System

Abstract

An object of a system according to an embodiment is to propose an appropriate product on the basis of an attribute or a field of interest of a user.SOLUTION: A system according to an embodiment includes a data collection part, a data analysis part, and a commodity proposal part. The data collection unit collects data related to attributes and fields of interest of a user. The data analysis unit analyzes the data collected by the data collection unit. The commodity proposal unit proposes a commodity on the basis of a result analyzed by the data analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not adequately proposed appropriate products based on the user's attributes and areas of interest, and there is room for improvement.

[0005] The system according to the embodiment aims to propose appropriate products based on the attributes and areas of interest of a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data analysis unit, and a product proposal unit. The data collection unit collects data related to a user's attributes and areas of interest. The data analysis unit analyzes the data collected by the data collection unit. The product proposal unit proposes products based on the results of the analysis by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest appropriate products based on the attributes and areas of interest of the user. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The platform according to an embodiment of the present invention is a system that uses generative AI to function as a platform between users and each company's own e-commerce site. This system utilizes data on the user's attributes and areas of interest, and also takes into account campaign information that is only available on each company's own e-commerce site, to suggest appropriate products that meet the user's needs. This allows the platform to easily find products that meet the user's needs and supports the growth of each company's own e-commerce site.

[0029] The platform according to the embodiment includes a data collection unit, a data analysis unit, and a product proposal unit. The data collection unit collects data related to a user's attributes and areas of interest. For example, the data collection unit collects information such as the user's age, gender, past purchase history, browsing history, and categories of interest. The data collection unit can also collect data related to the user's attributes and areas of interest from platforms such as SB, LINE, and Yahoo. The data analysis unit analyzes the data collected by the data collection unit. For example, the generation AI analyzes the user's preferences based on the data related to the user's attributes and areas of interest. The generation AI can also predict changes in the user's areas of interest using a machine learning algorithm. The generation AI can also analyze the user's data using statistical analysis. The product proposal unit proposes products based on the results of the analysis by the data analysis unit. For example, the generation AI can propose optimal products taking into account the user's preferences and campaign information. The generation AI can also use a recommendation engine to propose products based on the user's past behavioral history. The generation AI can also propose products tailored to the user's needs. This allows the platform according to the embodiment to suggest appropriate products based on the user's attributes and areas of interest. For example, if a user inputs a request such as "I'm looking for a new smartphone," the generation AI will suggest the optimal smartphone by taking into account the user's preferences and campaign information. User attributes - It is necessary to clarify what exactly refers to the user's attributes. For example, age, gender, occupation, etc. Areas of interest - It is necessary to clarify what exactly refers to the areas of interest. For example, hobbies, topics of interest, etc. Data collection - It is necessary to clarify the specific methods and means of data collection. For example, questionnaires, sensors, log data, etc. Data analysis - It is necessary to clarify the specific methods and algorithms of data analysis. For example, statistical analysis, machine learning algorithms, etc. Product proposal - It is necessary to clarify the specific methods and criteria for product proposal. For example, a recommendation engine, proposals based on the user's past behavioral history, etc.

[0030] The data collection unit can analyze a user's social media activity and detect changes in their areas of interest in real time. For example, the data collection unit can analyze a user's social media posts to detect changes in their areas of interest in real time. For example, if a user begins to frequently post about a new hobby, products related to that hobby can be suggested. The data collection unit can also analyze a user's social media following and like history to detect changes in the user's areas of interest. For example, if interest in a new brand or category increases, product suggestions can be made based on that information. The data collection unit can also analyze a user's comments and replies on social media to detect changes in their areas of interest. For example, if there is an increase in comments about a particular event or trend, related products can be suggested. This allows changes in a user's areas of interest to be detected in real time. Social media activity - It is necessary to clarify what exactly social media activity refers to. For example, post content, likes, shares, etc. Real-time - It is necessary to clarify the specific definition and criteria for real-time. For example, seconds, minutes, etc.

[0031] The data collection unit can predict potential interests by analyzing a user's purchase history, browsing history, and click patterns. For example, the data collection unit can integrate a user's purchase history and browsing history to predict potential interests. For example, it can identify product categories that the user frequently browses but has not purchased, and suggest products in those categories. The data collection unit can also analyze a user's click patterns to predict products that the user is interested in but has not yet purchased. For example, if a user repeatedly clicks on a page for a particular brand or product, it can suggest that product. The data collection unit can also predict changes in a user's interests based on the purchase history and browsing history. For example, it can compare products purchased in the past with products viewed recently to identify a category in which the user has become interested. This allows it to predict a user's potential interests. Purchase history: It is necessary to clarify what exactly a purchase history refers to, such as the products purchased and the date and time of purchase. Browsing history: It is necessary to clarify what exactly a browsing history refers to, such as the pages viewed and the duration of browsing. Click pattern: It is necessary to clarify what exactly a click pattern refers to, such as the links clicked and the frequency of clicks.

[0032] The data collection unit can analyze the user's location information and identify differences in areas of interest by region. The data collection unit can, for example, identify differences in areas of interest by region based on the user's location information. For example, it can analyze products and categories that are popular in a particular region and suggest them to users in that region. The data collection unit can also analyze the location information and identify areas of interest related to seasons and events in each region. For example, it can suggest products related to events held in a particular region. The data collection unit can also analyze purchasing patterns by region based on the user's location information. For example, it can identify product categories that are frequently purchased in a particular region and suggest them to users in that region. This makes it possible to identify differences in areas of interest by region. Location information - It is necessary to clarify what exactly location information refers to. For example, GPS data, IP address, etc.

[0033] The data collection unit can analyze a user's device usage patterns and make optimal product suggestions for each device. For example, the data collection unit can analyze a user's device usage patterns and make optimal product suggestions for each device. For example, mobile products can be suggested based on browsing history on a smartphone. The data collection unit can also identify a user's interests based on device usage patterns and make customized product suggestions for each device. For example, tablet accessories can be suggested to a user who frequently uses a tablet. The data collection unit can also analyze a user's device usage time and make optimal product suggestions at the optimal time. For example, products related to nighttime use can be suggested to a user who uses a smartphone at night. This makes it possible to make optimal product suggestions for each device. Device usage patterns - It is necessary to clarify what exactly device usage patterns refer to. For example, usage time, usage frequency, used apps, etc.

[0034] The data collection unit can collect and analyze campaign information for each company's own e-commerce website. The data collection unit, for example, collects campaign information for each company in real time and analyzes its effectiveness. For example, it identifies effective campaigns based on sales data during a specific campaign period. The data collection unit also analyzes the effectiveness of campaign information in real time and identifies the most effective campaign. For example, it can analyze that a specific discount rate or benefit is most effective. The data collection unit also analyzes campaign information in real time and identifies effective campaigns. For example, it can identify campaigns that are effective during a specific time period or on a specific day of the week. This allows the collection and analysis of campaign information for each company's own e-commerce website. Campaign information - It is necessary to clarify what exactly campaign information refers to. For example, discount information, promotion details, etc.

