system

Abstract

An object of a system according to an embodiment is to efficiently search for and present a product that meets a user's needs.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a selection unit, and a presentation unit. The receiving unit receives a search condition from a user. The analysis unit analyzes the condition received by the reception unit and searches the database on the EC site for a corresponding product. The selection unit selects the product retrieved by the analysis unit. The presentation unit presents the product selected by the selection unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult to efficiently search for products that meet users' needs on e-commerce sites.

[0005] The system according to the embodiment aims to efficiently search for and present products that meet the needs of a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a selection unit, and a presentation unit. The reception unit receives search conditions from a user. The analysis unit analyzes the conditions received by the reception unit and searches for corresponding products from a database on the EC site. The selection unit selects the products searched for by the analysis unit. The presentation unit presents the products selected by the selection unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently search for and present products that meet the needs of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A product search system according to an embodiment of the present invention uses a generation AI to select optimal products when a user searches for groceries or daily necessities on an e-commerce site. The product search system allows users to select products that best meet their needs by setting search criteria, such as low price, fast delivery, and high reputation. The product search system is also designed to be easily used by busy businesspeople by incorporating a voice recognition function. For example, a user inputs search criteria via voice or text. For example, the user may input a search criteria such as "Looking for products with low prices." These criteria are then input into the generation AI. The generation AI then analyzes the input criteria and searches for matching products from a database on the e-commerce site. The generation AI then selects optimal products based on information such as price, delivery time, and reputation. For example, the system may list products in descending order of price, and then select the optimal product taking delivery time and reputation into consideration. The product search system then presents the products selected by the generation AI to the user. The user can then select from the presented products. For example, the user may compare three products selected by the generation AI and select the most suitable product. This allows the user to efficiently search for and purchase products. For example, if you are looking for a cheap product, the generation AI will select the best product for you, saving you time. Also, by choosing a product with a good reputation, you can purchase a high-quality product. This allows the product search system to enable users to search for and purchase products efficiently. For example, if you are looking for a cheap product, the generation AI will select the best product for you, saving you time. Also, by choosing a product with a good reputation, you can purchase a high-quality product.

[0029] A product search system according to an embodiment includes a reception unit, an analysis unit, a selection unit, and a presentation unit. The reception unit receives search criteria from a user. The search criteria include, but are not limited to, price range, delivery time, and reputation. The reception unit receives voice input using, for example, voice recognition technology. The reception unit can also provide a text input interface and accept search criteria input in text. The reception unit can also accept image input using image recognition technology. For example, the reception unit can use voice recognition technology to convert a search criteria input by a user, such as "Look for products with low prices," into text. The reception unit can also provide a text input interface and accept search criteria entered by the user using a keyboard. The reception unit can also use image recognition technology to analyze an image taken by a user with a camera and accept the image as a search criteria. The analysis unit uses a generation AI to analyze the search criteria received by the reception unit and search for corresponding products from a database on an e-commerce site. The analysis is performed based on, for example, information such as price, delivery time, and reputation, but is not limited to, these examples. For example, the analysis unit uses the generation AI to list products in descending order of price. The analysis unit can also use the generation AI to search for products with short delivery times. The analysis unit can also use the generation AI to search for products with high reputations. For example, the analysis unit inputs a prompt to the generation AI, such as "List products with low prices," to list products in descending order of price. The analysis unit can also input a prompt to the generation AI, such as "Search for products with short delivery times," to search for products with short delivery times. The analysis unit can also input a prompt to the generation AI, such as "Search for products with high reputations," to search for products with high reputations. The selection unit uses the generation AI to select an optimal product from the products searched by the analysis unit. The selection is based on, for example, information such as price, delivery time, and reputation, but is not limited to such examples. For example, the selection unit uses the generation AI to select products with low prices, short delivery times, and high reputations. The selection unit can also use the generation AI to select an optimal product based on the user's past purchase history.The selection unit can also use the generation AI to select optimal products based on the user's preferences. For example, the selection unit can input a prompt to the generation AI, such as "Select a product that is inexpensive, has a short delivery time, and is highly rated," to select the optimal product. The selection unit can also input a prompt to the generation AI, such as "Select the optimal product based on your past purchase history," to select the optimal product. The selection unit can also input a prompt to the generation AI, such as "Select the optimal product based on the user's preferences," to select the optimal product. The presentation unit presents the products selected by the selection unit to the user. Presentation may include, but is not limited to, methods such as displaying the products on a screen or notifying the user by voice. For example, the presentation unit may display a list of the selected products on a screen. The presentation unit may also notify the user of the selected products by voice. The presentation unit may also display detailed information about the selected products. For example, the presentation unit may display a list of the selected products on a screen and display detailed information about the products selected by the user. The presentation unit may also notify the user of the selected products by voice and display detailed information about the products selected by the user. This allows the product search system according to the embodiment to select and present optimal products based on the user's search criteria. For example, if a user inputs by voice, "Find a cheap product," the product search system can list products in order of lowest price and select and present the most suitable product. Also, if a user inputs by text, "Find a product with a short delivery time," the product search system can search for products with a short delivery time, select and present the most suitable product. Also, if a user inputs by image, "Find a product with a high reputation," the product search system can search for products with a high reputation, select and present the most suitable product.

[0030] The reception unit can accept search conditions by voice or text. The reception unit, for example, accepts voice input using voice recognition technology. For example, the reception unit converts conditions input by voice by a user, such as "Look for products with low prices," into text. The reception unit can also provide a text input interface and accept search condition input in text. For example, the reception unit can accept conditions input by a user via a keyboard. This allows the user to input search conditions by voice or text. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can accept voice input using voice recognition technology, input the voice data to a generation AI, and the generation AI can convert the voice data into text data.

[0031] The analysis unit can search for products based on information such as price, delivery time, and reputation. The analysis unit, for example, uses a generation AI to list products in order of lowest price. For example, the analysis unit inputs a prompt to the generation AI, such as "List products with low prices," and lists products in order of lowest price. The analysis unit can also use the generation AI to search for products with short delivery times. For example, the analysis unit inputs a prompt to the generation AI, such as "Search for products with short delivery times," and searches for products with short delivery times. The analysis unit can also use the generation AI to search for products with high reputations. For example, the analysis unit inputs a prompt to the generation AI, such as "Search for products with high reputations," and searches for products with high reputations. This allows the optimal product to be searched for based on information such as price, delivery time, and reputation. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit inputs information such as price, delivery time, and reputation to the generation AI, and the generation AI searches for the optimal product.

[0032] The selection unit can select a product from the searched products. For example, the selection unit uses a generation AI to select a product that is inexpensive, has a short delivery time, and has a high reputation. For example, the selection unit inputs a prompt to the generation AI, such as "Select a product that is inexpensive, has a short delivery time, and has a high reputation," and selects the optimal product. The selection unit can also use the generation AI to select the optimal product based on the user's past purchase history. For example, the selection unit can input a prompt to the generation AI, such as "Select the optimal product based on your past purchase history," and select the optimal product. The selection unit can also use the generation AI to select the optimal product based on the user's preferences. For example, the selection unit can input a prompt to the generation AI, such as "Select the optimal product based on the user's preferences," and select the optimal product. This allows the optimal product to be selected from the searched products. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit can input information about the searched products to the generation AI, and the generation AI can select the optimal product.

[0033] The presentation unit can present the selected products to the user. The presentation unit, for example, displays a list of the selected products on a screen. For example, the presentation unit displays a list of the selected products on a screen and displays detailed information about the products selected by the user. The presentation unit can also notify the user of the selected products by voice. For example, the presentation unit can notify the user of the selected products by voice and display detailed information about the products selected by the user. In this way, the selected products can be presented to the user. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input information about the selected products to a generation AI, which can present the information to the user.

