system

Abstract

The system according to the embodiment aims to provide information regarding post-purchase life cycle management and to provide incentives. [Solution] A system according to an embodiment includes a display unit, a providing unit, and an incentive unit. The display unit displays product information. The providing unit provides information related to post-purchase lifecycle management based on the information displayed by the display unit. The incentive unit provides an incentive based on the information provided by the providing unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately address post-purchase lifecycle management or incentive provision, leaving room for improvement.

[0005] The system according to the embodiment aims to provide information regarding post-purchase life cycle management and to provide incentives. [Means for solving the problem]

[0006] A system according to an embodiment includes a display unit, a providing unit, and an incentive unit. The display unit displays product information. The providing unit provides information related to post-purchase lifecycle management based on the information displayed by the display unit. The incentive unit provides incentives based on the information provided by the providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide information on post-purchase life cycle management and provide incentives. [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 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 purchasing platform system according to an embodiment of the present invention is a system that recommends optimal purchases, taking into account the Sustainable Development Goals (SDGs) and environmental, social, and governance (ESG) perspectives, including post-purchase life cycle management (LCM) and incentives. This purchasing platform system allows users to search for products and displays a list of products offered by multiple suppliers. The system displays each product's price, customer reviews, and SDG / ESG ratings. For example, if a product is made from environmentally friendly materials, this information is displayed. Next, when a user selects a product, information about post-purchase life cycle management (LCM) is displayed, including information about the product's durability, recyclability, and disposal method. This allows users to make purchasing decisions based on the product's entire life cycle. Furthermore, a system that provides incentives at the time of purchase is introduced. For example, users who purchase environmentally friendly products are awarded points, which can be used as a discount on their next purchase. Users can also earn additional points by posting product reviews. This system allows users to select optimal products based not only on price but also on SDG / ESG perspectives and post-purchase life cycle management (LCM). Furthermore, incentives encourage users to make purchasing decisions. This allows users to make optimal purchases, including post-purchase life cycle management (LCM) and incentive provision, taking into account SDGs / ESG perspectives.

[0029] A purchasing platform system according to an embodiment includes a display unit, a provision unit, and an incentive unit. The display unit displays product information. The product information includes, but is not limited to, price, specifications, reviews, and SDG / ESG ratings. The display unit displays, for example, a list of products offered by multiple suppliers. The display unit displays the price of each product, customer comments and reviews, and SDG / ESG ratings. For example, if a product is made from environmentally friendly materials, this information is displayed. The provision unit provides information on post-purchase life cycle management (LCM) based on the information displayed by the display unit. The provision unit provides information on product durability, recyclability, disposal methods, etc. For example, when a user selects a product, the product's durability, recyclability, disposal methods, etc. are displayed. The incentive unit provides incentives based on the information provided by the provision unit. For example, the incentive unit awards points when an environmentally friendly product is purchased. These points can be used as a discount on the next purchase. In addition, additional points are awarded when a user posts a product review. As a result, the purchasing platform system according to the embodiment allows users to take into consideration the SDGs / ESG perspective and make optimal purchases, including post-purchase life cycle management (LCM) and incentive provision.

[0030] The purchasing platform system further includes an evaluation unit that sets evaluation criteria for SDGs / ESG. The evaluation unit sets the SDGs / ESG evaluation criteria. The SDGs / ESG evaluation criteria include, but are not limited to, environmental impact, social responsibility, and governance. For example, the evaluation unit may use carbon dioxide emissions during the manufacturing process of a product as a criterion to evaluate the environmental impact of the product. Furthermore, the evaluation unit may use working conditions and social contributions during the manufacturing process as a criterion to evaluate the social responsibility of the product. Furthermore, the evaluation unit may use corporate transparency and ethical management policies as a criterion to evaluate the governance of the product. This allows the evaluation unit to evaluate products from the perspective of SDGs / ESG. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit may input environmental impact data of the product into a generation AI, which then sets the evaluation criteria.