[0035] The data collection unit can analyze the effectiveness of campaign information in real time and identify the most effective campaign. The data collection unit, for example, collects campaign information from each company in real time and analyzes its effectiveness. For example, it identifies effective campaigns based on sales data during a specific campaign period. The data collection unit also analyzes the effectiveness of campaign information in real time and identifies the most effective campaign. For example, it can analyze that a specific discount rate or benefit is most effective. The data collection unit also analyzes campaign information in real time and identifies effective campaigns. For example, it can identify campaigns that are effective during specific times of day or on specific days of the week. This makes it possible to identify the most effective campaign in real time. Effectiveness - Specific evaluation criteria and methods for effectiveness need to be clarified. For example, sales increase, click rate, etc.

[0036] The data collection unit can analyze past campaign data and identify effective campaign patterns for each season or event. The data collection unit, for example, analyzes past campaign data and identifies effective campaign patterns for each season. For example, it identifies campaigns that are effective in the summer and uses them for the next campaign. The data collection unit also analyzes campaign data for each event and identifies effective campaign patterns. For example, it can identify campaigns that are effective for Christmas or Valentine's Day. The data collection unit also identifies effective campaign patterns for each season or event based on the past campaign data. For example, it can identify campaigns that offer discounts on products related to specific seasons or events. This makes it possible to identify effective campaign patterns for each season or event. Past campaign data - It is necessary to clarify what specifically the past campaign data refers to. For example, the content of past promotions, the implementation period, etc. Seasons and events - It is necessary to clarify what specifically the seasons and events refer to. For example, summer, winter, Christmas, Halloween, etc.

[0037] The data collection department can collect campaign information from different industries and apply success stories. For example, the data collection department can collect campaign information from different industries and apply success stories. For example, a successful campaign from the fashion industry can be applied to the home appliance industry. The data collection department can also analyze campaign information from other industries and apply success stories to its own campaigns. For example, a successful campaign from the food and beverage industry can be applied to the beauty industry. The data collection department can also collect campaign data from different industries and design its own campaigns based on success stories. For example, a successful campaign from the travel industry can be applied to the entertainment industry. This allows success stories from other industries to be applied. It is necessary to clarify what specific industries are referred to by different industries. For example, the fashion industry, the food industry, etc.

[0038] The data collection unit can visualize campaign information to enable users to intuitively understand it. The data collection unit, for example, visualizes campaign information to enable users to intuitively understand it. For example, it can visually display the effectiveness of the campaign using graphs and charts. The data collection unit can also visualize campaign information as infographics to enable users to easily understand it. For example, it can illustrate an overview of the campaign and the benefits. The data collection unit can also use visualized campaign information to enable users to intuitively understand it. For example, it can display the progress and effectiveness of the campaign in real time. This allows the campaign information to be visualized to enable intuitive understanding. Visualization - The specific method or means of visualization must be clarified. For example, graphs, charts, infographics, etc.

[0039] The data analysis unit can analyze a user's past requests and proposal history to make personalized suggestions. The data analysis unit, for example, analyzes a user's past requests and proposal history to make personalized product suggestions. For example, related new products can be suggested based on product categories requested in the past. The data analysis unit can also analyze a user's preferences based on the proposal history to make personalized suggestions. For example, similar products can be suggested based on proposals that have received high ratings in the past. The data analysis unit can also analyze a user's request history to make personalized suggestions. For example, if there are many requests for a particular brand or category, products from that brand or category can be suggested. This makes it possible to analyze a user's past requests and proposal history to make personalized suggestions. Personalized suggestions - The specific methods and criteria for personalized suggestions need to be clarified. For example, suggestions can be made based on the user's past behavioral history.

[0040] The data analysis unit can analyze other users' reactions to the user's request and prioritize the most popular proposal. The data analysis unit, for example, analyzes other users' reactions to the user's request and prioritizes the most popular proposal. For example, proposals that have received high ratings and many comments from other users can be displayed preferentially. The data analysis unit can also identify the most popular proposal based on other users' reaction data and prioritize the proposal. For example, it can select a proposal that has received many positive reactions to a specific request. The data analysis unit can also analyze other users' feedback on the user's request and prioritize the most popular proposal. For example, it can suggest related products based on proposals that have received the most support from other users. This makes it possible to analyze other users' reactions to the user's request and prioritize the most popular proposal. Reactions of other users - It is necessary to clarify what exactly is meant by reactions from other users. For example, reviews, comments, ratings, etc.

[0041] The data analysis unit can apply the user's request to products in different categories and make new suggestions. For example, the data analysis unit can apply the user's request to products in different categories and make new suggestions. For example, based on a request for a smartphone, it can suggest a smartwatch or a tablet. The data analysis unit can also apply the request to different categories and make new product suggestions. For example, based on a request for a fashion item, it can suggest accessories or shoes. The data analysis unit can also analyze the user's request and apply it to products in different categories and make suggestions. For example, based on a request for home decoration, it can suggest furniture or lighting. This makes it possible to apply the user's request to products in different categories and make new suggestions. Different categories - It is necessary to clarify what different categories specifically refer to. For example, electronic devices, clothing, etc.

[0042] The data analysis unit can analyze the user's requests and suggest related services and subscriptions. The data analysis unit, for example, analyzes the user's requests and suggests related services and subscriptions. For example, based on a request for fitness equipment, a fitness app subscription can be suggested. The data analysis unit can also suggest related services and subscriptions based on the request. For example, based on a request for cooking equipment, a cooking class subscription can be suggested. The data analysis unit can also analyze the user's requests and suggest related services and subscriptions. For example, based on a request for music equipment, a music streaming service subscription can be suggested. This makes it possible to analyze the user's requests and suggest related services and subscriptions. Service - It is necessary to clarify what exactly is meant by service. For example, a subscription service, an on-demand service, etc. Subscription - It is necessary to clarify what exactly is meant by subscription. For example, a monthly service, a yearly service, etc.

[0043] The data analysis unit can customize detailed information about suggested products to suit the user's areas of interest and display it individually. For example, the data analysis unit can customize detailed information about suggested products to suit the user's areas of interest and display it individually. For example, it can highlight product information in categories that interest the user. The data analysis unit can also customize and display detailed information about suggested products based on the user's areas of interest. For example, for a user who is interested in a particular brand or feature, that information can be displayed preferentially. The data analysis unit can also customize detailed information about suggested products to suit the user's areas of interest and display it individually. For example, it can display information related to products that the user has purchased in the past. This makes it possible to customize detailed information about suggested products to suit the user's areas of interest and display it individually. Customization - The specific method and criteria for customization must be clarified. For example, adjustments to suit the user's preferences, etc.

