system

Abstract

The system according to this embodiment aims to enable users to easily find the most suitable product. [Solution] The system according to this embodiment comprises a reception unit, a search unit, and a recommendation unit. The reception unit receives input of purchase purpose, preferences, and budget range. The search unit searches for the most suitable product on the e-commerce site based on the information received by the reception unit. The recommendation unit recommends the product found by the search unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the work for finding the optimal product on an EC site is complicated and the burden on the user is large.

[0005] The system according to an embodiment aims to enable a user to easily find an optimal product.

Means for Solving the Problems

[0006] The system according to an embodiment includes a reception unit, a search unit, and a recommendation unit. The reception unit receives inputs of a purchase purpose, preferences, and a budget range. The search unit searches for an optimal product on an EC site based on the information received by the reception unit. The recommendation unit recommends the product searched by the search unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment allows users to easily find the optimal product. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The e-commerce site purchase support system according to an embodiment of the present invention is a system that utilizes generative AI to streamline the purchase of multiple products. This system automatically selects and recommends necessary items within an appropriate budget based on the user's input of purchase purpose, preferences, and budget range. For example, the user inputs information such as "I want to buy new kitchenware," "I prefer modern designs," and "My budget is under 10,000 yen." This information is input into the generative AI. Next, the generative AI analyzes the input information and searches for the most suitable products on the e-commerce site. Based on the user's preferences and budget, the generative AI selects the most suitable items from among multiple products. For example, the generative AI searches for kitchenware with a modern design and selects products that can be purchased within the budget. The products selected by the generative AI are recommended to the user. The user reviews the products recommended by the generative AI and decides whether to purchase them. For example, the user reviews the list of kitchenware recommended by the generative AI, selects a product they like, and purchases it. This mechanism reduces the burden on the user in the series of tasks required to purchase multiple products at the lowest possible cost. Because the generating AI automatically selects and recommends the most suitable products, users can purchase items efficiently without any hassle. For example, if a user enters "I want to buy new kitchenware," the generating AI will search for modern kitchenware on the e-commerce site and select items that fit within the user's budget. The products selected by the generating AI are recommended to the user, who can then choose and purchase the items they like. In this way, by utilizing the generating AI, users can purchase items efficiently, significantly reducing the burden of the purchasing process. As a result, e-commerce purchase support systems reduce the burden on users during the purchasing process and enable them to purchase items efficiently.

[0029] The e-commerce site purchase support system according to this embodiment comprises a reception unit, a search unit, and a recommendation unit. The reception unit receives input of the purchase purpose, preferences, and budget range. For example, a user can input information such as "I want to buy new kitchenware," "I like modern designs," and "My budget is under 10,000 yen." The reception unit transmits the user's input information to a generating AI. The search unit uses the generating AI to search for the most suitable products on the e-commerce site based on the information received by the reception unit. For example, the generating AI searches for kitchenware with a modern design and selects products that can be purchased within the budget. The generating AI selects the most suitable items from multiple products based on the user's preferences and budget. The recommendation unit recommends the products found by the search unit to the user. For example, it presents the products selected by the generating AI to the user, who can then choose and purchase the product they like. As a result, the e-commerce site purchase support system according to this embodiment can reduce the burden of the purchase process by automatically selecting and recommending the most suitable products based on the user's purchase purpose, preferences, and budget range.

[0030] The reception desk accepts input regarding purchase purpose, preferences, and budget range. For example, a user can input information such as "I want to buy new kitchenware," "I prefer modern designs," and "My budget is under 10,000 yen." The reception desk provides a user-friendly interface to allow users to intuitively and easily input information. For example, it uses dropdown menus, checkboxes, and sliders to allow users to easily select options. It also incorporates a voice input function, enabling users to input information by voice. Furthermore, the reception desk has a function to learn the user's preferences and purchase patterns by referring to the user's past purchase and browsing history. This improves the accuracy of the information entered by the user and allows for more appropriate product suggestions. For example, a user who has previously purchased modern-design kitchenware will be given priority in displaying products with similar designs. The reception desk also analyzes the information entered by the user in real time and provides immediate feedback based on the input. For example, when a budget range is entered, the number and categories of products that can be purchased within that budget are displayed, allowing the user to have a more concrete image. In this way, the reception desk can efficiently and accurately collect user input information and smoothly provide it to the search desk in the next step.

[0031] The search unit uses generative AI to search for the most suitable products on the e-commerce site based on the information received by the reception unit. Specifically, the generative AI searches the vast product database on the e-commerce site based on the user's purchase purpose, preferences, and budget range. The generative AI analyzes the user's input information using natural language processing technology and extracts relevant keywords and categories. For example, from the input information "modern kitchenware," it extracts keywords such as "modern" and "kitchenware," and searches for products based on these keywords. The generative AI also analyzes product images using image recognition technology to identify products with designs that match the user's preferences. For example, it prioritizes displaying images of products with modern designs. Furthermore, the generative AI performs price filtering based on the user's budget range and selects products that can be purchased within that budget. As a result, the search unit can quickly and accurately find the most suitable products based on the user's input information and provide that information to the recommendation unit, which is the next step.

[0032] The recommendation section recommends products found by the search section to the user. Specifically, it presents products selected by the generation AI to the user, allowing the user to choose and purchase their favorite items. The recommendation section provides the user with a visually appealing interface, displaying detailed product information, images, and reviews. For example, it displays product images in a slideshow format, allowing users to examine products in detail. It also designs the layout to allow users to see product features, specifications, price, and user reviews at a glance. Furthermore, the recommendation section provides personalized recommendations based on the user's past purchase and browsing history. For example, it prioritizes displaying products similar to those previously purchased or products in the same category. The recommendation section also collects user feedback and continuously improves the accuracy of its recommendation algorithm. For example, if a user purchases a recommended product, that information is used to improve the accuracy of future recommendations. In this way, the recommendation section can suggest the most suitable products to the user and reduce the burden of the purchasing process.