[0034] The presentation unit can display information about products selected by the user. The presentation unit can, for example, display detailed information about the products selected by the user. For example, the presentation unit can list the selected products on a screen and display detailed information about the products selected by the user. The presentation unit can also notify the user of the selected products by voice and display detailed information about the products selected by the user. This makes it possible to display information about the products selected by the user. Some or all of the above-described processing in the presentation unit can be performed using, for example, AI, or can be performed without using AI. For example, the presentation unit can input information about the selected products to a generation AI, which can then present the information to the user.

[0035] The reception unit can analyze the user's past search history and suggest search criteria. For example, the reception unit retrieves the past search history from a database and analyzes it using an analysis algorithm. For example, the reception unit automatically displays product categories that the user has frequently searched for in the past as candidates. The reception unit can also prioritize suggesting search methods (voice, text, etc.) that the user has used in the past. For example, if the user has frequently used voice input in the past, the reception unit can prioritize suggesting voice input. The reception unit can also predict and suggest search criteria to be used during a specific time period based on the user's past search history. For example, if the user tends to search for specific products at night, the reception unit can suggest search criteria related to that time period. This makes it possible to suggest optimal search criteria based on the user's past search history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past search history data into a generation AI, which can then suggest optimal search criteria.

[0036] When receiving search criteria, the reception unit can filter based on the user's current purchasing intent and areas of interest. The reception unit, for example, estimates the user's current purchasing intent using an algorithm for estimating the user's current purchasing intent. For example, the reception unit estimates the user's purchasing intent based on the user's past purchase history and current browsing status. The reception unit can also identify the user's areas of interest using an algorithm for identifying the user's areas of interest. For example, the reception unit identifies the user's areas of interest based on the user's past search history and profile information. This allows search criteria to be filtered based on the user's purchasing intent and areas of interest. For example, if the user has a high purchasing intent, the reception unit can prioritize displaying products in a specific category. Also, if the user is interested in a specific area of ​​interest, the reception unit can prioritize displaying products in that area. Also, if the user has a low purchasing intent, the reception unit can prioritize displaying discounted products or products with special benefits. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's purchasing intent and areas of interest into a generation AI, which can then filter the search criteria.

[0037] When receiving search criteria, the reception unit can select a reception means depending on the user's input method. For example, when the user inputs search criteria by voice, the reception unit preferentially uses a voice recognition function. For example, the reception unit converts the user's voice input, such as "Look for products with low prices," into text using voice recognition technology. When the user inputs search criteria as text, the reception unit also provides a text input interface. For example, the reception unit can accept conditions entered by the user using a keyboard. When the user inputs search criteria using an image, the reception unit can also analyze the search criteria using an image recognition function. For example, the reception unit can analyze an image taken by the user with a camera and accept it as a search condition. This allows the optimal reception means to be selected depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input voice recognition technology, text analysis technology, and image recognition technology into a generation AI, which can then select the optimal reception means.

[0038] When accepting search conditions, the reception unit can prioritize accepting highly relevant conditions in consideration of the user's geographical location information. The reception unit, for example, uses GPS data to acquire the user's geographical location information. For example, the reception unit acquires the user's current location from the GPS data and prioritizes accepting highly relevant conditions based on the information. The reception unit can also acquire geographical location information using a user location information service. For example, the reception unit uses the user's location information service to prioritize displaying products from stores close to the user's current location. This allows highly relevant conditions to be prioritized based on the user's geographical location information. For example, products from stores close to the user's current location can be prioritized. Furthermore, if the user is in a specific area, search conditions for products available in that area can be prioritized. Furthermore, if the user is traveling, search conditions for products available in the area where the user is traveling can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI, which can then prioritize accepting highly relevant conditions.

[0039] When receiving search criteria, the reception unit can analyze the user's social media activity and suggest related criteria. For example, the reception unit retrieves the social media activity from a database and analyzes it using an analysis algorithm. For example, the reception unit suggests search criteria for products related to locations where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest search criteria for related products. For example, the reception unit analyzes the content of the user's posts and suggests search criteria for related products. The reception unit can also suggest search criteria for related products based on the activities of the user's friends on social media. For example, the reception unit suggests search criteria for related products based on products purchased by the user's friends. This makes it possible to suggest related criteria based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input social media activity data into a generation AI, which then suggests related criteria.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving search conditions. The reception unit, for example, retrieves past feedback from a database and analyzes it using an analysis algorithm. For example, the reception unit preferentially suggests search methods that the user has previously rated highly. The reception unit can also avoid search methods that the user has previously rated poorly. For example, the reception unit avoids search methods that the user has previously rated poorly and suggests an optimal search method. The reception unit can also suggest an optimal search method based on the user's past feedback. For example, the reception unit suggests an optimal search method based on the user's past feedback. This allows the reception method to be customized based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past feedback data into a generation AI, which then suggests an optimal reception method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the product. The analysis unit, for example, evaluates the importance of the product using an algorithm for evaluating the importance of the product. For example, the analysis unit evaluates the importance of the product based on the price of the product and the user's interest in it. The analysis unit can also adjust the level of detail of the analysis based on the importance of the product. For example, the analysis unit can provide detailed analysis results for products with high importance. Also, the analysis unit can provide concise analysis results for products with low importance. For example, the analysis unit can provide analysis results including detailed descriptions for products with high importance, and analysis results including concise descriptions for products with low importance. This allows the level of detail of the analysis to be adjusted based on the importance of the product. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input product importance data to a generation AI, which can adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply an analysis algorithm according to the product category. The analysis unit, for example, classifies the product category using an algorithm for classifying product categories. For example, the analysis unit classifies the product category based on the type and purpose of the product. The analysis unit can also apply different analysis algorithms according to the product category. For example, the analysis unit can apply an analysis algorithm that emphasizes freshness and expiration date to food products. Furthermore, the analysis unit can apply an analysis algorithm that emphasizes durability and frequency of use to daily necessities. For example, the analysis unit can apply an analysis algorithm that emphasizes freshness and expiration date to food products, and an analysis algorithm that emphasizes durability and frequency of use to daily necessities. This allows the application of an optimal analysis algorithm according to the product category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input product category data to a generation AI, which can then apply an optimal analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, retrieves past analysis results from a database and analyzes them using an analysis algorithm. For example, the analysis unit improves the accuracy of the analysis by referring to analysis results that the user has previously rated highly. The analysis unit can also avoid analysis results that the user has previously rated poorly. For example, the analysis unit selects the optimal analysis method by avoiding analysis results that the user has previously rated poorly. The analysis unit can also select the optimal analysis method based on the user's past analysis results. For example, the analysis unit selects the optimal analysis method based on the user's past analysis results. This improves the accuracy of the analysis based on the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into a generation AI, which then selects the optimal analysis method.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of product submission. The analysis unit, for example, obtains the time of product submission from a database and analyzes it using an analysis algorithm. For example, the analysis unit determines the priority of analysis based on the time of product submission. For example, the analysis unit prioritizes analysis of recently submitted products. In addition, the analysis unit can lower the analysis priority of older submitted products. For example, the analysis unit prioritizes analysis of recently submitted products and lowers the analysis priority of older submitted products. In this way, the analysis priority can be determined based on the time of product submission. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input product submission time data into a generation AI, and the generation AI can determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of products. The analysis unit evaluates the relevance of products using, for example, an algorithm for evaluating the relevance of products. For example, the analysis unit evaluates the relevance of products based on the similarity of products or related keywords. The analysis unit can also adjust the order of analysis based on the relevance of products. For example, the analysis unit prioritizes analysis of highly relevant products. Furthermore, the analysis unit can postpone the order of analysis of less relevant products. For example, the analysis unit prioritizes analysis of highly relevant products and postpones the order of analysis of less relevant products. In this way, the order of analysis can be adjusted based on the relevance of products. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input product relevance data to a generation AI, and the generation AI can adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise using an algorithm for evaluating the user's level of expertise. For example, the analysis unit evaluates the user's level of expertise based on the user's occupation and past usage history. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terms if the user has technical expertise. Also, if the user does not have technical expertise, the analysis unit can provide analysis results that are concise and easy to understand. For example, the analysis unit can provide analysis results that use a lot of technical terms if the user has technical expertise, and provide analysis results that are concise and easy to understand if the user does not have technical expertise. This allows the use of technical terms in the analysis to be adjusted according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI, which can adjust the use of technical terms in the analysis.