[0044] The data analysis unit can analyze a user's past purchase history and make bundle suggestions for related products. The data analysis unit, for example, analyzes a user's past purchase history and makes bundle suggestions for related products. For example, it can suggest accessories related to a smartphone previously purchased. The data analysis unit can also make bundle suggestions for related products based on the purchase history. For example, it can suggest cooking utensils related to kitchenware previously purchased. The data analysis unit can also analyze a user's purchase history and make bundle suggestions for related products. For example, it can suggest decorative items related to furniture previously purchased. This makes it possible to analyze a user's past purchase history and make bundle suggestions for related products. Bundle suggestions - The specific methods and criteria for bundle suggestions need to be clarified. For example, selling related products as a set.

[0045] The data analysis unit can use AR or VR technology to display suggested products, allowing users to actually try them on. For example, the data analysis unit can use AR technology to display suggested products, allowing users to actually try them on. For example, the data analysis unit can use AR to display furniture arranged in a home. The data analysis unit can also use VR technology to allow users to try suggested products in a virtual space. For example, a VR fitting room can be provided for trying on clothes. The data analysis unit can also use AR or VR technology to allow users to actually try suggested products. For example, a car interior can be experienced in VR. This allows AR and VR technology to be used to allow users to actually try on suggested products. Specific technologies and implementation methods for AR-AR (augmented reality) must be clarified. For example, smartphone apps, AR glasses, etc. Specific technologies and implementation methods for VR-VR (virtual reality) must be clarified. For example, VR headsets, VR simulations, etc.

[0046] The data analysis unit can link the display of suggested products with social media and incorporate the opinions of the user's friends and followers. For example, the data analysis unit can link the display of suggested products with social media and incorporate the opinions of the user's friends and followers. For example, it can display social media comments on the product page. The data analysis unit can also link with social media to customize suggested products based on the opinions of the user's friends and followers. For example, it can prioritize the display of products that friends have given high ratings. The data analysis unit can also link the display of suggested products with social media and incorporate the opinions of the user's friends and followers. For example, it can display reviews and ratings of products by friends. This allows the display of suggested products to be linked with social media and incorporate the opinions of the user's friends and followers. Social media - It is necessary to clarify what exactly social media refers to. For example, Facebook, Twitter, Instagram, etc.

[0047] The data analysis unit can analyze user feedback, identify common areas for improvement, and reflect them in the next proposal. The data analysis unit, for example, analyzes user feedback and identifies common areas for improvement. For example, it extracts problems pointed out by many users and reflects them in the next proposal. The data analysis unit can also identify common areas for improvement based on feedback data and reflect them in the next proposal. For example, it can reflect requests for improvement of specific functions or designs. The data analysis unit can also analyze user feedback, identify common areas for improvement, and reflect them in the next proposal. For example, it can reflect requests for improvement of specific categories or brands. This makes it possible to analyze user feedback, identify common areas for improvement, and reflect them in the next proposal. Feedback - It is necessary to clarify what exactly feedback refers to. For example, user opinions, ratings, comments, etc.

[0048] The data analysis unit can classify the content of the feedback by category and analyze the satisfaction level for each category. For example, the data analysis unit can classify user feedback by category and analyze the satisfaction level for each category. For example, the data analysis unit can score the satisfaction level for a specific category. The data analysis unit can also classify feedback data by category and analyze the satisfaction level for each category. For example, the data analysis unit can analyze the satisfaction level for a specific brand or product. The data analysis unit can also classify user feedback by category and analyze the satisfaction level for each category. For example, the satisfaction level for a specific function or design can be analyzed. This makes it possible to classify the content of the feedback by category and analyze the satisfaction level for each category. Category - It is necessary to clarify what exactly category refers to. For example, product category, service category, etc.

[0049] The data analysis unit can share the feedback with other users and collect community-based improvement suggestions. The data analysis unit, for example, shares the user's feedback with other users and collects community-based improvement suggestions. For example, the data analysis unit can publish the feedback and solicit improvement suggestions from other users. The data analysis unit also shares the feedback and collects community-based improvement suggestions. For example, a platform can be provided where users can post comments and suggestions in response to the feedback. The data analysis unit also shares the user's feedback with other users and collects community-based improvement suggestions. For example, other users' reactions and suggestions to the feedback can be collected and reflected in improvement measures. This makes it possible to share the feedback with other users and collect community-based improvement suggestions. Community-based improvement suggestions - Specific methods and criteria for community-based improvement suggestions need to be clarified. For example, collecting opinions from user groups, discussions in forums, etc.

[0050] The data analysis unit can visualize the feedback to enable the user to intuitively understand it. The data analysis unit, for example, visualizes the user's feedback to enable the user to intuitively understand it. For example, the content of the feedback can be displayed in a graph or chart. The data analysis unit can also visualize the feedback to enable the user to intuitively understand it. For example, the main points of the feedback can be displayed as an infographic. The data analysis unit can also visualize the user's feedback to enable the user to intuitively understand it. For example, the content of the feedback can be visually organized to highlight important points. This makes it possible to visualize the feedback to enable the user to intuitively understand it. Visualization - The specific method or means of visualization must be clarified. For example, graphs, charts, infographics, etc.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The data collection unit can collect the user's health data and make product suggestions based on the user's health condition. For example, the data collection unit can collect data from the user's fitness tracker and suggest fitness equipment to users who are not getting enough exercise. The data collection unit can also analyze the user's food records and suggest health foods to users with an unbalanced nutritional intake. Furthermore, the data collection unit can collect the user's sleep data and suggest sleep aids to users with poor sleep quality. This makes it possible to make product suggestions based on the user's health condition.

[0053] The data collection unit can collect data on the user's hobbies and lifestyle and make product suggestions related to the hobbies. For example, if the user likes outdoor activities, camping equipment and mountain climbing equipment can be suggested. If the user often enjoys music, the data collection unit can suggest musical instruments and audio equipment. Furthermore, if the user's hobby is cooking, the data collection unit can suggest cooking utensils and recipe books. This makes it possible to make product suggestions based on the user's hobbies and lifestyle.

[0054] The data collection unit can analyze the user's purchasing history and suggest subscription services. For example, it can suggest subscription services based on the product categories that the user frequently purchases. The data collection unit can also analyze the user's purchasing patterns and suggest subscription services tailored to specific seasons or events. Furthermore, the data collection unit can suggest related subscription services based on the user's purchasing history. This makes it possible to suggest subscription services based on the user's purchasing history.

[0055] The data collection unit can analyze the user's location information and suggest local specialties and products that are only available in that region. For example, if the user is traveling, it can suggest products related to local specialties and tourist attractions. The data collection unit can also provide information about campaigns and events that are only available in that region based on the user's location information. Furthermore, the data collection unit can analyze the user's location information and suggest popular products for each region. This makes it possible to suggest products based on the user's location information.