[0033] The reception desk can collect users' past purchase and browsing history. For example, the reception desk can collect information on products the user has purchased in the past to understand the user's preferences. The reception desk can also collect information on products the user has viewed in the past to understand the user's interests. This allows the reception desk to provide more accurate recommendations by collecting the user's past purchase and browsing history. Past purchase history includes, but is not limited to, purchase date and time, purchased items, and purchase frequency. Browsing history includes, but is not limited to, the categories of products viewed, viewing date and time, and number of views. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past purchase and browsing history into an AI, which can analyze this data to understand the user's preferences.

[0034] The search unit can filter products based on the user's preferences and budget, and select the most suitable product. For example, the search unit filters products on an e-commerce site based on the preferences and budget entered by the user. The search unit can also select the most suitable product based on the user's preferences and budget. Thus, the search unit can select the most suitable product by filtering products based on the user's preferences and budget. Filtering includes, but is not limited to, criteria such as price range, category, and brand. Some or all of the above processing in the search unit may be performed using or without a generative AI. For example, the search unit can input the user's preferences and budget into a generative AI, which can then filter products based on this information and select the most suitable product.

[0035] The recommendation section can present users with products selected by a generative AI. For example, the recommendation section can display products selected by the generative AI to the user, allowing the user to select and purchase their preferred product. This allows the recommendation section to present users with products selected by the generative AI, enabling them to efficiently select products. The generative AI includes, but is not limited to, machine learning models and natural language processing technologies. Some or all of the above-described processes in the recommendation section may be performed using AI or not. For example, the recommendation section can input products selected by the generative AI into the AI, which can then present these products to the user.

[0036] The recommendation unit can provide an interface for users to view and purchase products. For example, the recommendation unit provides an interface for users to view and purchase products. Furthermore, by providing an interface for users to view and purchase products, the recommendation unit can facilitate the purchase process. The interface includes, but is not limited to, functions such as usability, operation methods, and display content. Some or all of the above-described processes in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input the interface for users to view and purchase products into the AI, and the AI ​​can provide these interfaces.

[0037] The reception desk can analyze the user's past purchase and browsing history to complete inputs. For example, the reception desk can automatically suggest relevant purchase purposes and preferences based on products the user has purchased in the past. It can also suggest purchase purposes and preferences that the user might be interested in based on products the user has viewed in the past. Furthermore, the reception desk can suggest input candidates based on specific brands or categories from the user's past purchase history. In this way, the reception desk completes user inputs by analyzing past purchase and browsing history, reducing the effort required for input. Input completion includes, but is not limited to, autocomplete functions and recommended input candidates. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past purchase and browsing history into AI, which can then analyze this data to complete inputs.

[0038] The input system can suggest input options based on the user's current living situation and areas of interest when the user inputs their purchase purpose and preferences. For example, if the user has recently moved, the input system can suggest items needed for their new home. It can also suggest items related to a particular hobby if the user is interested in that hobby. Furthermore, if the user plans to attend a specific event, it can suggest items related to that event. This allows the input system to provide more appropriate input by suggesting input options based on the user's living situation and areas of interest. Living situation includes, but is not limited to, family structure, occupation, and lifestyle. Areas of interest include, but are not limited to, hobbies, topics of interest, and social media accounts followed. Some or all of the above processing in the input system may be performed using AI or not. For example, the input system can input data on the user's living situation and areas of interest into an AI, which can then analyze this data and suggest input options.

[0039] The reception desk can suggest highly relevant input options when the user inputs their purchase purpose and preferences, taking into account the user's geographical location. For example, if the user lives in a specific region, the reception desk can suggest popular items in that region. It can also suggest items needed at the user's travel destination if the user is traveling. Furthermore, if the user is near a specific store, the reception desk can suggest items available for purchase at that store. This allows the reception desk to suggest more relevant input options by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data, IP addresses, and location services. Some or all of the processing described above in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into an AI, which can then analyze this data to suggest highly relevant input options.

[0040] The reception desk can analyze the user's social media activity when they input their purchase purpose and preferences, and suggest relevant input options. For example, the reception desk can suggest items that the user might be interested in based on posts the user has shared on social media. It can also suggest relevant items based on posts from influencers the user follows. Furthermore, it can suggest relevant items based on the activities of groups and communities the user participates in. In this way, the reception desk can suggest input options based on the user's interests by analyzing their social media activity. Social media activity includes, but is not limited to, posts, likes and shares, and follower count. Some or all of the processing described above in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's social media activity into an AI, which can then analyze this data and suggest relevant input options.

[0041] The search unit can optimize search results by referencing the user's past purchase and browsing history during a search. For example, the search unit can prioritize displaying products related to items the user has previously purchased. It can also prioritize displaying products related to items the user has previously viewed. Furthermore, the search unit can prioritize displaying products of specific brands or categories based on the user's past purchase history. This improves the accuracy of search results by referencing past purchase and browsing history. Optimization of search results includes, but is not limited to, ranking algorithms and the use of user feedback. Some or all of the above processing in the search unit may be performed using or without a generative AI. For example, the search unit can input the user's past purchase and browsing history into a generative AI, which can then analyze this data to optimize search results.

[0042] The search unit can filter search results based on the user's current lifestyle and areas of interest. For example, if the user has recently moved, the search unit will prioritize displaying items needed for their new home. It can also prioritize displaying items related to a specific hobby if the user is interested in that hobby. Furthermore, if the user is planning to attend a particular event, the search unit can prioritize displaying items related to that event. This allows the search unit to provide more relevant products by filtering search results based on the user's lifestyle and areas of interest. Filtering search results may include, but is not limited to, criteria such as category, price range, and brand. Some or all of the above processing in the search unit may be performed using or without generative AI. For example, the search unit can input data on the user's lifestyle and areas of interest into a generative AI, which can then analyze this data to filter the search results.

[0043] The search unit can prioritize searching for highly relevant products by considering the user's geographical location during a search. For example, if the user lives in a specific region, the search unit can prioritize displaying products popular in that region. It can also prioritize displaying products available for purchase at the user's travel destination if the user is traveling. Furthermore, if the user is near a specific store, the search unit can prioritize displaying products available for purchase at that store. This allows the search unit to provide more relevant products by considering the user's geographical location. Relevant products include, but are not limited to, the user's past purchase history, browsing history, and current areas of interest. Some or all of the above processing in the search unit may be performed using or without a generative AI. For example, the search unit can input the user's geographical location information into a generative AI, which can then analyze this data to prioritize searching for highly relevant products.

