system

Abstract

The system according to this embodiment aims to analyze users' purchase history and behavioral data to recommend optimal products and services, promote cooperation among users, and provide special products and services that are only available within specific communities. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a recommendation unit, a feedback collection unit, a development 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 understand the user's preferences and needs. The recommendation unit recommends products and services based on the analysis results obtained by the analysis unit. The feedback collection unit collects user feedback. The development unit develops and improves products based on the feedback collected by the feedback collection unit. 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 room for improvement not only in recommending products and services by utilizing the purchase history and behavior data of users, but also in promoting cooperation between users and providing special products and services that are only available in a specific community.

[0005] The system according to the embodiment aims to analyze the purchase history and behavior data of users, recommend optimal products and services, promote cooperation between users, and provide special products and services that are only available in a specific community.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a recommendation unit, a feedback collection unit, a development unit, and a provision unit. The data collection unit collects user purchase history and behavioral data. The analysis unit analyzes the data collected by the data collection unit to understand user preferences and needs. The recommendation unit recommends products and services based on the analysis results obtained by the analysis unit. The feedback collection unit collects user feedback. The development unit develops and improves products based on the feedback collected by the feedback collection unit. The provision unit provides special products and services that are only available in specific communities. [Effects of the Invention]

[0007] The system according to this embodiment analyzes users' purchase history and behavioral data to recommend optimal products and services, promotes cooperation among users, and can provide special products and services that are only available within specific communities. [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 (for example, 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 AI ​​agent system according to an embodiment of the present invention is a system that not only analyzes users' purchase history and behavioral data to recommend optimal products and services, but also builds an ecosystem that promotes cooperation among users. In this ecosystem, new products are developed and existing products are improved based on reviews and feedback provided by users, and special products and services that can only be obtained in specific communities are provided. For example, the AI ​​agent system collects users' purchase history and behavioral data. This includes products and services purchased by the user, browsing history, click history, etc. Next, the AI ​​analyzes the collected data to understand the user's preferences and needs. For example, for users who frequently purchase products in a particular category, products in that category are recommended preferentially. Furthermore, the AI ​​agent system builds an ecosystem that promotes cooperation among users. Users provide reviews and feedback on purchased products and services, and new products are developed and existing products are improved based on this feedback. For example, if a user provides feedback that a particular product should be made easier to use, the product will be improved based on that feedback. In addition, this ecosystem provides special products and services that can only be obtained in specific communities. For example, exclusive products that can only be purchased by members of a specific community, or special services that can only be used within that community, can be offered. This improves user engagement and revitalizes the community. As a result, the AI ​​agent system can be expected to improve user engagement, shorten the product development cycle, and simultaneously increase sales and customer satisfaction. Specifically, it enables personalized recommendations using big data and machine learning, a continuous product improvement process utilizing user-generated content, and an incentive system that incorporates community-specific benefits and rewards. This allows the AI ​​agent system to analyze users' purchase history and behavioral data, recommend the most suitable products and services, and build an ecosystem that promotes collaboration among users.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, a feedback collection unit, a development unit, and a provision unit. The collection unit collects user purchase history and behavioral data. The collection unit can collect data such as products and services purchased by the user, browsing history, and click history. For example, the collection unit can collect website browsing history. The collection unit can also collect user click history. Furthermore, the collection unit can collect data on products purchased by the user. The analysis unit analyzes the data collected by the collection unit to understand the user's preferences and needs. For example, the analysis unit can analyze the collected data to understand the user's purchasing patterns. For example, the analysis unit can analyze user preferences. Furthermore, the analysis unit can understand user needs. The recommendation unit recommends products and services based on the analysis results obtained by the analysis unit. For example, the recommendation unit can prioritize recommending products in a specific category to users who frequently purchase products in that category. The recommendation department can, for example, recommend products based on user preferences. The recommendation department can also recommend services based on user needs. The feedback collection department collects user feedback. For example, the feedback collection department can collect user reviews and feedback. For example, the feedback collection department can collect user ratings. The feedback collection department can also collect user comments. The development department develops and improves products based on the feedback collected by the feedback collection department. For example, the development department can develop new products based on the collected feedback. For example, the development department can improve existing products. The development department can also improve 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 limited-edition products that can only be purchased by members of a specific community. For example, the supply department can provide special services that are only available within a community.Furthermore, the service provider can offer services with special benefits to community members. This allows the AI ​​agent system according to the embodiment to analyze users' purchase history and behavioral data, recommend optimal products and services, and build an ecosystem that promotes cooperation among users.

[0030] The data collection unit collects user purchase history and behavioral data. Specifically, it collects data such as products and services purchased by users, browsing history, and click history. For example, when collecting website browsing history, it obtains detailed data such as which pages users visited, which links they clicked, and how long they stayed on those pages. This allows the unit to understand users' interests and preferences. By collecting click history, it is possible to identify which advertisements and products users were interested in. Furthermore, by collecting data on products purchased by users, it is possible to analyze users' purchasing patterns and preferences in detail. The data collection unit centrally manages this data and updates it in real time, ensuring that it always maintains the latest user information. This allows the data collection unit to accurately understand user behavior and preferences and build a foundation for providing this information to other departments.

[0031] The analytics department analyzes data collected by the data collection department to understand user preferences and needs. Specifically, it uses AI to analyze the collected data and reveal user purchasing patterns and behavioral characteristics. For example, if a user frequently purchases products in a particular category, it can be determined that they have a high level of interest in that category. Based on this data, the analytics department conducts a detailed analysis of user preferences to identify the most suitable products and services for each individual user. Furthermore, by analyzing user behavior data, it is possible to understand what needs users have. For example, if a user frequently uses a particular service, it can be determined that they have a high need for that service. Based on this information, the analytics department develops strategies to provide products and services that meet user needs. In addition, by analyzing past data and trends, the analytics department can predict future user needs and formulate long-term marketing strategies.