[0056] The data collection unit can integrate a user's purchasing history and social media activity to predict the user's interests. For example, it can identify product categories that the user frequently mentions on social media and suggest products in those categories. The data collection unit can also analyze the user's purchasing history and social media following information to suggest products from brands that the user is interested in. Furthermore, the data collection unit can suggest trending products based on the user's social media activity. This makes it possible to make product suggestions based on the user's purchasing history and social media activity.

[0057] The data collection unit can analyze the user's purchase history and make bundle suggestions for related products. For example, it can suggest accessories or complementary products related to products the user has previously purchased. The data collection unit can also suggest bundle sales or discount campaigns based on the user's purchase history. Furthermore, the data collection unit can analyze the user's purchase history and make bundle suggestions for related products. This makes it possible to make bundle suggestions based on the user's purchase history.

[0058] The data collection unit can analyze the user's purchase history and browsing history to suggest related services and subscriptions. For example, related subscription services can be suggested based on the product categories that the user frequently purchases. The data collection unit can also suggest services and subscriptions that the user is interested in based on the user's browsing history. Furthermore, the data collection unit can analyze the user's purchase history and browsing history to suggest related services and subscriptions. This makes it possible to suggest services and subscriptions based on the user's purchase history and browsing history.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The data collection unit collects data on the user's attributes and areas of interest. For example, it collects information such as the user's age, gender, past purchase history, browsing history, and categories of interest. The data collection unit can also collect data on the user's attributes and areas of interest from platforms such as SB, LINE, and Yahoo. Step 2: The data analysis unit analyzes the data collected by the data collection unit. For example, the generation AI analyzes the user's preferences based on data on the user's attributes and areas of interest. The generation AI can also predict changes in the user's areas of interest using machine learning algorithms. Furthermore, the generation AI can analyze the user's data using statistical analysis. Step 3: The product proposal unit proposes products based on the results of the analysis by the data analysis unit. For example, the generation AI considers the user's preferences and campaign information to propose optimal products. The generation AI can also use a recommendation engine to make product proposals based on the user's past behavioral history. Furthermore, the generation AI can also make product proposals based on the user's requests.

[0061] (Example 2) The platform according to an embodiment of the present invention is a system that uses generative AI to function as a platform between users and each company's own e-commerce site. This system utilizes data on the user's attributes and areas of interest, and also takes into account campaign information that is only available on each company's own e-commerce site, to suggest appropriate products that meet the user's needs. This allows the platform to easily find products that meet the user's needs and supports the growth of each company's own e-commerce site.

[0062] The platform according to the embodiment includes a data collection unit, a data analysis unit, and a product proposal unit. The data collection unit collects data related to a user's attributes and areas of interest. For example, the data collection unit collects information such as the user's age, gender, past purchase history, browsing history, and categories of interest. The data collection unit can also collect data related to the user's attributes and areas of interest from platforms such as SB, LINE, and Yahoo. The data analysis unit analyzes the data collected by the data collection unit. For example, the generation AI analyzes the user's preferences based on the data related to the user's attributes and areas of interest. The generation AI can also predict changes in the user's areas of interest using a machine learning algorithm. The generation AI can also analyze the user's data using statistical analysis. The product proposal unit proposes products based on the results of the analysis by the data analysis unit. For example, the generation AI can propose optimal products taking into account the user's preferences and campaign information. The generation AI can also use a recommendation engine to propose products based on the user's past behavioral history. The generation AI can also propose products tailored to the user's needs. This allows the platform according to the embodiment to suggest appropriate products based on the user's attributes and areas of interest. For example, if a user inputs a request such as "I'm looking for a new smartphone," the generation AI will suggest the optimal smartphone by taking into account the user's preferences and campaign information. User attributes - It is necessary to clarify what exactly refers to the user's attributes. For example, age, gender, occupation, etc. Areas of interest - It is necessary to clarify what exactly refers to the areas of interest. For example, hobbies, topics of interest, etc. Data collection - It is necessary to clarify the specific methods and means of data collection. For example, questionnaires, sensors, log data, etc. Data analysis - It is necessary to clarify the specific methods and algorithms of data analysis. For example, statistical analysis, machine learning algorithms, etc. Product proposal - It is necessary to clarify the specific methods and criteria for product proposal. For example, a recommendation engine, proposals based on the user's past behavioral history, etc.

[0063] The data collection unit can analyze a user's social media activity and detect changes in their areas of interest in real time. For example, the data collection unit can analyze a user's social media posts to detect changes in their areas of interest in real time. For example, if a user begins to frequently post about a new hobby, products related to that hobby can be suggested. The data collection unit can also analyze a user's social media following and like history to detect changes in the user's areas of interest. For example, if interest in a new brand or category increases, product suggestions can be made based on that information. The data collection unit can also analyze a user's comments and replies on social media to detect changes in their areas of interest. For example, if there is an increase in comments about a particular event or trend, related products can be suggested. This allows changes in a user's areas of interest to be detected in real time. Social media activity - It is necessary to clarify what exactly social media activity refers to. For example, post content, likes, shares, etc. Real-time - It is necessary to clarify the specific definition and criteria for real-time. For example, seconds, minutes, etc.

[0064] The data collection unit can predict potential interests by analyzing a user's purchase history, browsing history, and click patterns. For example, the data collection unit can integrate a user's purchase history and browsing history to predict potential interests. For example, it can identify product categories that the user frequently browses but has not purchased, and suggest products in those categories. The data collection unit can also analyze a user's click patterns to predict products that the user is interested in but has not yet purchased. For example, if a user repeatedly clicks on a page for a particular brand or product, it can suggest that product. The data collection unit can also predict changes in a user's interests based on the purchase history and browsing history. For example, it can compare products purchased in the past with products viewed recently to identify a category in which the user has become interested. This allows it to predict a user's potential interests. Purchase history: It is necessary to clarify what exactly a purchase history refers to, such as the products purchased and the date and time of purchase. Browsing history: It is necessary to clarify what exactly a browsing history refers to, such as the pages viewed and the duration of browsing. Click pattern: It is necessary to clarify what exactly a click pattern refers to, such as the links clicked and the frequency of clicks.

[0065] The data collection unit can use the emotion estimation function to analyze emotions from user posts and reviews and identify categories with positive emotions. For example, the data collection unit performs emotion analysis on user posts and reviews to identify categories with positive emotions. For example, if there are many positive reviews about a particular product or brand, it can suggest products in that category. The data collection unit can also use the emotion estimation function to identify topics with positive emotions from user posts. For example, if there are many positive posts about a particular event or activity, it can suggest related products. The data collection unit can also analyze user reviews and comments to identify categories with positive emotions. For example, if there are many positive reviews about a particular feature or design, it can suggest products with those features. This allows it to identify categories with positive user emotions. Emotion estimation function: The specific method and algorithm of the emotion estimation function must be clarified. For example, text analysis, voice analysis, etc. Posts: It must be clarified what exactly posts refer to. For example, social media posts, blog articles, etc. Reviews: It must be clarified what exactly reviews refer to. For example, product reviews, service reviews, etc.