[0047] The selection unit can improve the accuracy of selection by taking into account the interrelationships between products during selection. The selection unit evaluates the interrelationships between products, for example, using an algorithm for evaluating the interrelationships between products. For example, the selection unit evaluates the interrelationships between products based on the relevance and interaction of the products. The selection unit can also improve the accuracy of selection based on the interrelationships between products. For example, the selection unit preferentially selects highly related products. The selection unit can also analyze the interrelationships between products and select the optimal product. For example, the selection unit preferentially selects highly related products and improves the accuracy of selection based on the interrelationships between products. This improves the accuracy of selection by taking into account the interrelationships between products. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input product interrelationship data to a generation AI, which can improve the accuracy of selection.

[0048] When making a selection, the selection unit can make a selection taking into account the attribute information of the submitter of the product. The selection unit evaluates the attribute information of the submitter using, for example, an algorithm that evaluates the attribute information of the submitter of the product. For example, the selection unit evaluates the attribute information of the submitter based on the submitter's reliability and past ratings. The selection unit can also make a selection based on the submitter's attribute information. For example, the selection unit selects a product taking into account the submitter's reliability. The selection unit can also select a product based on the submitter's past ratings. For example, the selection unit selects a product taking into account the submitter's reliability and selects a product based on the submitter's past ratings. In this way, the selection is made taking into account the attribute information of the submitter of the product. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the submitter's attribute information data to a generation AI, and the generation AI can make the selection.

[0049] When making a selection, the selection unit can weight the selection based on the frequency of submission of the product. The selection unit, for example, uses an algorithm for evaluating the frequency of submission of the product to evaluate the frequency of submission of the product. For example, the selection unit evaluates the frequency of submission of the product based on the number of times the product is submitted or the submission interval. The selection unit can also weight the selection based on the frequency of submission of the product. For example, the selection unit preferentially selects products with a high submission frequency. The selection unit can also lower the selection weight for products with a low submission frequency. For example, the selection unit preferentially selects products with a high submission frequency and lowers the selection weight for products with a low submission frequency. In this way, the selection is weighted based on the frequency of submission of the product. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input product submission frequency data to a generation AI, and the generation AI can weight the selection.

[0050] The selection unit can make a selection taking into consideration the geographic distribution of the product. The selection unit, for example, evaluates the geographic distribution of the product using an algorithm for evaluating the geographic distribution of the product. For example, the selection unit evaluates the geographic distribution of the product based on the delivery area and sales area of ​​the product. The selection unit can also make a selection based on the geographic distribution of the product. For example, the selection unit preferentially selects products close to the user's current location. The selection unit can also analyze the geographic distribution of the product and select the optimal product. For example, the selection unit preferentially selects products close to the user's current location and makes a selection based on the geographic distribution of the product. In this way, the selection is made taking into consideration the geographic distribution of the product. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input geographic distribution data of the product to a generation AI, which then makes the selection.

[0051] The selection unit can improve the accuracy of the selection by referring to literature related to the product during selection. The selection unit, for example, retrieves literature related to the product from a database and analyzes it using an analysis algorithm. For example, the selection unit can improve the accuracy of the selection by referring to reviews and ratings related to the product. The selection unit can also improve the accuracy of the selection by referring to research papers and articles related to the product. For example, the selection unit can improve the accuracy of the selection by referring to reviews and ratings related to the product and research papers and articles related to the product. The selection unit can also improve the accuracy of the selection by referring to user feedback related to the product. For example, the selection unit can improve the accuracy of the selection by referring to user feedback related to the product. This improves the accuracy of the selection by referring to literature related to the product. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input literature data related to the product into a generation AI, which can improve the accuracy of the selection.

[0052] When making a selection, the selection unit can take into consideration the market value of the product. The selection unit, for example, evaluates the market value of the product using an algorithm for evaluating the market value of the product. For example, the selection unit evaluates the market value of the product based on the price of the product and the balance between supply and demand. The selection unit can also make a selection based on the market value of the product. For example, the selection unit preferentially selects products with high market value. The selection unit can also lower the selection priority of products with low market value. For example, the selection unit preferentially selects products with high market value and lowers the selection priority of products with low market value. In this way, the selection is made taking into consideration the market value of the product. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input market value data of the product to a generation AI, and the generation AI can make the selection.

[0053] The presentation unit can select the optimal display method by referring to the user's past operation history when presenting the display. The presentation unit, for example, retrieves the past operation history from a database and analyzes it using an analysis algorithm. For example, the presentation unit preferentially provides display methods that the user has previously rated highly. The presentation unit can also avoid display methods that the user has previously rated poorly. For example, the presentation unit selects the optimal display method by avoiding display methods that the user has previously rated poorly. The presentation unit can also select the optimal display method based on the user's past operation history. For example, the presentation unit selects the optimal display method based on the user's past operation history. This makes it possible to select the optimal display method based on the user's past operation history. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input past operation history data to a generation AI, which can select the optimal display method.

[0054] The presentation unit can customize the display content according to the user's current task when presenting the display. The presentation unit, for example, evaluates the current task using an algorithm for evaluating the user's current task. For example, the presentation unit evaluates the current task based on the user's current work content and work priority. The presentation unit can also customize the display content according to the user's current task. For example, when the user is shopping, the presentation unit can prioritize displaying information about related products. Furthermore, when the user is working, the presentation unit can prioritize displaying information related to work. For example, when the user is shopping, the presentation unit prioritizes displaying information about related products, and when the user is working, the presentation unit prioritizes displaying information related to work. This allows the display content to be customized according to the user's current task. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's current task data into a generation AI, which can customize the display content.

[0055] The presentation unit can improve the display method by reflecting the user's feedback when presenting the display. The presentation unit, for example, obtains the feedback from a database and analyzes it using an analysis algorithm. For example, the presentation unit preferentially provides display methods that the user has given a high rating. The presentation unit can also avoid display methods that the user has given a low rating. For example, the presentation unit avoids display methods that the user has given a low rating and selects an optimal display method. The presentation unit can also dynamically improve the display method based on the user's feedback. For example, the presentation unit dynamically improves the display method based on the user's feedback. This allows the display method to be improved based on the user's feedback. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input feedback data to a generation AI, which can improve the display method.