[0032] The recommendation department recommends products and services based on the analysis results obtained by the analysis department. Specifically, it selects and proposes the most suitable products and services to users according to their preferences and needs. For example, users who frequently purchase products in a particular category will be given priority in recommendations of products in that category. It can also propose new products and services that users may be interested in based on their past purchase history and behavioral data. The recommendation department uses AI to analyze users' preferences and needs in real time and make optimal recommendations. This allows users to easily find products and services that match their interests and needs, increasing their willingness to purchase. Furthermore, the recommendation department collects user feedback and continuously improves the accuracy of its recommendations. This allows the recommendation department to always provide users with the best possible suggestions and improve their satisfaction.

[0033] The Feedback Collection Department collects user feedback. Specifically, it collects and analyzes reviews and feedback provided by users. For example, by collecting ratings and comments on products purchased by users, it is possible to understand product quality and service satisfaction. Furthermore, based on the feedback provided by users, it is possible to identify areas for product improvement and new needs. The Feedback Collection Department centrally manages this data and provides it to other departments, thereby building a foundation for product development and service improvement. In addition, by collecting user feedback in real time and responding quickly, the Feedback Collection Department can improve user satisfaction. As a result, the Feedback Collection Department can provide products and services that reflect user voices and build trusting relationships with users.

[0034] The Development Department develops and improves products based on feedback collected by the Feedback Collection Department. Specifically, it analyzes user feedback to develop new products and improve existing ones. For example, it adds new features or improves existing ones based on user requests and complaints. By developing new products that meet user needs, it can meet user expectations. The Development Department works to quickly incorporate feedback and improve the quality of products and services. Furthermore, the Development Department formulates long-term product development strategies based on user feedback and implements continuous improvements. As a result, the Development Department can provide high-quality products and services that meet user needs and improve user satisfaction.

[0035] The service provider will offer exclusive products and services that are only available within specific communities. Specifically, this includes limited-edition products that can only be purchased by members of a particular community, and special services that can only be used within that community. For example, by offering members early access to limited-edition products or services with special benefits, the service provider can enhance the value of the community. Furthermore, events and campaigns will be held to promote interaction within the community and strengthen connections among members. Through these exclusive products and services, the service provider will provide added value to community members and revitalize the community. In addition, the service provider will collect community feedback and continuously improve the quality of the products and services it offers. This allows the service provider to consistently provide high-quality products and services to community members and improve community satisfaction.

[0036] The data collection unit can analyze the user's past purchase history and select the optimal data collection method. For example, the data collection unit can prioritize collecting relevant data based on the product categories that the user frequently purchases. For example, the data collection unit can collect data related to specific seasons or events from the user's purchase history. For example, the data collection unit can analyze the user's purchasing patterns and collect data at the optimal time. This allows the optimal data collection method to be selected 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, for example, or without AI. For example, the data collection unit can input the user's purchase history data into a generating AI and have the generating AI select the optimal data collection method.

[0037] The data collection unit can filter data based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. For example, the data collection unit can collect relevant data according to the user's living situation (e.g., moving, marriage). For example, the data collection unit can collect appropriate data based on the user's current activities (e.g., traveling, working). This allows for the collection of more relevant data by filtering the data based on the user's current living situation and areas of interest. 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 living situation data into a generating AI and have the generating AI perform data filtering.

[0038] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data on products and services related to the user's current location. For example, the data collection unit can collect region-specific promotions and event information based on the user's location information. For example, the data collection unit can analyze the user's movement patterns and collect relevant data. This allows for the priority collection of highly relevant data by considering the user's geographical location information. 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 location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0039] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to products and services shared by the user on social media. For example, the data collection unit can collect relevant data based on the user's interests and preferences on social media. For example, the data collection unit can collect data at the optimal time based on the user's social media activity time. This allows for the collection of relevant data by analyzing the user's social media activity. 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 social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0040] 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 can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0041] 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 product data. For example, the analysis unit can apply a usage frequency analysis algorithm to service data. For example, the analysis unit can apply a sentiment analysis algorithm to user reviews. By applying different analysis algorithms depending on the data category, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0042] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may prioritize the analysis of current data while referring to past data. For example, the analysis unit may adjust the priority of analysis according to the data collection timing. This allows for more appropriate analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis according to the relevance of the data. By adjusting the order of analysis based on the relevance of the data, more appropriate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0044] 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 high-importance products. For example, the recommendation unit can provide simplified recommendations for low-importance products. The recommendation unit can also determine the priority of recommendations based on the importance of the products. This allows for more appropriate recommendations by adjusting the level of detail based on the importance of the products. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the importance of the products into a generating AI and have the generating AI adjust the level of detail in the recommendations.

[0045] 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 apply a recommendation algorithm based on technical specifications. For fashion items, for example, the recommendation unit can apply a recommendation algorithm based on trend analysis. For books, for example, the recommendation unit can apply a recommendation algorithm based on the user's reading history. By applying different recommendation algorithms depending on the product category, more appropriate recommendations become possible. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the product category into a generating AI and have the generating AI execute the application of different recommendation algorithms.

[0046] The recommendation unit can determine the priority of recommendations based on the product submission date. For example, the recommendation unit can prioritize recommending new products. For example, the recommendation unit can recommend seasonal products at the appropriate time. For example, the recommendation unit can make recommendations at the optimal time based on the user's past purchase history. This makes it possible to make more appropriate recommendations by determining the priority of recommendations based on the product submission date. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the product submission date into a generating AI and have the generating AI perform the determination of recommendation priorities.