[0031] The purchasing platform system further includes a point management unit that manages the point system. The point management unit manages the point system. The point system includes, for example, but is not limited to, criteria for awarding points and methods for using points. For example, the point management unit sets criteria for awarding points when purchasing environmentally friendly products. The point management unit can also set criteria for awarding additional points when users post product reviews. The point management unit also manages methods for using points. For example, the point management unit can set a system that allows users to use points as a discount on their next purchase. This allows the point management unit to manage a point system that provides incentives to users. Some or all of the above-described processing in the point management unit may be performed using, for example, AI, or may be performed without AI. For example, the point management unit can input user purchase history data into a generation AI, which can then set criteria for awarding points.

[0032] The purchasing platform system further includes an evaluation unit that evaluates the quality of the review. The evaluation unit evaluates the quality of the review. Examples of review quality include, but are not limited to, the reliability, detail, and user ratings of the review. For example, the evaluation unit can use the purchase history and rating history of the reviewer to evaluate the reliability of the review. Furthermore, the evaluation unit can use the specificity and detail of the review content to evaluate the detail of the review. Furthermore, the evaluation unit can evaluate the quality of the review based on user ratings. For example, the evaluation unit can use the number of "helpful" ratings given by other users for the review as a criterion. This allows the evaluation unit to evaluate the quality of the user's review and provide reliable information. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input review content data into a generation AI and have the generation AI evaluate the quality of the review.

[0033] The display unit can display the price of each product, comment reviews from purchasers, and SDG / ESG ratings. The display unit, for example, displays the price of each product. Prices include, but are not limited to, prices including tax and discounted prices. The display unit, for example, displays comment reviews from purchasers. Comment reviews include, but are not limited to, user ratings and comment length. The display unit, for example, displays SDG / ESG ratings. SDG / ESG ratings include, but are not limited to, rating scores and rating criteria. This allows the display unit to allow the user to check the price, reviews, and SDG / ESG ratings of the product. Some or all of the above-described processing on the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input product price data into a generation AI and display price information using the generation AI.

[0034] The providing unit can provide information on the durability, recyclability, and disposal method of a product. The providing unit, for example, provides information on the durability of a product. Durability includes, for example, a usage period and durability test results, but is not limited to these examples. The providing unit, for example, provides information on the recyclability of a product. Recyclability includes, for example, a recycling rate and a recycling method, but is not limited to these examples. The providing unit, for example, provides information on a disposal method of a product. Disposal methods include, for example, disposal procedures and disposal locations, but are not limited to these examples. This allows the providing unit to allow a user to check information on the product's life cycle. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input product durability data into a generation AI, and the generation AI can provide durability information.

[0035] When displaying, the display unit can analyze the user's past purchase history and prioritize displaying highly relevant products. For example, the display unit can prioritize displaying products in the same category as products purchased by the user in the past. For example, the display unit can also prioritize displaying products of the same brand as products that the user has previously given high ratings to. For example, the display unit can also prioritize displaying complementary products to products purchased by the user in the past. In this way, the display unit can display highly relevant products based on the user's past purchase history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's purchase history data into a generation AI, which can then display highly relevant products.

[0036] The display unit can filter products based on the user's current areas of interest and trends when displaying the products. For example, the display unit can prioritize displaying products related to keywords recently searched by the user. For example, the display unit can also prioritize displaying products related to trends the user is following on social media. For example, the display unit can also prioritize displaying products related to articles recently viewed by the user. This allows the display unit to display products based on the user's areas of interest and trends. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's search history data into a generation AI and filter products using the generation AI.

[0037] When displaying, the display unit can prioritize displaying regionally limited products in consideration of the user's geographical location information. The display unit, for example, prioritizes displaying products from stores close to the user's current location. The display unit can also, for example, prioritize displaying products that are popular in the user's area. The display unit can also, for example, prioritize displaying products related to events being held in the user's area. This allows the display unit to display regionally limited products based on the user's geographical location information. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's geographical location data into a generation AI and display regionally limited products using the generation AI.