[0044] The search unit can analyze a user's social media activity during a search and find relevant products. For example, the search unit can prioritize displaying products that a user might be interested in based on posts they have shared on social media. It can also prioritize displaying relevant products based on posts from influencers the user follows. Furthermore, it can prioritize displaying relevant products based on the activities of groups and communities the user participates in. This allows the search unit to provide products tailored to the user's interests by analyzing their social media activity. Analysis of social media activity includes, but is not limited to, methods such as text mining and network analysis. Some or all of the processing described above in the search unit may be performed using or without generative AI. For example, the search unit can input data on a user's social media activity into a generative AI, which can then analyze this data to find relevant products.

[0045] The recommendation unit can adjust the level of detail in recommendations based on the importance of the products. For example, for important products, the recommendation unit can provide recommendations that include detailed descriptions and multiple images. For less important products, the recommendation unit can provide recommendations that include concise descriptions and one image. Furthermore, for products of moderate importance, the recommendation unit can provide recommendations that include descriptions of moderate detail and two images. In this way, the recommendation unit can provide users with the most relevant information by adjusting the level of detail in recommendations based on the importance of the products. Product importance includes, but is not limited to, sales data, user ratings, and inventory status. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input product importance data into AI, which can then analyze this data to adjust the level of detail in the recommendations.

[0046] The recommendation unit can apply different recommendation algorithms depending on the product category when making recommendations. For example, the recommendation unit can apply a recommendation algorithm that emphasizes performance and price for home appliances. It can also apply a recommendation algorithm that emphasizes design and trends for fashion items. Furthermore, it can apply a recommendation algorithm that emphasizes quality and expiration date for food products. This allows the recommendation unit to provide more appropriate recommendations by applying different recommendation algorithms depending on the product category. Recommendation algorithms include, but are not limited to, collaborative filtering and content-based filtering. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input product category data into an AI, which can then analyze this data and apply different recommendation algorithms.

[0047] The recommendation system can prioritize recommendations based on when the products were submitted. For example, the recommendation system can prioritize recommendations for new products. It can also prioritize recommendations for products on sale. Furthermore, it can prioritize recommendations for products with low stock. In this way, the recommendation system can provide users with the most suitable products by prioritizing recommendations based on when the products were submitted. Submission dates include, but are not limited to, release dates, arrival dates, and campaign periods. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input product submission date data into an AI, which can then analyze this data to determine the recommendation priority.

[0048] The recommendation system can adjust the order of recommendations based on product relevance. For example, it can display products most relevant to the user's preferences at the top. It can also display products most relevant to the user's budget at the top. Furthermore, it can display products most relevant to the user's purchase purpose at the top. In this way, the recommendation system can provide the user with the most suitable products by adjusting the order of recommendations based on product relevance. Product relevance includes, but is not limited to, category matching, the user's past purchase history, and browsing history. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input product relevance data into AI, which can then analyze this data to adjust the order of recommendations.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The reception desk can provide trend information related to the user's purchase purpose and preferences based on the user's input information. For example, if the user inputs "I like modern designs," the reception desk will display current trends and popular products in modern design. Similarly, if the user inputs "I want to buy new kitchenware," the reception desk can introduce the latest trends and new products in kitchenware. Furthermore, if the user inputs "My budget is under 10,000 yen," the reception desk can suggest trend products that can be purchased within that budget. In this way, the reception desk enables users to select products based on the latest information by providing trend information based on the user's input information. Trend information includes, but is not limited to, the latest designs, popular brands, and recent reviews. Some or all of the processing described above in the reception desk may be performed using AI or not. For example, the reception desk can input trend information into an AI, which can then analyze this data and provide it to the user.

[0051] The search function can evaluate the eco-friendliness of products based on user input and prioritize displaying eco-friendly products. For example, if a user inputs "I'm looking for eco-friendly products," the search function will prioritize displaying products that use environmentally friendly materials and manufacturing methods. If a user inputs "My budget is under 10,000 yen," the search function can also select eco-friendly products that fit within that budget. Furthermore, if a user inputs "I prefer modern designs," the search function can prioritize displaying eco-friendly products with modern designs. In this way, the search function supports environmentally conscious product selection by prioritizing eco-friendly products based on user input. The evaluation of eco-friendliness includes, but is not limited to, the renewable nature of materials, the environmental impact of the manufacturing process, and the recyclability of the product. Some or all of the above processing in the search function may be performed using AI or not. For example, the search function can input eco-friendliness data into an AI, which can then analyze this data to select eco-friendly products.

[0052] The reception desk can provide coupons and discount information related to the user's purchase purpose and preferences based on the information entered by the user. For example, if the reception desk enters "I want to buy new kitchenware," it will display coupons and discount information applicable to kitchenware. Similarly, if the reception desk enters "I prefer modern designs," it can provide discount information applicable to modern design products. Furthermore, if the reception desk enters "My budget is under 10,000 yen," it can suggest coupons applicable to products that can be purchased within that budget. In this way, the reception desk enables users to purchase products more affordably by providing coupons and discount information based on the user's input. Coupons and discount information include, but are not limited to, limited-time sales, discount coupons for specific brands, and discounts based on purchase amount. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input coupons and discount information into an AI, which can then analyze this data and provide it to the user.

[0053] The recommendation section can display product reviews and ratings based on user input. For example, if a user inputs "I want to buy new kitchenware," the recommendation section will display reviews and ratings for that product. If a user inputs "I prefer modern designs," the recommendation section can also provide reviews and ratings for products with modern designs. Furthermore, if a user inputs "My budget is under 10,000 yen," the recommendation section can display reviews and ratings for products that can be purchased within that budget. This allows the recommendation section to display product reviews and ratings based on user input, enabling users to check product quality and satisfaction. Reviews and ratings include, but are not limited to, user comments, star ratings, and buyer feedback. Some or all of the above processing in the recommendation section may be performed using AI or not. For example, the recommendation section can input review and rating data into an AI, which can then analyze this data and provide it to the user.