[0066] The data collection unit can analyze the user's location information and identify differences in areas of interest by region. The data collection unit can, for example, identify differences in areas of interest by region based on the user's location information. For example, it can analyze products and categories that are popular in a particular region and suggest them to users in that region. The data collection unit can also analyze the location information and identify areas of interest related to seasons and events in each region. For example, it can suggest products related to events held in a particular region. The data collection unit can also analyze purchasing patterns by region based on the user's location information. For example, it can identify product categories that are frequently purchased in a particular region and suggest them to users in that region. This makes it possible to identify differences in areas of interest by region. Location information - It is necessary to clarify what exactly location information refers to. For example, GPS data, IP address, etc.

[0067] The data collection unit can analyze a user's device usage patterns and make optimal product suggestions for each device. For example, the data collection unit can analyze a user's device usage patterns and make optimal product suggestions for each device. For example, mobile products can be suggested based on browsing history on a smartphone. The data collection unit can also identify a user's interests based on device usage patterns and make customized product suggestions for each device. For example, tablet accessories can be suggested to a user who frequently uses a tablet. The data collection unit can also analyze a user's device usage time and make optimal product suggestions at the optimal time. For example, products related to nighttime use can be suggested to a user who uses a smartphone at night. This makes it possible to make optimal product suggestions for each device. Device usage patterns - It is necessary to clarify what exactly device usage patterns refer to. For example, usage time, usage frequency, used apps, etc.

[0068] The data collection unit can use the emotion estimation function to analyze what emotions a user has during a specific time period and suggest products that are optimal for that time period. The data collection unit, for example, uses the emotion estimation function to analyze what emotions a user has during a specific time period. For example, relaxation products can be suggested for a user who wants to relax at night. The data collection unit can also suggest products that are optimal for a specific time period based on the user's emotion data. For example, an energy drink can be suggested for a user who needs energy in the morning. The data collection unit can also use the emotion estimation function to analyze the user's emotional changes and suggest products that are optimal for that time period. For example, stress relief products can be suggested during times when stress levels are high. This makes it possible to suggest products that are optimal for a specific time period. Specific time period - It is necessary to clarify what specific time period refers to. For example, morning, noon, night, etc.

[0069] The data collection unit can collect and analyze campaign information for each company's own e-commerce website. The data collection unit, for example, collects campaign information for each company in real time and analyzes its effectiveness. For example, it identifies effective campaigns based on sales data during a specific campaign period. The data collection unit also analyzes the effectiveness of campaign information in real time and identifies the most effective campaign. For example, it can analyze that a specific discount rate or benefit is most effective. The data collection unit also analyzes campaign information in real time and identifies effective campaigns. For example, it can identify campaigns that are effective during a specific time period or on a specific day of the week. This allows the collection and analysis of campaign information for each company's own e-commerce website. Campaign information - It is necessary to clarify what exactly campaign information refers to. For example, discount information, promotion details, etc.

[0070] The data collection unit can analyze the effectiveness of campaign information in real time and identify the most effective campaign. The data collection unit, for example, collects campaign information from each company in real time and analyzes its effectiveness. For example, it identifies effective campaigns based on sales data during a specific campaign period. The data collection unit also analyzes the effectiveness of campaign information in real time and identifies the most effective campaign. For example, it can analyze that a specific discount rate or benefit is most effective. The data collection unit also analyzes campaign information in real time and identifies effective campaigns. For example, it can identify campaigns that are effective during specific times of day or on specific days of the week. This makes it possible to identify the most effective campaign in real time. Effectiveness - Specific evaluation criteria and methods for effectiveness need to be clarified. For example, sales increase, click rate, etc.

[0071] The data collection unit can analyze past campaign data and identify effective campaign patterns for each season or event. The data collection unit, for example, analyzes past campaign data and identifies effective campaign patterns for each season. For example, it identifies campaigns that are effective in the summer and uses them for the next campaign. The data collection unit also analyzes campaign data for each event and identifies effective campaign patterns. For example, it can identify campaigns that are effective for Christmas or Valentine's Day. The data collection unit also identifies effective campaign patterns for each season or event based on the past campaign data. For example, it can identify campaigns that offer discounts on products related to specific seasons or events. This makes it possible to identify effective campaign patterns for each season or event. Past campaign data - It is necessary to clarify what specifically the past campaign data refers to. For example, the content of past promotions, the implementation period, etc. Seasons and events - It is necessary to clarify what specifically the seasons and events refer to. For example, summer, winter, Christmas, Halloween, etc.

[0072] The data collection unit can use the emotion estimation function to analyze the user's emotions toward campaign information and suggest campaigns that elicit positive emotions. The data collection unit, for example, uses the emotion estimation function to analyze the user's emotions toward campaign information. For example, if there are many positive emotions toward a particular campaign, the data collection unit can suggest that campaign. The data collection unit can also suggest campaigns that elicit positive emotions based on the user's emotion data. For example, if a particular discount rate or benefit elicits positive emotions, the data collection unit can suggest that campaign. The data collection unit can also use the emotion estimation function to analyze changes in the user's emotions and suggest campaigns that elicit positive emotions. For example, it can suggest campaigns that elicit positive emotions during particular times of the day or on particular days of the week. This makes it possible to suggest campaigns that elicit positive emotions from the user. Positive emotions - It is necessary to clarify what exactly positive emotions refer to. For example, joy, satisfaction, etc.

[0073] The data collection department can collect campaign information from different industries and apply success stories. For example, the data collection department can collect campaign information from different industries and apply success stories. For example, a successful campaign from the fashion industry can be applied to the home appliance industry. The data collection department can also analyze campaign information from other industries and apply success stories to its own campaigns. For example, a successful campaign from the food and beverage industry can be applied to the beauty industry. The data collection department can also collect campaign data from different industries and design its own campaigns based on success stories. For example, a successful campaign from the travel industry can be applied to the entertainment industry. This allows success stories from other industries to be applied. It is necessary to clarify what specific industries are referred to by different industries. For example, the fashion industry, the food industry, etc.

[0074] The data collection unit can visualize campaign information to enable users to intuitively understand it. The data collection unit, for example, visualizes campaign information to enable users to intuitively understand it. For example, it can visually display the effectiveness of the campaign using graphs and charts. The data collection unit can also visualize campaign information as infographics to enable users to easily understand it. For example, it can illustrate an overview of the campaign and the benefits. The data collection unit can also use visualized campaign information to enable users to intuitively understand it. For example, it can display the progress and effectiveness of the campaign in real time. This allows the campaign information to be visualized to enable intuitive understanding. Visualization - The specific method or means of visualization must be clarified. For example, graphs, charts, infographics, etc.

