System

Abstract

An object of a system according to an embodiment is to provide personalized information based on a product put into a shopping cart.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives commodity information. The analysis unit analyzes the commodity information received by the reception unit. The generation unit generates personalized information on the basis of the information analyzed by the analysis unit. The providing unit provides the information generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately provide personalized information based on the items added to a shopping cart, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized information based on the products added to a shopping cart. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit receives product information. The analyzing unit analyzes the product information received by the receiving unit. The generating unit generates personalized information based on the information analyzed by the analyzing unit. The providing unit provides the information generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized information based on the items added to the shopping cart. [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 shopping support system according to an embodiment of the present invention supports efficient and time-saving shopping by using a generation AI to customize personalized information when a user adds an item to a shopping cart. When a user adds an item to a shopping cart, the shopping support system transmits the information to the generation AI, which then analyzes the information about the added item and generates personalized information optimal for the user. This information is customized based on the user's purchasing history and preferences. For example, in the shopping support system, a user selects an item on an e-commerce site and adds it to the cart. This information is input into the generation AI. The shopping support system then uses the generation AI to analyze the information about the added item. The generation AI analyzes information about the item, such as its category, price, and brand, and generates personalized information optimal for the user. For example, if the item added to the cart by the user is an electronic device, the generation AI generates information about accessories and warranty services related to the item. The generated personalized information is customized based on the user's purchasing history and preferences. For example, the generation AI provides optimal information to the user based on the user's past purchases and viewed items. This allows the user to shop efficiently and without waste. As a result, the shopping support system automatically provides information related to products added to the user's cart, saving the user the trouble of searching for the information they need. In addition, the generation AI customizes the information based on the user's preferences, making it easier for the user to find the product that's best for them. As a result, the shopping support system allows the user to shop efficiently and without waste. For example, the shopping support system automatically provides information related to products added to the user's cart, saving the user the trouble of searching for the information they need. In addition, the generation AI customizes the information based on the user's preferences, making it easier for the user to find the product that's best for them.

[0029] A shopping support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives product information. The product information includes, but is not limited to, product names, prices, categories, and brands. For example, the reception unit receives product information when a user selects a product on an e-commerce site and adds it to a cart. The reception unit can also receive information when a user adds a product to a shopping cart at a store. The analysis unit analyzes the product information received by the reception unit. The analysis unit analyzes information such as product categories, prices, and brands. The analysis unit can analyze the product information using data mining, statistical analysis, machine learning algorithms, and the like. For example, the analysis unit analyzes product categories and extracts related information. The analysis unit can also analyze product prices and provide information according to price ranges. The analysis unit can also analyze product brands and provide information related to the brands. The generation unit generates personalized information based on the information analyzed by the analysis unit. The generation unit generates information based on a user's purchasing history and preferences using a generation AI. For example, the generation unit generates related information based on products previously purchased or viewed by the user. The generation unit can also generate information on related products, accessories, warranty services, and the like using a generation AI. For example, if the product added to the cart by the user is an electronic device, the generation unit generates information on accessories and warranty services related to the product. The provision unit provides the information generated by the generation unit to the user. For example, the provision unit displays the generated information on the user's device. The provision unit can also send the generated information to the user as an email or a notification. For example, the provision unit notifies the user of the generated information via their smartphone. This allows the shopping support system according to the embodiment to streamline the user's shopping experience.

[0030] The analysis unit can analyze at least one piece of information from the product category, price, and brand. The analysis unit, for example, analyzes the product category. For example, the analysis unit analyzes the product category and extracts related information. The analysis unit can also analyze the product price. For example, the analysis unit can analyze the product price and provide information according to the price range. The analysis unit can also analyze the product brand. For example, the analysis unit can analyze the product brand and provide information related to the brand. In this way, by analyzing detailed product information, more accurate personalized information can be generated.

[0031] The generation unit can generate personalized information based on the user's purchase history or preferences. The generation unit generates information based on, for example, the user's purchase history. For example, the generation unit generates related information based on products the user has previously purchased or viewed. The generation unit can also generate information based on the user's preferences. For example, the generation unit provides the user with optimal information based on the user's preferences. In this way, by generating information based on the user's purchase history or preferences, optimal information can be provided to the user.

[0032] The providing unit can provide the generated information to the user. For example, the providing unit displays the generated information on the user's device. For example, the providing unit notifies the user of the generated information on a smartphone of the user. The providing unit can also send the generated information to the user by email or as a notification. For example, the providing unit sends the generated information to the user by email. In this way, by providing the generated information to the user, the user can quickly obtain the information he or she needs.

[0033] The generation unit may generate information on at least one of related products, accessories, and warranty services. The generation unit may, for example, generate information on related products. For example, the generation unit may generate product information related to a product added to a cart by a user. The generation unit may also generate information on accessories. For example, the generation unit may generate information on accessories related to a product added to a cart by a user. The generation unit may also generate information on warranty services. For example, the generation unit may generate information on warranty services related to a product added to a cart by a user. In this way, additional value can be provided to the user by generating information on related products, accessories, warranty services, etc.