[0038] The display unit can analyze the user's social media activity and display related products when displaying the product. For example, the display unit can prioritize displaying products that the user has "liked" on social media. For example, the display unit can also prioritize displaying products introduced by influencers the user follows. For example, the display unit can also prioritize displaying products that are trending in groups the user participates in. This allows the display unit to display related products based on the user's social media activity. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's social media data into a generation AI and have the generation AI display related products.

[0039] When providing the information, the providing unit can improve the accuracy of the information by referring to past data related to the product's life cycle. The providing unit, for example, provides information related to the durability of the product based on past data. The providing unit can also provide information related to the recyclability of the product based on past data. The providing unit can also provide information related to how to dispose of the product based on past data. This allows the providing unit to improve the accuracy of the information based on past data. 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 past data of the product into the generating AI, and the generating AI can improve the accuracy of the information.

[0040] The providing unit can provide different life cycle information for each product category at the time of providing. For example, in the case of electronic products, the providing unit provides information on recycling methods and disposal methods. For example, in the case of clothing, the providing unit can also provide information on durability and recyclability. For example, in the case of food, the providing unit can also provide information on expiration dates and disposal methods. This allows the providing unit to provide appropriate life cycle information for each product category. 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 into a generating AI, which can provide life cycle information for each category.

[0041] The providing unit can determine the priority of the life cycle information based on the purchase date of the product when providing the information. For example, immediately after purchase, the providing unit can prioritize providing information on how to use the product. For example, the providing unit can also provide information on how to maintain the product as the period of use increases. For example, the providing unit can also provide information on how to recycle or dispose of the product as the product approaches the end of its lifespan. This allows the providing unit to provide appropriate life cycle information based on the purchase date of the product. 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 data on the purchase date of the product into a generating AI, and the generating AI can determine the priority of the life cycle information.

[0042] When providing the information, the providing unit can improve the accuracy of the information by referring to related literature for the product. For example, the providing unit can provide information on durability by referring to research papers on the product. For example, the providing unit can also provide information on recyclability by referring to technical documentation for the product. For example, the providing unit can also provide information on disposal methods by referring to a product usage guide. This allows the providing unit to improve the accuracy of the information based on related literature. 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 related literature data for the product into a generation AI, which can improve the accuracy of the information.

[0043] When providing an incentive, the incentive unit can analyze the user's past purchasing behavior and select the optimal incentive. For example, the incentive unit can provide a discount on products in the same category as products the user has previously purchased. For example, the incentive unit can also provide points for products of the same brand as products that the user has previously given high ratings to. For example, the incentive unit can also provide incentives for complementary products to products the user has previously purchased. This allows the incentive unit to provide the optimal incentive based on the user's past purchasing behavior. Some or all of the above-mentioned processing in the incentive unit may be performed using, for example, AI, or may be performed without using AI. For example, the incentive unit can input the user's purchase history data into a generation AI, which can select the optimal incentive.

[0044] When providing an incentive, the incentive unit can adjust the amount of the incentive based on the user's current purchasing willingness. For example, if the user shows high purchasing willingness, the incentive unit can increase the amount of the incentive. For example, if the user shows low purchasing willingness, the incentive unit can also decrease the amount of the incentive. For example, if the user shows medium purchasing willingness, the incentive unit can also moderately adjust the amount of the incentive. This allows the incentive unit to adjust the amount of the incentive according to the user's purchasing willingness. Some or all of the above-mentioned processing in the incentive unit may be performed using, for example, AI, or may be performed without using AI. For example, the incentive unit can input the user's purchasing willingness data into a generation AI and have the generation AI adjust the amount of the incentive.

[0045] When providing incentives, the incentive unit can provide region-specific incentives by taking into account the user's geographical location information. For example, the incentive unit can provide discount coupons that can be used at stores close to the user's current location. For example, the incentive unit can also provide incentives related to events held in the user's area. For example, the incentive unit can also provide incentives for products that are popular in the user's area. This allows the incentive unit to provide region-specific incentives based on the user's geographical location information. Some or all of the above-mentioned processing in the incentive unit may be performed using AI, for example, or may be performed without using AI. For example, the incentive unit can input the user's geographical location data into a generation AI, which can then provide region-specific incentives.