[0075] The data collection unit can use the emotion estimation function to customize and individually optimize campaign information according to the user's emotions. The data collection unit, for example, uses the emotion estimation function to customize campaign information according to the user's emotions. For example, a campaign with many perks can be suggested to a user with positive emotions. The data collection unit also individually optimizes campaign information based on the user's emotion data. For example, a campaign with a high discount rate can be suggested to a user with negative emotions. The data collection unit also uses the emotion estimation function to customize campaign information according to changes in the user's emotions. For example, a campaign that is optimal for that time period can be suggested to a user whose emotions change during a specific time period or day of the week. This allows the campaign information to be customized and individually optimized according to the user's emotions. Customization - The specific method and criteria for customization must be clarified. For example, adjustments based on the user's preferences, etc.

[0076] The data analysis unit can analyze a user's past requests and proposal history to make personalized suggestions. The data analysis unit, for example, analyzes a user's past requests and proposal history to make personalized product suggestions. For example, related new products can be suggested based on product categories requested in the past. The data analysis unit can also analyze a user's preferences based on the proposal history to make personalized suggestions. For example, similar products can be suggested based on proposals that have received high ratings in the past. The data analysis unit can also analyze a user's request history to make personalized suggestions. For example, if there are many requests for a particular brand or category, products from that brand or category can be suggested. This makes it possible to analyze a user's past requests and proposal history to make personalized suggestions. Personalized suggestions - The specific methods and criteria for personalized suggestions need to be clarified. For example, suggestions can be made based on the user's past behavioral history.

[0077] The data analysis unit can analyze other users' reactions to the user's request and prioritize the most popular proposal. The data analysis unit, for example, analyzes other users' reactions to the user's request and prioritizes the most popular proposal. For example, proposals that have received high ratings and many comments from other users can be displayed preferentially. The data analysis unit can also identify the most popular proposal based on other users' reaction data and prioritize the proposal. For example, it can select a proposal that has received many positive reactions to a specific request. The data analysis unit can also analyze other users' feedback on the user's request and prioritize the most popular proposal. For example, it can suggest related products based on proposals that have received the most support from other users. This makes it possible to analyze other users' reactions to the user's request and prioritize the most popular proposal. Reactions of other users - It is necessary to clarify what exactly is meant by reactions from other users. For example, reviews, comments, ratings, etc.

[0078] The data analysis unit can use the emotion estimation function to analyze the user's emotions regarding the request and make suggestions that elicit positive emotions. For example, the data analysis unit can use the emotion estimation function to analyze the user's emotions regarding the request and make suggestions that elicit positive emotions. For example, the data analysis unit can suggest product categories for which the user has positive emotions. The data analysis unit can also make suggestions that elicit positive emotions based on emotion data regarding the user's request. For example, the data analysis unit can suggest related products based on products for which the user has felt positive in the past. The data analysis unit can also use the emotion estimation function to analyze changes in the user's emotions regarding the request and make suggestions that elicit positive emotions. For example, if there are many positive emotions regarding a specific request, the data analysis unit can suggest products related to that request. This makes it possible to analyze the user's emotions regarding the request and make suggestions that elicit positive emotions. Positive emotions - It is necessary to clarify what positive emotions specifically refer to. For example, joy, satisfaction, etc.

[0079] The data analysis unit can apply the user's request to products in different categories and make new suggestions. For example, the data analysis unit can apply the user's request to products in different categories and make new suggestions. For example, based on a request for a smartphone, it can suggest a smartwatch or a tablet. The data analysis unit can also apply the request to different categories and make new product suggestions. For example, based on a request for a fashion item, it can suggest accessories or shoes. The data analysis unit can also analyze the user's request and apply it to products in different categories and make suggestions. For example, based on a request for home decoration, it can suggest furniture or lighting. This makes it possible to apply the user's request to products in different categories and make new suggestions. Different categories - It is necessary to clarify what different categories specifically refer to. For example, electronic devices, clothing, etc.

[0080] The data analysis unit can analyze the user's requests and suggest related services and subscriptions. The data analysis unit, for example, analyzes the user's requests and suggests related services and subscriptions. For example, based on a request for fitness equipment, a fitness app subscription can be suggested. The data analysis unit can also suggest related services and subscriptions based on the request. For example, based on a request for cooking equipment, a cooking class subscription can be suggested. The data analysis unit can also analyze the user's requests and suggest related services and subscriptions. For example, based on a request for music equipment, a music streaming service subscription can be suggested. This makes it possible to analyze the user's requests and suggest related services and subscriptions. Service - It is necessary to clarify what exactly is meant by service. For example, a subscription service, an on-demand service, etc. Subscription - It is necessary to clarify what exactly is meant by subscription. For example, a monthly service, a yearly service, etc.

[0081] The data analysis unit uses the emotion estimation function to analyze the user's emotions regarding the request in real time and make suggestions at the optimal timing. The data analysis unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the request in real time and make suggestions at the optimal timing. For example, it suggests products when positive emotions are increasing. The data analysis unit also makes product suggestions at the optimal timing based on the user's emotion data. For example, it can suggest relaxation products when the user is relaxing. The data analysis unit also uses the emotion estimation function to analyze the user's emotional changes in real time and make suggestions at the optimal timing. For example, it can suggest stress relief products when stress is increasing. This makes it possible to analyze the user's emotions regarding the request in real time and make suggestions at the optimal timing. Real-time - The specific definition and criteria of real-time need to be clarified. For example, seconds, minutes, etc.

[0082] The data analysis unit can customize detailed information about suggested products to suit the user's areas of interest and display it individually. For example, the data analysis unit can customize detailed information about suggested products to suit the user's areas of interest and display it individually. For example, it can highlight product information in categories that interest the user. The data analysis unit can also customize and display detailed information about suggested products based on the user's areas of interest. For example, for a user who is interested in a particular brand or feature, that information can be displayed preferentially. The data analysis unit can also customize detailed information about suggested products to suit the user's areas of interest and display it individually. For example, it can display information related to products that the user has purchased in the past. This makes it possible to customize detailed information about suggested products to suit the user's areas of interest and display it individually. Customization - The specific method and criteria for customization must be clarified. For example, adjustments to suit the user's preferences, etc.

[0083] The data analysis unit can analyze a user's past purchase history and make bundle suggestions for related products. The data analysis unit, for example, analyzes a user's past purchase history and makes bundle suggestions for related products. For example, it can suggest accessories related to a smartphone previously purchased. The data analysis unit can also make bundle suggestions for related products based on the purchase history. For example, it can suggest cooking utensils related to kitchenware previously purchased. The data analysis unit can also analyze a user's purchase history and make bundle suggestions for related products. For example, it can suggest decorative items related to furniture previously purchased. This makes it possible to analyze a user's past purchase history and make bundle suggestions for related products. Bundle suggestions - The specific methods and criteria for bundle suggestions need to be clarified. For example, selling related products as a set.