[0034] The reception unit can analyze the user's past purchase history and select an appropriate reception method. For example, the reception unit may prioritize reception of product categories that the user has frequently purchased in the past. The reception unit can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the reception method to be used during a specific time period based on the user's past purchase history. This allows the optimal reception method to be selected by analyzing the user's past purchase history, improving user convenience. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past purchase history into a generation AI and have the generation AI select the optimal reception method.

[0035] When receiving product information, the reception unit can perform filtering based on the user's current shopping situation or areas of interest. For example, the reception unit preferentially receives information related to products added to the user's current shopping cart. The reception unit can also filter and receive related product information based on the user's areas of interest. The reception unit can also preferentially receive specific product information based on the user's current shopping situation (e.g., during a sale period). In this way, by filtering based on the user's current shopping situation and areas of interest, highly relevant information can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's current shopping situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0036] When receiving product information, the reception unit can select an appropriate reception means depending on the user's input method. For example, when the user inputs product information by voice, the reception unit receives the information using voice recognition technology. Furthermore, when the user inputs product information by text, the reception unit can also receive the information using text analysis technology. Furthermore, when the user inputs product information by image, the reception unit can also receive the information using image recognition technology. This improves user convenience by selecting the optimal reception means 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 using AI. For example, the reception unit can input the user's input method to the generation AI and cause the generation AI to select the optimal reception means.

[0037] When receiving product information, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving product information related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving product information related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving product information related to stores near the user's home. In this way, by taking the user's geographical location information into consideration, highly relevant information can be preferentially received. 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 can input the user's geographical location information to the generation AI and cause the generation AI to receive highly relevant information.

[0038] The reception unit can analyze the user's social media activity and receive related information when receiving product information. For example, the reception unit receives product information related to a location 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 receive related product information. The reception unit can also receive related product information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, highly relevant information can be received preferentially. 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 social media activity data into the generation AI and cause the generation AI to receive related information.

[0039] The reception unit can adjust the reception method by reflecting the user's past feedback when receiving product information. For example, the reception unit can suggest an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the reception method. This makes it possible to provide an optimal reception method by reflecting the user's past feedback. 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 can input the user's past feedback data into the generation AI and have the generation AI adjust the reception method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the product. For example, the analysis unit performs a detailed analysis of important products. The analysis unit can also perform a concise analysis of general products. The analysis unit can also perform a special analysis of products in which the user is particularly interested. In this way, by adjusting the level of detail of the analysis based on the importance of the product, it is possible to provide the user with necessary information. 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 the generation AI and have the generation AI adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the product category. For example, in the case of electronic devices, the analysis unit applies a technical analysis algorithm. In addition, in the case of food, the analysis unit can also apply an analysis algorithm based on nutritional value or expiration date. In addition, in the case of clothing, the analysis unit can also apply an analysis algorithm based on material or size. In this way, by applying different analysis algorithms depending on the product category, more accurate analysis results can be provided. 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 into the generation AI and cause the generation AI to apply the analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can adjust the analysis algorithm based on, for example, feedback provided by the user in the past. The analysis unit can also preferentially apply a specific analysis method based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the accuracy of the analysis. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the order of analysis based on the product launch date. For example, the analysis unit prioritizes analysis of new products. The analysis unit can also prioritize analysis of products during sale periods. The analysis unit can also determine the analysis priority for seasonal products based on the launch date. This allows timely information to be provided by determining the analysis priority based on the product launch date. 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 launch date data into the generation AI and have the generation AI determine the analysis order.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of products. For example, the analysis unit prioritizes analysis of highly relevant products. The analysis unit can also postpone analysis of less relevant products. The analysis unit can also adjust the order of analysis based on the user's level of interest. By adjusting the order of analysis based on the relevance of products, information important to the user can be provided preferentially. 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 the generation AI and have the generation AI adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of knowledge. For example, if the user has specialized knowledge, the analysis unit can provide analysis results that make extensive use of technical terms. Furthermore, if the user only has general knowledge, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. 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 the user's knowledge level data into a generation AI and have the generation AI execute the use of technical terms.

[0046] The generation unit can adjust the level of detail of the information to be generated based on the importance of the product at the time of generation. For example, the generation unit provides detailed information for important products. The generation unit can also provide concise information for general products. The generation unit can also provide special information for products in which the user is particularly interested. In this way, by adjusting the level of detail of the information based on the importance of the product, it is possible to provide the information necessary for the user. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input product importance data into the generation AI and cause the generation AI to adjust the level of detail of the information.

