system

Abstract

An object of a system according to an embodiment is to provide a consumer with optimal purchase timing and discount information, and to allow a store establishment company to secure a budget necessary for SEO countermeasures.SOLUTION: A system includes a collection part, a provision part, an operation part, means, and a budget part. The collection unit collects a purchase history and a browsing history of a consumer. The providing unit provides appropriate purchase timing or discount information based on the data collected by the collecting unit. The operating unit operates points that are not used. The means increases the exposure opportunity of its own company merchandise by the increase of profits due to the increase of the number of consumers and point operation. The budget section secures a budget for devoting a budget required for the SEO countermeasure.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 technology has made it difficult for consumers to obtain information on optimal purchase timing and discounts, and for retailers to secure a sufficient budget for SEO measures.

[0005] The system according to the embodiment aims to provide consumers with optimal purchase timing and discount information, and to enable store owners to secure the budget necessary for SEO measures. [Means for solving the problem]

[0006] The system according to the embodiment comprises a collection unit, a provision unit, an operation unit, a means, and a budget unit. The collection unit collects consumer purchase and browsing histories. The provision unit provides appropriate purchase timing or discount information based on the data collected by the collection unit. The operation unit manages unused points. The means increases the number of consumers and revenues from point management, thereby increasing exposure opportunities for the company's products. The budget unit secures a budget to allocate the necessary funds for SEO measures. [Effects of the Invention]

[0007] The system according to the embodiment provides consumers with optimal purchase timing and discount information, and enables store owners to secure the budget necessary for SEO measures. [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 AI-driven e-commerce site according to an embodiment of the present invention is a system that solves problems for both consumers and merchants. The AI-driven e-commerce site collects consumers' purchase and browsing histories and provides optimal purchase timing and discount information. It also manages unused points and develops them as assets. Furthermore, by increasing the number of consumers and increasing revenue through point management, merchants can increase the exposure of their products. For example, an AI-driven e-commerce site collects consumers' purchase and browsing histories. For example, it collects data such as the category, price range, and purchase frequency of products purchased by consumers in the past. Then, based on the collected data, it provides optimal purchase timing and discount information. For example, when a consumer is planning to purchase a specific product, AI can analyze the product's price fluctuations and discount information and suggest the optimal purchase timing. Furthermore, the AI-driven e-commerce site manages unused points and increases their value. For example, AI can manage consumers' accumulated points and increase their value. This allows consumers to make effective use of their points. Meanwhile, merchants can increase the exposure of their products by increasing the number of consumers and increasing revenue through point management. For example, as the number of consumers increases, a store's products become more widely known. Furthermore, increased revenue from point management allows store owners to allocate a sufficient budget to SEO strategies. This allows store owners to increase exposure for their products and boost sales. AI-driven e-commerce sites solve problems for both consumers and store owners, enabling sustainable growth. AI-driven e-commerce sites can collect consumers' purchase and browsing histories and provide optimal purchase timing and discount information. Unused points can also be used to increase the value of points. Furthermore, an increase in the number of consumers and increased revenue from point management allows store owners to increase exposure for their products and boost sales.

[0029] An AI-driven e-commerce website according to an embodiment includes a collection unit, a provision unit, an operation unit, an exposure unit, and a budget unit. The collection unit collects consumer purchase and browsing histories. The consumer purchase and browsing histories include, but are not limited to, purchase dates and times, product categories, and viewed pages. The collection unit collects data such as the categories and price ranges of products previously purchased by the consumer, and purchase frequency. The collection unit can also collect product pages and search histories viewed by the consumer. For example, the collection unit records the number of times and duration a consumer views a specific product. The provision unit provides optimal purchase timing and discount information based on the data collected by the collection unit. For example, when a consumer is planning to purchase a specific product, the provision unit analyzes price fluctuations and discount information for that product and suggests the optimal purchase timing. The provision unit can also provide limited-time discount information and coupon codes to the consumer. For example, the provision unit notifies the consumer of discount information for products the consumer is considering purchasing. The operation unit manages unused points to increase the value of the points. For example, the operations department uses AI to manage points held by consumers, increasing their value. The operations department can also adjust the interest rate and management method for points. For example, the operations department sets the investment interest rate for points and increases their value. The exposure department increases exposure opportunities for the company's products by increasing the number of consumers and increasing revenue through point management. For example, the exposure department ensures that the products of store operators are recognized by more consumers by increasing the number of consumers. The exposure department also ensures that the increased revenue through point management allows store operators to allocate sufficient budgets to SEO measures. For example, the exposure department exposes store operators' products through advertising campaigns and promotional events. The budget department allocates the necessary budget for SEO measures. For example, the budget department ensures that store operators can allocate sufficient budgets to SEO measures. The budget department can also adjust the allocation of budgets for SEO measures. For example, the budget department allocates budgets to SEO measures such as keyword optimization, link building, and content marketing.As a result, the AI-driven e-commerce site according to the embodiment can collect consumers' purchase and browsing histories and provide optimal purchase timing and discount information. It can also utilize unused points to increase the value of points. Furthermore, by increasing the number of consumers and increasing revenue through point utilization, store owners can increase exposure opportunities for their products and improve sales.

[0030] The collection unit can collect the purchase history and browsing history of consumers. The collection unit collects data such as the category, price range, and purchase frequency of products purchased by consumers in the past. For example, the collection unit records the date and time and price when a consumer purchased a specific product. The collection unit can also collect the product pages viewed by consumers and search history. For example, the collection unit records the number of times and time when a consumer viewed a specific product. The collection unit can also collect reviews and ratings of products purchased by consumers in the past. For example, the collection unit analyzes the consumer's purchasing patterns based on the reviews and ratings of products purchased by the consumer. This makes it possible to effectively collect the consumer's purchase history and browsing history.

[0031] The providing unit can provide appropriate purchase timing or discount information based on the collected data. For example, when a consumer is planning to purchase a specific product, the providing unit analyzes price fluctuations and discount information for that product and suggests the optimal purchase timing. For example, the providing unit analyzes the price history of a product that the consumer is considering purchasing and predicts when the price will drop. The providing unit can also provide limited-time discount information and coupon codes to the consumer. For example, the providing unit notifies the consumer of discount information for a product that the consumer is considering purchasing. The providing unit can also provide discount information for products that the consumer has previously purchased. For example, the providing unit provides discount information when the consumer repurchases a product that the consumer has previously purchased. This makes it possible to provide the consumer with the optimal purchase timing and discount information.

[0032] The investment department can invest unused points and increase their value. For example, the investment department uses AI to invest points held by consumers and increase their value. For example, the investment department sets the investment interest rate for points and increases their value. The investment department can also adjust the interest rate and investment method for points. For example, the investment department can adjust the investment interest rate for points to maximize their value. The investment department can also diversify the investment method for points. For example, the investment department can diversify points into different investment targets. This allows consumers' points to be managed effectively and their value to be increased.

[0033] The Exposure Department has a means to increase exposure opportunities for its products by increasing the number of consumers and increasing revenue through point management. For example, the Exposure Department ensures that the products of the store-opening company are recognized by more consumers as the number of consumers increases. For example, as the number of consumers increases, the Exposure Department exposes the store-opening company's products in advertising campaigns and promotional events. In addition, the Exposure Department ensures that the store-opening company can allocate a sufficient budget to SEO measures by increasing revenue through point management. For example, the Exposure Department uses SEO measures to display the store-opening company's products at the top of search engines. This increases the store-opening company's product exposure opportunities and improves sales.

[0034] The budget department has a means for allocating the necessary budget for SEO measures. For example, the budget department ensures that store owners can allocate a sufficient budget to SEO measures. For example, the budget department adjusts the budget allocation for SEO measures and implements effective SEO measures. The budget department can also optimize the budget allocation for SEO measures. For example, the budget department allocates the budget to SEO measures such as keyword optimization, link building, and content marketing. This allows store owners to allocate a sufficient budget to SEO measures.

[0035] The collection unit can analyze the user's past purchase history and browsing history and select the optimal collection method. The collection unit, for example, prioritizes collection of product categories that the user frequently purchases. For example, the collection unit analyzes the user's past purchase history and selects the optimal collection method. The collection unit can also re-collect products that the user viewed in the past but did not purchase to attract their interest. For example, the collection unit analyzes the user's browsing history and selects the optimal collection method. The collection unit can also perform periodic collection based on the user's purchase frequency. For example, the collection unit analyzes the user's purchase frequency and selects the optimal collection method. This enables efficient data collection by selecting the optimal collection method based on the user's past behavior.