[0056] The presentation unit can select the optimal display method by taking into consideration the user's device information when presenting the display. The presentation unit, for example, uses the device type and setting information to acquire the user's device information. For example, if the user is using a smartphone, the presentation unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the presentation unit can provide a display method optimized for a large screen. For example, if the user is using a smartphone, the presentation unit can provide a display method tailored to the screen size, and if the user is using a tablet, the presentation unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the presentation unit can provide a simple and highly visible display method. For example, if the user is using a smartwatch, the presentation unit can provide a simple and highly visible display method. This allows the optimal display method to be selected based on the user's device information. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input device information data to a generation AI, which can select the optimal display method.

[0057] The presentation unit can make the display content multilingual according to the user's language setting when presenting it. The presentation unit, for example, uses the language setting of the device to acquire the user's language setting. For example, the presentation unit automatically sets the display content based on the language setting of the user's device. The presentation unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the presentation unit provides the display content in that language. This makes it possible to make the display content multilingual according to the user's language setting. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input language setting data to a generation AI, which can then make the display content multilingual.

[0058] The presentation unit can customize the display method by reflecting the user's past feedback when presenting the display. The presentation unit, for example, retrieves past feedback from a database and analyzes it using an analysis algorithm. For example, the presentation unit preferentially provides display methods that the user has given a high rating. The presentation unit can also avoid display methods that the user has given a low rating. For example, the presentation unit selects the optimal display method by avoiding display methods that the user has given a low rating. The presentation unit can also dynamically customize the display method based on the user's past feedback. For example, the presentation unit dynamically customizes the display method based on the user's past feedback. This allows the display method to be customized based on the user's past feedback. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input feedback data to a generation AI, which can customize the display method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can analyze the user's past purchase history and automatically suggest product categories that the user frequently purchases. For example, the reception unit can analyze data on products purchased in the past by the user and identify the most frequently purchased category. The reception unit can also suggest products that the user purchases in relation to a particular season or event. For example, if the user tends to purchase certain products every year at Christmas, the reception unit can suggest products related to that time of year. Furthermore, the reception unit can learn the user's purchasing patterns, predict when the next purchase will be made, and send a reminder. This allows the user to purchase the products they need in a timely manner.

[0061] The analysis unit can search for products taking into account the user's current weather information. For example, the analysis unit can obtain weather forecast data for the user's area and suggest waterproof products and umbrellas on rainy days, or cooling products and beverages on hot days. Furthermore, the analysis unit can analyze seasonal weather patterns and suggest products appropriate for each season. For example, it can suggest heaters and winter clothing in winter, and air conditioners and sunglasses in summer. This allows users to easily find products that suit the weather conditions.

[0062] The selection unit can select products taking into account the user's health data. For example, the selection unit can obtain data from the user's fitness tracker or health app and suggest products according to the user's health condition. The selection unit can also prioritize products that do not contain allergens by taking into account the user's allergy information. Furthermore, the selection unit can suggest appropriate foods and supplements based on the user's dietary restrictions and diet goals. This allows the user to easily select health-conscious products.

[0063] The presentation unit can present products based on the user's visual preferences. For example, the presentation unit can analyze the designs and colors of products that the user has previously rated highly and prioritize products with similar designs and colors. If the user prefers a particular brand or style, the presentation unit can also prioritize products of that brand or style. Furthermore, the presentation unit can customize and display product images and videos based on the user's visual preferences. This allows the user to easily find visually appealing products.

[0064] The reception unit can filter search conditions based on the user's current purchasing intent and areas of interest. For example, if the user has a high purchasing intent, the reception unit can prioritize displaying products in a specific category. Also, if the user is interested in a specific area of ​​interest, the reception unit can prioritize displaying products in that area. Furthermore, if the user has a low purchasing intent, the reception unit can also prioritize displaying discounted products or products with special offers. This makes it possible to provide optimal search conditions based on the user's purchasing intent and areas of interest.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives search criteria from the user. Search criteria include price range, delivery time, reputation, etc. The reception unit can receive voice input using voice recognition technology, and can also provide a text input interface to receive search criteria input in text. Furthermore, the reception unit can also receive image input using image recognition technology. Step 2: The analysis unit uses the generation AI to analyze the conditions received by the reception unit and search for matching products in the database on the e-commerce site. The analysis is based on information such as price, delivery time, and reputation. Step 3: The selection unit uses the generation AI to select the best product from the products searched by the analysis unit, based on information such as price, delivery time, and reputation. Step 4: The presentation unit presents the product selected by the selection unit to the user. The presentation may be displayed on a screen or by audio notification.

[0067] (Example 2) A product search system according to an embodiment of the present invention uses a generation AI to select optimal products when a user searches for groceries or daily necessities on an e-commerce site. The product search system allows users to select products that best meet their needs by setting search criteria, such as low price, fast delivery, and high reputation. The product search system is also designed to be easily used by busy businesspeople by incorporating a voice recognition function. For example, a user inputs search criteria via voice or text. For example, the user may input a search criteria such as "Looking for products with low prices." These criteria are then input into the generation AI. The generation AI then analyzes the input criteria and searches for matching products from a database on the e-commerce site. The generation AI then selects optimal products based on information such as price, delivery time, and reputation. For example, the system may list products in descending order of price, and then select the optimal product taking delivery time and reputation into consideration. The product search system then presents the products selected by the generation AI to the user. The user can then select from the presented products. For example, the user may compare three products selected by the generation AI and select the most suitable product. This allows the user to efficiently search for and purchase products. For example, if you are looking for a cheap product, the generation AI will select the best product for you, saving you time. Also, by choosing a product with a good reputation, you can purchase a high-quality product. This allows the product search system to enable users to search for and purchase products efficiently. For example, if you are looking for a cheap product, the generation AI will select the best product for you, saving you time. Also, by choosing a product with a good reputation, you can purchase a high-quality product.

[0068] A product search system according to an embodiment includes a reception unit, an analysis unit, a selection unit, and a presentation unit. The reception unit receives search criteria from a user. The search criteria include, but are not limited to, price range, delivery time, and reputation. The reception unit receives voice input using, for example, voice recognition technology. The reception unit can also provide a text input interface and accept search criteria input in text. The reception unit can also accept image input using image recognition technology. For example, the reception unit can use voice recognition technology to convert a search criteria input by a user, such as "Look for products with low prices," into text. The reception unit can also provide a text input interface and accept search criteria entered by the user using a keyboard. The reception unit can also use image recognition technology to analyze an image taken by a user with a camera and accept the image as a search criteria. The analysis unit uses a generation AI to analyze the search criteria received by the reception unit and search for corresponding products from a database on an e-commerce site. The analysis is performed based on, for example, information such as price, delivery time, and reputation, but is not limited to, these examples. For example, the analysis unit uses the generation AI to list products in descending order of price. The analysis unit can also use the generation AI to search for products with short delivery times. The analysis unit can also use the generation AI to search for products with high reputations. For example, the analysis unit inputs a prompt to the generation AI, such as "List products with low prices," to list products in descending order of price. The analysis unit can also input a prompt to the generation AI, such as "Search for products with short delivery times," to search for products with short delivery times. The analysis unit can also input a prompt to the generation AI, such as "Search for products with high reputations," to search for products with high reputations. The selection unit uses the generation AI to select an optimal product from the products searched by the analysis unit. The selection is based on, for example, information such as price, delivery time, and reputation, but is not limited to such examples. For example, the selection unit uses the generation AI to select products with low prices, short delivery times, and high reputations. The selection unit can also use the generation AI to select an optimal product based on the user's past purchase history.The selection unit can also use the generation AI to select optimal products based on the user's preferences. For example, the selection unit can input a prompt to the generation AI, such as "Select a product that is inexpensive, has a short delivery time, and is highly rated," to select the optimal product. The selection unit can also input a prompt to the generation AI, such as "Select the optimal product based on your past purchase history," to select the optimal product. The selection unit can also input a prompt to the generation AI, such as "Select the optimal product based on the user's preferences," to select the optimal product. The presentation unit presents the products selected by the selection unit to the user. Presentation may include, but is not limited to, methods such as displaying the products on a screen or notifying the user by voice. For example, the presentation unit may display a list of the selected products on a screen. The presentation unit may also notify the user of the selected products by voice. The presentation unit may also display detailed information about the selected products. For example, the presentation unit may display a list of the selected products on a screen and display detailed information about the products selected by the user. The presentation unit may also notify the user of the selected products by voice and display detailed information about the products selected by the user. This allows the product search system according to the embodiment to select and present optimal products based on the user's search criteria. For example, if a user inputs by voice, "Find a cheap product," the product search system can list products in order of lowest price and select and present the most suitable product. Also, if a user inputs by text, "Find a product with a short delivery time," the product search system can search for products with a short delivery time, select and present the most suitable product. Also, if a user inputs by image, "Find a product with a high reputation," the product search system can search for products with a high reputation, select and present the most suitable product.