[0047] During generation, the generation unit can apply different generation algorithms depending on the product category. For example, in the case of electronic devices, the generation unit applies a generation algorithm that provides technical information. In addition, in the case of food, the generation unit can also apply a generation algorithm that provides information based on nutritional value and expiration date. In addition, in the case of clothing, the generation unit can also apply a generation algorithm that provides information based on material and size. In this way, by applying different generation algorithms depending on the product category, more accurate information can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input product category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0048] During generation, the generation unit can improve the accuracy of generation based on the user's past generation results. The generation unit, for example, adjusts the generation algorithm based on feedback provided by the user in the past. The generation unit can also preferentially apply a specific generation method based on the user's past generation results. The generation unit can also analyze the user's past generation results and improve the accuracy of generation. In this way, the accuracy of generation is improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0049] At the time of generation, the generation unit can determine the order of information to be generated based on the product release date. For example, the generation unit can prioritize generating information for new products. The generation unit can also prioritize generating information for products during sale periods. The generation unit can also determine the priority of information for seasonal products based on the release date. This allows timely information to be provided by determining the priority of information based on the product release date. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input product release date data into the generation AI and have the generation AI determine the order of information.

[0050] The generation unit can adjust the order of information to be generated based on the relevance of products during generation. For example, the generation unit can generate information preferentially for highly relevant products. The generation unit can also postpone generating information for less relevant products. The generation unit can also adjust the order of information based on the user's level of interest. In this way, by adjusting the order of information based on the relevance of products, information that is important to the user can be provided preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input product relevance data into the generation AI and cause the generation AI to adjust the order of information.

[0051] The generation unit can adjust the use of technical terms in the information to be generated according to the user's knowledge level during generation. For example, if the user has specialized knowledge, the generation unit can provide information that uses a lot of technical terms. Furthermore, if the user only has general knowledge, the generation unit can also provide concise, easy-to-understand information. The generation unit can also adjust the way the information is presented according to the user's level of expertise. This allows the provision of information that is easy for the user to understand by adjusting the use of technical terms in the information according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's knowledge level data into the generation AI and have the generation AI execute the use of technical terms.

[0052] The providing unit can adjust the level of detail of the information to be provided based on the importance of the product when providing the information. For example, the providing unit provides detailed information for important products. The providing unit can also provide concise information for general products. The providing unit can also provide special information for products in which the user is particularly interested. In this way, by adjusting the level of detail of the information based on the importance of the product, it is possible to provide the information necessary for the user. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input product importance data to the generating AI and cause the generating AI to adjust the level of detail of the information.

[0053] The providing unit can apply different providing algorithms depending on the product category when providing information. For example, in the case of electronic devices, the providing unit applies a providing algorithm that provides technical information. Furthermore, in the case of food, the providing unit can also apply a providing algorithm that provides information based on nutritional value and expiration date. Furthermore, in the case of clothing, the providing unit can also apply a providing algorithm that provides information based on material and size. In this way, by applying different providing algorithms depending on the product category, more accurate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input product category data to the generation AI and cause the generation AI to apply the providing algorithm.

[0054] The providing unit can improve the accuracy of the provision based on the user's past provision results when providing the data. The providing unit can adjust the provision algorithm based on, for example, feedback provided by the user in the past. The providing unit can also preferentially apply a specific provision method based on the user's past provision results. The providing unit can also analyze the user's past provision results and improve the accuracy of the provision. This improves the accuracy of the provision by referring to the user's past provision results. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0055] The providing unit can determine the order of information to be provided based on the product launch date at the time of provision. For example, the providing unit can provide information preferentially for new products. The providing unit can also provide information preferentially for products during sale periods. The providing unit can also determine the priority of information for seasonal products based on the launch date. This allows timely information to be provided by determining the priority of information based on the product launch date. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input product launch date data to the generation AI and have the generation AI determine the order of information.

[0056] The providing unit can adjust the order of information to be provided based on the relevance of the products when providing the information. For example, the providing unit can provide information preferentially for highly relevant products. The providing unit can also provide information later for less relevant products. The providing unit can also adjust the order of information based on the user's level of interest. In this way, by adjusting the order of information based on the relevance of the products, information that is important to the user can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input product relevance data to a generation AI and cause the generation AI to adjust the order of the information.

[0057] The providing unit can adjust the use of technical terms in the information to be provided according to the user's knowledge level when providing the information. For example, if the user has specialized knowledge, the providing unit can provide information that uses a lot of technical terms. Furthermore, if the user only has general knowledge, the providing unit can also provide concise and easy-to-understand information. The providing unit can also adjust the way the information is expressed according to the user's level of expertise. This allows the provision of information that is easy for the user to understand by adjusting the use of technical terms in the information according to the user's level of expertise. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's knowledge level data into a generating AI and cause the generating AI to use technical terms.

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

[0059] The reception unit can preferentially receive discount information for specific products based on the user's purchase history. For example, it can preferentially receive discount information related to a product category that the user has frequently purchased in the past. The reception unit can also predict when the user will repurchase a product that they have previously purchased and provide discount information at that timing. Furthermore, the reception unit can analyze the user's preference for a specific brand from the user's purchase history and preferentially receive discount information for products from that brand. In this way, providing discount information based on the user's purchase history can increase the user's desire to purchase.