[0084] The data analysis unit can use the emotion estimation function to analyze the user's emotions toward the proposed product and propose a display method that elicits positive emotions. For example, the data analysis unit can use the emotion estimation function to analyze the user's emotions toward the proposed product and propose a display method that elicits positive emotions. For example, it can highlight product categories that elicit positive emotions. The data analysis unit can also propose a display method that elicits positive emotions based on the user's emotion data. For example, if a specific design or color elicits positive emotions, it can highlight those elements. The data analysis unit can also use the emotion estimation function to analyze the user's emotional changes and propose a display method that elicits positive emotions. For example, it can customize the display method according to a specific time period or situation. This makes it possible to propose a display method that will elicit positive emotions toward the proposed product. Positive emotions - It is necessary to clarify what specifically positive emotions refer to. For example, joy, satisfaction, etc.

[0085] The data analysis unit can use AR or VR technology to display suggested products, allowing users to actually try them on. For example, the data analysis unit can use AR technology to display suggested products, allowing users to actually try them on. For example, the data analysis unit can use AR to display furniture arranged in a home. The data analysis unit can also use VR technology to allow users to try suggested products in a virtual space. For example, a VR fitting room can be provided for trying on clothes. The data analysis unit can also use AR or VR technology to allow users to actually try suggested products. For example, a car interior can be experienced in VR. This allows AR and VR technology to be used to allow users to actually try on suggested products. Specific technologies and implementation methods for AR-AR (augmented reality) must be clarified. For example, smartphone apps, AR glasses, etc. Specific technologies and implementation methods for VR-VR (virtual reality) must be clarified. For example, VR headsets, VR simulations, etc.

[0086] The data analysis unit can link the display of suggested products with social media and incorporate the opinions of the user's friends and followers. For example, the data analysis unit can link the display of suggested products with social media and incorporate the opinions of the user's friends and followers. For example, it can display social media comments on the product page. The data analysis unit can also link with social media to customize suggested products based on the opinions of the user's friends and followers. For example, it can prioritize the display of products that friends have given high ratings. The data analysis unit can also link the display of suggested products with social media and incorporate the opinions of the user's friends and followers. For example, it can display reviews and ratings of products by friends. This allows the display of suggested products to be linked with social media and incorporate the opinions of the user's friends and followers. Social media - It is necessary to clarify what exactly social media refers to. For example, Facebook, Twitter, Instagram, etc.

[0087] The data analysis unit uses the emotion estimation function to display a purchase support message that matches the user's emotions, thereby increasing their willingness to purchase. The data analysis unit, for example, uses the emotion estimation function to display a purchase support message that matches the user's emotions. For example, an encouraging message can be displayed to a user with positive emotions. The data analysis unit also customizes the purchase support message based on the user's emotion data. For example, a message that gives a sense of security can be displayed to a user with negative emotions. The data analysis unit also uses the emotion estimation function to display a purchase support message that matches the user's emotional changes. For example, a message encouraging relaxation can be displayed to a user who is experiencing increased stress. This allows the display of a purchase support message that matches the user's emotions, increasing their willingness to purchase. Emotion estimation function - The specific method and algorithm of the emotion estimation function must be clarified. For example, text analysis, voice analysis, etc.

[0088] The data analysis unit can analyze user feedback, identify common areas for improvement, and reflect them in the next proposal. The data analysis unit, for example, analyzes user feedback and identifies common areas for improvement. For example, it extracts problems pointed out by many users and reflects them in the next proposal. The data analysis unit can also identify common areas for improvement based on feedback data and reflect them in the next proposal. For example, it can reflect requests for improvement of specific functions or designs. The data analysis unit can also analyze user feedback, identify common areas for improvement, and reflect them in the next proposal. For example, it can reflect requests for improvement of specific categories or brands. This makes it possible to analyze user feedback, identify common areas for improvement, and reflect them in the next proposal. Feedback - It is necessary to clarify what exactly feedback refers to. For example, user opinions, ratings, comments, etc.

[0089] The data analysis unit can classify the content of the feedback by category and analyze the satisfaction level for each category. For example, the data analysis unit can classify user feedback by category and analyze the satisfaction level for each category. For example, the data analysis unit can score the satisfaction level for a specific category. The data analysis unit can also classify feedback data by category and analyze the satisfaction level for each category. For example, the data analysis unit can analyze the satisfaction level for a specific brand or product. The data analysis unit can also classify user feedback by category and analyze the satisfaction level for each category. For example, the satisfaction level for a specific function or design can be analyzed. This makes it possible to classify the content of the feedback by category and analyze the satisfaction level for each category. Category - It is necessary to clarify what exactly category refers to. For example, product category, service category, etc.

[0090] The data analysis unit can use the emotion estimation function to analyze emotions contained in the feedback and propose improvement measures to elicit positive emotions. The data analysis unit, for example, uses the emotion estimation function to analyze emotions contained in the feedback and propose improvement measures to elicit positive emotions. For example, it proposes positive improvement measures for feedback that contains a lot of negative emotions. The data analysis unit can also analyze user feedback using the emotion estimation function and propose improvement measures to elicit positive emotions. For example, it can propose positive solutions to specific problems. The data analysis unit can also use the emotion estimation function to analyze emotions contained in the feedback and propose improvement measures to elicit positive emotions. For example, it can propose specific improvement measures to improve user satisfaction. This makes it possible to analyze emotions contained in the feedback and propose improvement measures to elicit positive emotions. Positive emotions - It is necessary to clarify what specifically positive emotions refer to. For example, joy, satisfaction, etc.

[0091] The data analysis unit can share the feedback with other users and collect community-based improvement suggestions. The data analysis unit, for example, shares the user's feedback with other users and collects community-based improvement suggestions. For example, the data analysis unit can publish the feedback and solicit improvement suggestions from other users. The data analysis unit also shares the feedback and collects community-based improvement suggestions. For example, a platform can be provided where users can post comments and suggestions in response to the feedback. The data analysis unit also shares the user's feedback with other users and collects community-based improvement suggestions. For example, other users' reactions and suggestions to the feedback can be collected and reflected in improvement measures. This makes it possible to share the feedback with other users and collect community-based improvement suggestions. Community-based improvement suggestions - Specific methods and criteria for community-based improvement suggestions need to be clarified. For example, collecting opinions from user groups, discussions in forums, etc.

[0092] The data analysis unit can visualize the feedback to enable the user to intuitively understand it. The data analysis unit, for example, visualizes the user's feedback to enable the user to intuitively understand it. For example, the content of the feedback can be displayed in a graph or chart. The data analysis unit can also visualize the feedback to enable the user to intuitively understand it. For example, the main points of the feedback can be displayed as an infographic. The data analysis unit can also visualize the user's feedback to enable the user to intuitively understand it. For example, the content of the feedback can be visually organized to highlight important points. This makes it possible to visualize the feedback to enable the user to intuitively understand it. Visualization - The specific method or means of visualization must be clarified. For example, graphs, charts, infographics, etc.