[0036] When collecting purchase history or browsing history, the collection unit can filter the data based on the user's current areas of interest or living situation. The collection unit, for example, prioritizes collection of product categories in which the user is currently interested. For example, the collection unit analyzes the user's current areas of interest and selects an optimal collection method. The collection unit can also collect related products based on the user's living situation (for example, moving or getting married). For example, the collection unit analyzes the user's living situation and selects an optimal collection method. The collection unit can also adjust the data to be collected based on the user's current purchasing desires. For example, the collection unit analyzes the user's purchasing desires and selects an optimal collection method. In this way, more relevant data can be collected by filtering data based on the user's current interests or living situation.

[0037] When collecting purchase history or browsing history, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit analyzes voice data to collect the purchase history. For example, the collection unit analyzes the user's voice input and selects the optimal collection method. Furthermore, if the user uses text input, the collection unit can also analyze text data to collect the browsing history. For example, the collection unit analyzes the user's text input and selects the optimal collection method. Furthermore, if the user uses image input, the collection unit can analyze image data to collect related product information. For example, the collection unit analyzes the user's image input and selects the optimal collection method. This enables efficient data collection by selecting the optimal collection means depending on the user's input method.

[0038] When collecting purchase history or browsing history, the collection unit can prioritize collection of highly relevant data by taking into account the user's geographical location information. The collection unit, for example, prioritizes collection of data on specific stores or products in the area where the user is currently located. For example, the collection unit analyzes the user's geographical location information and selects an optimal collection method. The collection unit can also collect data on areas frequently visited by the user and provide related product information. For example, the collection unit analyzes the user's geographical location information and selects an optimal collection method. The collection unit can also collect area-specific discount information based on the user's geographical location information. For example, the collection unit analyzes the user's geographical location information and selects an optimal collection method. This enables more effective data collection by preferentially collecting highly relevant data based on the user's geographical location information.

[0039] The collection unit can analyze the user's social media activities and collect related data when collecting purchase history and browsing history. The collection unit, for example, collects data about products that the user has "liked" or commented on on social media. For example, the collection unit analyzes the user's social media activities and selects the optimal collection method. The collection unit can also analyze the content of the user's posts on social media to collect related product information. For example, the collection unit analyzes the user's social media activities and selects the optimal collection method. The collection unit can also collect related product data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the user's social media activities and selects the optimal collection method. In this way, highly relevant data can be collected by analyzing the user's social media activities.

[0040] When collecting purchase history or browsing history, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit adjusts the type of data to be collected, for example, based on feedback provided by the user in the past. For example, the collection unit analyzes the user's past feedback and selects the optimal collection method. The collection unit can also optimize the collection timing based on the user's feedback. For example, the collection unit analyzes the user's feedback and selects the optimal collection method. The collection unit can also customize the collection means (audio, text, image, etc.) by reflecting the user's feedback. For example, the collection unit analyzes the user's feedback and selects the optimal collection method. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0041] The providing unit can adjust the level of detail of the provided information based on the importance of the product when providing the information. For example, in the case of an expensive product, the providing unit provides detailed specifications and review information. For example, the providing unit provides optimal information based on the price and reviews of the product. The providing unit can also provide concise information in the case of a product that is used on a daily basis. For example, the providing unit provides optimal information based on the frequency of use and category of the product. The providing unit can also provide special promotion information in the case of a new product or a limited product. For example, the providing unit provides optimal information based on the release date and limited offers of the product. In this way, by adjusting the level of detail of the information based on the importance of the product, more appropriate information can be provided.

[0042] The providing unit can apply different providing algorithms depending on the product category when providing information. For example, in the case of electronic devices, the providing unit provides information that emphasizes technical specifications and functions. For example, the providing unit provides optimal information based on the product category. In addition, the providing unit can also provide information that emphasizes visuals in the case of fashion items. For example, the providing unit provides optimal information based on the design and style of the product. In addition, the providing unit can also provide ingredient and nutritional information in the case of food. For example, the providing unit provides optimal information based on the ingredients and nutritional value of the product. This makes it possible to provide more effective information by adjusting the providing algorithm depending on the product category.

[0043] The providing unit can improve the accuracy of the information provided by referring to the user's past providing results. The providing unit provides information on similar products based on, for example, evaluations of products purchased by the user in the past. For example, the providing unit analyzes the user's past providing results and provides optimal information. The providing unit can also provide information on related products based on information on products viewed by the user in the past. For example, the providing unit analyzes the user's past providing results and provides optimal information. The providing unit can also analyze the user's past purchasing history and provide information on optimal products. For example, the providing unit analyzes the user's past providing results and provides optimal information. In this way, the accuracy of the information provided can be improved by referring to the user's past providing results.

[0044] The providing unit can determine the priority of the information to be provided based on the price fluctuations of the product at the time of providing the information. For example, the providing unit preferentially provides information on products whose prices have dropped significantly. For example, the providing unit analyzes price fluctuations of products and provides the most appropriate information. The providing unit can also provide information on products whose prices are on the rise early. For example, the providing unit analyzes the price history of products and provides the most appropriate information. The providing unit can also periodically provide information on products whose prices are stable. For example, the providing unit analyzes price fluctuations of products and provides the most appropriate information. In this way, by determining the priority of information based on price fluctuations of products, more appropriate information can be provided.

[0045] The providing unit can adjust the order of provided information based on the relevance of the products when providing the information. For example, the providing unit prioritizes providing information about products that are highly relevant to products that the user has purchased in the past. For example, the providing unit analyzes the user's past purchase history and provides optimal information. The providing unit can also provide information about products that are highly relevant to the product the user is currently browsing. For example, the providing unit analyzes the user's current browsing history and provides optimal information. The providing unit can also provide information about highly relevant products based on the user's interests and concerns. For example, the providing unit analyzes the user's interests and concerns and provides optimal information. This enables more effective information provision by adjusting the order of information based on the relevance of the products.

[0046] The providing unit can adjust the use of technical terms in the provided information according to the user's level of expertise when providing the information. For example, if the user has technical expertise, the providing unit provides information using detailed technical terms. For example, the providing unit analyzes the user's level of expertise and provides optimal information. Furthermore, if the user is a beginner, the providing unit can also provide information explained in simple terms. For example, the providing unit analyzes the user's level of expertise and provides optimal information. Furthermore, the providing unit can adjust the optimal level of technical terms based on the user's past search history. For example, the providing unit analyzes the user's past search history and provides optimal information. In this way, by adjusting the use of technical terms according to the user's level of expertise, more appropriate information can be provided.

[0047] When investing points, the investment department can analyze the user's past consumption behavior and select the optimal investment method. For example, the investment department can propose a similar investment method based on an investment method that the user has used successfully in the past. For example, the investment department can analyze the user's past consumption behavior and select the optimal investment method. The investment department can also analyze the user's past consumption behavior and select an investment method that minimizes risk. For example, the investment department can analyze the user's past consumption behavior and select the optimal investment method. The investment department can also propose the optimal investment timing based on the user's past consumption behavior. For example, the investment department can analyze the user's past consumption behavior and select the optimal investment method. In this way, the optimal investment method can be selected by analyzing the user's past consumption behavior.

[0048] When managing points, the management department can customize the management method based on the user's current living situation. For example, the management department selects a low-risk management method according to the user's current living situation. For example, the management department analyzes the user's living situation and selects the optimal management method. The management department can also adjust the management method based on the user's living situation (e.g., moving or marriage). For example, the management department analyzes the user's living situation and selects the optimal management method. The management department can also suggest the optimal management method based on the user's current income situation. For example, the management department analyzes the user's income situation and selects the optimal management method. This allows for more appropriate management by customizing the management method based on the user's current living situation.

[0049] The management department can improve the management method by reflecting user feedback when managing points. The management department adjusts the management method based on, for example, feedback provided by the user. For example, the management department analyzes user feedback and selects the optimal management method. The management department can also customize the management means based on user feedback. For example, the management department analyzes user feedback and selects the optimal management method. The management department can also optimize the management timing by reflecting user feedback. For example, the management department analyzes user feedback and selects the optimal management method. In this way, the management method can be optimized by reflecting user feedback.

[0050] When managing points, the management department can select the optimal management method by taking into account the user's geographical location information. The management department selects the optimal management method by taking into account, for example, the economic situation in the area where the user is currently located. For example, the management department analyzes the user's geographical location information and selects the optimal management method. The management department can also analyze economic trends in areas that the user frequently visits and adjust the management method. For example, the management department analyzes the user's geographical location information and selects the optimal management method. The management department can also propose a region-specific management method based on the user's geographical location information. For example, the management department analyzes the user's geographical location information and selects the optimal management method. This enables more effective management by selecting the optimal management method based on the user's geographical location information.

[0051] When managing points, the management department can analyze the user's social media activity and suggest management methods. The management department can suggest management methods, for example, based on information about investments that the user has "liked" or commented on on social media. For example, the management department can analyze the user's social media activity and select the optimal management method. The management department can also analyze the content of the user's social media posts and suggest related management methods. For example, the management department can analyze the user's social media activity and select the optimal management method. The management department can also suggest related management methods based on the activity of the user's friends on social media. For example, the management department can analyze the user's social media activity and select the optimal management method. In this way, by analyzing the user's social media activity, it is possible to suggest highly relevant management methods.