[0060] The generation unit can generate review information for a specific product based on the user's purchasing history. For example, the generation unit collects reviews from other users for products that the user has previously purchased, and summarizes and provides the review information. The generation unit can also generate review information for related products based on ratings of products that the user has previously purchased. Furthermore, the generation unit can customize and provide review information for a specific product based on the user's preferences. In this way, providing review information based on the user's purchasing history can help the user select a product.

[0061] The reception unit can prioritize receiving area-specific promotional information based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving information about sales and events being held in that area. Also, if the user is traveling, the reception unit can prioritize receiving promotional information about tourist spots and restaurants in the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving sales information about stores near the user's home. This makes it possible to provide highly relevant promotional information based on the user's geographical location information.

[0062] The generation unit can generate customization options for a specific product based on the user's purchasing history. For example, the generation unit analyzes customization options for products previously purchased by the user and proposes new customization options based on that information. The generation unit can also generate customization options for a specific product based on the user's preferences. Furthermore, the generation unit can analyze the user's preferences for a specific brand from the user's purchasing history and generate customization options for products of that brand. This improves user satisfaction by providing customization options based on the user's purchasing history.

[0063] The reception unit can analyze the user's social media activity and prioritize receiving related product information. For example, it can prioritize receiving product information related to places 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 prioritize receiving related product information. Furthermore, the reception unit can also refer to the activities of the user's friends on social media and prioritize receiving related product information. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant product information.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit receives product information. Product information includes product name, price, category, brand, etc. For example, when a user selects a product on an e-commerce site and adds it to a cart, or when a user places a product in a shopping cart at a store, the information is received. Step 2: The analysis unit analyzes the product information received by the reception unit. The analysis unit analyzes information such as product category, price, and brand, and analyzes the product information using data mining, statistical analysis, machine learning algorithms, etc. For example, the analysis unit analyzes the product category and extracts related information, analyzes the price and provides information according to the price range, or analyzes the brand and provides information related to the brand. Step 3: The generation unit generates personalized information based on the information analyzed by the analysis unit. The generation unit uses generation AI to generate information based on the user's purchasing history and preferences. For example, it generates related information based on products the user has previously purchased or viewed, or generates information on related products, accessories, warranty services, etc. Step 4: The providing unit provides the information generated by the generating unit to the user. The providing unit displays the generated information on the user's device or sends it to the user as an email or a notification. For example, the generated information is notified to the user's smartphone.

[0066] (Example 2) A shopping support system according to an embodiment of the present invention supports efficient and time-saving shopping by using a generation AI to customize personalized information when a user adds an item to a shopping cart. When a user adds an item to a shopping cart, the shopping support system transmits the information to the generation AI, which then analyzes the information about the added item and generates personalized information optimal for the user. This information is customized based on the user's purchasing history and preferences. For example, in the shopping support system, a user selects an item on an e-commerce site and adds it to the cart. This information is input into the generation AI. The shopping support system then uses the generation AI to analyze the information about the added item. The generation AI analyzes information about the item, such as its category, price, and brand, and generates personalized information optimal for the user. For example, if the item added to the cart by the user is an electronic device, the generation AI generates information about accessories and warranty services related to the item. The generated personalized information is customized based on the user's purchasing history and preferences. For example, the generation AI provides optimal information to the user based on the user's past purchases and viewed items. This allows the user to shop efficiently and without waste. As a result, the shopping support system automatically provides information related to products added to the user's cart, saving the user the trouble of searching for the information they need. In addition, the generation AI customizes the information based on the user's preferences, making it easier for the user to find the product that's best for them. As a result, the shopping support system allows the user to shop efficiently and without waste. For example, the shopping support system automatically provides information related to products added to the user's cart, saving the user the trouble of searching for the information they need. In addition, the generation AI customizes the information based on the user's preferences, making it easier for the user to find the product that's best for them.

[0067] A shopping support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives product information. The product information includes, but is not limited to, product names, prices, categories, and brands. For example, the reception unit receives product information when a user selects a product on an e-commerce site and adds it to a cart. The reception unit can also receive information when a user adds a product to a shopping cart at a store. The analysis unit analyzes the product information received by the reception unit. The analysis unit analyzes information such as product categories, prices, and brands. The analysis unit can analyze the product information using data mining, statistical analysis, machine learning algorithms, and the like. For example, the analysis unit analyzes product categories and extracts related information. The analysis unit can also analyze product prices and provide information according to price ranges. The analysis unit can also analyze product brands and provide information related to the brands. The generation unit generates personalized information based on the information analyzed by the analysis unit. The generation unit generates information based on a user's purchasing history and preferences using a generation AI. For example, the generation unit generates related information based on products previously purchased or viewed by the user. The generation unit can also generate information on related products, accessories, warranty services, and the like using a generation AI. For example, if the product added to the cart by the user is an electronic device, the generation unit generates information on accessories and warranty services related to the product. The provision unit provides the information generated by the generation unit to the user. For example, the provision unit displays the generated information on the user's device. The provision unit can also send the generated information to the user as an email or a notification. For example, the provision unit notifies the user of the generated information via their smartphone. This allows the shopping support system according to the embodiment to streamline the user's shopping experience.