[0046] When providing an incentive, the incentive unit can analyze the user's social media activity and select the type of incentive. For example, the incentive unit can provide an incentive for a product that the user has "liked" on social media. For example, the incentive unit can also provide an incentive for a product introduced by an influencer the user follows. For example, the incentive unit can also provide an incentive for a product that is a hot topic in a group the user participates in. This allows the incentive unit to provide optimal incentives based on the user's social media activity. Some or all of the above-described processing in the incentive unit may be performed using, or without, AI. For example, the incentive unit can input the user's social media data into a generation AI, which can select the type of incentive.

[0047] The evaluation unit can improve the accuracy of the evaluation by referring to past evaluation data of the product during the evaluation. The evaluation unit, for example, evaluates the durability of the product based on the past evaluation data. The evaluation unit can also evaluate the recyclability of the product based on the past evaluation data. The evaluation unit can also evaluate the disposal method of the product based on the past evaluation data. This allows the evaluation unit to improve the accuracy of the evaluation based on the past evaluation data. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input past evaluation data of the product into the generation AI, and the generation AI can improve the accuracy of the evaluation.

[0048] The evaluation unit can apply different evaluation criteria to each product category during evaluation. For example, in the case of electronic products, the evaluation unit applies evaluation criteria related to durability and recyclability. For example, in the case of clothing, the evaluation unit can also apply evaluation criteria related to durability and recyclability. For example, in the case of food, the evaluation unit can also apply evaluation criteria related to expiration dates and disposal methods. This allows the evaluation unit to apply appropriate evaluation criteria to each product category. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input product category data into a generation AI, and the generation AI can apply evaluation criteria for each category.

[0049] During evaluation, the evaluation unit can determine the priority of evaluations based on the purchase date of the product. For example, immediately after purchase, the evaluation unit can prioritize evaluation of the product's initial quality. For example, the evaluation unit can also prioritize evaluation of the product's durability as the period of use increases. For example, the evaluation unit can also prioritize evaluation of the product's recyclability and disposal method as the product approaches the end of its lifespan. This allows the evaluation unit to perform appropriate evaluations based on the purchase date of the product. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the purchase date of the product into the generation AI, and the generation AI can determine the priority of evaluations.

[0050] During the evaluation, the evaluation unit can improve the accuracy of the evaluation by referring to literature related to the product. For example, the evaluation unit can evaluate durability by referring to research papers on the product. For example, the evaluation unit can also evaluate recyclability by referring to technical documentation on the product. For example, the evaluation unit can evaluate disposal methods by referring to a product usage guide. This allows the evaluation unit to improve the accuracy of the evaluation based on the related literature. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input literature data related to the product into a generation AI, which can improve the accuracy of the evaluation.

[0051] During point management, the point management unit can analyze the user's past point usage history and select the optimal point awarding method. For example, the point management unit awards more points to product categories in which the user has used many points in the past. For example, the point management unit can also award more points to product categories that the user has given high ratings in the past. For example, the point management unit can also award more points to product categories that the user has frequently purchased in the past. This allows the point management unit to provide the optimal point awarding method based on the user's past point usage history. Some or all of the above-mentioned processing in the point management unit may be performed using, or without, AI, for example. For example, the point management unit can input the user's point usage history data into a generation AI, which can select the optimal point awarding method.

[0052] During point management, the point management unit can adjust the amount of points awarded based on the user's current purchasing willingness. For example, if the user shows a high purchasing willingness, the point management unit can increase the amount of points awarded. For example, if the user shows a low purchasing willingness, the point management unit can also decrease the amount of points awarded. For example, if the user shows a medium purchasing willingness, the point management unit can also moderately adjust the amount of points awarded. This allows the point management unit to adjust the amount of points awarded according to the user's purchasing willingness. Some or all of the above-described processing in the point management unit may be performed using, for example, AI, or may be performed without using AI. For example, the point management unit can input the user's purchasing willingness data into a generation AI, and the generation AI can adjust the amount of points awarded.