[0052] When managing points, the management department can customize the management method by reflecting the user's past feedback. The management department adjusts the management method, for example, based on feedback provided by the user in the past. For example, the management department analyzes the user's feedback and selects the optimal management method. The management department can also customize the management means based on the user's feedback. For example, the management department analyzes the user's feedback and selects the optimal management method. The management department can also optimize the management timing by reflecting the user's feedback. For example, the management department analyzes the user's feedback and selects the optimal management method. In this way, the management method can be optimized by reflecting the user's past feedback.

[0053] When displaying a product, the display unit can analyze the user's past purchasing behavior and select the optimal display method. For example, the display unit preferentially displays information about products that are highly related to products purchased by the user in the past. For example, the display unit analyzes the user's past purchasing behavior and selects the optimal display method. The display unit can also analyze the user's past purchasing behavior and select the optimal display timing. For example, the display unit analyzes the user's past purchasing behavior and selects the optimal display method. The display unit can also select the optimal display means based on the user's past purchasing behavior. For example, the display unit analyzes the user's past purchasing behavior and selects the optimal display method. In this way, the optimal display method can be selected by analyzing the user's past purchasing behavior.

[0054] When displaying a product, the display unit can customize the display means based on the user's current areas of interest. For example, the display unit prioritizes display of product categories in which the user is currently interested. For example, the display unit analyzes the user's current areas of interest and selects the optimal display method. The display unit can also display information about related products based on the user's current areas of interest. For example, the display unit analyzes the user's current areas of interest and selects the optimal display method. The display unit can also select the optimal display means based on the user's current areas of interest. For example, the display unit analyzes the user's current areas of interest and selects the optimal display method. This enables more effective product display by customizing the display means based on the user's current areas of interest.

[0055] The exposure unit can improve the exposure method by reflecting user feedback when exposing a product. The exposure unit, for example, adjusts the exposure method based on feedback provided by the user. For example, the exposure unit analyzes user feedback and selects the optimal exposure method. The exposure unit can also customize the exposure means based on user feedback. For example, the exposure unit analyzes user feedback and selects the optimal exposure method. The exposure unit can also optimize the exposure timing by reflecting user feedback. For example, the exposure unit analyzes user feedback and selects the optimal exposure method. In this way, the exposure method can be optimized by reflecting user feedback.

[0056] When exposing a product, the exposure unit can select the optimal exposure method taking into consideration the user's geographical location information. For example, the exposure unit prioritizes exposing information about specific stores and products in the area where the user is currently located. For example, the exposure unit analyzes the user's geographical location information and selects the optimal exposure method. The exposure unit can also expose information about products in areas that the user frequently visits. For example, the exposure unit analyzes the user's geographical location information and selects the optimal exposure method. The exposure unit can also expose information about products that are limited to a specific area based on the user's geographical location information. For example, the exposure unit analyzes the user's geographical location information and selects the optimal exposure method. This enables more effective product exposure by selecting the optimal exposure method based on the user's geographical location information.

[0057] When exposing a product, the exposure unit can analyze the user's social media activity and suggest an exposure method. For example, the exposure unit prioritizes exposing information about products that the user has "liked" or commented on on social media. For example, the exposure unit analyzes the user's social media activity and selects the optimal exposure method. The exposure unit can also analyze the content of the user's social media posts to expose information about related products. For example, the exposure unit analyzes the user's social media activity and selects the optimal exposure method. The exposure unit can also expose information about related products by referring to the activity of the user's friends on social media. For example, the exposure unit analyzes the user's social media activity and selects the optimal exposure method. In this way, highly relevant exposure methods can be suggested by analyzing the user's social media activity.

[0058] The exposure unit can customize the exposure method by reflecting the user's past feedback when exposing a product. The exposure unit adjusts the exposure method based on, for example, feedback provided by the user in the past. For example, the exposure unit analyzes the user's feedback and selects the optimal exposure method. The exposure unit can also customize the exposure means based on the user's feedback. For example, the exposure unit analyzes the user's feedback and selects the optimal exposure method. The exposure unit can also optimize the exposure timing by reflecting the user's feedback. For example, the exposure unit analyzes the user's feedback and selects the optimal exposure method. In this way, the exposure method can be optimized by reflecting the user's past feedback.

[0059] When allocating the budget for SEO measures, the budget department can make the most optimal budget allocation by referring to past effectiveness data. For example, the budget department will prioritize budget allocation to SEO measures that have been effective in the past. For example, the budget department will analyze past effectiveness data and make the most optimal budget allocation. The budget department can also determine the priority of budget allocation based on past effectiveness data. For example, the budget department will analyze past effectiveness data and make the most optimal budget allocation. In this way, optimal budget allocation becomes possible by referring to past effectiveness data.

[0060] The budget unit can customize the budget allocation when allocating the budget for SEO measures by analyzing the user's current purchasing behavior. For example, the budget unit analyzes the user's current purchasing behavior and allocates the budget to the most appropriate SEO measures. For example, the budget unit analyzes the user's current purchasing behavior and allocates the optimal budget. The budget unit can also adjust the budget allocation for SEO measures based on the user's purchasing intentions. For example, the budget unit analyzes the user's purchasing intentions and allocates the optimal budget. The budget unit can also determine the priority of budget allocation based on the user's current purchasing behavior. For example, the budget unit analyzes the user's current purchasing behavior and allocates the optimal budget. In this way, optimal budget allocation is possible by analyzing the user's current purchasing behavior.

[0061] The budget unit can improve the budget allocation method by reflecting user feedback when allocating the budget for SEO measures. For example, the budget unit adjusts the budget allocation for SEO measures based on feedback provided by the user. For example, the budget unit analyzes user feedback and makes optimal budget allocation. The budget unit can also customize the budget allocation method based on user feedback. For example, the budget unit analyzes user feedback and makes optimal budget allocation. The budget unit can also determine the priority of budget allocation by reflecting user feedback. For example, the budget unit analyzes user feedback and makes optimal budget allocation. In this way, the budget allocation method can be optimized by reflecting user feedback.

[0062] The budget unit can optimally allocate the budget for SEO measures by taking into account the user's geographical location information. The budget unit optimally allocates the budget by taking into account, for example, the economic situation in the area where the user is currently located. For example, the budget unit analyzes the user's geographical location information and optimally allocates the budget. The budget unit can also analyze economic trends in areas frequently visited by the user and adjust the budget allocation. For example, the budget unit analyzes the user's geographical location information and optimally allocates the budget. The budget unit can also allocate the budget to area-specific SEO measures based on the user's geographical location information. For example, the budget unit analyzes the user's geographical location information and optimally allocates the budget. This makes it possible to optimally allocate the budget based on the user's geographical location information.

[0063] When allocating a budget for SEO measures, the budget unit can analyze the user's social media activity and suggest budget allocations. For example, the budget unit suggests budget allocations based on information about products that the user has "liked" or commented on on social media. For example, the budget unit analyzes the user's social media activity and makes optimal budget allocations. The budget unit can also analyze the content of the user's social media posts and allocate the budget to related SEO measures. For example, the budget unit analyzes the user's social media activity and makes optimal budget allocations. The budget unit can also allocate the budget to related SEO measures based on the activity of the user's friends on social media. For example, the budget unit analyzes the user's social media activity and makes optimal budget allocations. In this way, optimal budget allocations are possible by analyzing the user's social media activity.

[0064] The budget unit can customize the budget allocation method by reflecting the user's past feedback when allocating the budget for SEO measures. The budget unit adjusts the budget allocation for SEO measures based on, for example, feedback provided by the user in the past. For example, the budget unit analyzes the user's feedback and makes optimal budget allocation. The budget unit can also customize the budget allocation method based on the user's feedback. For example, the budget unit analyzes the user's feedback and makes optimal budget allocation. The budget unit can also determine the priority of budget allocation by reflecting the user's feedback. For example, the budget unit analyzes the user's feedback and makes optimal budget allocation. In this way, the budget allocation method can be optimized by reflecting the user's past feedback.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The collection unit can analyze the user's past purchase history and browsing history and select the optimal collection method. For example, it prioritizes collection of product categories that the user frequently purchases. For example, the collection unit analyzes the user's past purchase history and selects the optimal collection method. The collection unit can also re-collect products that the user viewed in the past but did not purchase to attract the user's interest. For example, the collection unit analyzes the user's browsing history and selects the optimal collection method. The collection unit can also periodically collect data based on the user's purchase frequency. For example, the collection unit analyzes the user's purchase frequency and selects the optimal collection method. This enables efficient data collection by selecting the optimal collection method based on the user's past behavior.