[0068] The analysis unit can analyze at least one piece of information from the product category, price, and brand. The analysis unit, for example, analyzes the product category. For example, the analysis unit analyzes the product category and extracts related information. The analysis unit can also analyze the product price. For example, the analysis unit can analyze the product price and provide information according to the price range. The analysis unit can also analyze the product brand. For example, the analysis unit can analyze the product brand and provide information related to the brand. In this way, by analyzing detailed product information, more accurate personalized information can be generated.

[0069] The generation unit can generate personalized information based on the user's purchase history or preferences. The generation unit generates information based on, for example, the user's purchase history. For example, the generation unit generates related information based on products the user has previously purchased or viewed. The generation unit can also generate information based on the user's preferences. For example, the generation unit provides the user with optimal information based on the user's preferences. In this way, by generating information based on the user's purchase history or preferences, optimal information can be provided to the user.

[0070] The providing unit can provide the generated information to the user. For example, the providing unit displays the generated information on the user's device. For example, the providing unit notifies the user of the generated information on a smartphone of the user. The providing unit can also send the generated information to the user by email or as a notification. For example, the providing unit sends the generated information to the user by email. In this way, by providing the generated information to the user, the user can quickly obtain the information he or she needs.

[0071] The generation unit may generate information on at least one of related products, accessories, and warranty services. The generation unit may, for example, generate information on related products. For example, the generation unit may generate product information related to a product added to a cart by a user. The generation unit may also generate information on accessories. For example, the generation unit may generate information on accessories related to a product added to a cart by a user. The generation unit may also generate information on warranty services. For example, the generation unit may generate information on warranty services related to a product added to a cart by a user. In this way, additional value can be provided to the user by generating information on related products, accessories, warranty services, etc.

[0072] The reception unit can estimate the user's emotions and adjust the timing of receiving product information based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay receiving product information and receive it in a relaxed state. Furthermore, if the user is excited, the reception unit can quickly receive product information and immediately start analysis. Furthermore, if the user is tired, the reception unit can receive product information using a simple interface, saving effort. This allows the timing of receiving product information to be adjusted according to the user's emotions, reducing stress for the user and providing a comfortable shopping experience. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The reception unit can analyze the user's past purchase history and select an appropriate reception method. For example, the reception unit may prioritize reception of product categories that the user has frequently purchased in the past. The reception unit can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the reception method to be used during a specific time period based on the user's past purchase history. This allows the optimal reception method to be selected by analyzing the user's past purchase history, improving user convenience. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past purchase history into a generation AI and have the generation AI select the optimal reception method.

[0074] When receiving product information, the reception unit can perform filtering based on the user's current shopping situation or areas of interest. For example, the reception unit preferentially receives information related to products added to the user's current shopping cart. The reception unit can also filter and receive related product information based on the user's areas of interest. The reception unit can also preferentially receive specific product information based on the user's current shopping situation (e.g., during a sale period). In this way, by filtering based on the user's current shopping situation and areas of interest, highly relevant information can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's current shopping situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0075] When receiving product information, the reception unit can select an appropriate reception means depending on the user's input method. For example, when the user inputs product information by voice, the reception unit receives the information using voice recognition technology. Furthermore, when the user inputs product information by text, the reception unit can also receive the information using text analysis technology. Furthermore, when the user inputs product information by image, the reception unit can also receive the information using image recognition technology. This improves user convenience by selecting the optimal reception means 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 using AI. For example, the reception unit can input the user's input method to the generation AI and cause the generation AI to select the optimal reception means.

[0076] The reception unit can estimate the user's emotions and determine the priority of the product information to be received based on the estimated user's emotions. For example, when the user is relaxed, the reception unit receives product information with normal priority. Furthermore, when the user is in a hurry, the reception unit can prioritize important product information. Furthermore, when the user is excited, the reception unit can prioritize highly relevant product information. This allows the system to provide information tailored to the user's needs by determining the priority of product information according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the product information.

[0077] When receiving product information, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving product information related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving product information related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving product information related to stores near the user's home. In this way, by taking the user's geographical location information into consideration, highly relevant information can be preferentially received. 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 can input the user's geographical location information to the generation AI and cause the generation AI to receive highly relevant information.

[0078] The reception unit can analyze the user's social media activity and receive related information when receiving product information. For example, the reception unit receives product information related to a location 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 receive related product information. The reception unit can also receive related product information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, highly relevant information can be received preferentially. 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 social media activity data into the generation AI and cause the generation AI to receive related information.