[0093] The data analysis unit can use the emotion estimation function to analyze the emotion of the feedback in real time and immediately propose a countermeasure. The data analysis unit, for example, uses the emotion estimation function to analyze the emotion of the feedback in real time and immediately propose a countermeasure. For example, for feedback with a lot of negative emotions, it can quickly propose an improvement measure. The data analysis unit can also analyze the emotion of the feedback in real time and immediately propose a countermeasure. For example, it can propose a specific improvement measure to bring out positive emotions. The data analysis unit can also use the emotion estimation function to analyze the emotion of the feedback in real time and immediately propose a countermeasure. For example, it can quickly propose a countermeasure to improve user satisfaction. This makes it possible to analyze the emotion of the feedback in real time and immediately propose a countermeasure. Real-time - The specific definition and criteria of real-time need to be clarified. For example, seconds, minutes, etc.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The data collection unit can collect the user's health data and make product suggestions based on the user's health condition. For example, the data collection unit can collect data from the user's fitness tracker and suggest fitness equipment to users who are not getting enough exercise. The data collection unit can also analyze the user's food records and suggest health foods to users with an unbalanced nutritional intake. Furthermore, the data collection unit can collect the user's sleep data and suggest sleep aids to users with poor sleep quality. This makes it possible to make product suggestions based on the user's health condition.

[0096] The data collection unit can collect data on the user's hobbies and lifestyle and make product suggestions related to the hobbies. For example, if the user likes outdoor activities, camping equipment and mountain climbing equipment can be suggested. If the user often enjoys music, the data collection unit can suggest musical instruments and audio equipment. Furthermore, if the user's hobby is cooking, the data collection unit can suggest cooking utensils and recipe books. This makes it possible to make product suggestions based on the user's hobbies and lifestyle.

[0097] The data collection unit can analyze the user's purchasing history and suggest subscription services. For example, it can suggest subscription services based on the product categories that the user frequently purchases. The data collection unit can also analyze the user's purchasing patterns and suggest subscription services tailored to specific seasons or events. Furthermore, the data collection unit can suggest related subscription services based on the user's purchasing history. This makes it possible to suggest subscription services based on the user's purchasing history.

[0098] The data collection unit can use the emotion estimation function to make product suggestions based on the user's emotions. For example, if the user is feeling stressed, the data collection unit can suggest relaxation goods or stress relief items. Also, if the user is feeling positive emotions, the data collection unit can suggest entertainment products or products related to hobbies. Furthermore, the data collection unit can make product suggestions according to the user's emotions based on the user's emotion data. This makes it possible to make product suggestions based on the user's emotions.

[0099] The data collection unit can analyze the user's location information and suggest local specialties and products that are only available in that region. For example, if the user is traveling, it can suggest products related to local specialties and tourist attractions. The data collection unit can also provide information about campaigns and events that are only available in that region based on the user's location information. Furthermore, the data collection unit can analyze the user's location information and suggest popular products for each region. This makes it possible to suggest products based on the user's location information.

[0100] The data collection unit can integrate a user's purchasing history and social media activity to predict the user's interests. For example, it can identify product categories that the user frequently mentions on social media and suggest products in those categories. The data collection unit can also analyze the user's purchasing history and social media following information to suggest products from brands that the user is interested in. Furthermore, the data collection unit can suggest trending products based on the user's social media activity. This makes it possible to make product suggestions based on the user's purchasing history and social media activity.

[0101] The data collection unit can use the emotion estimation function to provide a customized message based on the user's emotions. For example, if the user has negative emotions, the data collection unit can provide an encouraging message or suggest ways to relax. If the user has positive emotions, the data collection unit can provide a thank you message or special offer information. Furthermore, the data collection unit can provide a customized message based on the user's emotions, based on the user's emotion data. This makes it possible to provide a customized message based on the user's emotions.

[0102] The data collection unit can analyze the user's purchase history and make bundle suggestions for related products. For example, it can suggest accessories or complementary products related to products the user has previously purchased. The data collection unit can also suggest bundle sales or discount campaigns based on the user's purchase history. Furthermore, the data collection unit can analyze the user's purchase history and make bundle suggestions for related products. This makes it possible to make bundle suggestions based on the user's purchase history.

[0103] The data collection unit can use the emotion estimation function to provide purchasing support based on the user's emotions. For example, if the user is unsure about a purchase, it can provide messages and reviews that give the user a sense of security. Also, if the user has positive emotions, the data collection unit can provide messages and special offer information that encourage the purchase. Furthermore, the data collection unit can provide purchasing support that corresponds to the user's emotions based on the user's emotion data. This makes it possible to provide purchasing support based on the user's emotions.

[0104] The data collection unit can analyze the user's purchase history and browsing history to suggest related services and subscriptions. For example, related subscription services can be suggested based on the product categories that the user frequently purchases. The data collection unit can also suggest services and subscriptions that the user is interested in based on the user's browsing history. Furthermore, the data collection unit can analyze the user's purchase history and browsing history to suggest related services and subscriptions. This makes it possible to suggest services and subscriptions based on the user's purchase history and browsing history.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The data collection unit collects data on the user's attributes and areas of interest. For example, it collects information such as the user's age, gender, past purchase history, browsing history, and categories of interest. The data collection unit can also collect data on the user's attributes and areas of interest from platforms such as SB, LINE, and Yahoo. Step 2: The data analysis unit analyzes the data collected by the data collection unit. For example, the generation AI analyzes the user's preferences based on data on the user's attributes and areas of interest. The generation AI can also predict changes in the user's areas of interest using machine learning algorithms. Furthermore, the generation AI can analyze the user's data using statistical analysis. Step 3: The product proposal unit proposes products based on the results of the analysis by the data analysis unit. For example, the generation AI considers the user's preferences and campaign information to propose optimal products. The generation AI can also use a recommendation engine to make product proposals based on the user's past behavioral history. Furthermore, the generation AI can also make product proposals based on the user's requests.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] 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.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects data on user attributes and areas of interest; a data analysis unit that analyzes the data collected by the data collection unit; a product suggestion unit that suggests products based on the results of the analysis by the data analysis unit. A system characterized by:

2. The data collection unit Analyzing the user's social media activity to detect changes in the areas of interest in real time 2. The system of claim 1.

3. The data collection unit Analyzing the user's purchase history, browsing history, and click patterns to predict potential interests 2. The system of claim 1.

4. The data collection unit Analyze sentiment from the user's posts and reviews to identify categories with positive sentiment 2. The system of claim 1.

5. The data collection unit Analyzing the location information of the user and identifying differences in the areas of interest by region 2. The system of claim 1.

6. The data collection unit Analyze the device usage patterns of the user and make optimal product suggestions for each device.

2. The system of claim 1.

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