[0047] 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. For example, the recommendation unit can recommend less relevant products later. For example, the recommendation unit can adjust the order of recommendations according to the relevance of the products. This allows for more appropriate recommendations by adjusting the order of recommendations based on the relevance of the products. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the relevance of products into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0048] The feedback collection unit can analyze the user's past feedback history to select the optimal collection method when collecting feedback. For example, if the user has provided detailed feedback in the past, the feedback collection unit can provide a feedback form that includes detailed questions. For example, if the user has provided concise feedback in the past, the feedback collection unit can provide a feedback form that includes concise questions. For example, the feedback collection unit can request feedback at the optimal time based on the user's past feedback history. This allows the optimal collection method to be selected by analyzing the user's past feedback history. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's feedback history data into a generating AI and have the generating AI select the optimal collection method.

[0049] The feedback collection unit can prioritize the collection of highly relevant feedback by considering the user's geographical location information during feedback collection. For example, the feedback collection unit can prioritize the collection of feedback related to the area where the user is currently located. For example, the feedback collection unit can collect region-specific feedback based on the user's location information. For example, the feedback collection unit can analyze the user's movement patterns and collect relevant feedback. This allows for the priority collection of highly relevant feedback by considering the user's geographical location information. Some or all of the above-described processes in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's location information data into a generating AI and have the generating AI perform the collection of highly relevant feedback.

[0050] The development department can analyze past user feedback during development to select the optimal development method. For example, the development department can determine the direction of development based on feedback previously provided by users. For example, the development department can extract areas for improvement from past user feedback and reflect them in development. For example, the development department can analyze past user feedback and formulate an optimal development schedule. In this way, the optimal development method can be selected by analyzing past user feedback. Some or all of the above processes in the development department may be performed using AI, for example, or not using AI. For example, the development department can input user feedback data into a generating AI and have the generating AI select the optimal development method.

[0051] The development department can customize the development process based on the user's current living situation. For example, the development department can adjust development priorities according to the user's current living situation. For example, the development department can customize the development process based on the user's living situation. For example, the development department can adjust the development schedule taking into account the user's current living situation. This allows for more appropriate development by customizing the development process based on the user's current living situation. Some or all of the above processes in the development department may be performed using AI, for example, or without AI. For example, the development department can input user living situation data into a generating AI and have the generating AI perform the customization of the development process.

[0052] The development department can select the optimal development method during development by considering the user's geographical location information. For example, the development department may prioritize development items related to the area where the user is currently located. For example, the development department may select a region-specific development method based on the user's location information. For example, the development department may analyze the user's movement patterns and select the optimal development method. In this way, the optimal development method can be selected by considering the user's geographical location information. Some or all of the above processes in the development department may be performed using AI, for example, or not using AI. For example, the development department can input user location data into a generating AI and have the generating AI select the optimal development method.

[0053] The development department can analyze users' social media activity during development and propose development methods. For example, the development department can determine the direction of development based on opinions shared by users on social media. For example, the development department can propose development methods based on users' social media activity. For example, the development department can analyze users' social media feedback and reflect it in development. In this way, development methods can be proposed by analyzing users' social media activity. Some or all of the above processes in the development department may be performed using AI, for example, or not using AI. For example, the development department can input user social media data into a generating AI and have the generating AI execute proposals for development methods.

[0054] The delivery unit can analyze the user's past purchase history at the time of delivery to select the optimal delivery method. For example, the delivery unit can provide related products based on products the user has purchased in the past. For example, the delivery unit can select the optimal delivery timing from the user's purchase history. For example, the delivery unit can analyze the user's purchase patterns to select the optimal delivery method. In this way, the optimal delivery method can be selected by analyzing the user's past purchase history. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's purchase history data into a generating AI and have the generating AI select the optimal delivery method.

[0055] The delivery unit can customize the means of providing products based on the user's current living situation at the time of delivery. For example, the delivery unit can adjust the priority of products to be provided according to the user's current living situation. For example, the delivery unit can customize the means of providing products based on the user's living situation. For example, the delivery unit can adjust the schedule of products to be provided, taking into account the user's current living situation. This makes it possible to provide more appropriate products by customizing the means of providing products based on the user's current living situation. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI. For example, the delivery unit can input user living situation data into a generating AI and have the generating AI perform the customization of the means of providing products.

[0056] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, the service provider can prioritize providing products related to the user's current location. For example, the service provider can provide region-specific products based on the user's location information. For example, the service provider can analyze the user's movement patterns and select the optimal service delivery method. This allows the service provider to select the optimal service delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's location information data into a generating AI and have the generating AI select the optimal service delivery method.

[0057] The service provider can analyze the user's social media activity at the time of service provision and propose ways to provide the product. For example, the service provider can provide products related to products that the user has shared on social media. For example, the service provider can propose ways to provide the product based on the user's social media activity. For example, the service provider can analyze the user's social media feedback and reflect it in the products it provides. In this way, by analyzing the user's social media activity, it is possible to propose ways to provide the product. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI execute suggestions for ways to provide the product.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The analytics unit can consider the user's health data when analyzing their purchase history and behavioral data. For example, if a user is using a health management app, the system can collect that data and recommend the most suitable products and services based on the user's health status. For instance, if a user is on a diet, low-calorie foods and fitness-related products can be prioritized for recommendation. Furthermore, if a user has a specific allergy, products that address that allergy can be recommended. This enables personalized recommendations tailored to the user's health condition.

[0060] The recommendation system can analyze users' purchase history and behavioral data while considering their hobbies and interests. For example, if a user is interested in a particular sport, it can prioritize recommending products and services related to that sport. Similarly, if a user prefers a particular music genre, it can recommend products and services related to that genre. This enables personalized recommendations tailored to the user's tastes and interests.