[0054] The search function can display product customization options based on user input. For example, if a user enters "I want to buy new kitchenware," the search function will display customizable kitchenware. If a user enters "I prefer modern designs," the search function can also provide customization options for modern design products. Furthermore, if a user enters "My budget is under 10,000 yen," the search function can display customizable products within that budget. In this way, the search function allows users to select products that suit their preferences by displaying product customization options based on their input. Customization options include, but are not limited to, color, size, material, and additional features. Some or all of the above processing in the search function may be performed using AI or not. For example, the search function can input customization option data into an AI, which can then analyze this data and provide it to the user.

[0055] The reception desk can introduce communities and forums related to the user's purchase purpose and preferences based on the information the user enters. For example, if the reception desk enters "I want to buy new kitchenware," it will introduce communities and forums related to kitchenware. Similarly, if the user enters "I prefer modern design," it can introduce communities and forums related to modern design. Furthermore, if the user enters "My budget is under 10,000 yen," it can introduce communities and forums related to products available within that budget. In this way, the reception desk allows users to share information and exchange opinions with other users by introducing relevant communities and forums based on their input. These communities and forums include, but are not limited to, online bulletin boards, social media groups, and expert blogs. Some or all of the processing described above in the reception desk may be performed using AI or not. For example, the reception desk can input community and forum information into an AI, which can then analyze this data and provide it to the user.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk accepts input regarding the purpose of purchase, preferences, and budget range. For example, a user can enter information such as "I want to buy new kitchenware," "I prefer modern designs," and "My budget is under 10,000 yen." The reception desk then sends the user's input information to the generating AI. Step 2: The search unit uses a generation AI to search for the most suitable products on the e-commerce site based on the information received by the reception unit. For example, the generation AI searches for modern kitchenware and selects products that can be purchased within the budget. The generation AI selects the most suitable items from multiple products based on the user's preferences and budget. Step 3: The recommendation unit recommends products found by the search unit to the user. For example, the generation AI presents products to the user, who can then choose and purchase the product they like.

[0058] (Example of form 2) The e-commerce site purchase support system according to an embodiment of the present invention is a system that utilizes generative AI to streamline the purchase of multiple products. This system automatically selects and recommends necessary items within an appropriate budget based on the user's input of purchase purpose, preferences, and budget range. For example, the user inputs information such as "I want to buy new kitchenware," "I prefer modern designs," and "My budget is under 10,000 yen." This information is input into the generative AI. Next, the generative AI analyzes the input information and searches for the most suitable products on the e-commerce site. Based on the user's preferences and budget, the generative AI selects the most suitable items from among multiple products. For example, the generative AI searches for kitchenware with a modern design and selects products that can be purchased within the budget. The products selected by the generative AI are recommended to the user. The user reviews the products recommended by the generative AI and decides whether to purchase them. For example, the user reviews the list of kitchenware recommended by the generative AI, selects a product they like, and purchases it. This mechanism reduces the burden on the user in the series of tasks required to purchase multiple products at the lowest possible cost. Because the generating AI automatically selects and recommends the most suitable products, users can purchase items efficiently without any hassle. For example, if a user enters "I want to buy new kitchenware," the generating AI will search for modern kitchenware on the e-commerce site and select items that fit within the user's budget. The products selected by the generating AI are recommended to the user, who can then choose and purchase the items they like. In this way, by utilizing the generating AI, users can purchase items efficiently, significantly reducing the burden of the purchasing process. As a result, e-commerce purchase support systems reduce the burden on users during the purchasing process and enable them to purchase items efficiently.

[0059] The e-commerce site purchase support system according to this embodiment comprises a reception unit, a search unit, and a recommendation unit. The reception unit receives input of the purchase purpose, preferences, and budget range. For example, a user can input information such as "I want to buy new kitchenware," "I like modern designs," and "My budget is under 10,000 yen." The reception unit transmits the user's input information to a generating AI. The search unit uses the generating AI to search for the most suitable products on the e-commerce site based on the information received by the reception unit. For example, the generating AI searches for kitchenware with a modern design and selects products that can be purchased within the budget. The generating AI selects the most suitable items from multiple products based on the user's preferences and budget. The recommendation unit recommends the products found by the search unit to the user. For example, it presents the products selected by the generating AI to the user, who can then choose and purchase the product they like. As a result, the e-commerce site purchase support system according to this embodiment can reduce the burden of the purchase process by automatically selecting and recommending the most suitable products based on the user's purchase purpose, preferences, and budget range.

[0060] The reception desk accepts input regarding purchase purpose, preferences, and budget range. For example, a user can input information such as "I want to buy new kitchenware," "I prefer modern designs," and "My budget is under 10,000 yen." The reception desk provides a user-friendly interface to allow users to intuitively and easily input information. For example, it uses dropdown menus, checkboxes, and sliders to allow users to easily select options. It also incorporates a voice input function, enabling users to input information by voice. Furthermore, the reception desk has a function to learn the user's preferences and purchase patterns by referring to the user's past purchase and browsing history. This improves the accuracy of the information entered by the user and allows for more appropriate product suggestions. For example, a user who has previously purchased modern-design kitchenware will be given priority in displaying products with similar designs. The reception desk also analyzes the information entered by the user in real time and provides immediate feedback based on the input. For example, when a budget range is entered, the number and categories of products that can be purchased within that budget are displayed, allowing the user to have a more concrete image. In this way, the reception desk can efficiently and accurately collect user input information and smoothly provide it to the search desk in the next step.

[0061] The search unit uses generative AI to search for the most suitable products on the e-commerce site based on the information received by the reception unit. Specifically, the generative AI searches the vast product database on the e-commerce site based on the user's purchase purpose, preferences, and budget range. The generative AI analyzes the user's input information using natural language processing technology and extracts relevant keywords and categories. For example, from the input information "modern kitchenware," it extracts keywords such as "modern" and "kitchenware," and searches for products based on these keywords. The generative AI also analyzes product images using image recognition technology to identify products with designs that match the user's preferences. For example, it prioritizes displaying images of products with modern designs. Furthermore, the generative AI performs price filtering based on the user's budget range and selects products that can be purchased within that budget. As a result, the search unit can quickly and accurately find the most suitable products based on the user's input information and provide that information to the recommendation unit, which is the next step.