[0067] The providing unit can adjust the level of detail of the provided information based on the importance of the product when providing it. For example, for an expensive product, detailed specifications and review information are provided. For example, the providing unit provides optimal information based on the price and reviews of the product. The providing unit can also provide concise information for products that are used on a daily basis. For example, the providing unit provides optimal information based on the frequency of use and category of the product. The providing unit can also provide special promotion information for new products and limited-edition products. For example, the providing unit provides optimal information based on the release date and limited offers of the product. In this way, by adjusting the level of detail of the information based on the importance of the product, more appropriate information can be provided.

[0068] When investing points, the investment department can analyze the user's past consumption behavior and select the optimal investment method. For example, the investment department can propose a similar investment method based on an investment method that the user has used successfully in the past. For example, the investment department can analyze the user's past consumption behavior and select the optimal investment method. The investment department can also analyze the user's past consumption behavior and select an investment method that minimizes risk. For example, the investment department can analyze the user's past consumption behavior and select the optimal investment method. The investment department can also propose the optimal investment timing based on the user's past consumption behavior. For example, the investment department can analyze the user's past consumption behavior and select the optimal investment method. In this way, the optimal investment method can be selected by analyzing the user's past consumption behavior.

[0069] When exposing a product, the exposure unit can customize the exposure means based on the user's current areas of interest. For example, the exposure unit prioritizes the exposure of product categories in which the user is currently interested. For example, the exposure unit analyzes the user's current areas of interest and selects the optimal exposure method. The exposure unit can also expose information about related products based on the user's current areas of interest. For example, the exposure unit analyzes the user's current areas of interest and selects the optimal exposure method. The exposure unit can also select the optimal exposure means based on the user's current areas of interest. For example, the exposure unit analyzes the user's current areas of interest and selects the optimal exposure method. This allows for more effective product exposure by customizing the exposure means based on the user's current areas of interest.

[0070] When allocating the budget for SEO measures, the budget department can refer to past effectiveness data to make optimal budget allocations. For example, the budget can be allocated preferentially to SEO measures that have been effective in the past. For example, the budget department can analyze past effectiveness data and make optimal budget allocations. The budget department can also determine the priority of budget allocations based on past effectiveness data. For example, the budget department can analyze past effectiveness data and make optimal budget allocations. In this way, optimal budget allocations are possible by referring to past effectiveness data.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The collection unit collects the consumer's purchase history and browsing history. Specifically, it collects data such as purchase date and time, product category, viewed pages, purchase frequency, price range, and search history. For example, it also records the number of times and time that the consumer viewed a particular product. Step 2: The provision unit provides optimal purchase timing and discount information based on the data collected by the collection unit. Specifically, it analyzes product price fluctuations and discount information and suggests optimal purchase timing to consumers. It can also provide limited-time discount information and coupon codes. Step 3: The investment department manages unused points and increases their value. Specifically, they use AI to manage points and adjust the investment interest rate and investment method to increase their value. Step 4: The Promotion Department will increase exposure opportunities for their products by increasing the number of consumers and increasing revenue through point management. Specifically, they will expose the products of participating companies in advertising campaigns and promotional events. Step 5: The budget department allocates the necessary budget for SEO measures, specifically, keyword optimization, link building, content marketing, and other SEO strategies.

[0073] (Example 2) The AI-driven e-commerce site according to an embodiment of the present invention is a system that solves problems for both consumers and merchants. The AI-driven e-commerce site collects consumers' purchase and browsing histories and provides optimal purchase timing and discount information. It also manages unused points and develops them as assets. Furthermore, by increasing the number of consumers and increasing revenue through point management, merchants can increase the exposure of their products. For example, an AI-driven e-commerce site collects consumers' purchase and browsing histories. For example, it collects data such as the category, price range, and purchase frequency of products purchased by consumers in the past. Then, based on the collected data, it provides optimal purchase timing and discount information. For example, when a consumer is planning to purchase a specific product, AI can analyze the product's price fluctuations and discount information and suggest the optimal purchase timing. Furthermore, the AI-driven e-commerce site manages unused points and increases their value. For example, AI can manage consumers' accumulated points and increase their value. This allows consumers to make effective use of their points. Meanwhile, merchants can increase the exposure of their products by increasing the number of consumers and increasing revenue through point management. For example, as the number of consumers increases, a store's products become more widely known. Furthermore, increased revenue from point management allows store owners to allocate a sufficient budget to SEO strategies. This allows store owners to increase exposure for their products and boost sales. AI-driven e-commerce sites solve problems for both consumers and store owners, enabling sustainable growth. AI-driven e-commerce sites can collect consumers' purchase and browsing histories and provide optimal purchase timing and discount information. Unused points can also be used to increase the value of points. Furthermore, an increase in the number of consumers and increased revenue from point management allows store owners to increase exposure for their products and boost sales.

[0074] An AI-driven e-commerce website according to an embodiment includes a collection unit, a provision unit, an operation unit, an exposure unit, and a budget unit. The collection unit collects consumer purchase and browsing histories. The consumer purchase and browsing histories include, but are not limited to, purchase dates and times, product categories, and viewed pages. The collection unit collects data such as the categories and price ranges of products previously purchased by the consumer, and purchase frequency. The collection unit can also collect product pages and search histories viewed by the consumer. For example, the collection unit records the number of times and duration a consumer views a specific product. The provision unit provides optimal purchase timing and discount information based on the data collected by the collection unit. For example, when a consumer is planning to purchase a specific product, the provision unit analyzes price fluctuations and discount information for that product and suggests the optimal purchase timing. The provision unit can also provide limited-time discount information and coupon codes to the consumer. For example, the provision unit notifies the consumer of discount information for products the consumer is considering purchasing. The operation unit manages unused points to increase the value of the points. For example, the operations department uses AI to manage points held by consumers, increasing their value. The operations department can also adjust the interest rate and management method for points. For example, the operations department sets the investment interest rate for points and increases their value. The exposure department increases exposure opportunities for the company's products by increasing the number of consumers and increasing revenue through point management. For example, the exposure department ensures that the products of store operators are recognized by more consumers by increasing the number of consumers. The exposure department also ensures that the increased revenue through point management allows store operators to allocate sufficient budgets to SEO measures. For example, the exposure department exposes store operators' products through advertising campaigns and promotional events. The budget department allocates the necessary budget for SEO measures. For example, the budget department ensures that store operators can allocate sufficient budgets to SEO measures. The budget department can also adjust the allocation of budgets for SEO measures. For example, the budget department allocates budgets to SEO measures such as keyword optimization, link building, and content marketing.As a result, the AI-driven e-commerce site according to the embodiment can collect consumers' purchase and browsing histories and provide optimal purchase timing and discount information. It can also utilize unused points to increase the value of points. Furthermore, by increasing the number of consumers and increasing revenue through point utilization, store owners can increase exposure opportunities for their products and improve sales.

[0075] The collection unit can collect the purchase history and browsing history of consumers. The collection unit collects data such as the category, price range, and purchase frequency of products purchased by consumers in the past. For example, the collection unit records the date and time and price when a consumer purchased a specific product. The collection unit can also collect the product pages viewed by consumers and search history. For example, the collection unit records the number of times and time when a consumer viewed a specific product. The collection unit can also collect reviews and ratings of products purchased by consumers in the past. For example, the collection unit analyzes the consumer's purchasing patterns based on the reviews and ratings of products purchased by the consumer. This makes it possible to effectively collect the consumer's purchase history and browsing history.

[0076] The providing unit can provide appropriate purchase timing or discount information based on the collected data. For example, when a consumer is planning to purchase a specific product, the providing unit analyzes price fluctuations and discount information for that product and suggests the optimal purchase timing. For example, the providing unit analyzes the price history of a product that the consumer is considering purchasing and predicts when the price will drop. The providing unit can also provide limited-time discount information and coupon codes to the consumer. For example, the providing unit notifies the consumer of discount information for a product that the consumer is considering purchasing. The providing unit can also provide discount information for products that the consumer has previously purchased. For example, the providing unit provides discount information when the consumer repurchases a product that the consumer has previously purchased. This makes it possible to provide the consumer with the optimal purchase timing and discount information.

[0077] The investment department can invest unused points and increase their value. For example, the investment department uses AI to invest points held by consumers and increase their value. For example, the investment department sets the investment interest rate for points and increases their value. The investment department can also adjust the interest rate and investment method for points. For example, the investment department can adjust the investment interest rate for points to maximize their value. The investment department can also diversify the investment method for points. For example, the investment department can diversify points into different investment targets. This allows consumers' points to be managed effectively and their value to be increased.