[0069] The reception unit can accept search conditions by voice or text. The reception unit, for example, accepts voice input using voice recognition technology. For example, the reception unit converts conditions input by voice by a user, such as "Look for products with low prices," into text. The reception unit can also provide a text input interface and accept search condition input in text. For example, the reception unit can accept conditions input by a user via a keyboard. This allows the user to input search conditions by voice or text. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can accept voice input using voice recognition technology, input the voice data to a generation AI, and the generation AI can convert the voice data into text data.

[0070] The analysis unit can search for products based on information such as price, delivery time, and reputation. The analysis unit, for example, uses a generation AI to list products in order of lowest price. For example, the analysis unit inputs a prompt to the generation AI, such as "List products with low prices," and lists products in order of lowest price. The analysis unit can also use the generation AI to search for products with short delivery times. For example, the analysis unit inputs a prompt to the generation AI, such as "Search for products with short delivery times," and searches for products with short delivery times. The analysis unit can also use the generation AI to search for products with high reputations. For example, the analysis unit inputs a prompt to the generation AI, such as "Search for products with high reputations," and searches for products with high reputations. This allows the optimal product to be searched for based on information such as price, delivery time, and reputation. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit inputs information such as price, delivery time, and reputation to the generation AI, and the generation AI searches for the optimal product.

[0071] The selection unit can select a product from the searched products. For example, the selection unit uses a generation AI to select a product that is inexpensive, has a short delivery time, and has a high reputation. For example, the selection unit inputs a prompt to the generation AI, such as "Select a product that is inexpensive, has a short delivery time, and has a high reputation," and selects the optimal product. The selection unit can also use the generation AI to select the optimal product based on the user's past purchase history. For example, the selection unit can input a prompt to the generation AI, such as "Select the optimal product based on your past purchase history," and select the optimal product. The selection unit can also use the generation AI to select the optimal product based on the user's preferences. For example, the selection unit can input a prompt to the generation AI, such as "Select the optimal product based on the user's preferences," and select the optimal product. This allows the optimal product to be selected from the searched products. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit can input information about the searched products to the generation AI, and the generation AI can select the optimal product.

[0072] The presentation unit can present the selected products to the user. The presentation unit, for example, displays a list of the selected products on a screen. For example, the presentation unit displays a list of the selected products on a screen and displays detailed information about the products selected by the user. The presentation unit can also notify the user of the selected products by voice. For example, the presentation unit can notify the user of the selected products by voice and display detailed information about the products selected by the user. In this way, the selected products can be presented to the user. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input information about the selected products to a generation AI, which can present the information to the user.

[0073] The presentation unit can display information about products selected by the user. The presentation unit can, for example, display detailed information about the products selected by the user. For example, the presentation unit can list the selected products on a screen and display detailed information about the products selected by the user. The presentation unit can also notify the user of the selected products by voice and display detailed information about the products selected by the user. This makes it possible to display information about the products selected by the user. Some or all of the above-described processing in the presentation unit can be performed using, for example, AI, or can be performed without using AI. For example, the presentation unit can input information about the selected products to a generation AI, which can then present the information to the user.

[0074] The reception unit can estimate the user's emotion and adjust the search criterion input method based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion using an emotion estimation algorithm. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This allows the search criterion input method to be adjusted according to the user's emotion. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception unit can prioritize voice input to enable quick input of search criteria. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotion.

[0075] The reception unit can analyze the user's past search history and suggest search criteria. For example, the reception unit retrieves the past search history from a database and analyzes it using an analysis algorithm. For example, the reception unit automatically displays product categories that the user has frequently searched for in the past as candidates. The reception unit can also prioritize suggesting search methods (voice, text, etc.) that the user has used in the past. For example, if the user has frequently used voice input in the past, the reception unit can prioritize suggesting voice input. The reception unit can also predict and suggest search criteria to be used during a specific time period based on the user's past search history. For example, if the user tends to search for specific products at night, the reception unit can suggest search criteria related to that time period. This makes it possible to suggest optimal search criteria based on the user's past search history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past search history data into a generation AI, which can then suggest optimal search criteria.

[0076] When receiving search criteria, the reception unit can filter based on the user's current purchasing intent and areas of interest. The reception unit, for example, estimates the user's current purchasing intent using an algorithm for estimating the user's current purchasing intent. For example, the reception unit estimates the user's purchasing intent based on the user's past purchase history and current browsing status. The reception unit can also identify the user's areas of interest using an algorithm for identifying the user's areas of interest. For example, the reception unit identifies the user's areas of interest based on the user's past search history and profile information. This allows search criteria to be filtered based on the user's purchasing intent and areas of interest. For example, if the user has a high purchasing intent, the reception unit can prioritize displaying products in a specific category. Also, if the user is interested in a specific area of ​​interest, the reception unit can prioritize displaying products in that area. Also, if the user has a low purchasing intent, the reception unit can prioritize displaying discounted products or products with special benefits. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's purchasing intent and areas of interest into a generation AI, which can then filter the search criteria.

[0077] When receiving search criteria, the reception unit can select a reception means depending on the user's input method. For example, when the user inputs search criteria by voice, the reception unit preferentially uses a voice recognition function. For example, the reception unit converts the user's voice input, such as "Look for products with low prices," into text using voice recognition technology. When the user inputs search criteria as text, the reception unit also provides a text input interface. For example, the reception unit can accept conditions entered by the user using a keyboard. When the user inputs search criteria using an image, the reception unit can also analyze the search criteria using an image recognition function. For example, the reception unit can analyze an image taken by the user with a camera and accept it as a search condition. This allows the optimal reception means to be selected depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input voice recognition technology, text analysis technology, and image recognition technology into a generation AI, which can then select the optimal reception means.

[0078] The reception unit can estimate the user's emotion and determine the priority of search conditions based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion using an emotion estimation algorithm. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This makes it possible to determine the priority of search conditions based on the user's emotion. For example, if the user is feeling stressed, the reception unit can prioritize displaying simple search conditions. On the other hand, if the user is relaxed, the reception unit can prioritize displaying detailed search conditions. On the other hand, if the user is in a hurry, the reception unit can prioritize displaying conditions that can quickly obtain search results. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotion.