[0053] The point management unit can provide area-specific points by taking into account the user's geographical location information when managing points. The point management unit can, for example, provide points that can be used at stores close to the user's current location. The point management unit can also provide points related to events held in the user's area, for example. The point management unit can also provide points for popular products in the user's area, for example. This allows the point management unit to provide area-specific points based on the user's geographical location information. Some or all of the above-mentioned processing in the point management unit may be performed using AI, for example, or may be performed without using AI. For example, the point management unit can input the user's geographical location data into a generation AI, which can provide area-specific points.

[0054] During point management, the point management unit can analyze the user's social media activity and select a point awarding method. For example, the point management unit provides points for products that the user has "liked" on social media. For example, the point management unit can also provide points for products introduced by influencers the user follows. For example, the point management unit can also provide points for products that are popular in groups the user participates in. This allows the point management unit to provide an optimal point awarding method based on the user's social media activity. Some or all of the above-described processing in the point management unit may be performed using, for example, AI, or may be performed without using AI. For example, the point management unit can input the user's social media data into a generation AI, which can select a point awarding method.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The purchasing platform system may further include a recommendation unit that analyzes a user's purchasing history and recommends products based on the user's preferences. The recommendation unit, for example, preferentially recommends products in the same category as products previously purchased by the user. The recommendation unit may also recommend products from the same brand as products previously rated highly by the user. The recommendation unit may also recommend complementary products to products previously purchased by the user. This allows the recommendation unit to recommend highly relevant products based on the user's purchasing history. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit may input the user's purchasing history data into a generation AI, which may then recommend highly relevant products.

[0057] The purchasing platform system may further include a region recommendation unit that recommends region-exclusive products taking into account the user's geographical location information. The region recommendation unit, for example, recommends products from stores close to the user's current location. The region recommendation unit may also recommend products that are popular in the user's region. The region recommendation unit may also recommend products related to events held in the user's region. This allows the region recommendation unit to recommend region-exclusive products based on the user's geographical location information. Some or all of the above-described processing in the region recommendation unit may be performed using, or without, AI, for example. For example, the region recommendation unit may input the user's geographical location data into a generation AI, which may then recommend region-exclusive products.

[0058] The purchasing platform system may further include a social recommendation unit that analyzes the user's social media activity and recommends related products. The social recommendation unit, for example, recommends products that the user has "liked" on social media. The social recommendation unit may also recommend products introduced by influencers the user follows. The social recommendation unit may also recommend products that are trending in groups the user participates in. This allows the social recommendation unit to recommend related products based on the user's social media activity. Some or all of the above-described processing in the social recommendation unit may be performed using, or without, AI. For example, the social recommendation unit may input the user's social media data into a generation AI, which then recommends related products.

[0059] The purchasing platform system may further include a purchasing motivation incentive unit that estimates a user's purchasing motivation and adjusts the amount of incentive based on the estimated purchasing motivation. For example, the purchasing motivation incentive unit may increase the amount of incentive if the user indicates high purchasing motivation. For example, the purchasing motivation incentive unit may decrease the amount of incentive if the user indicates low purchasing motivation. For example, the purchasing motivation incentive unit may moderately adjust the amount of incentive if the user indicates medium purchasing motivation. This allows the purchasing motivation incentive unit to adjust the amount of incentive according to the user's purchasing motivation. Some or all of the above-described processing in the purchasing motivation incentive unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing motivation incentive unit may input the user's purchasing motivation data into a generation AI, which may adjust the amount of incentive.

[0060] The purchasing platform system may further include a history incentive unit that analyzes a user's purchasing history and provides incentives based on the user's preferences. The history incentive unit may, for example, provide discounts on products in the same category as products previously purchased by the user. The history incentive unit may, for example, provide points for products of the same brand as products previously rated by the user. The history incentive unit may, for example, provide incentives for complementary products to products previously purchased by the user. This allows the history incentive unit to provide optimal incentives based on the user's purchasing history. Some or all of the above-described processing in the history incentive unit may be performed using, or without, AI. For example, the history incentive unit may input the user's purchasing history data into a generation AI, which may then provide optimal incentives.