[0079] The reception unit can adjust the reception method by reflecting the user's past feedback when receiving product information. For example, the reception unit can suggest an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the reception method. This makes it possible to provide an optimal reception method by reflecting the user's past feedback. 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 can input the user's past feedback data into the generation AI and have the generation AI adjust the reception method.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's 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 also provide concise analysis results that focus on the main points. If the user is excited, the analysis unit can also provide analysis results with visually stimulating effects. This allows the analysis results to be easily understood by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the product. For example, the analysis unit performs a detailed analysis of important products. The analysis unit can also perform a concise analysis of general products. The analysis unit can also perform a special analysis of products in which the user is particularly interested. In this way, by adjusting the level of detail of the analysis based on the importance of the product, it is possible to provide the user with necessary information. 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 the generation AI and have the generation AI adjust the level of detail of the analysis.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the product category. For example, in the case of electronic devices, the analysis unit applies a technical analysis algorithm. In addition, in the case of food, the analysis unit can also apply an analysis algorithm based on nutritional value or expiration date. In addition, in the case of clothing, the analysis unit can also apply an analysis algorithm based on material or size. In this way, by applying different analysis algorithms depending on the product category, more accurate analysis results can be provided. 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 into the generation AI and cause the generation AI to apply the analysis algorithm.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can adjust the analysis algorithm based on, for example, feedback provided by the user in the past. The analysis unit can also preferentially apply a specific analysis method based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the accuracy of the analysis. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0085] During analysis, the analysis unit can determine the order of analysis based on the product launch date. For example, the analysis unit prioritizes analysis of new products. The analysis unit can also prioritize analysis of products during sale periods. The analysis unit can also determine the analysis priority for seasonal products based on the launch date. This allows timely information to be provided by determining the analysis priority based on the product launch date. 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 launch date data into the generation AI and have the generation AI determine the analysis order.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of products. For example, the analysis unit prioritizes analysis of highly relevant products. The analysis unit can also postpone analysis of less relevant products. The analysis unit can also adjust the order of analysis based on the user's level of interest. By adjusting the order of analysis based on the relevance of products, information important to the user can be provided preferentially. 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 the generation AI and have the generation AI adjust the order of analysis.

[0087] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of knowledge. For example, if the user has specialized knowledge, the analysis unit can provide analysis results that make extensive use of technical terms. Furthermore, if the user only has general knowledge, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. 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 the user's knowledge level data into a generation AI and have the generation AI execute the use of technical terms.

[0088] The generation unit can estimate the user's emotions and adjust the way the information is presented based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can provide detailed information. If the user is in a hurry, the generation unit can provide concise information that focuses on the main points. If the user is excited, the generation unit can provide information with visually stimulating effects. This allows the user to be provided with information that is easy to understand by adjusting the way the information is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the information is presented.

[0089] The generation unit can adjust the level of detail of the information to be generated based on the importance of the product at the time of generation. For example, the generation unit provides detailed information for important products. The generation unit can also provide concise information for general products. The generation unit can also provide special information for products in which the user is particularly interested. In this way, by adjusting the level of detail of the information based on the importance of the product, it is possible to provide the information necessary for the user. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input product importance data into the generation AI and cause the generation AI to adjust the level of detail of the information.

[0090] During generation, the generation unit can apply different generation algorithms depending on the product category. For example, in the case of electronic devices, the generation unit applies a generation algorithm that provides technical information. In addition, in the case of food, the generation unit can also apply a generation algorithm that provides information based on nutritional value and expiration date. In addition, in the case of clothing, the generation unit can also apply a generation algorithm that provides information based on material and size. In this way, by applying different generation algorithms depending on the product category, more accurate information can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input product category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0091] During generation, the generation unit can improve the accuracy of generation based on the user's past generation results. The generation unit, for example, adjusts the generation algorithm based on feedback provided by the user in the past. The generation unit can also preferentially apply a specific generation method based on the user's past generation results. The generation unit can also analyze the user's past generation results and improve the accuracy of generation. In this way, the accuracy of generation is improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0092] The generation unit can estimate the user's emotions and adjust the length of the information to be generated based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can provide short, concise information. Furthermore, if the user is relaxed, the generation unit can provide detailed information. Furthermore, if the user is excited, the generation unit can provide information with visually stimulating effects. By adjusting the length of information according to the user's emotions, optimal information can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the information.

[0093] At the time of generation, the generation unit can determine the order of information to be generated based on the product release date. For example, the generation unit can prioritize generating information for new products. The generation unit can also prioritize generating information for products during sale periods. The generation unit can also determine the priority of information for seasonal products based on the release date. This allows timely information to be provided by determining the priority of information based on the product release date. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input product release date data into the generation AI and have the generation AI determine the order of information.

[0094] The generation unit can adjust the order of information to be generated based on the relevance of products during generation. For example, the generation unit can generate information preferentially for highly relevant products. The generation unit can also postpone generating information for less relevant products. The generation unit can also adjust the order of information based on the user's level of interest. In this way, by adjusting the order of information based on the relevance of products, information that is important to the user can be provided preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input product relevance data into the generation AI and cause the generation AI to adjust the order of information.

[0095] The generation unit can adjust the use of technical terms in the information to be generated according to the user's knowledge level during generation. For example, if the user has specialized knowledge, the generation unit can provide information that uses a lot of technical terms. Furthermore, if the user only has general knowledge, the generation unit can also provide concise, easy-to-understand information. The generation unit can also adjust the way the information is presented according to the user's level of expertise. This allows the provision of information that is easy for the user to understand by adjusting the use of technical terms in the information according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's knowledge level data into the generation AI and have the generation AI execute the use of technical terms.