[0062] The recommendation section recommends products found by the search section to the user. Specifically, it presents products selected by the generation AI to the user, allowing the user to choose and purchase their favorite items. The recommendation section provides the user with a visually appealing interface, displaying detailed product information, images, and reviews. For example, it displays product images in a slideshow format, allowing users to examine products in detail. It also designs the layout to allow users to see product features, specifications, price, and user reviews at a glance. Furthermore, the recommendation section provides personalized recommendations based on the user's past purchase and browsing history. For example, it prioritizes displaying products similar to those previously purchased or products in the same category. The recommendation section also collects user feedback and continuously improves the accuracy of its recommendation algorithm. For example, if a user purchases a recommended product, that information is used to improve the accuracy of future recommendations. In this way, the recommendation section can suggest the most suitable products to the user and reduce the burden of the purchasing process.

[0063] The reception desk can collect users' past purchase and browsing history. For example, the reception desk can collect information on products the user has purchased in the past to understand the user's preferences. The reception desk can also collect information on products the user has viewed in the past to understand the user's interests. This allows the reception desk to provide more accurate recommendations by collecting the user's past purchase and browsing history. Past purchase history includes, but is not limited to, purchase date and time, purchased items, and purchase frequency. Browsing history includes, but is not limited to, the categories of products viewed, viewing date and time, and number of views. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past purchase and browsing history into an AI, which can analyze this data to understand the user's preferences.

[0064] The search unit can filter products based on the user's preferences and budget, and select the most suitable product. For example, the search unit filters products on an e-commerce site based on the preferences and budget entered by the user. The search unit can also select the most suitable product based on the user's preferences and budget. Thus, the search unit can select the most suitable product by filtering products based on the user's preferences and budget. Filtering includes, but is not limited to, criteria such as price range, category, and brand. Some or all of the above processing in the search unit may be performed using or without a generative AI. For example, the search unit can input the user's preferences and budget into a generative AI, which can then filter products based on this information and select the most suitable product.

[0065] The recommendation section can present users with products selected by a generative AI. For example, the recommendation section can display products selected by the generative AI to the user, allowing the user to select and purchase their preferred product. This allows the recommendation section to present users with products selected by the generative AI, enabling them to efficiently select products. The generative AI includes, but is not limited to, machine learning models and natural language processing technologies. Some or all of the above-described processes in the recommendation section may be performed using AI or not. For example, the recommendation section can input products selected by the generative AI into the AI, which can then present these products to the user.

[0066] The recommendation unit can provide an interface for users to view and purchase products. For example, the recommendation unit provides an interface for users to view and purchase products. Furthermore, by providing an interface for users to view and purchase products, the recommendation unit can facilitate the purchase process. The interface includes, but is not limited to, functions such as usability, operation methods, and display content. Some or all of the above-described processes in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input the interface for users to view and purchase products into the AI, and the AI ​​can provide these interfaces.

[0067] The reception desk can estimate the user's emotions and adjust the input method for purchase purpose and preferences based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of purchase purpose and preferences. In this way, the reception desk reduces user stress and improves input accuracy by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can analyze this data to estimate the user's emotions and adjust the input method.

[0068] The reception desk can analyze the user's past purchase and browsing history to complete inputs. For example, the reception desk can automatically suggest relevant purchase purposes and preferences based on products the user has purchased in the past. It can also suggest purchase purposes and preferences that the user might be interested in based on products the user has viewed in the past. Furthermore, the reception desk can suggest input candidates based on specific brands or categories from the user's past purchase history. In this way, the reception desk completes user inputs by analyzing past purchase and browsing history, reducing the effort required for input. Input completion includes, but is not limited to, autocomplete functions and recommended input candidates. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past purchase and browsing history into AI, which can then analyze this data to complete inputs.

[0069] The input system can suggest input options based on the user's current living situation and areas of interest when the user inputs their purchase purpose and preferences. For example, if the user has recently moved, the input system can suggest items needed for their new home. It can also suggest items related to a particular hobby if the user is interested in that hobby. Furthermore, if the user plans to attend a specific event, it can suggest items related to that event. This allows the input system to provide more appropriate input by suggesting input options based on the user's living situation and areas of interest. Living situation includes, but is not limited to, family structure, occupation, and lifestyle. Areas of interest include, but are not limited to, hobbies, topics of interest, and social media accounts followed. Some or all of the above processing in the input system may be performed using AI or not. For example, the input system can input data on the user's living situation and areas of interest into an AI, which can then analyze this data and suggest input options.

[0070] The reception desk can estimate the user's emotions and determine the priority of inputs based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize displaying important input items and postpone other items. If the user is relaxed, the reception desk can also display all input items equally, allowing the user to choose freely. Furthermore, if the user is in a hurry, the reception desk can display only the most important input items, enabling quick completion of the input. In this way, the reception desk reduces user stress and improves input efficiency by determining the priority of inputs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can analyze this data to estimate the user's emotions and determine the priority of inputs.

[0071] The reception desk can suggest highly relevant input options when the user inputs their purchase purpose and preferences, taking into account the user's geographical location. For example, if the user lives in a specific region, the reception desk can suggest popular items in that region. It can also suggest items needed at the user's travel destination if the user is traveling. Furthermore, if the user is near a specific store, the reception desk can suggest items available for purchase at that store. This allows the reception desk to suggest more relevant input options by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data, IP addresses, and location services. Some or all of the processing described above in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into an AI, which can then analyze this data to suggest highly relevant input options.

[0072] The reception desk can analyze the user's social media activity when they input their purchase purpose and preferences, and suggest relevant input options. For example, the reception desk can suggest items that the user might be interested in based on posts the user has shared on social media. It can also suggest relevant items based on posts from influencers the user follows. Furthermore, it can suggest relevant items based on the activities of groups and communities the user participates in. In this way, the reception desk can suggest input options based on the user's interests by analyzing their social media activity. Social media activity includes, but is not limited to, posts, likes and shares, and follower count. Some or all of the processing described above in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's social media activity into an AI, which can then analyze this data and suggest relevant input options.