[0061] The feedback collection unit can consider the user's social background when analyzing their purchase history and behavioral data. For example, if a user belongs to a particular culture or community, it can prioritize recommending products and services related to that culture or community. Similarly, if a user participates in a specific social event, it can recommend products and services related to that event. This enables personalized recommendations tailored to the user's social background.

[0062] The development team can consider users' environmental data when analyzing their purchase history and behavioral data. For example, if a user lives in a specific climate or region, they can prioritize recommending products and services suitable for that environment. Similarly, if a user tends to purchase products related to a particular season, they can recommend products related to that season. This enables personalized recommendations based on the user's environmental data.

[0063] The service provider can consider the user's economic situation when analyzing user purchase history and behavioral data. For example, if a user tends to purchase products in a certain price range, the service provider can prioritize recommending products and services suitable for that price range. Similarly, if a user tends to take advantage of specific discounts or promotions, the service provider can recommend such products and services. This enables personalized recommendations tailored to the user's economic situation.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects user purchase history and behavioral data. For example, it can collect data such as products and services purchased by the user, browsing history, and click history. Step 2: The analysis unit analyzes the data collected by the collection unit to understand user preferences and needs. For example, by analyzing the collected data, it can understand user purchasing patterns, preferences, and needs. Step 3: The recommendation unit recommends products and services based on the analysis results obtained by the analysis unit. For example, users who frequently purchase products in a particular category can be given priority in recommending products in that category. Step 4: The feedback collection unit collects user feedback. For example, it can collect reviews, feedback, ratings, and comments provided by users. Step 5: The development department develops and improves products based on the feedback collected by the feedback collection department. For example, they can develop new products or improve existing products based on the collected feedback. Step 6: The provider offers special products or services that are only available within a specific community. For example, they can offer limited-edition products that can only be purchased by members of a particular community, or special services that can only be used within that community.

[0066] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that not only analyzes users' purchase history and behavioral data to recommend optimal products and services, but also builds an ecosystem that promotes cooperation among users. In this ecosystem, new products are developed and existing products are improved based on reviews and feedback provided by users, and special products and services that can only be obtained in specific communities are provided. For example, the AI ​​agent system collects users' purchase history and behavioral data. This includes products and services purchased by the user, browsing history, click history, etc. Next, the AI ​​analyzes the collected data to understand the user's preferences and needs. For example, for users who frequently purchase products in a particular category, products in that category are recommended preferentially. Furthermore, the AI ​​agent system builds an ecosystem that promotes cooperation among users. Users provide reviews and feedback on purchased products and services, and new products are developed and existing products are improved based on this feedback. For example, if a user provides feedback that a particular product should be made easier to use, the product will be improved based on that feedback. In addition, this ecosystem provides special products and services that can only be obtained in specific communities. For example, exclusive products that can only be purchased by members of a specific community, or special services that can only be used within that community, can be offered. This improves user engagement and revitalizes the community. As a result, the AI ​​agent system can be expected to improve user engagement, shorten the product development cycle, and simultaneously increase sales and customer satisfaction. Specifically, it enables personalized recommendations using big data and machine learning, a continuous product improvement process utilizing user-generated content, and an incentive system that incorporates community-specific benefits and rewards. This allows the AI ​​agent system to analyze users' purchase history and behavioral data, recommend the most suitable products and services, and build an ecosystem that promotes collaboration among users.

[0067] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, a feedback collection unit, a development unit, and a provision unit. The collection unit collects user purchase history and behavioral data. The collection unit can collect data such as products and services purchased by the user, browsing history, and click history. For example, the collection unit can collect website browsing history. The collection unit can also collect user click history. Furthermore, the collection unit can collect data on products purchased by the user. The analysis unit analyzes the data collected by the collection unit to understand the user's preferences and needs. For example, the analysis unit can analyze the collected data to understand the user's purchasing patterns. For example, the analysis unit can analyze user preferences. Furthermore, the analysis unit can understand user needs. The recommendation unit recommends products and services based on the analysis results obtained by the analysis unit. For example, the recommendation unit can prioritize recommending products in a specific category to users who frequently purchase products in that category. The recommendation department can, for example, recommend products based on user preferences. The recommendation department can also recommend services based on user needs. The feedback collection department collects user feedback. For example, the feedback collection department can collect user reviews and feedback. For example, the feedback collection department can collect user ratings. The feedback collection department can also collect user comments. The development department develops and improves products based on the feedback collected by the feedback collection department. For example, the development department can develop new products based on the collected feedback. For example, the development department can improve existing products. The development department can also improve 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 limited-edition products that can only be purchased by members of a specific community. For example, the supply department can provide special services that are only available within a community.Furthermore, the service provider can offer services with special benefits to community members. This allows the AI ​​agent system according to the embodiment to analyze users' purchase history and behavioral data, recommend optimal products and services, and build an ecosystem that promotes cooperation among users.

[0068] The data collection unit collects user purchase history and behavioral data. Specifically, it collects data such as products and services purchased by users, browsing history, and click history. For example, when collecting website browsing history, it obtains detailed data such as which pages users visited, which links they clicked, and how long they stayed on those pages. This allows the unit to understand users' interests and preferences. By collecting click history, it is possible to identify which advertisements and products users were interested in. Furthermore, by collecting data on products purchased by users, it is possible to analyze users' purchasing patterns and preferences in detail. The data collection unit centrally manages this data and updates it in real time, ensuring that it always maintains the latest user information. This allows the data collection unit to accurately understand user behavior and preferences and build a foundation for providing this information to other departments.