[0061] The purchasing platform system may further include a history point management unit that analyzes a user's purchase history and adjusts the point awarding method based on the user's preferences. The history point management unit, for example, awards more points to product categories in which the user has used many points in the past. The history point management unit may also award more points to product categories in which the user has given high ratings in the past. The history point management unit may also award more points to product categories in which the user has frequently purchased in the past. This allows the history point management unit to provide an optimal point awarding method based on the user's purchase history. Some or all of the above-described processing in the history point management unit may be performed using, for example, AI, or may be performed without using AI. For example, the history point management unit may input the user's purchase history data into a generation AI, which may then provide an optimal point awarding method.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The display unit displays product information. Product information includes price, specifications, reviews, SDG / ESG ratings, etc. The display unit displays a list of products offered by multiple suppliers, and displays the price of each product, customer comments and reviews, and SDG / ESG ratings. For example, information about products made from environmentally friendly materials is displayed. Step 2: The providing unit provides information about post-purchase life cycle management (LCM) based on the information displayed by the display unit. The providing unit provides information such as the durability, recyclability, and disposal method of the product. For example, when the user selects a product, the durability, recyclability, and disposal method of the product are displayed. Step 3: The incentive unit provides incentives based on the information provided by the provision unit. For example, the incentive unit provides points when an environmentally friendly product is purchased. These points can be used as a discount for the next purchase. In addition, additional points can be provided when the user posts a review of the product.

[0064] (Example 2) A purchasing platform system according to an embodiment of the present invention is a system that recommends optimal purchases, taking into account the Sustainable Development Goals (SDGs) and environmental, social, and governance (ESG) perspectives, including post-purchase life cycle management (LCM) and incentives. This purchasing platform system allows users to search for products and displays a list of products offered by multiple suppliers. The system displays each product's price, customer reviews, and SDG / ESG ratings. For example, if a product is made from environmentally friendly materials, this information is displayed. Next, when a user selects a product, information about post-purchase life cycle management (LCM) is displayed, including information about the product's durability, recyclability, and disposal method. This allows users to make purchasing decisions based on the product's entire life cycle. Furthermore, a system that provides incentives at the time of purchase is introduced. For example, users who purchase environmentally friendly products are awarded points, which can be used as a discount on their next purchase. Users can also earn additional points by posting product reviews. This system allows users to select optimal products based not only on price but also on SDG / ESG perspectives and post-purchase life cycle management (LCM). Furthermore, incentives encourage users to make purchasing decisions. This allows users to make optimal purchases, including post-purchase life cycle management (LCM) and incentive provision, taking into account SDGs / ESG perspectives.

[0065] A purchasing platform system according to an embodiment includes a display unit, a provision unit, and an incentive unit. The display unit displays product information. The product information includes, but is not limited to, price, specifications, reviews, and SDG / ESG ratings. The display unit displays, for example, a list of products offered by multiple suppliers. The display unit displays the price of each product, customer comments and reviews, and SDG / ESG ratings. For example, if a product is made from environmentally friendly materials, this information is displayed. The provision unit provides information on post-purchase life cycle management (LCM) based on the information displayed by the display unit. The provision unit provides information on product durability, recyclability, disposal methods, etc. For example, when a user selects a product, the product's durability, recyclability, disposal methods, etc. are displayed. The incentive unit provides incentives based on the information provided by the provision unit. For example, the incentive unit awards points when an environmentally friendly product is purchased. These points can be used as a discount on the next purchase. In addition, additional points are awarded when a user posts a product review. As a result, the purchasing platform system according to the embodiment allows users to take into consideration the SDGs / ESG perspective and make optimal purchases, including post-purchase life cycle management (LCM) and incentive provision.