[0096] The providing unit can estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide detailed information. If the user is in a hurry, the providing unit can provide concise information that focuses on the main points. If the user is excited, the providing unit can provide information with visually stimulating effects. This allows the user to be provided with information that is easy to understand by adjusting the way information is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way information is presented.

[0097] The providing unit can adjust the level of detail of the information to be provided based on the importance of the product when providing the information. For example, the providing unit provides detailed information for important products. The providing unit can also provide concise information for general products. The providing unit can also provide special information for products in which the user is particularly interested. In this way, by adjusting the level of detail of the information based on the importance of the product, it is possible to provide the information necessary for the user. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input product importance data to the generating AI and cause the generating AI to adjust the level of detail of the information.

[0098] The providing unit can apply different providing algorithms depending on the product category when providing information. For example, in the case of electronic devices, the providing unit applies a providing algorithm that provides technical information. Furthermore, in the case of food, the providing unit can also apply a providing algorithm that provides information based on nutritional value and expiration date. Furthermore, in the case of clothing, the providing unit can also apply a providing algorithm that provides information based on material and size. In this way, by applying different providing algorithms depending on the product category, more accurate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input product category data to the generation AI and cause the generation AI to apply the providing algorithm.

[0099] The providing unit can improve the accuracy of the provision based on the user's past provision results when providing the data. The providing unit can adjust the provision algorithm based on, for example, feedback provided by the user in the past. The providing unit can also preferentially apply a specific provision method based on the user's past provision results. The providing unit can also analyze the user's past provision results and improve the accuracy of the provision. This improves the accuracy of the provision by referring to the user's past provision results. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0100] The providing unit can estimate the user's emotions and adjust the length of the information to be provided based on the estimated user emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point information. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is excited, the providing unit can provide information with visually stimulating effects. By adjusting the length of information according to the user's emotions, optimal information can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the information.

[0101] The providing unit can determine the order of information to be provided based on the product launch date at the time of provision. For example, the providing unit can provide information preferentially for new products. The providing unit can also provide information preferentially for products during sale periods. The providing unit can also determine the priority of information for seasonal products based on the launch date. This allows timely information to be provided by determining the priority of information based on the product launch date. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input product launch date data to the generation AI and have the generation AI determine the order of information.

[0102] The providing unit can adjust the order of information to be provided based on the relevance of the products when providing the information. For example, the providing unit can provide information preferentially for highly relevant products. The providing unit can also provide information later for less relevant products. The providing unit can also adjust the order of information based on the user's level of interest. In this way, by adjusting the order of information based on the relevance of the products, information that is important to the user can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input product relevance data to a generation AI and cause the generation AI to adjust the order of the information.

[0103] The providing unit can adjust the use of technical terms in the information to be provided according to the user's knowledge level when providing the information. For example, if the user has specialized knowledge, the providing unit can provide information that uses a lot of technical terms. Furthermore, if the user only has general knowledge, the providing unit can also provide concise and easy-to-understand information. The providing unit can also adjust the way the information is expressed according to the user's level of expertise. This allows the provision of information that is easy for the user to understand by adjusting the use of technical terms in the information according to the user's level of expertise. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's knowledge level data into a generating AI and cause the generating AI to use technical terms. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 receives information when a user selects a product on an e-commerce site and adds it to a cart. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information such as the product category, price, and brand. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates information based on the user's purchasing history and preferences using a generation AI. The provision unit is realized by the output device 40 of the smart device 14 and displays the generated information on the user's device. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, and provision 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 receives information when a user selects a product on an e-commerce site and adds it to a cart. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information such as product category, price, and brand. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates information based on the user's purchasing history and preferences using a generation AI. The provision unit is realized by the speaker 240 of the smart glasses 214 and notifies the user of the generated information. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 receives information when a user selects a product on an e-commerce site and adds the product to a cart. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information such as the product category, price, and brand. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates information based on the user's purchasing history and preferences using a generation AI. The provision unit is realized by the display 343 of the headset type terminal 314 and displays the generated information to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 receives information when a user selects a product on an e-commerce site and adds it to a cart. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information such as the product category, price, and brand. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates information based on the user's purchase history and preferences using a generation AI. The provision unit is realized by the speaker 240 of the robot 414 and notifies the user of the generated information.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The reception unit can preferentially receive discount information for specific products based on the user's purchase history. For example, it can preferentially receive discount information related to a product category that the user has frequently purchased in the past. The reception unit can also predict when the user will repurchase a product that they have previously purchased and provide discount information at that timing. Furthermore, the reception unit can analyze the user's preference for a specific brand from the user's purchase history and preferentially receive discount information for products from that brand. In this way, providing discount information based on the user's purchase history can increase the user's desire to purchase.

[0106] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing information about products with a relaxing effect. Also, if the user is excited, the analysis unit can prioritize analyzing information about entertainment-related products. Furthermore, if the user is tired, the analysis unit can prioritize analyzing information about health foods and relaxation products. In this way, by adjusting the analysis priority according to the user's emotions, it is possible to provide the user with the most suitable information.