[0069] The analytics department analyzes data collected by the data collection department to understand user preferences and needs. Specifically, it uses AI to analyze the collected data and reveal user purchasing patterns and behavioral characteristics. For example, if a user frequently purchases products in a particular category, it can be determined that they have a high level of interest in that category. Based on this data, the analytics department conducts a detailed analysis of user preferences to identify the most suitable products and services for each individual user. Furthermore, by analyzing user behavior data, it is possible to understand what needs users have. For example, if a user frequently uses a particular service, it can be determined that they have a high need for that service. Based on this information, the analytics department develops strategies to provide products and services that meet user needs. In addition, by analyzing past data and trends, the analytics department can predict future user needs and formulate long-term marketing strategies.

[0070] The recommendation department recommends products and services based on the analysis results obtained by the analysis department. Specifically, it selects and proposes the most suitable products and services to users according to their preferences and needs. For example, users who frequently purchase products in a particular category will be given priority in recommendations of products in that category. It can also propose new products and services that users may be interested in based on their past purchase history and behavioral data. The recommendation department uses AI to analyze users' preferences and needs in real time and make optimal recommendations. This allows users to easily find products and services that match their interests and needs, increasing their willingness to purchase. Furthermore, the recommendation department collects user feedback and continuously improves the accuracy of its recommendations. This allows the recommendation department to always provide users with the best possible suggestions and improve their satisfaction.

[0071] The Feedback Collection Department collects user feedback. Specifically, it collects and analyzes reviews and feedback provided by users. For example, by collecting ratings and comments on products purchased by users, it is possible to understand product quality and service satisfaction. Furthermore, based on the feedback provided by users, it is possible to identify areas for product improvement and new needs. The Feedback Collection Department centrally manages this data and provides it to other departments, thereby building a foundation for product development and service improvement. In addition, by collecting user feedback in real time and responding quickly, the Feedback Collection Department can improve user satisfaction. As a result, the Feedback Collection Department can provide products and services that reflect user voices and build trusting relationships with users.

[0072] The Development Department develops and improves products based on feedback collected by the Feedback Collection Department. Specifically, it analyzes user feedback to develop new products and improve existing ones. For example, it adds new features or improves existing ones based on user requests and complaints. By developing new products that meet user needs, it can meet user expectations. The Development Department works to quickly incorporate feedback and improve the quality of products and services. Furthermore, the Development Department formulates long-term product development strategies based on user feedback and implements continuous improvements. As a result, the Development Department can provide high-quality products and services that meet user needs and improve user satisfaction.

[0073] The service provider will offer exclusive products and services that are only available within specific communities. Specifically, this includes limited-edition products that can only be purchased by members of a particular community, and special services that can only be used within that community. For example, by offering members early access to limited-edition products or services with special benefits, the service provider can enhance the value of the community. Furthermore, events and campaigns will be held to promote interaction within the community and strengthen connections among members. Through these exclusive products and services, the service provider will provide added value to community members and revitalize the community. In addition, the service provider will collect community feedback and continuously improve the quality of the products and services it offers. This allows the service provider to consistently provide high-quality products and services to community members and improve community satisfaction.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can refrain from collecting data and collect it when the user is relaxed. For example, if the user is excited, the data collection unit can immediately collect data to obtain real-time behavioral data. For example, if the user is tired, the data collection unit can postpone data collection and collect it after the user has rested. By adjusting the timing of data collection according to the user's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The data collection unit can analyze the user's past purchase history and select the optimal data collection method. For example, the data collection unit can prioritize collecting relevant data based on the product categories that the user frequently purchases. For example, the data collection unit can collect data related to specific seasons or events from the user's purchase history. For example, the data collection unit can analyze the user's purchasing patterns and collect data at the optimal time. This allows the optimal data collection method to be selected 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, for example, or without AI. For example, the data collection unit can input the user's purchase history data into a generating AI and have the generating AI select the optimal data collection method.

[0076] The data collection unit can filter data based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. For example, the data collection unit can collect relevant data according to the user's living situation (e.g., moving, marriage). For example, the data collection unit can collect appropriate data based on the user's current activities (e.g., traveling, working). This allows for the collection of more relevant data by filtering the data based on the user's current living situation and areas of interest. 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 living situation data into a generating AI and have the generating AI perform data filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is excited, the data collection unit may prioritize collecting real-time behavioral data. For example, if the user is relaxed, the data collection unit may prioritize collecting past purchase and browsing history. For example, if the user is stressed, the data collection unit may prioritize collecting emotion-related data. This allows for more appropriate data collection by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may 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, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data on products and services related to the user's current location. For example, the data collection unit can collect region-specific promotions and event information based on the user's location information. For example, the data collection unit can analyze the user's movement patterns and collect relevant data. This allows for the priority collection of highly relevant data by considering the user's geographical location information. 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 location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0079] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to products and services shared by the user on social media. For example, the data collection unit can collect relevant data based on the user's interests and preferences on social media. For example, the data collection unit can collect data at the optimal time based on the user's social media activity time. This allows for the collection of relevant data by analyzing the user's social media activity. 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 social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0080] 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. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. For example, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without 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.

[0081] 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 can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] 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 product data. For example, the analysis unit can apply a usage frequency analysis algorithm to service data. For example, the analysis unit can apply a sentiment analysis algorithm to user reviews. By applying different analysis algorithms depending on the data category, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0083] 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 result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually appealing analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without 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.

[0084] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may prioritize the analysis of current data while referring to past data. For example, the analysis unit may adjust the priority of analysis according to the data collection timing. This allows for more appropriate analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis according to the relevance of the data. By adjusting the order of analysis based on the relevance of the data, more appropriate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on the estimated emotions. For example, if the user is relaxed, the recommendation unit can provide detailed recommendations. If the user is in a hurry, the recommendation unit can provide concise recommendations that get straight to the point. If the user is excited, the recommendation unit can provide visually appealing recommendations. By adjusting the way recommendations are presented according to the user's emotions, more appropriate recommendations become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input user emotion data into a generative AI and have the generative AI adjust the way recommendations are presented.