[0073] The search unit can estimate the user's emotions and adjust the search algorithm based on the estimated emotions. For example, if the user is relaxed, the search unit can use a generative AI to apply a search algorithm that provides a wide range of options. If the user is in a hurry, the search unit can also use a generative AI to apply a search algorithm that prioritizes displaying the most relevant products. Furthermore, if the user is excited, the search unit can use a generative AI to apply a search algorithm that prioritizes displaying visually appealing products. In this way, the search unit can provide more appropriate search results by adjusting the search algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using or without the generative AI. For example, the search unit can input user emotion data into the generative AI, which can analyze this data and adjust the search algorithm.

[0074] The search unit can optimize search results by referencing the user's past purchase and browsing history during a search. For example, the search unit can prioritize displaying products related to items the user has previously purchased. It can also prioritize displaying products related to items the user has previously viewed. Furthermore, the search unit can prioritize displaying products of specific brands or categories based on the user's past purchase history. This improves the accuracy of search results by referencing past purchase and browsing history. Optimization of search results includes, but is not limited to, ranking algorithms and the use of user feedback. Some or all of the above processing in the search unit may be performed using or without a generative AI. For example, the search unit can input the user's past purchase and browsing history into a generative AI, which can then analyze this data to optimize search results.

[0075] The search unit can filter search results based on the user's current lifestyle and areas of interest. For example, if the user has recently moved, the search unit will prioritize displaying items needed for their new home. It can also prioritize displaying items related to a specific hobby if the user is interested in that hobby. Furthermore, if the user is planning to attend a particular event, the search unit can prioritize displaying items related to that event. This allows the search unit to provide more relevant products by filtering search results based on the user's lifestyle and areas of interest. Filtering search results may include, but is not limited to, criteria such as category, price range, and brand. Some or all of the above processing in the search unit may be performed using or without generative AI. For example, the search unit can input data on the user's lifestyle and areas of interest into a generative AI, which can then analyze this data to filter the search results.

[0076] The search unit can estimate the user's emotions and adjust the display order of search results based on the estimated emotions. For example, if the user is relaxed, the search unit may display search results randomly to provide a wide range of options. If the user is in a hurry, the search unit may also display the most relevant products at the top. Furthermore, if the user is excited, the search unit may display visually appealing products at the top. In this way, the search unit can provide the user with the most suitable products by adjusting the display order of search results according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using or without generative AI. For example, the search unit can input user emotion data into a generative AI, which can then analyze this data to adjust the display order of search results.

[0077] The search unit can prioritize searching for highly relevant products by considering the user's geographical location during a search. For example, if the user lives in a specific region, the search unit can prioritize displaying products popular in that region. It can also prioritize displaying products available for purchase at the user's travel destination if the user is traveling. Furthermore, if the user is near a specific store, the search unit can prioritize displaying products available for purchase at that store. This allows the search unit to provide more relevant products by considering the user's geographical location. Relevant products include, but are not limited to, the user's past purchase history, browsing history, and current areas of interest. Some or all of the above processing in the search unit may be performed using or without a generative AI. For example, the search unit can input the user's geographical location information into a generative AI, which can then analyze this data to prioritize searching for highly relevant products.

[0078] The search unit can analyze a user's social media activity during a search and find relevant products. For example, the search unit can prioritize displaying products that a user might be interested in based on posts they have shared on social media. It can also prioritize displaying relevant products based on posts from influencers the user follows. Furthermore, it can prioritize displaying relevant products based on the activities of groups and communities the user participates in. This allows the search unit to provide products tailored to the user's interests by analyzing their social media activity. Analysis of social media activity includes, but is not limited to, methods such as text mining and network analysis. Some or all of the processing described above in the search unit may be performed using or without generative AI. For example, the search unit can input data on a user's social media activity into a generative AI, which can then analyze this data to find relevant products.

[0079] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, the recommendation unit can provide recommendations with detailed explanations. If the user is in a hurry, the recommendation unit can provide concise recommendations that get straight to the point. Furthermore, if the user is excited, the recommendation unit can provide visually appealing recommendations. In this way, the recommendation unit can provide more appropriate recommendations by adjusting the way recommendations are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input user emotion data into a generative AI, which can then analyze this data to adjust the way recommendations are presented.

[0080] The recommendation unit can adjust the level of detail in recommendations based on the importance of the products. For example, for important products, the recommendation unit can provide recommendations that include detailed descriptions and multiple images. For less important products, the recommendation unit can provide recommendations that include concise descriptions and one image. Furthermore, for products of moderate importance, the recommendation unit can provide recommendations that include descriptions of moderate detail and two images. In this way, the recommendation unit can provide users with the most relevant information by adjusting the level of detail in recommendations based on the importance of the products. Product importance includes, but is not limited to, sales data, user ratings, and inventory status. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input product importance data into AI, which can then analyze this data to adjust the level of detail in the recommendations.

[0081] The recommendation unit can apply different recommendation algorithms depending on the product category when making recommendations. For example, the recommendation unit can apply a recommendation algorithm that emphasizes performance and price for home appliances. It can also apply a recommendation algorithm that emphasizes design and trends for fashion items. Furthermore, it can apply a recommendation algorithm that emphasizes quality and expiration date for food products. This allows the recommendation unit to provide more appropriate recommendations by applying different recommendation algorithms depending on the product category. Recommendation algorithms include, but are not limited to, collaborative filtering and content-based filtering. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input product category data into an AI, which can then analyze this data and apply different recommendation algorithms.

[0082] The recommendation unit can estimate the user's mood and adjust the length of recommendations based on that mood. For example, if the user is relaxed, the recommendation unit can provide longer recommendations with detailed descriptions. If the user is in a hurry, the recommendation unit can provide short, to-the-point recommendations. Furthermore, if the user is excited, the recommendation unit can provide visually appealing recommendations. In this way, the recommendation unit can provide more appropriate recommendations by adjusting the length of recommendations according to the user's mood. Mood estimation is achieved using a mood estimation function, for example, using a mood engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input user mood data into a generative AI, which can analyze this data to adjust the length of recommendations.