[0078] The Exposure Department has a means to increase exposure opportunities for its products by increasing the number of consumers and increasing revenue through point management. For example, the Exposure Department ensures that the products of the store-opening company are recognized by more consumers as the number of consumers increases. For example, as the number of consumers increases, the Exposure Department exposes the store-opening company's products in advertising campaigns and promotional events. In addition, the Exposure Department ensures that the store-opening company can allocate a sufficient budget to SEO measures by increasing revenue through point management. For example, the Exposure Department uses SEO measures to display the store-opening company's products at the top of search engines. This increases the store-opening company's product exposure opportunities and improves sales.

[0079] The budget department has a means for allocating the necessary budget for SEO measures. For example, the budget department ensures that store owners can allocate a sufficient budget to SEO measures. For example, the budget department adjusts the budget allocation for SEO measures and implements effective SEO measures. The budget department can also optimize the budget allocation for SEO measures. For example, the budget department allocates the budget to SEO measures such as keyword optimization, link building, and content marketing. This allows store owners to allocate a sufficient budget to SEO measures.

[0080] The collection unit can estimate a user's emotions and adjust the timing of collecting purchase history and browsing history based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing and collects data when the user is relaxed. For example, the collection unit analyzes the user's facial expressions and voice to estimate the emotion. Furthermore, if the user is excited, the collection unit can collect data immediately and update the data in real time. For example, the collection unit collects the user's heart rate and electrodermal activity using a sensor to estimate the emotion. Furthermore, if the user is tired, the collection unit can adjust the collection timing and collect data while the user is resting. For example, the collection unit analyzes the user's activity log to estimate the emotion. This allows for more appropriate data collection by adjusting the collection timing according to the user's emotions.

[0081] The collection unit can analyze the user's past purchase history and browsing history and select the optimal collection method. The collection unit, for example, prioritizes collection of product categories that the user frequently purchases. For example, the collection unit analyzes the user's past purchase history and selects the optimal collection method. The collection unit can also re-collect products that the user viewed in the past but did not purchase to attract their interest. For example, the collection unit analyzes the user's browsing history and selects the optimal collection method. The collection unit can also perform periodic collection based on the user's purchase frequency. For example, the collection unit analyzes the user's purchase frequency and selects the optimal collection method. This enables efficient data collection by selecting the optimal collection method based on the user's past behavior.

[0082] When collecting purchase history or browsing history, the collection unit can filter the data based on the user's current areas of interest or living situation. The collection unit, for example, prioritizes collection of product categories in which the user is currently interested. For example, the collection unit analyzes the user's current areas of interest and selects an optimal collection method. The collection unit can also collect related products based on the user's living situation (for example, moving or getting married). For example, the collection unit analyzes the user's living situation and selects an optimal collection method. The collection unit can also adjust the data to be collected based on the user's current purchasing desires. For example, the collection unit analyzes the user's purchasing desires and selects an optimal collection method. In this way, more relevant data can be collected by filtering data based on the user's current interests or living situation.

[0083] When collecting purchase history or browsing history, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit analyzes voice data to collect the purchase history. For example, the collection unit analyzes the user's voice input and selects the optimal collection method. Furthermore, if the user uses text input, the collection unit can also analyze text data to collect the browsing history. For example, the collection unit analyzes the user's text input and selects the optimal collection method. Furthermore, if the user uses image input, the collection unit can analyze image data to collect related product information. For example, the collection unit analyzes the user's image input and selects the optimal collection method. This enables efficient data collection by selecting the optimal collection means depending on the user's input method.

[0084] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit sets a high priority for data to be collected immediately. For example, the collection unit analyzes the user's facial expressions and voice to estimate the emotion. Also, if the user is relaxed, the collection unit can set a low priority for data to be collected and collect more detailed data. For example, the collection unit collects the user's heart rate and electrodermal activity using a sensor to estimate the emotion. Also, if the user is feeling stressed, the collection unit can adjust the priority of data to be collected and collect only important data. For example, the collection unit analyzes the user's activity log to estimate the emotion. In this way, by determining the priority of data based on the user's emotions, more important data can be collected preferentially.

[0085] When collecting purchase history or browsing history, the collection unit can prioritize collection of highly relevant data by taking into account the user's geographical location information. The collection unit, for example, prioritizes collection of data on specific stores or products in the area where the user is currently located. For example, the collection unit analyzes the user's geographical location information and selects an optimal collection method. The collection unit can also collect data on areas frequently visited by the user and provide related product information. For example, the collection unit analyzes the user's geographical location information and selects an optimal collection method. The collection unit can also collect area-specific discount information based on the user's geographical location information. For example, the collection unit analyzes the user's geographical location information and selects an optimal collection method. This enables more effective data collection by preferentially collecting highly relevant data based on the user's geographical location information.

[0086] The collection unit can analyze the user's social media activities and collect related data when collecting purchase history and browsing history. The collection unit, for example, collects data about products that the user has "liked" or commented on on social media. For example, the collection unit analyzes the user's social media activities and selects the optimal collection method. The collection unit can also analyze the content of the user's posts on social media to collect related product information. For example, the collection unit analyzes the user's social media activities and selects the optimal collection method. The collection unit can also collect related product data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the user's social media activities and selects the optimal collection method. In this way, highly relevant data can be collected by analyzing the user's social media activities.

[0087] When collecting purchase history or browsing history, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit adjusts the type of data to be collected, for example, based on feedback provided by the user in the past. For example, the collection unit analyzes the user's past feedback and selects the optimal collection method. The collection unit can also optimize the collection timing based on the user's feedback. For example, the collection unit analyzes the user's feedback and selects the optimal collection method. The collection unit can also customize the collection means (audio, text, image, etc.) by reflecting the user's feedback. For example, the collection unit analyzes the user's feedback and selects the optimal collection method. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0088] The providing unit can estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide detailed information to increase purchasing motivation. For example, the providing unit can analyze the user's facial expressions and voice to estimate the user's emotions. Furthermore, if the user is in a hurry, the providing unit can provide concise, to-the-point information. For example, the providing unit can collect the user's heart rate and electrodermal activity using a sensor to estimate the user's emotions. Furthermore, if the user is excited, the providing unit can provide visually appealing information to stimulate purchasing motivation. For example, the providing unit can analyze the user's activity log to estimate the user's emotions. This allows for more effective information provision by adjusting the way information is presented based on the user's emotions.

[0089] The providing unit can adjust the level of detail of the provided information based on the importance of the product when providing the information. For example, in the case of an expensive product, the providing unit provides detailed specifications and review information. For example, the providing unit provides optimal information based on the price and reviews of the product. The providing unit can also provide concise information in the case of a product that is used on a daily basis. For example, the providing unit provides optimal information based on the frequency of use and category of the product. The providing unit can also provide special promotion information in the case of a new product or a limited product. For example, the providing unit provides optimal information based on the release date and limited offers of the product. In this way, by adjusting the level of detail of the information based on the importance of the product, more appropriate information can be provided.

[0090] The providing unit can apply different providing algorithms depending on the product category when providing information. For example, in the case of electronic devices, the providing unit provides information that emphasizes technical specifications and functions. For example, the providing unit provides optimal information based on the product category. In addition, the providing unit can also provide information that emphasizes visuals in the case of fashion items. For example, the providing unit provides optimal information based on the design and style of the product. In addition, the providing unit can also provide ingredient and nutritional information in the case of food. For example, the providing unit provides optimal information based on the ingredients and nutritional value of the product. This makes it possible to provide more effective information by adjusting the providing algorithm depending on the product category.

[0091] The providing unit can improve the accuracy of the information provided by referring to the user's past providing results. The providing unit provides information on similar products based on, for example, evaluations of products purchased by the user in the past. For example, the providing unit analyzes the user's past providing results and provides optimal information. The providing unit can also provide information on related products based on information on products viewed by the user in the past. For example, the providing unit analyzes the user's past providing results and provides optimal information. The providing unit can also analyze the user's past purchasing history and provide information on optimal products. For example, the providing unit analyzes the user's past providing results and provides optimal information. In this way, the accuracy of the information provided can be improved by referring to the user's past providing results.

[0092] The providing unit can estimate the user's emotions and adjust the length of the information to be provided based on the estimated user emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point information. For example, the providing unit can analyze the user's facial expressions and voice to estimate the emotions. Furthermore, if the user is relaxed, the providing unit can provide longer information including detailed explanations. For example, the providing unit can collect the user's heart rate and electrodermal activity using a sensor to estimate the emotions. Furthermore, if the user is excited, the providing unit can provide information with visually stimulating effects. For example, the providing unit can analyze the user's activity log to estimate the emotions. This allows for more effective information provision by adjusting the length of the information based on the user's emotions.