[0087] 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 high-importance products. For example, the recommendation unit can provide simplified recommendations for low-importance products. The recommendation unit can also determine the priority of recommendations based on the importance of the products. This allows for more appropriate recommendations by adjusting the level of detail based on the importance of the products. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the importance of the products into a generating AI and have the generating AI adjust the level of detail in the recommendations.

[0088] 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 apply a recommendation algorithm based on technical specifications. For fashion items, for example, the recommendation unit can apply a recommendation algorithm based on trend analysis. For books, for example, the recommendation unit can apply a recommendation algorithm based on the user's reading history. By applying different recommendation algorithms depending on the product category, more appropriate recommendations become possible. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the product category into a generating AI and have the generating AI execute the application of different recommendation algorithms.

[0089] The recommendation unit can estimate the user's emotions and adjust the length of recommendations based on the estimated emotions. For example, if the user is in a hurry, the recommendation unit can provide short, concise recommendations. If the user is relaxed, the recommendation unit can provide detailed recommendations. If the user is excited, the recommendation unit can provide visually appealing recommendations. By adjusting the length of recommendations according to the user's emotions, more appropriate recommendations can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input user emotion data into the generative AI and have the generative AI adjust the length of the recommendations.

[0090] The recommendation unit can determine the priority of recommendations based on the product submission date. For example, the recommendation unit can prioritize recommending new products. For example, the recommendation unit can recommend seasonal products at the appropriate time. For example, the recommendation unit can make recommendations at the optimal time based on the user's past purchase history. This makes it possible to make more appropriate recommendations by determining the priority of recommendations based on the product submission date. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the product submission date into a generating AI and have the generating AI perform the determination of recommendation priorities.

[0091] 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. For example, the recommendation unit can recommend less relevant products later. For example, the recommendation unit can adjust the order of recommendations according to the relevance of the products. This allows for more appropriate recommendations by adjusting the order of recommendations based on the relevance of the products. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the relevance of products into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0092] The feedback collection unit can estimate the user's emotions and adjust the timing of feedback collection based on the estimated emotions. For example, if the user is satisfied, the feedback collection unit can immediately request feedback. For example, if the user is dissatisfied, the feedback collection unit can request feedback after a certain period of time. For example, if the user has neutral emotions, the feedback collection unit can request feedback at an appropriate time. This allows for the collection of more appropriate feedback by adjusting the timing of feedback collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the feedback collection unit may be performed using AI, or not using AI. For example, the feedback collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of feedback collection.

[0093] The feedback collection unit can analyze the user's past feedback history to select the optimal collection method when collecting feedback. For example, if the user has provided detailed feedback in the past, the feedback collection unit can provide a feedback form that includes detailed questions. For example, if the user has provided concise feedback in the past, the feedback collection unit can provide a feedback form that includes concise questions. For example, the feedback collection unit can request feedback at the optimal time based on the user's past feedback history. This allows the optimal collection method to be selected by analyzing the user's past feedback history. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's feedback history data into a generating AI and have the generating AI select the optimal collection method.

[0094] The feedback collection unit can estimate the user's emotions and determine the priority of feedback to collect based on the estimated user emotions. For example, if the user is satisfied, the feedback collection unit can prioritize collecting positive feedback. For example, if the user is dissatisfied, the feedback collection unit can prioritize collecting negative feedback. For example, if the user has neutral emotions, the feedback collection unit can collect balanced feedback. This allows for the collection of more appropriate feedback by determining the priority of feedback to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. 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.

[0095] The feedback collection unit can prioritize the collection of highly relevant feedback by considering the user's geographical location information during feedback collection. For example, the feedback collection unit can prioritize the collection of feedback related to the area where the user is currently located. For example, the feedback collection unit can collect region-specific feedback based on the user's location information. For example, the feedback collection unit can analyze the user's movement patterns and collect relevant feedback. This allows for the priority collection of highly relevant feedback by considering the user's geographical location information. Some or all of the above-described processes in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's location information data into a generating AI and have the generating AI perform the collection of highly relevant feedback.

[0096] The development department can estimate user emotions and adjust development methods based on the estimated user emotions. For example, if the user is satisfied, the development department will continue with the current development methods. For example, if the user is dissatisfied, the development department can review and improve development methods. For example, if the user has neutral emotions, the development department can fine-tune development methods. This allows for more appropriate development by adjusting development methods according to user emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines 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 development department may be performed using AI or not using AI. For example, the development department can input user emotion data into a generative AI and have the generative AI adjust development methods.

[0097] The development department can analyze past user feedback during development to select the optimal development method. For example, the development department can determine the direction of development based on feedback previously provided by users. For example, the development department can extract areas for improvement from past user feedback and reflect them in development. For example, the development department can analyze past user feedback and formulate an optimal development schedule. In this way, the optimal development method can be selected by analyzing past user feedback. Some or all of the above processes in the development department may be performed using AI, for example, or not using AI. For example, the development department can input user feedback data into a generating AI and have the generating AI select the optimal development method.

[0098] The development department can customize the development process based on the user's current living situation. For example, the development department can adjust development priorities according to the user's current living situation. For example, the development department can customize the development process based on the user's living situation. For example, the development department can adjust the development schedule taking into account the user's current living situation. This allows for more appropriate development by customizing the development process based on the user's current living situation. Some or all of the above processes in the development department may be performed using AI, for example, or without AI. For example, the development department can input user living situation data into a generating AI and have the generating AI perform the customization of the development process.

[0099] The development department can estimate user emotions and determine development priorities based on those estimated emotions. For example, if the user is satisfied, the development department can prioritize current development items. If the user is dissatisfied, the development department can prioritize items that need improvement. If the user has neutral emotions, the development department can prioritize balanced development items. This allows for more appropriate development by determining development priorities according to user emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the development department may be performed using AI or not. For example, the development department can input user emotion data into a generative AI and have the generative AI determine development priorities.