[0083] The recommendation system can prioritize recommendations based on when the products were submitted. For example, the recommendation system can prioritize recommendations for new products. It can also prioritize recommendations for products on sale. Furthermore, it can prioritize recommendations for products with low stock. In this way, the recommendation system can provide users with the most suitable products by prioritizing recommendations based on when the products were submitted. Submission dates include, but are not limited to, release dates, arrival dates, and campaign periods. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input product submission date data into an AI, which can then analyze this data to determine the recommendation priority.

[0084] The recommendation system can adjust the order of recommendations based on product relevance. For example, it can display products most relevant to the user's preferences at the top. It can also display products most relevant to the user's budget at the top. Furthermore, it can display products most relevant to the user's purchase purpose at the top. In this way, the recommendation system can provide the user with the most suitable products by adjusting the order of recommendations based on product relevance. Product relevance includes, but is not limited to, category matching, the user's past purchase history, and browsing history. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input product relevance data into AI, which can then analyze this data to adjust the order of recommendations.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The reception desk can provide trend information related to the user's purchase purpose and preferences based on the user's input information. For example, if the user inputs "I like modern designs," the reception desk will display current trends and popular products in modern design. Similarly, if the user inputs "I want to buy new kitchenware," the reception desk can introduce the latest trends and new products in kitchenware. Furthermore, if the user inputs "My budget is under 10,000 yen," the reception desk can suggest trend products that can be purchased within that budget. In this way, the reception desk enables users to select products based on the latest information by providing trend information based on the user's input information. Trend information includes, but is not limited to, the latest designs, popular brands, and recent reviews. Some or all of the processing described above in the reception desk may be performed using AI or not. For example, the reception desk can input trend information into an AI, which can then analyze this data and provide it to the user.

[0087] The search function can evaluate the eco-friendliness of products based on user input and prioritize displaying eco-friendly products. For example, if a user inputs "I'm looking for eco-friendly products," the search function will prioritize displaying products that use environmentally friendly materials and manufacturing methods. If a user inputs "My budget is under 10,000 yen," the search function can also select eco-friendly products that fit within that budget. Furthermore, if a user inputs "I prefer modern designs," the search function can prioritize displaying eco-friendly products with modern designs. In this way, the search function supports environmentally conscious product selection by prioritizing eco-friendly products based on user input. The evaluation of eco-friendliness includes, but is not limited to, the renewable nature of materials, the environmental impact of the manufacturing process, and the recyclability of the product. Some or all of the above processing in the search function may be performed using AI or not. For example, the search function can input eco-friendliness data into an AI, which can then analyze this data to select eco-friendly products.

[0088] The recommendation unit can estimate the user's emotions and adjust the timing of recommendations based on those emotions. For example, if the user is feeling stressed, the recommendation unit can provide recommendations during a relaxed time. It can also provide immediate recommendations if the user is relaxed. Furthermore, if the user is in a hurry, the recommendation unit can provide recommendations quickly. This allows the recommendation unit to provide recommendations at a more appropriate time by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input user emotion data into a generative AI, which can then analyze this data to adjust the timing of recommendations.

[0089] The reception desk can provide coupons and discount information related to the user's purchase purpose and preferences based on the information entered by the user. For example, if the reception desk enters "I want to buy new kitchenware," it will display coupons and discount information applicable to kitchenware. Similarly, if the reception desk enters "I prefer modern designs," it can provide discount information applicable to modern design products. Furthermore, if the reception desk enters "My budget is under 10,000 yen," it can suggest coupons applicable to products that can be purchased within that budget. In this way, the reception desk enables users to purchase products more affordably by providing coupons and discount information based on the user's input. Coupons and discount information include, but are not limited to, limited-time sales, discount coupons for specific brands, and discounts based on purchase amount. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input coupons and discount information into an AI, which can then analyze this data and provide it to the user.

[0090] The search unit can estimate the user's emotions and adjust the display format of search results based on the estimated emotions. For example, if the user is relaxed, the search unit can display search results in a list format containing detailed information. If the user is in a hurry, the search unit can also display search results in a concise, point-by-point card format. Furthermore, if the user is excited, the search unit can display search results in a visually appealing grid format. In this way, the search unit can provide more appropriate search results by adjusting the display format of search results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using or without generative AI. For example, the search unit can input user emotion data into a generative AI, which can analyze this data and adjust the display format of the search results.

[0091] The recommendation section can display product reviews and ratings based on user input. For example, if a user inputs "I want to buy new kitchenware," the recommendation section will display reviews and ratings for that product. If a user inputs "I prefer modern designs," the recommendation section can also provide reviews and ratings for products with modern designs. Furthermore, if a user inputs "My budget is under 10,000 yen," the recommendation section can display reviews and ratings for products that can be purchased within that budget. This allows the recommendation section to display product reviews and ratings based on user input, enabling users to check product quality and satisfaction. Reviews and ratings include, but are not limited to, user comments, star ratings, and buyer feedback. Some or all of the above processing in the recommendation section may be performed using AI or not. For example, the recommendation section can input review and rating data into an AI, which can then analyze this data and provide it to the user.

[0092] The reception desk can estimate the user's emotions and provide feedback on the input based on the estimated emotions. For example, if the reception desk is stressed, it can provide positive feedback on the input to alleviate the user's feelings. If the user is relaxed, the reception desk can also provide detailed feedback to confirm the user's input. Furthermore, if the user is in a hurry, the reception desk can provide quick feedback to facilitate confirmation of the input. In this way, the reception desk reduces user stress and improves input accuracy by providing feedback on the input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can then analyze this data to provide feedback on the input.

[0093] The search function can display product customization options based on user input. For example, if a user enters "I want to buy new kitchenware," the search function will display customizable kitchenware. If a user enters "I prefer modern designs," the search function can also provide customization options for modern design products. Furthermore, if a user enters "My budget is under 10,000 yen," the search function can display customizable products within that budget. In this way, the search function allows users to select products that suit their preferences by displaying product customization options based on their input. Customization options include, but are not limited to, color, size, material, and additional features. Some or all of the above processing in the search function may be performed using AI or not. For example, the search function can input customization option data into an AI, which can then analyze this data and provide it to the user.