[0093] The providing unit can determine the priority of the information to be provided based on the price fluctuations of the product at the time of providing the information. For example, the providing unit preferentially provides information on products whose prices have dropped significantly. For example, the providing unit analyzes price fluctuations of products and provides the most appropriate information. The providing unit can also provide information on products whose prices are on the rise early. For example, the providing unit analyzes the price history of products and provides the most appropriate information. The providing unit can also regularly provide information on products whose prices are stable. For example, the providing unit analyzes price fluctuations of products and provides the most appropriate information. In this way, by determining the priority of information based on price fluctuations of products, more appropriate information can be provided.

[0094] The providing unit can adjust the order of provided information based on the relevance of the products when providing the information. For example, the providing unit prioritizes providing information about products that are highly relevant to products that the user has purchased in the past. For example, the providing unit analyzes the user's past purchase history and provides optimal information. The providing unit can also provide information about products that are highly relevant to the product the user is currently browsing. For example, the providing unit analyzes the user's current browsing history and provides optimal information. The providing unit can also provide information about highly relevant products based on the user's interests and concerns. For example, the providing unit analyzes the user's interests and concerns and provides optimal information. This enables more effective information provision by adjusting the order of information based on the relevance of the products.

[0095] The providing unit can adjust the use of technical terms in the provided information according to the user's level of expertise when providing the information. For example, if the user has technical expertise, the providing unit provides information using detailed technical terms. For example, the providing unit analyzes the user's level of expertise and provides optimal information. Furthermore, if the user is a beginner, the providing unit can also provide information explained in simple terms. For example, the providing unit analyzes the user's level of expertise and provides optimal information. Furthermore, the providing unit can adjust the optimal level of technical terms based on the user's past search history. For example, the providing unit analyzes the user's past search history and provides optimal information. In this way, by adjusting the use of technical terms according to the user's level of expertise, more appropriate information can be provided.

[0096] The management unit can estimate the user's emotions and adjust the point management method based on the estimated user emotions. For example, if the user is relaxed, the management unit selects a low-risk management method. For example, the management unit analyzes the user's facial expressions and voice to estimate emotions. The management unit can also select a high-risk management method if the user is excited. For example, the management unit collects the user's heart rate and electrodermal activity using a sensor to estimate emotions. The management unit can also temporarily stop point management if the user is feeling stressed. For example, the management unit analyzes the user's activity log to estimate emotions. This allows for more appropriate point management by adjusting the point management method based on the user's emotions.

[0097] When investing points, the investment department can analyze the user's past consumption behavior and select the optimal investment method. For example, the investment department can propose a similar investment method based on an investment method that the user has used successfully in the past. For example, the investment department can analyze the user's past consumption behavior and select the optimal investment method. The investment department can also analyze the user's past consumption behavior and select an investment method that minimizes risk. For example, the investment department can analyze the user's past consumption behavior and select the optimal investment method. The investment department can also propose the optimal investment timing based on the user's past consumption behavior. For example, the investment department can analyze the user's past consumption behavior and select the optimal investment method. In this way, the optimal investment method can be selected by analyzing the user's past consumption behavior.

[0098] When managing points, the management department can customize the management method based on the user's current living situation. For example, the management department selects a low-risk management method according to the user's current living situation. For example, the management department analyzes the user's living situation and selects the optimal management method. The management department can also adjust the management method based on the user's living situation (e.g., moving or marriage). For example, the management department analyzes the user's living situation and selects the optimal management method. The management department can also suggest the optimal management method based on the user's current income situation. For example, the management department analyzes the user's income situation and selects the optimal management method. This allows for more appropriate management by customizing the management method based on the user's current living situation.

[0099] The management department can improve the management method by reflecting user feedback when managing points. The management department adjusts the management method based on, for example, feedback provided by the user. For example, the management department analyzes user feedback and selects the optimal management method. The management department can also customize the management means based on user feedback. For example, the management department analyzes user feedback and selects the optimal management method. The management department can also optimize the management timing by reflecting user feedback. For example, the management department analyzes user feedback and selects the optimal management method. In this way, the management method can be optimized by reflecting user feedback.

[0100] The management unit can estimate the user's emotions and determine the priority of point management based on the estimated user emotions. For example, if the user is relaxed, the management unit sets the priority of point management high. For example, the management unit analyzes the user's facial expressions and voice to estimate emotions. The management unit can also set the priority of point management low if the user is feeling stressed. For example, the management unit collects the user's heart rate and electrodermal activity using a sensor to estimate emotions. The management unit can also adjust the priority of point management if the user is excited. For example, the management unit analyzes the user's activity log to estimate emotions. This enables more appropriate management by determining the priority of point management based on the user's emotions.

[0101] When managing points, the management department can select the optimal management method by taking into account the user's geographical location information. The management department selects the optimal management method by taking into account, for example, the economic situation in the area where the user is currently located. For example, the management department analyzes the user's geographical location information and selects the optimal management method. The management department can also analyze economic trends in areas that the user frequently visits and adjust the management method. For example, the management department analyzes the user's geographical location information and selects the optimal management method. The management department can also propose a region-specific management method based on the user's geographical location information. For example, the management department analyzes the user's geographical location information and selects the optimal management method. This enables more effective management by selecting the optimal management method based on the user's geographical location information.

[0102] When managing points, the management department can analyze the user's social media activity and suggest management methods. The management department can suggest management methods, for example, based on information about investments that the user has "liked" or commented on on social media. For example, the management department can analyze the user's social media activity and select the optimal management method. The management department can also analyze the content of the user's social media posts and suggest related management methods. For example, the management department can analyze the user's social media activity and select the optimal management method. The management department can also suggest related management methods based on the activity of the user's friends on social media. For example, the management department can analyze the user's social media activity and select the optimal management method. In this way, by analyzing the user's social media activity, it is possible to suggest highly relevant management methods.

[0103] When managing points, the management department can customize the management method by reflecting the user's past feedback. The management department adjusts the management method, for example, based on feedback provided by the user in the past. For example, the management department analyzes the user's feedback and selects the optimal management method. The management department can also customize the management means based on the user's feedback. For example, the management department analyzes the user's feedback and selects the optimal management method. The management department can also optimize the management timing by reflecting the user's feedback. For example, the management department analyzes the user's feedback and selects the optimal management method. In this way, the management method can be optimized by reflecting the user's past feedback.

[0104] The exposure unit can estimate the user's emotions and adjust the product exposure method based on the estimated user emotions. For example, if the user is relaxed, the exposure unit provides detailed product information. For example, the exposure unit analyzes the user's facial expressions and voice to estimate the user's emotions. Furthermore, if the user is in a hurry, the exposure unit can provide concise and to-the-point product information. For example, the exposure unit collects the user's heart rate and electrodermal activity using a sensor to estimate the user's emotions. Furthermore, if the user is excited, the exposure unit can provide visually appealing product information. For example, the exposure unit analyzes the user's activity log to estimate the user's emotions. This allows for more effective product exposure by adjusting the product exposure method based on the user's emotions.

[0105] When displaying a product, the display unit can analyze the user's past purchasing behavior and select the optimal display method. For example, the display unit preferentially displays information about products that are highly related to products purchased by the user in the past. For example, the display unit analyzes the user's past purchasing behavior and selects the optimal display method. The display unit can also analyze the user's past purchasing behavior and select the optimal display timing. For example, the display unit analyzes the user's past purchasing behavior and selects the optimal display method. The display unit can also select the optimal display means based on the user's past purchasing behavior. For example, the display unit analyzes the user's past purchasing behavior and selects the optimal display method. In this way, the optimal display method can be selected by analyzing the user's past purchasing behavior.

[0106] When displaying a product, the display unit can customize the display means based on the user's current areas of interest. For example, the display unit prioritizes display of product categories in which the user is currently interested. For example, the display unit analyzes the user's current areas of interest and selects the optimal display method. The display unit can also display information about related products based on the user's current areas of interest. For example, the display unit analyzes the user's current areas of interest and selects the optimal display method. The display unit can also select the optimal display means based on the user's current areas of interest. For example, the display unit analyzes the user's current areas of interest and selects the optimal display method. This enables more effective product display by customizing the display means based on the user's current areas of interest.

[0107] The exposure unit can improve the exposure method by reflecting user feedback when exposing a product. The exposure unit, for example, adjusts the exposure method based on feedback provided by the user. For example, the exposure unit analyzes user feedback and selects the optimal exposure method. The exposure unit can also customize the exposure means based on user feedback. For example, the exposure unit analyzes user feedback and selects the optimal exposure method. The exposure unit can also optimize the exposure timing by reflecting user feedback. For example, the exposure unit analyzes user feedback and selects the optimal exposure method. In this way, the exposure method can be optimized by reflecting user feedback.

[0108] The exposure unit can estimate the user's emotions and determine the product exposure priority based on the estimated user emotions. For example, if the user is relaxed, the exposure unit sets the product exposure priority high. For example, the exposure unit analyzes the user's facial expressions and voice to estimate the user's emotions. The exposure unit can also set the product exposure priority low if the user is feeling stressed. For example, the exposure unit collects the user's heart rate and electrodermal activity using a sensor to estimate the user's emotions. The exposure unit can also adjust the product exposure priority if the user is excited. For example, the exposure unit analyzes the user's activity log to estimate the user's emotions. This enables more effective product exposure by determining the product exposure priority based on the user's emotions.