[0100] The development department can select the optimal development method during development by considering the user's geographical location information. For example, the development department may prioritize development items related to the area where the user is currently located. For example, the development department may select a region-specific development method based on the user's location information. For example, the development department may analyze the user's movement patterns and select the optimal development method. In this way, the optimal development method can be selected by considering the user's geographical location information. Some or all of the above processes in the development department may be performed using AI, for example, or not using AI. For example, the development department can input user location data into a generating AI and have the generating AI select the optimal development method.

[0101] The development department can analyze users' social media activity during development and propose development methods. For example, the development department can determine the direction of development based on opinions shared by users on social media. For example, the development department can propose development methods based on users' social media activity. For example, the development department can analyze users' social media feedback and reflect it in development. In this way, development methods can be proposed by analyzing users' social media activity. Some or all of the above processes in the development department may be performed using AI, for example, or not using AI. For example, the development department can input user social media data into a generating AI and have the generating AI execute proposals for development methods.

[0102] The service provider can estimate the user's emotions and determine the priority of products to offer based on those estimated emotions. For example, if the user is satisfied, the service provider can prioritize offering the current product. For example, if the user is dissatisfied, the service provider can prioritize offering an improved product. For example, if the user has neutral emotions, the service provider can offer a balanced product. This allows for more appropriate product offerings by determining the priority of products 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 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 provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of products.

[0103] The delivery unit can analyze the user's past purchase history at the time of delivery to select the optimal delivery method. For example, the delivery unit can provide related products based on products the user has purchased in the past. For example, the delivery unit can select the optimal delivery timing from the user's purchase history. For example, the delivery unit can analyze the user's purchase patterns to select the optimal delivery method. In this way, the optimal delivery method can be selected by analyzing the user's past purchase history. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's purchase history data into a generating AI and have the generating AI select the optimal delivery method.

[0104] The delivery unit can customize the means of providing products based on the user's current living situation at the time of delivery. For example, the delivery unit can adjust the priority of products to be provided according to the user's current living situation. For example, the delivery unit can customize the means of providing products based on the user's living situation. For example, the delivery unit can adjust the schedule of products to be provided, taking into account the user's current living situation. This makes it possible to provide more appropriate products by customizing the means of providing products based on the user's current living situation. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI. For example, the delivery unit can input user living situation data into a generating AI and have the generating AI perform the customization of the means of providing products.

[0105] The service provider can estimate the user's emotions and adjust how the products are displayed based on those estimated emotions. For example, if the user is satisfied, the service provider can display detailed product information. If the user is dissatisfied, the service provider can display concise product information that gets straight to the point. If the user has neutral emotions, the service provider can display balanced product information. By adjusting how the products are displayed according to the user's emotions, it becomes possible to provide more appropriate products. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust how the products are displayed.

[0106] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, the service provider can prioritize providing products related to the user's current location. For example, the service provider can provide region-specific products based on the user's location information. For example, the service provider can analyze the user's movement patterns and select the optimal service delivery method. This allows the service provider to select the optimal service delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's location information data into a generating AI and have the generating AI select the optimal service delivery method.

[0107] The service provider can analyze the user's social media activity at the time of service provision and propose ways to provide the product. For example, the service provider can provide products related to products that the user has shared on social media. For example, the service provider can propose ways to provide the product based on the user's social media activity. For example, the service provider can analyze the user's social media feedback and reflect it in the products it provides. In this way, by analyzing the user's social media activity, it is possible to propose ways to provide the product. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI execute suggestions for ways to provide the product.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The analytics unit can consider the user's health data when analyzing their purchase history and behavioral data. For example, if a user is using a health management app, the system can collect that data and recommend the most suitable products and services based on the user's health status. For instance, if a user is on a diet, low-calorie foods and fitness-related products can be prioritized for recommendation. Furthermore, if a user has a specific allergy, products that address that allergy can be recommended. This enables personalized recommendations tailored to the user's health condition.

[0110] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is relaxed, detailed analysis results can be notified. If the user is in a hurry, concise analysis results that get straight to the point can be notified. If the user is excited, visually appealing analysis results can be notified. In this way, by adjusting the notification method of analysis results according to the user's emotions, it becomes possible to provide more appropriate analysis results.

[0111] The recommendation system can analyze users' purchase history and behavioral data while considering their hobbies and interests. For example, if a user is interested in a particular sport, it can prioritize recommending products and services related to that sport. Similarly, if a user prefers a particular music genre, it can recommend products and services related to that genre. This enables personalized recommendations tailored to the user's tastes and interests.

[0112] The recommendation system 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 that get straight to the point. If the user is excited, it can provide visually appealing recommendations. By adjusting the timing of recommendations according to the user's emotions, it becomes possible to provide more appropriate recommendations.

[0113] The feedback collection unit can consider the user's social background when analyzing their purchase history and behavioral data. For example, if a user belongs to a particular culture or community, it can prioritize recommending products and services related to that culture or community. Similarly, if a user participates in a specific social event, it can recommend products and services related to that event. This enables personalized recommendations tailored to the user's social background.

[0114] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on those emotions. For example, if the user is satisfied, it can request detailed feedback. If the user is dissatisfied, it can request concise feedback. If the user has neutral emotions, it can request balanced feedback. By adjusting the feedback collection method according to the user's emotions, more appropriate feedback can be collected.

[0115] The development team can consider users' environmental data when analyzing their purchase history and behavioral data. For example, if a user lives in a specific climate or region, they can prioritize recommending products and services suitable for that environment. Similarly, if a user tends to purchase products related to a particular season, they can recommend products related to that season. This enables personalized recommendations based on the user's environmental data.