[0079] When accepting search conditions, the reception unit can prioritize accepting highly relevant conditions in consideration of the user's geographical location information. The reception unit, for example, uses GPS data to acquire the user's geographical location information. For example, the reception unit acquires the user's current location from the GPS data and prioritizes accepting highly relevant conditions based on the information. The reception unit can also acquire geographical location information using a user location information service. For example, the reception unit uses the user's location information service to prioritize displaying products from stores close to the user's current location. This allows highly relevant conditions to be prioritized based on the user's geographical location information. For example, products from stores close to the user's current location can be prioritized. Furthermore, if the user is in a specific area, search conditions for products available in that area can be prioritized. Furthermore, if the user is traveling, search conditions for products available in the area where the user is traveling can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI, which can then prioritize accepting highly relevant conditions.

[0080] When receiving search criteria, the reception unit can analyze the user's social media activity and suggest related criteria. For example, the reception unit retrieves the social media activity from a database and analyzes it using an analysis algorithm. For example, the reception unit suggests search criteria for products related to locations where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest search criteria for related products. For example, the reception unit analyzes the content of the user's posts and suggests search criteria for related products. The reception unit can also suggest search criteria for related products based on the activities of the user's friends on social media. For example, the reception unit suggests search criteria for related products based on products purchased by the user's friends. This makes it possible to suggest related criteria based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input social media activity data into a generation AI, which then suggests related criteria.

[0081] The reception unit can customize the reception method by reflecting the user's past feedback when receiving search conditions. The reception unit, for example, retrieves past feedback from a database and analyzes it using an analysis algorithm. For example, the reception unit preferentially suggests search methods that the user has previously rated highly. The reception unit can also avoid search methods that the user has previously rated poorly. For example, the reception unit avoids search methods that the user has previously rated poorly and suggests an optimal search method. The reception unit can also suggest an optimal search method based on the user's past feedback. For example, the reception unit suggests an optimal search method based on the user's past feedback. This allows the reception method to be customized based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past feedback data into a generation AI, which then suggests an optimal reception method.

[0082] The analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion using an emotion estimation algorithm. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on heart rate fluctuations. This makes it possible to adjust the way the analysis is presented based on the user's emotion. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. If the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotion.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the product. The analysis unit, for example, evaluates the importance of the product using an algorithm for evaluating the importance of the product. For example, the analysis unit evaluates the importance of the product based on the price of the product and the user's interest in it. The analysis unit can also adjust the level of detail of the analysis based on the importance of the product. For example, the analysis unit can provide detailed analysis results for products with high importance. Also, the analysis unit can provide concise analysis results for products with low importance. For example, the analysis unit can provide analysis results including detailed descriptions for products with high importance, and analysis results including concise descriptions for products with low importance. This allows the level of detail of the analysis to be adjusted based on the importance of the product. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input product importance data to a generation AI, which can adjust the level of detail of the analysis.

[0084] During analysis, the analysis unit can apply an analysis algorithm according to the product category. The analysis unit, for example, classifies the product category using an algorithm for classifying product categories. For example, the analysis unit classifies the product category based on the type and purpose of the product. The analysis unit can also apply different analysis algorithms according to the product category. For example, the analysis unit can apply an analysis algorithm that emphasizes freshness and expiration date to food products. Furthermore, the analysis unit can apply an analysis algorithm that emphasizes durability and frequency of use to daily necessities. For example, the analysis unit can apply an analysis algorithm that emphasizes freshness and expiration date to food products, and an analysis algorithm that emphasizes durability and frequency of use to daily necessities. This allows the application of an optimal analysis algorithm according to the product category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input product category data to a generation AI, which can then apply an optimal analysis algorithm.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, retrieves past analysis results from a database and analyzes them using an analysis algorithm. For example, the analysis unit improves the accuracy of the analysis by referring to analysis results that the user has previously rated highly. The analysis unit can also avoid analysis results that the user has previously rated poorly. For example, the analysis unit selects the optimal analysis method by avoiding analysis results that the user has previously rated poorly. The analysis unit can also select the optimal analysis method based on the user's past analysis results. For example, the analysis unit selects the optimal analysis method based on the user's past analysis results. This improves the accuracy of the analysis based on the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into a generation AI, which then selects the optimal analysis method.

[0086] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion using an emotion estimation algorithm. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This allows the length of the analysis to be adjusted based on the user's emotion. For example, if the user is in a hurry, the analysis unit can provide a short, to-the-point analysis result. If the user is relaxed, the analysis unit can provide a longer analysis result with detailed explanations. If the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotion.

[0087] During analysis, the analysis unit can determine the priority of analysis based on the time of product submission. The analysis unit, for example, obtains the time of product submission from a database and analyzes it using an analysis algorithm. For example, the analysis unit determines the priority of analysis based on the time of product submission. For example, the analysis unit prioritizes analysis of recently submitted products. In addition, the analysis unit can lower the analysis priority of older submitted products. For example, the analysis unit prioritizes analysis of recently submitted products and lowers the analysis priority of older submitted products. In this way, the analysis priority can be determined based on the time of product submission. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input product submission time data into a generation AI, and the generation AI can determine the analysis priority.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of products. The analysis unit evaluates the relevance of products using, for example, an algorithm for evaluating the relevance of products. For example, the analysis unit evaluates the relevance of products based on the similarity of products or related keywords. The analysis unit can also adjust the order of analysis based on the relevance of products. For example, the analysis unit prioritizes analysis of highly relevant products. Furthermore, the analysis unit can postpone the order of analysis of less relevant products. For example, the analysis unit prioritizes analysis of highly relevant products and postpones the order of analysis of less relevant products. In this way, the order of analysis can be adjusted based on the relevance of products. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input product relevance data to a generation AI, and the generation AI can adjust the order of analysis.

[0089] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise using an algorithm for evaluating the user's level of expertise. For example, the analysis unit evaluates the user's level of expertise based on the user's occupation and past usage history. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terms if the user has technical expertise. Also, if the user does not have technical expertise, the analysis unit can provide analysis results that are concise and easy to understand. For example, the analysis unit can provide analysis results that use a lot of technical terms if the user has technical expertise, and provide analysis results that are concise and easy to understand if the user does not have technical expertise. This allows the use of technical terms in the analysis to be adjusted according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI, which can adjust the use of technical terms in the analysis.

[0090] The selection unit can estimate the user's emotions and adjust the selection criteria based on the estimated user emotions. The selection unit, for example, estimates the user's emotions using an emotion estimation algorithm. For example, the selection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The selection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the selection unit analyzes the tone and speed of the voice to calculate an emotion score. The selection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the selection unit calculates an emotion score based on heart rate fluctuations. This allows the selection criteria to be adjusted based on the user's emotions. For example, if the user is relaxed, the selection unit can provide detailed selection criteria. If the user is in a hurry, the selection unit can provide simple selection criteria. If the user is excited, the selection unit can provide selection criteria with visually stimulating effects. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0091] The selection unit can improve the accuracy of selection by taking into account the interrelationships between products during selection. The selection unit evaluates the interrelationships between products, for example, using an algorithm for evaluating the interrelationships between products. For example, the selection unit evaluates the interrelationships between products based on the relevance and interaction of the products. The selection unit can also improve the accuracy of selection based on the interrelationships between products. For example, the selection unit preferentially selects highly related products. The selection unit can also analyze the interrelationships between products and select the optimal product. For example, the selection unit preferentially selects highly related products and improves the accuracy of selection based on the interrelationships between products. This improves the accuracy of selection by taking into account the interrelationships between products. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input product interrelationship data to a generation AI, which can improve the accuracy of selection.