[0109] When exposing a product, the exposure unit can select the optimal exposure method taking into consideration the user's geographical location information. For example, the exposure unit prioritizes exposing information about specific stores and products in the area where the user is currently located. For example, the exposure unit analyzes the user's geographical location information and selects the optimal exposure method. The exposure unit can also expose information about products in areas that the user frequently visits. For example, the exposure unit analyzes the user's geographical location information and selects the optimal exposure method. The exposure unit can also expose information about products that are limited to a specific area based on the user's geographical location information. For example, the exposure unit analyzes the user's geographical location information and selects the optimal exposure method. This enables more effective product exposure by selecting the optimal exposure method based on the user's geographical location information.

[0110] When exposing a product, the exposure unit can analyze the user's social media activity and suggest an exposure method. For example, the exposure unit prioritizes exposing information about products that the user has "liked" or commented on on social media. For example, the exposure unit analyzes the user's social media activity and selects the optimal exposure method. The exposure unit can also analyze the content of the user's social media posts to expose information about related products. For example, the exposure unit analyzes the user's social media activity and selects the optimal exposure method. The exposure unit can also expose information about related products by referring to the activity of the user's friends on social media. For example, the exposure unit analyzes the user's social media activity and selects the optimal exposure method. In this way, highly relevant exposure methods can be suggested by analyzing the user's social media activity.

[0111] The exposure unit can customize the exposure method by reflecting the user's past feedback when exposing a product. The exposure unit adjusts the exposure method based on, for example, feedback provided by the user in the past. For example, the exposure unit analyzes the user's feedback and selects the optimal exposure method. The exposure unit can also customize the exposure means based on the user's feedback. For example, the exposure unit analyzes the user's feedback and selects the optimal exposure method. The exposure unit can also optimize the exposure timing by reflecting the user's feedback. For example, the exposure unit analyzes the user's feedback and selects the optimal exposure method. In this way, the exposure method can be optimized by reflecting the user's past feedback.

[0112] The budget unit can estimate the user's emotions and adjust the budget allocation for SEO measures based on the estimated user emotions. For example, if the user is relaxed, the budget unit can set a higher budget allocation for SEO measures. For example, the budget unit can analyze the user's facial expressions and voice to estimate emotions. The budget unit can also set a lower budget allocation for SEO measures if the user is feeling stressed. For example, the budget unit can collect the user's heart rate and electrodermal activity using a sensor to estimate emotions. The budget unit can also adjust the budget allocation for SEO measures if the user is excited. For example, the budget unit can analyze the user's activity log to estimate emotions. This allows for more effective SEO measures by adjusting the budget allocation for SEO measures based on the user's emotions.

[0113] When allocating the budget for SEO measures, the budget department can make the most optimal budget allocation by referring to past effectiveness data. For example, the budget department will prioritize budget allocation to SEO measures that have been effective in the past. For example, the budget department will analyze past effectiveness data and make the most optimal budget allocation. The budget department can also determine the priority of budget allocation based on past effectiveness data. For example, the budget department will analyze past effectiveness data and make the most optimal budget allocation. In this way, optimal budget allocation becomes possible by referring to past effectiveness data.

[0114] The budget unit can customize the budget allocation when allocating the budget for SEO measures by analyzing the user's current purchasing behavior. For example, the budget unit analyzes the user's current purchasing behavior and allocates the budget to the most appropriate SEO measures. For example, the budget unit analyzes the user's current purchasing behavior and allocates the optimal budget. The budget unit can also adjust the budget allocation for SEO measures based on the user's purchasing intentions. For example, the budget unit analyzes the user's purchasing intentions and allocates the optimal budget. The budget unit can also determine the priority of budget allocation based on the user's current purchasing behavior. For example, the budget unit analyzes the user's current purchasing behavior and allocates the optimal budget. In this way, optimal budget allocation is possible by analyzing the user's current purchasing behavior.

[0115] The budget unit can improve the budget allocation method by reflecting user feedback when allocating the budget for SEO measures. For example, the budget unit adjusts the budget allocation for SEO measures based on feedback provided by the user. For example, the budget unit analyzes user feedback and makes optimal budget allocation. The budget unit can also customize the budget allocation method based on user feedback. For example, the budget unit analyzes user feedback and makes optimal budget allocation. The budget unit can also determine the priority of budget allocation by reflecting user feedback. For example, the budget unit analyzes user feedback and makes optimal budget allocation. In this way, the budget allocation method can be optimized by reflecting user feedback.

[0116] The budgeting unit can estimate the user's emotions and determine the priority of SEO measures based on the estimated user emotions. For example, if the user is relaxed, the budgeting unit can set the priority of SEO measures to be high. For example, the budgeting unit can analyze the user's facial expressions and voice to estimate the emotions. The budgeting unit can also set the priority of SEO measures to be low if the user is feeling stressed. For example, the budgeting unit can collect the user's heart rate and electrodermal activity using a sensor to estimate the emotions. The budgeting unit can also adjust the priority of SEO measures if the user is excited. For example, the budgeting unit can analyze the user's activity log to estimate the emotions. This allows for more effective SEO measures by determining the priority of SEO measures based on the user's emotions.

[0117] The budget unit can optimally allocate the budget for SEO measures by taking into account the user's geographical location information. The budget unit optimally allocates the budget by taking into account, for example, the economic situation in the area where the user is currently located. For example, the budget unit analyzes the user's geographical location information and optimally allocates the budget. The budget unit can also analyze economic trends in areas frequently visited by the user and adjust the budget allocation. For example, the budget unit analyzes the user's geographical location information and optimally allocates the budget. The budget unit can also allocate the budget to area-specific SEO measures based on the user's geographical location information. For example, the budget unit analyzes the user's geographical location information and optimally allocates the budget. This makes it possible to optimally allocate the budget based on the user's geographical location information.

[0118] When allocating a budget for SEO measures, the budget unit can analyze the user's social media activity and suggest budget allocations. For example, the budget unit suggests budget allocations based on information about products that the user has "liked" or commented on on social media. For example, the budget unit analyzes the user's social media activity and makes optimal budget allocations. The budget unit can also analyze the content of the user's social media posts and allocate the budget to related SEO measures. For example, the budget unit analyzes the user's social media activity and makes optimal budget allocations. The budget unit can also allocate the budget to related SEO measures based on the activity of the user's friends on social media. For example, the budget unit analyzes the user's social media activity and makes optimal budget allocations. In this way, optimal budget allocations are possible by analyzing the user's social media activity.

[0119] The budget unit can customize the budget allocation method by reflecting the user's past feedback when allocating the budget for SEO measures. The budget unit adjusts the budget allocation for SEO measures based on, for example, feedback provided by the user in the past. For example, the budget unit analyzes the user's feedback and makes optimal budget allocation. The budget unit can also customize the budget allocation method based on the user's feedback. For example, the budget unit analyzes the user's feedback and makes optimal budget allocation. The budget unit can also determine the priority of budget allocation by reflecting the user's feedback. For example, the budget unit analyzes the user's feedback and makes optimal budget allocation. In this way, the budget allocation method can be optimized by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, provision unit, management unit, exposure unit, and budget unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects consumer purchase and browsing histories using the camera 42 and microphone 38B of the smart device 14, and the collected history is analyzed by the specific processing unit 290 of the data processing device 12. The provision unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, provides consumers with optimal purchase timing and discount information. The management unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, manages consumers' points and increases their value. The exposure unit, implemented, for example, by the control unit 46A of the smart device 14, exposes products of participating companies to consumers. The budget unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, allocates the budget necessary for SEO measures. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, provision unit, management unit, exposure unit, and budget unit, described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects a consumer's purchase history and browsing history using the camera 42 and microphone 238 of the smart glasses 214, and the collected history is analyzed by the specific processing unit 290 of the data processing device 12. The provision unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, provides consumers with optimal purchase timing and discount information. The management unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, manages consumers' points and increases their value. The exposure unit, implemented, for example, by the control unit 46A of the smart glasses 214, exposes products of participating companies to consumers. The budget unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, allocates a budget necessary for SEO measures. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, provision unit, management unit, exposure unit, and budget unit, described above, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects consumer purchase and browsing histories using the camera 42 and microphone 238 of the headset terminal 314, and the collected history is analyzed by the specific processing unit 290 of the data processing device 12. The provision unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, provides consumers with optimal purchase timing and discount information. The management unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, manages consumers' points and increases their value. The exposure unit, implemented, for example, by the control unit 46A of the headset terminal 314, exposes products of participating companies to consumers. The budget unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, allocates the budget necessary for SEO measures. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, provision unit, management unit, exposure unit, and budget unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects consumer purchase and browsing histories using the camera 42 and microphone 238 of the robot 414, and the collected history is analyzed by the specific processing unit 290 of the data processing device 12. The provision unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides consumers with optimal purchase timing and discount information. The management unit, realized, for example, by the specific processing unit 290 of the data processing device 12, manages consumers' points and increases their value. The exposure unit, realized, for example, by the control unit 46A of the robot 414, exposes products of participating companies to consumers. The budget unit, realized, for example, by the specific processing unit 290 of the data processing device 12, allocates the budget necessary for SEO measures.