[0094] The recommendation system can estimate the user's emotions and adjust the frequency of recommendations based on those emotions. For example, if the user is stressed, the recommendation system can reduce the frequency of recommendations to lessen the user's burden. Conversely, if the user is relaxed, the recommendation system can increase the frequency of recommendations to provide the user with more options. Furthermore, if the user is in a hurry, the recommendation system can provide only the essential recommendations to allow for quick product selection. In this way, the recommendation system can provide more appropriate recommendations by adjusting the frequency of recommendations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation unit can input user sentiment data into a generating AI, which can then analyze this data and adjust the frequency of recommendations.

[0095] The reception desk can introduce communities and forums related to the user's purchase purpose and preferences based on the information the user enters. For example, if the reception desk enters "I want to buy new kitchenware," it will introduce communities and forums related to kitchenware. Similarly, if the user enters "I prefer modern design," it can introduce communities and forums related to modern design. Furthermore, if the user enters "My budget is under 10,000 yen," it can introduce communities and forums related to products available within that budget. In this way, the reception desk allows users to share information and exchange opinions with other users by introducing relevant communities and forums based on their input. These communities and forums include, but are not limited to, online bulletin boards, social media groups, and expert blogs. Some or all of the processing described above in the reception desk may be performed using AI or not. For example, the reception desk can input community and forum information into an AI, which can then analyze this data and provide it to the user.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The reception desk accepts input regarding the purpose of purchase, preferences, and budget range. For example, a user can enter information such as "I want to buy new kitchenware," "I prefer modern designs," and "My budget is under 10,000 yen." The reception desk then sends the user's input information to the generating AI. Step 2: The search unit uses a generation AI to search for the most suitable products on the e-commerce site based on the information received by the reception unit. For example, the generation AI searches for modern kitchenware and selects products that can be purchased within the budget. The generation AI selects the most suitable items from multiple products based on the user's preferences and budget. Step 3: The recommendation unit recommends products found by the search unit to the user. For example, the generation AI presents products to the user, who can then choose and purchase the product they like.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0100] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0101] Each of the multiple elements described above, including the reception unit, search unit, and recommendation unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing the user to input their purchase purpose, preferences, and budget range. The search unit is implemented by the identification processing unit 290 of the data processing unit 12, using a generating AI to search for the most suitable product. The recommendation unit is implemented by the output device 40 of the smart device 14, presenting the product selected by the generating AI to the user. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0108] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0109] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0110] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0111] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0112] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the reception unit, search unit, and recommendation unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to input their purchase purpose, preferences, and budget range by voice. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which searches for the optimal product using a generating AI. The recommendation unit is implemented by, for example, the speaker 240 of the smart glasses 214, which presents the products selected by the generating AI to the user by voice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, search unit, and recommendation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input their purchase purpose, preferences, and budget range by voice. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which searches for the optimal product using a generating AI. The recommendation unit is implemented by, for example, the display 343 of the headset terminal 314, which displays the product selected by the generating AI to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

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

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

[0141] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0142] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the reception unit, search unit, and recommendation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing the user to input their purchase purpose, preferences, and budget range by voice. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which searches for the optimal product using a generating AI. The recommendation unit is implemented by, for example, the speaker 240 of the robot 414, which presents the products selected by the generating AI to the user by voice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0151] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0153] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0154] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0155] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0159] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0160] 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.

[0161] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0162] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0163] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0164] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0166] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0167] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0168] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0169] (Note 1) A reception desk that accepts input regarding purchase purpose, preferences, and budget range, A search unit searches for the most suitable product on the e-commerce site based on the information received by the aforementioned reception unit, The system includes a recommendation unit that recommends products found by the search unit to the user. A system characterized by the following features. (Note 2) The aforementioned reception unit is Collects users' past purchase and browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned search unit, Filter products based on user preferences and budget to select the most suitable product. The system described in Appendix 1, characterized by the features described herein. (Note 4) The recommendation unit is, The AI ​​generates and presents products selected by the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The recommendation unit is, Provides an interface for users to view and purchase products. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for purchase purpose and preferences based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past purchase and browsing history to complete input. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users input their purchase purpose and preferences, the system suggests input options based on their current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users input their purchase purpose and preferences, the system will consider their geographical location to suggest highly relevant input options. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input their purchase purpose and preferences, the system analyzes their social media activity and suggests relevant input options. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned search unit, It estimates the user's sentiment and adjusts the search algorithm based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned search unit, When searching, the system optimizes search results by referencing the user's past purchase and browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, When searching, the search results are filtered based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, It estimates the user's sentiment and adjusts the display order of search results based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, When searching, the system prioritizes finding highly relevant products by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, When you search, we analyze your social media activity and search for relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 18) The recommendation unit is, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The recommendation unit is, When making recommendations, adjust the level of detail based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 20) The recommendation unit is, When making recommendations, different recommendation algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The recommendation unit is, It estimates the user's emotions and adjusts the length of recommendations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The recommendation unit is, When making recommendations, the priority of recommendations is determined based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The recommendation unit is, When making recommendations, adjust the order of recommendations based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts input regarding purchase purpose, preferences, and budget range, A search unit searches for the most suitable product on the e-commerce site based on the information received by the aforementioned reception unit, The system includes a recommendation unit that recommends products found by the search unit to the user. A system characterized by the following features.

2. The aforementioned reception unit is Collects users' past purchase and browsing history. The system according to feature 1.

3. The aforementioned search unit, Filter products based on user preferences and budget to select the most suitable product. The system according to feature 1.

4. The recommendation unit is, The AI ​​generates and presents products selected by the user. The system according to feature 1.

5. The recommendation unit is, Provides an interface for users to view and purchase products. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for purchase purpose and preferences based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past purchase and browsing history to complete input. The system according to feature 1.

8. The aforementioned reception unit is When users input their purchase purpose and preferences, the system suggests input options based on their current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When users input their purchase purpose and preferences, the system will consider their geographical location to suggest highly relevant input options. The system according to feature 1.

Images