[0107] The generation unit can generate review information for a specific product based on the user's purchasing history. For example, the generation unit collects reviews from other users for products that the user has previously purchased, and summarizes and provides the review information. The generation unit can also generate review information for related products based on ratings of products that the user has previously purchased. Furthermore, the generation unit can customize and provide review information for a specific product based on the user's preferences. In this way, providing review information based on the user's purchasing history can help the user select a product.

[0108] The providing unit can estimate the user's emotions and adjust the format of the information to be provided based on the estimated user's emotions. For example, if the user is relaxed, detailed text information can be provided. If the user is in a hurry, information in a bulleted list format that focuses on the main points can be provided. Furthermore, if the user is excited, information in a visually appealing infographic format can be provided. In this way, by adjusting the format of information according to the user's emotions, it is possible to provide information that is easy for the user to understand.

[0109] The reception unit can prioritize receiving area-specific promotional information based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving information about sales and events being held in that area. Also, if the user is traveling, the reception unit can prioritize receiving promotional information about tourist spots and restaurants in the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving sales information about stores near the user's home. This makes it possible to provide highly relevant promotional information based on the user's geographical location information.

[0110] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated user emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the user is excited, analysis results with added visually stimulating effects can be provided. In this way, by adjusting the level of detail of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0111] The generation unit can generate customization options for a specific product based on the user's purchasing history. For example, the generation unit analyzes customization options for products previously purchased by the user and proposes new customization options based on that information. The generation unit can also generate customization options for a specific product based on the user's preferences. Furthermore, the generation unit can analyze the user's preferences for a specific brand from the user's purchasing history and generate customization options for products of that brand. This improves user satisfaction by providing customization options based on the user's purchasing history.

[0112] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the estimated user's emotions. For example, if the user is relaxed, the information can be provided at a normal timing. If the user is in a hurry, the information can be provided quickly. Furthermore, if the user is excited, the information can be provided at an appropriate timing. In this way, by adjusting the timing of providing information according to the user's emotions, the information can be provided at the optimal timing for the user.

[0113] The reception unit can analyze the user's social media activity and prioritize receiving related product information. For example, it can prioritize receiving product information related to places 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 prioritize receiving related product information. Furthermore, the reception unit can also refer to the activities of the user's friends on social media and prioritize receiving related product information. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant product information.

[0114] The analysis unit can estimate the user's emotions and adjust the order of analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis can be performed in the normal order. If the user is in a hurry, important information can be prioritized in the analysis. Furthermore, if the user is excited, highly relevant information can be prioritized in the analysis. In this way, by adjusting the order of analysis according to the user's emotions, it is possible to provide the user with the most appropriate information.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit receives product information. Product information includes product name, price, category, brand, etc. For example, when a user selects a product on an e-commerce site and adds it to a cart, or when a user places a product in a shopping cart at a store, the information is received. Step 2: The analysis unit analyzes the product information received by the reception unit. The analysis unit analyzes information such as product category, price, and brand, and analyzes the product information using data mining, statistical analysis, machine learning algorithms, etc. For example, the analysis unit analyzes the product category and extracts related information, analyzes the price and provides information according to the price range, or analyzes the brand and provides information related to the brand. Step 3: The generation unit generates personalized information based on the information analyzed by the analysis unit. The generation unit uses generation AI to generate information based on the user's purchasing history and preferences. For example, it generates related information based on products the user has previously purchased or viewed, or generates information on related products, accessories, warranty services, etc. Step 4: The providing unit provides the information generated by the generating unit to the user. The providing unit displays the generated information on the user's device or sends it to the user as an email or a notification. For example, the generated information is notified to the user's smartphone.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

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

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

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

[0140] The 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.

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

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

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

[0144] Fig. 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.

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

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

[0147] In the 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.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0151] The data processing system 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.

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

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

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

[0156] The 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.

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

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 product information; an analysis unit that analyzes the product information received by the reception unit; a generation unit that generates personalized information based on the information analyzed by the analysis unit; a providing unit that provides the information generated by the generating unit; Equipped with A system characterized by:

2. The analysis unit Analyze at least one of the following information: product category, price, and brand 2. The system of claim 1.

3. The generation unit Generate personalized information based on a user's purchasing history or preferences 2. The system of claim 1.

4. The providing unit Providing generated information to the user 2. The system of claim 1.

5. The generation unit Generate information on at least one of related products, accessories, and warranty services 2. The system of claim 1.

6. The reception unit Estimates user emotions and adjusts the timing of receiving product information based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past purchase history and select the appropriate reception method 2. The system of claim 1.

8. The reception unit Filter product information as it arrives based on the user's current shopping context or interests 2. The system of claim 1.

9. The reception unit When accepting product information, select the appropriate acceptance method depending on the user's input method 2. The system of claim 1.

10. The reception unit Estimate the user's emotions and determine the priority of the product information to be received based on the estimated user emotions.

2. The system of claim 1.

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