation unit is, When making recommendations, the priority of recommendations is determined based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The recommendation unit is, When making recommendations, adjust the order of recommendations based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback collection unit is We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback collection unit is When collecting feedback, the system selects the optimal collection method by referring to the user's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback collection unit is When collecting feedback, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback collection unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback collection unit is When collecting feedback, the optimal collection method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned improvement unit is, It estimates user sentiment and adjusts improvement methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned improvement unit is, When making improvements, we analyze past user feedback to select the most suitable improvement method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned improvement unit is, When making improvements, customize the methods of improvement based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned improvement unit is, We estimate user emotions and determine improvement priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned improvement unit is, When making improvements, the optimal improvement method will be selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned improvement unit is, During the improvement process, we analyze users' social media activity and propose ways to make improvements. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned supply unit is, We estimate the user's emotions and adjust the way we offer special products and services based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned supply unit is, When providing the product, the system will refer to the user's past purchase history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned supply unit is, When providing the service, we offer special products and services based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned supply unit is, It estimates user emotions and prioritizes special products and services based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0205] 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. A data collection unit that collects user purchase history and behavioral data, An analysis unit analyzes the data collected by the aforementioned collection unit to identify user preferences and behavioral patterns, A recommendation unit that recommends products and services based on preferences and behavioral patterns identified by the aforementioned analysis unit, A feedback collection unit that collects user reviews and feedback, The Improvement Unit analyzes the reviews and feedback collected by the aforementioned Feedback Collection Unit and uses this information to develop new products and improve existing products. It includes a provision department that offers special products and services that are only available within a specific community. A system characterized by the following features.

2. The aforementioned collection unit is The system collects data such as products the user has previously purchased, products they have viewed, and advertisements they have clicked on. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to identify user preferences and behavioral patterns. The system according to feature 1.

4. The recommendation unit is, Based on identified preferences and behavioral patterns, the system recommends the most suitable products and services to the user. The system according to feature 1.

5. The aforementioned feedback collection unit is Collect user reviews and feedback. The system according to feature 1.

6. The aforementioned improvement unit is, We analyze collected reviews and feedback to develop new products and improve existing ones. The system according to feature 1.

7. The aforementioned supply unit is, We offer special products and services that are only available within specific communities. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of collecting purchase history and behavioral data based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past purchase history and select the optimal data collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting purchase history and behavioral data, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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