[0092] When making a selection, the selection unit can make a selection taking into account the attribute information of the submitter of the product. The selection unit evaluates the attribute information of the submitter using, for example, an algorithm that evaluates the attribute information of the submitter of the product. For example, the selection unit evaluates the attribute information of the submitter based on the submitter's reliability and past ratings. The selection unit can also make a selection based on the submitter's attribute information. For example, the selection unit selects a product taking into account the submitter's reliability. The selection unit can also select a product based on the submitter's past ratings. For example, the selection unit selects a product taking into account the submitter's reliability and selects a product based on the submitter's past ratings. In this way, the selection is made taking into account the attribute information of the submitter of the product. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the submitter's attribute information data to a generation AI, and the generation AI can make the selection.

[0093] When making a selection, the selection unit can weight the selection based on the frequency of submission of the product. The selection unit, for example, uses an algorithm for evaluating the frequency of submission of the product to evaluate the frequency of submission of the product. For example, the selection unit evaluates the frequency of submission of the product based on the number of times the product is submitted or the submission interval. The selection unit can also weight the selection based on the frequency of submission of the product. For example, the selection unit preferentially selects products with a high submission frequency. The selection unit can also lower the selection weight for products with a low submission frequency. For example, the selection unit preferentially selects products with a high submission frequency and lowers the selection weight for products with a low submission frequency. In this way, the selection is weighted based on the frequency of submission of the product. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input product submission frequency data to a generation AI, and the generation AI can weight the selection.

[0094] The selection unit can estimate the user's emotion and adjust the order in which the selection results are displayed based on the estimated user's emotion. The selection unit, for example, estimates the user's emotion using an emotion estimation algorithm. For example, the selection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The selection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the selection unit analyzes the tone and speed of the voice and calculates an emotion score. The selection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the selection unit calculates an emotion score based on heart rate fluctuations. This allows the order in which the selection results are displayed to be adjusted based on the user's emotion. For example, if the user is relaxed, the selection unit can provide detailed selection results. If the user is in a hurry, the selection unit can provide concise selection results. If the user is excited, the selection unit can provide selection results with visually stimulating effects. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0095] The selection unit can make a selection taking into consideration the geographic distribution of the product. The selection unit, for example, evaluates the geographic distribution of the product using an algorithm for evaluating the geographic distribution of the product. For example, the selection unit evaluates the geographic distribution of the product based on the delivery area and sales area of ​​the product. The selection unit can also make a selection based on the geographic distribution of the product. For example, the selection unit preferentially selects products close to the user's current location. The selection unit can also analyze the geographic distribution of the product and select the optimal product. For example, the selection unit preferentially selects products close to the user's current location and makes a selection based on the geographic distribution of the product. In this way, the selection is made taking into consideration the geographic distribution of the product. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input geographic distribution data of the product to a generation AI, which then makes the selection.

[0096] The selection unit can improve the accuracy of the selection by referring to literature related to the product during selection. The selection unit, for example, retrieves literature related to the product from a database and analyzes it using an analysis algorithm. For example, the selection unit can improve the accuracy of the selection by referring to reviews and ratings related to the product. The selection unit can also improve the accuracy of the selection by referring to research papers and articles related to the product. For example, the selection unit can improve the accuracy of the selection by referring to reviews and ratings related to the product and research papers and articles related to the product. The selection unit can also improve the accuracy of the selection by referring to user feedback related to the product. For example, the selection unit can improve the accuracy of the selection by referring to user feedback related to the product. This improves the accuracy of the selection by referring to literature related to the product. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input literature data related to the product into a generation AI, which can improve the accuracy of the selection.

[0097] When making a selection, the selection unit can take into consideration the market value of the product. The selection unit, for example, evaluates the market value of the product using an algorithm for evaluating the market value of the product. For example, the selection unit evaluates the market value of the product based on the price of the product and the balance between supply and demand. The selection unit can also make a selection based on the market value of the product. For example, the selection unit preferentially selects products with high market value. The selection unit can also lower the selection priority of products with low market value. For example, the selection unit preferentially selects products with high market value and lowers the selection priority of products with low market value. In this way, the selection is made taking into consideration the market value of the product. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input market value data of the product to a generation AI, and the generation AI can make the selection.

[0098] The presentation unit can estimate the user's emotion and adjust the presentation display method based on the estimated user's emotion. The presentation unit, for example, estimates the user's emotion using an emotion estimation algorithm. For example, the presentation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The presentation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the presentation unit can analyze the tone and speed of the voice and calculate an emotion score. The presentation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the presentation unit can calculate an emotion score based on heart rate fluctuations. This allows the presentation display method to be adjusted based on the user's emotion. For example, if the user is nervous, the presentation unit can provide a simple, highly visible display method. If the user is relaxed, the presentation unit can provide a display method that includes detailed information. If the user is in a hurry, the presentation unit can provide a display method that focuses on the main points. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using AI, or may be performed without using AI. For example, the presentation unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0099] The presentation unit can select the optimal display method by referring to the user's past operation history when presenting the display. The presentation unit, for example, retrieves the past operation history from a database and analyzes it using an analysis algorithm. For example, the presentation unit preferentially provides display methods that the user has previously rated highly. The presentation unit can also avoid display methods that the user has previously rated poorly. For example, the presentation unit selects the optimal display method by avoiding display methods that the user has previously rated poorly. The presentation unit can also select the optimal display method based on the user's past operation history. For example, the presentation unit selects the optimal display method based on the user's past operation history. This makes it possible to select the optimal display method based on the user's past operation history. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input past operation history data to a generation AI, which can select the optimal display method.

[0100] The presentation unit can customize the display content according to the user's current task when presenting the display. The presentation unit, for example, evaluates the current task using an algorithm for evaluating the user's current task. For example, the presentation unit evaluates the current task based on the user's current work content and work priority. The presentation unit can also customize the display content according to the user's current task. For example, when the user is shopping, the presentation unit can prioritize displaying information about related products. Furthermore, when the user is working, the presentation unit can prioritize displaying information related to work. For example, when the user is shopping, the presentation unit prioritizes displaying information about related products, and when the user is working, the presentation unit prioritizes displaying information related to work. This allows the display content to be customized according to the user's current task. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's current task data into a generation AI, which can customize the display content.

[0101] The presentation unit can improve the display method by reflecting the user's feedback when presenting the display. The presentation unit, for example, obtains the feedback from a database and analyzes it using an analysis algorithm. For example, the presentation unit preferentially provides display methods that the user has given a high rating. The presentation unit can also avoid display methods that the user has given a low rating. For example, the presentation unit avoids display methods that the user has given a low rating and selects an optimal display method. The presentation unit can also dynamically improve the display method based on the user's feedback. For example, the presentation unit dynamically improves the display method based on the user's feedback. This allows the display method to be improved based on the user's feedback. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input feedback data to a generation AI, which can improve the display method.

[0102] The presentation unit can estimate the user's emotions and adjust the presented operation procedures based on the estimated user's emotions. The presentation unit, for example, estimates the user's emotions using an emotion estimation algorithm. For example, the presentation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The presentation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the presentation unit analyzes the tone and speed of the voice and calculates an emotion score. The presentation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the presentation unit calculates an emotion score based on heart rate fluctuations. This allows the presented operation procedures to be adjusted based on the user's emotions. For example, if the user is nervous, the presentation unit can provide simple and intuitive operation procedures. If the user is relaxed, the presentation unit can provide detailed operation procedures. If the user is in a hurry, the presentation unit can provide quick operation procedures. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using AI, or may be performed without using AI. For example, the presentation unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0103] The presentation unit can select the optimal display method by taking into consideration the user's device information when presenting the display. The presentation unit, for example, uses the device type and setting information to acquire the user's device information. For example, if the user is using a smartphone, the presentation unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the presentation unit can provide a display method optimized for a large screen. For example, if the user is using a smartphone, the presentation unit can provide a display method tailored to the screen size, and if the user is using a tablet, the presentation unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the presentation unit can provide a simple and highly visible display method. For example, if the user is using a smartwatch, the presentation unit can provide a simple and highly visible display method. This allows the optimal display method to be selected based on the user's device information. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input device information data to a generation AI, which can select the optimal display method.