[0116] The development team can estimate user sentiment and adjust development progress based on that estimation. For example, if users are satisfied, the current development schedule can be maintained. If users are dissatisfied, the development schedule can be reviewed and improved. If users have a neutral sentiment, the development schedule can be fine-tuned. This allows for more effective development by adjusting development progress according to user sentiment.

[0117] The service provider can consider the user's economic situation when analyzing user purchase history and behavioral data. For example, if a user tends to purchase products in a certain price range, the service provider can prioritize recommending products and services suitable for that price range. Similarly, if a user tends to take advantage of specific discounts or promotions, the service provider can recommend such products and services. This enables personalized recommendations tailored to the user's economic situation.

[0118] The service provider can estimate the user's emotions and adjust the way they describe the products based on those emotions. For example, if the user is satisfied, they can provide a detailed product description. If the user is dissatisfied, they can provide a concise product description that gets straight to the point. If the user has neutral emotions, they can provide a balanced product description. By adjusting the way products are described according to the user's emotions, it becomes possible to provide more appropriate products.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The data collection unit collects user purchase history and behavioral data. For example, it can collect data such as products and services purchased by the user, browsing history, and click history. Step 2: The analysis unit analyzes the data collected by the collection unit to understand user preferences and needs. For example, by analyzing the collected data, it can understand user purchasing patterns, preferences, and needs. Step 3: The recommendation unit recommends products and services based on the analysis results obtained by the analysis unit. For example, users who frequently purchase products in a particular category can be given priority in recommending products in that category. Step 4: The feedback collection unit collects user feedback. For example, it can collect reviews, feedback, ratings, and comments provided by users. Step 5: The development department develops and improves products based on the feedback collected by the feedback collection department. For example, they can develop new products or improve existing products based on the collected feedback. Step 6: The provider offers special products or services that are only available within a specific community. For example, they can offer limited-edition products that can only be purchased by members of a particular community, or special services that can only be used within that community.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, feedback collection unit, development 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 control unit 46A of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to understand the user's preferences and needs. The recommendation unit recommends products and services based on the analysis results using the specific processing unit 290 of the data processing unit 12. The feedback collection unit collects user feedback using the control unit 46A of the smart device 14. The development unit develops and improves products based on the feedback using the specific processing unit 290 of the data processing unit 12. The provision unit provides special products and services that are only available in a specific community using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, feedback collection unit, development unit, and provision unit, is implemented, for example, 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 control unit 46A of the smart glasses 214. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to understand the user's preferences and needs. The recommendation unit recommends products and services based on the analysis results using the specific processing unit 290 of the data processing unit 12. The feedback collection unit collects user feedback using the control unit 46A of the smart glasses 214. The development unit develops and improves products based on the feedback using the specific processing unit 290 of the data processing unit 12. The provision unit provides special products and services that are only available in a specific community using the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, feedback collection unit, development unit, and provision unit, is implemented, for example, by 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 by the control unit 46A of the headset terminal 314. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 to understand the user's preferences and needs. The recommendation unit recommends products and services based on the analysis results by the specific processing unit 290 of the data processing unit 12. The feedback collection unit collects user feedback by the control unit 46A of the headset terminal 314. The development unit develops and improves products based on the feedback by the specific processing unit 290 of the data processing unit 12. The provision unit provides special products and services that are only available in a specific community by the control unit 46A of the headset terminal 314. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, feedback collection unit, development unit, and provision unit, is implemented, for example, by 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 by the control unit 46A of the robot 414. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 to understand the user's preferences and needs. The recommendation unit recommends products and services based on the analysis results by the specific processing unit 290 of the data processing unit 12. The feedback collection unit collects user feedback by the control unit 46A of the robot 414. The development unit develops and improves products based on the feedback by the specific processing unit 290 of the data processing unit 12. The provision unit provides special products and services that are only available in a specific community by the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) A data collection unit that collects user purchase history and behavioral data, The analysis unit analyzes the data collected by the aforementioned collection unit to understand the user's preferences and needs, A recommendation unit that recommends products and services based on the analysis results obtained by the aforementioned analysis unit, A feedback collection unit that collects user feedback, Based on the feedback collected by the aforementioned feedback collection unit, the development unit carries out product development and improvement, 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 We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) 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 4) The aforementioned collection unit is During data collection, 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 5) 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 6) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) 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 10) 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 11) 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 12) 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 13) 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 14) 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 15) 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 16) 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 17) 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 18) 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 19) 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 20) The aforementioned feedback collection unit is It estimates the user's emotions and adjusts the timing of feedback collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback collection unit is When collecting feedback, the system analyzes the user's past feedback history to select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback collection unit is It estimates the user's emotions and determines the priority of feedback to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback collection unit is When collecting feedback, the system prioritizes collecting highly relevant feedback by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned development department, We estimate user emotions and adjust development methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned development department, During development, we analyze past user feedback to select the optimal development method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned development department, During development, the development process is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned development department, We estimate user sentiment and determine development priorities based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned development department, During development, the optimal development method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned development department, During development, we analyze users' social media activity and propose development methods. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of products to offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, the system analyzes the user's past purchase history to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, At the time of delivery, the method of providing the product will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, We estimate the user's emotions and adjust how products are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) 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. (Note 35) The aforementioned supply unit is, When providing the product, we analyze the user's social media activity and suggest ways to deliver the product. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 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, The analysis unit analyzes the data collected by the aforementioned collection unit to understand the user's preferences and needs, A recommendation unit that recommends products and services based on the analysis results obtained by the aforementioned analysis unit, A feedback collection unit that collects user feedback, Based on the feedback collected by the aforementioned feedback collection unit, the development unit carries out product development and improvement, 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 We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

3. 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.

4. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system according to feature 1.

8. 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 according to feature 1.

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