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

[0121] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is excited, the priority of data to be collected immediately is set high. For example, the collection unit analyzes the user's facial expressions and voice to estimate the emotions. Furthermore, if the user is relaxed, the collection unit can set the priority of data to be collected low and collect more detailed data. For example, the collection unit collects the user's heart rate and electrodermal activity using a sensor to estimate the emotions. Furthermore, if the user is feeling stressed, the collection unit can adjust the priority of data to be collected and collect only important data. For example, the collection unit analyzes the user's activity log to estimate the emotions. In this way, by determining the priority of data based on the user's emotions, more important data can be collected preferentially.

[0122] The providing unit can estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions. For example, if the user is relaxed, detailed information can be provided to increase purchasing motivation. For example, the providing unit can analyze the user's facial expressions and voice to estimate the user's emotions. Furthermore, if the user is in a hurry, the providing unit can provide concise, to-the-point information. For example, the providing unit can collect the user's heart rate and electrodermal activity using a sensor to estimate the user's emotions. Furthermore, if the user is excited, the providing unit can provide visually appealing information to stimulate purchasing motivation. For example, the providing unit can analyze the user's activity log to estimate the user's emotions. This allows for more effective information provision by adjusting the way information is presented based on the user's emotions.

[0123] The management unit can estimate the user's emotions and adjust the point management method based on the estimated user emotions. For example, if the user is relaxed, a low-risk management method is selected. For example, the management unit analyzes the user's facial expressions and voice to estimate their emotions. Furthermore, if the user is excited, the management unit can select a high-risk management method. For example, the management unit collects the user's heart rate and electrodermal activity using a sensor to estimate their emotions. Furthermore, the management unit can temporarily stop point management if the user is feeling stressed. For example, the management unit analyzes the user's activity log to estimate their emotions. This allows for more appropriate management by adjusting the point management method based on the user's emotions.

[0124] The exposure unit can estimate the user's emotions and adjust the product exposure method based on the estimated user emotions. For example, if the user is relaxed, detailed product information is provided. For example, the exposure unit analyzes the user's facial expressions and voice to estimate the user's emotions. Furthermore, if the user is in a hurry, the exposure unit can provide concise and to-the-point product information. For example, the exposure unit collects the user's heart rate and electrodermal activity using a sensor to estimate the user's emotions. Furthermore, if the user is excited, the exposure unit can provide visually appealing product information. For example, the exposure unit analyzes the user's activity log to estimate the user's emotions. This allows for more effective product exposure by adjusting the product exposure method based on the user's emotions.

[0125] The budget unit can estimate a user's emotions and adjust the budget allocation for SEO measures based on the estimated user emotions. For example, if the user is relaxed, the budget allocation for SEO measures can be set higher. For example, the budget unit can analyze the user's facial expressions and voice to estimate emotions. The budget unit can also set a lower budget allocation for SEO measures if the user is feeling stressed. For example, the budget unit can collect the user's heart rate and electrodermal activity using a sensor to estimate emotions. The budget unit can also adjust the budget allocation for SEO measures if the user is excited. For example, the budget unit can analyze the user's activity log to estimate emotions. This allows for more effective SEO measures by adjusting the budget allocation for SEO measures based on the user's emotions.

[0126] The collection unit can analyze the user's past purchase history and browsing history and select the optimal collection method. For example, it prioritizes collection of product categories that the user frequently purchases. For example, the collection unit analyzes the user's past purchase history and selects the optimal collection method. The collection unit can also re-collect products that the user viewed in the past but did not purchase to attract the user's interest. For example, the collection unit analyzes the user's browsing history and selects the optimal collection method. The collection unit can also periodically collect data based on the user's purchase frequency. For example, the collection unit analyzes the user's purchase frequency and selects the optimal collection method. This enables efficient data collection by selecting the optimal collection method based on the user's past behavior.

[0127] The providing unit can adjust the level of detail of the provided information based on the importance of the product when providing it. For example, for an expensive product, detailed specifications and review information are provided. For example, the providing unit provides optimal information based on the price and reviews of the product. The providing unit can also provide concise information for products that are used on a daily basis. For example, the providing unit provides optimal information based on the frequency of use and category of the product. The providing unit can also provide special promotion information for new products and limited-edition products. For example, the providing unit provides optimal information based on the release date and limited offers of the product. In this way, by adjusting the level of detail of the information based on the importance of the product, more appropriate information can be provided.

[0128] When investing points, the investment department can analyze the user's past consumption behavior and select the optimal investment method. For example, the investment department can propose a similar investment method based on an investment method that the user has used successfully in the past. For example, the investment department can analyze the user's past consumption behavior and select the optimal investment method. The investment department can also analyze the user's past consumption behavior and select an investment method that minimizes risk. For example, the investment department can analyze the user's past consumption behavior and select the optimal investment method. The investment department can also propose the optimal investment timing based on the user's past consumption behavior. For example, the investment department can analyze the user's past consumption behavior and select the optimal investment method. In this way, the optimal investment method can be selected by analyzing the user's past consumption behavior.

[0129] When exposing a product, the exposure unit can customize the exposure means based on the user's current areas of interest. For example, the exposure unit prioritizes the exposure of product categories in which the user is currently interested. For example, the exposure unit analyzes the user's current areas of interest and selects the optimal exposure method. The exposure unit can also expose information about related products based on the user's current areas of interest. For example, the exposure unit analyzes the user's current areas of interest and selects the optimal exposure method. The exposure unit can also select the optimal exposure means based on the user's current areas of interest. For example, the exposure unit analyzes the user's current areas of interest and selects the optimal exposure method. This allows for more effective product exposure by customizing the exposure means based on the user's current areas of interest.

[0130] When allocating the budget for SEO measures, the budget department can refer to past effectiveness data to make optimal budget allocations. For example, the budget can be allocated preferentially to SEO measures that have been effective in the past. For example, the budget department can analyze past effectiveness data and make optimal budget allocations. The budget department can also determine the priority of budget allocations based on past effectiveness data. For example, the budget department can analyze past effectiveness data and make optimal budget allocations. In this way, optimal budget allocations are possible by referring to past effectiveness data.

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

[0132] Step 1: The collection unit collects the consumer's purchase history and browsing history. Specifically, it collects data such as purchase date and time, product category, viewed pages, purchase frequency, price range, and search history. For example, it also records the number of times and time that the consumer viewed a particular product. Step 2: The provision unit provides optimal purchase timing and discount information based on the data collected by the collection unit. Specifically, it analyzes product price fluctuations and discount information and suggests optimal purchase timing to consumers. It can also provide limited-time discount information and coupon codes. Step 3: The investment department manages unused points and increases their value. Specifically, they use AI to manage points and adjust the investment interest rate and investment method to increase their value. Step 4: The Promotion Department will increase exposure opportunities for their products by increasing the number of consumers and increasing revenue through point management. Specifically, they will expose the products of participating companies in advertising campaigns and promotional events. Step 5: The budget department allocates the necessary budget for SEO measures, specifically, keyword optimization, link building, content marketing, and other SEO strategies.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0204] [Explanation of symbols]

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

Claims

1. a collection unit that collects consumer purchase history and browsing history; a providing unit that provides appropriate purchase timing or discount information based on the data collected by the collecting unit; The management department manages unused points, The Exposure Department increases exposure opportunities for the company's products by increasing the number of consumers and increasing revenue through point management, and A budget department is in place to allocate the necessary budget for SEO measures. A system characterized by:

2. The collecting unit Collecting consumer purchasing and browsing history The system of claim 1 .

3. The providing unit Providing appropriate purchase timing or discount information based on collected data The system of claim 1 .

4. The operation unit Manage unused points to increase their value The system of claim 1 .

5. The exposed portion is A means to increase exposure of your company's products by increasing the number of consumers and increasing revenue through point management The system of claim 1 .

6. The budget department: How to allocate the necessary budget for SEO The system of claim 1 .

7. The collecting unit Estimate user emotions and adjust the timing of collecting purchase and browsing histories based on the estimated user emotions. The system of claim 1 .

8. The collecting unit Analyze users' past purchase and browsing history to select the optimal collection method The system of claim 1 .

Images