[0104] The presentation unit can make the display content multilingual according to the user's language setting when presenting it. The presentation unit, for example, uses the language setting of the device to acquire the user's language setting. For example, the presentation unit automatically sets the display content based on the language setting of the user's device. The presentation unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the presentation unit provides the display content in that language. This makes it possible to make the display content multilingual according to the user's language setting. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input language setting data to a generation AI, which can then make the display content multilingual.

[0105] The presentation unit can customize the display method by reflecting the user's past feedback when presenting the display. The presentation unit, for example, retrieves past feedback from a database and analyzes it using an analysis algorithm. For example, the presentation unit preferentially provides display methods that the user has given a high rating. The presentation unit can also avoid display methods that the user has given a low rating. For example, the presentation unit selects the optimal display method by avoiding display methods that the user has given a low rating. The presentation unit can also dynamically customize the display method based on the user's past feedback. For example, the presentation unit dynamically customizes the display method based on the user's past feedback. This allows the display method to be customized based on the user's past feedback. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input feedback data to a generation AI, which can customize the display method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, selection unit, and presentation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and provides voice recognition technology and a text input interface. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes search conditions using a generation AI. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects optimal products. The presentation unit is realized by the output device 40 of the smart device 14 and presents the selected products to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, selection unit, and presentation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides voice recognition technology and a text input interface. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes search conditions using a generation AI. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects optimal products. The presentation unit is realized by the speaker 240 of the smart glasses 214 and presents the selected products to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, selection unit, and presentation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and provides voice recognition technology and a text input interface. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes search conditions using a generation AI. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects optimal products. The presentation unit is realized by the display 343 of the headset type terminal 314 and presents the selected products to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, selection unit, and presentation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides voice recognition technology and a text input interface. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes search conditions using a generation AI. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects the optimal product. The presentation unit is realized by the speaker 240 of the robot 414 and presents the selected product to the user.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can analyze the user's past purchase history and automatically suggest product categories that the user frequently purchases. For example, the reception unit can analyze data on products purchased in the past by the user and identify the most frequently purchased category. The reception unit can also suggest products that the user purchases in relation to a particular season or event. For example, if the user tends to purchase certain products every year at Christmas, the reception unit can suggest products related to that time of year. Furthermore, the reception unit can learn the user's purchasing patterns, predict when the next purchase will be made, and send a reminder. This allows the user to purchase the products they need in a timely manner.

[0108] The analysis unit can search for products taking into account the user's current weather information. For example, the analysis unit can obtain weather forecast data for the user's area and suggest waterproof products and umbrellas on rainy days, or cooling products and beverages on hot days. Furthermore, the analysis unit can analyze seasonal weather patterns and suggest products appropriate for each season. For example, it can suggest heaters and winter clothing in winter, and air conditioners and sunglasses in summer. This allows users to easily find products that suit the weather conditions.

[0109] The selection unit can select products taking into account the user's health data. For example, the selection unit can obtain data from the user's fitness tracker or health app and suggest products according to the user's health condition. The selection unit can also prioritize products that do not contain allergens by taking into account the user's allergy information. Furthermore, the selection unit can suggest appropriate foods and supplements based on the user's dietary restrictions and diet goals. This allows the user to easily select health-conscious products.

[0110] The presentation unit can present products based on the user's visual preferences. For example, the presentation unit can analyze the designs and colors of products that the user has previously rated highly and prioritize products with similar designs and colors. If the user prefers a particular brand or style, the presentation unit can also prioritize products of that brand or style. Furthermore, the presentation unit can customize and display product images and videos based on the user's visual preferences. This allows the user to easily find visually appealing products.

[0111] The reception unit can estimate the user's emotions and adjust the search criteria input method based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input, allowing the user to quickly input search criteria. This makes it possible to provide the optimal search criteria input method according to the user's emotions.

[0112] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. Furthermore, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. This makes it possible to provide optimal analysis results according to the user's emotions.

[0113] The selection unit can estimate the user's emotion and adjust the selection criteria based on the estimated emotion. For example, if the user is relaxed, the selection unit can provide detailed selection criteria. If the user is in a hurry, the selection unit can provide simple selection criteria. Furthermore, if the user is excited, the selection unit can provide selection criteria with visually stimulating effects. In this way, it is possible to provide optimal selection criteria according to the user's emotion.

[0114] The presentation unit can estimate the user's emotions and adjust the presentation display method based on the estimated emotions. For example, if the user is nervous, the presentation unit can provide a simple, highly visible presentation method. If the user is relaxed, the presentation unit can provide a presentation method including detailed information. Furthermore, if the user is in a hurry, the presentation unit can also provide a presentation method that focuses on the main points. In this way, it is possible to provide the optimal presentation method according to the user's emotions.

[0115] The presentation unit can estimate the user's emotions and adjust the presented operation procedures based on the estimated emotions. For example, if the user is nervous, the presentation unit can provide simple and intuitive operation procedures. If the user is relaxed, the presentation unit can provide detailed operation procedures. Furthermore, if the user is in a hurry, the presentation unit can also provide procedures that allow for quick operation. In this way, it is possible to provide optimal operation procedures according to the user's emotions.

[0116] The reception unit can filter search conditions based on the user's current purchasing intent and areas of interest. For example, if the user has a high purchasing intent, the reception unit can prioritize displaying products in a specific category. Also, if the user is interested in a specific area of ​​interest, the reception unit can prioritize displaying products in that area. Furthermore, if the user has a low purchasing intent, the reception unit can also prioritize displaying discounted products or products with special offers. This makes it possible to provide optimal search conditions based on the user's purchasing intent and areas of interest.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives search criteria from the user. Search criteria include price range, delivery time, reputation, etc. The reception unit can receive voice input using voice recognition technology, and can also provide a text input interface to receive search criteria input in text. Furthermore, the reception unit can also receive image input using image recognition technology. Step 2: The analysis unit uses the generation AI to analyze the conditions received by the reception unit and search for matching products in the database on the e-commerce site. The analysis is based on information such as price, delivery time, and reputation. Step 3: The selection unit uses the generation AI to select the best product from the products searched by the analysis unit, based on information such as price, delivery time, and reputation. Step 4: The presentation unit presents the product selected by the selection unit to the user. The presentation may be displayed on a screen or by audio notification.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives search conditions from a user; an analysis unit that analyzes the conditions received by the reception unit and searches for corresponding products from a database on the EC site; a selection unit for selecting the product searched by the analysis unit; a presentation unit that presents the product selected by the selection unit to the user. A system characterized by:

2. The reception unit Accept search terms by voice or text 2. The system of claim 1.

3. The analysis unit Search for products based on price, delivery time, and reviews 2. The system of claim 1.

4. The selection unit Select a product from the search results 2. The system of claim 1.

5. The presentation unit Present the selected products to the user 2. The system of claim 1.

6. The presentation unit Display information about the product selected by the user 2. The system of claim 1.

7. The reception unit Inferring user emotions and adjusting search criteria input methods based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyzes users' past search history and suggests search terms 2. The system of claim 1.

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