[0066] The purchasing platform system further includes an evaluation unit that sets evaluation criteria for SDGs / ESG. The evaluation unit sets the SDGs / ESG evaluation criteria. The SDGs / ESG evaluation criteria include, but are not limited to, environmental impact, social responsibility, and governance. For example, the evaluation unit may use carbon dioxide emissions during the manufacturing process of a product as a criterion to evaluate the environmental impact of the product. Furthermore, the evaluation unit may use working conditions and social contributions during the manufacturing process as a criterion to evaluate the social responsibility of the product. Furthermore, the evaluation unit may use corporate transparency and ethical management policies as a criterion to evaluate the governance of the product. This allows the evaluation unit to evaluate products from the perspective of SDGs / ESG. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit may input environmental impact data of the product into a generation AI, which then sets the evaluation criteria.

[0067] The purchasing platform system further includes a point management unit that manages the point system. The point management unit manages the point system. The point system includes, for example, but is not limited to, criteria for awarding points and methods for using points. For example, the point management unit sets criteria for awarding points when purchasing environmentally friendly products. The point management unit can also set criteria for awarding additional points when users post product reviews. The point management unit also manages methods for using points. For example, the point management unit can set a system that allows users to use points as a discount on their next purchase. This allows the point management unit to manage a point system that provides incentives to users. Some or all of the above-described processing in the point management unit may be performed using, for example, AI, or may be performed without AI. For example, the point management unit can input user purchase history data into a generation AI, which can then set criteria for awarding points.

[0068] The purchasing platform system further includes an evaluation unit that evaluates the quality of the review. The evaluation unit evaluates the quality of the review. Examples of review quality include, but are not limited to, the reliability, detail, and user ratings of the review. For example, the evaluation unit can use the purchase history and rating history of the reviewer to evaluate the reliability of the review. Furthermore, the evaluation unit can use the specificity and detail of the review content to evaluate the detail of the review. Furthermore, the evaluation unit can evaluate the quality of the review based on user ratings. For example, the evaluation unit can use the number of "helpful" ratings given by other users for the review as a criterion. This allows the evaluation unit to evaluate the quality of the user's review and provide reliable information. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input review content data into a generation AI and have the generation AI evaluate the quality of the review.

[0069] The display unit can display the price of each product, comment reviews from purchasers, and SDG / ESG ratings. The display unit, for example, displays the price of each product. Prices include, but are not limited to, prices including tax and discounted prices. The display unit, for example, displays comment reviews from purchasers. Comment reviews include, but are not limited to, user ratings and comment length. The display unit, for example, displays SDG / ESG ratings. SDG / ESG ratings include, but are not limited to, rating scores and rating criteria. This allows the display unit to allow the user to check the price, reviews, and SDG / ESG ratings of the product. Some or all of the above-described processing on the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input product price data into a generation AI and display price information using the generation AI.

[0070] The providing unit can provide information on the durability, recyclability, and disposal method of a product. The providing unit, for example, provides information on the durability of a product. Durability includes, for example, a usage period and durability test results, but is not limited to these examples. The providing unit, for example, provides information on the recyclability of a product. Recyclability includes, for example, a recycling rate and a recycling method, but is not limited to these examples. The providing unit, for example, provides information on a disposal method of a product. Disposal methods include, for example, disposal procedures and disposal locations, but are not limited to these examples. This allows the providing unit to allow a user to check information on the product's life cycle. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input product durability data into a generation AI, and the generation AI can provide durability information.

[0071] The display unit can estimate the user's emotions and adjust the order of displayed products based on the estimated user's emotions. For example, if the user is feeling stressed, the display unit can prioritize displaying products with a relaxing effect. For example, if the user is excited, the display unit can prioritize displaying energetic products. For example, if the user is tired, the display unit can prioritize displaying products with a relaxing effect. This allows the display unit to display optimal products 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, 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 display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the user's emotion data into the generation AI and adjust the order of products using the generation AI.

[0072] When displaying, the display unit can analyze the user's past purchase history and prioritize displaying highly relevant products. For example, the display unit can prioritize displaying products in the same category as products purchased by the user in the past. For example, the display unit can also prioritize displaying products of the same brand as products that the user has previously given high ratings to. For example, the display unit can also prioritize displaying complementary products to products purchased by the user in the past. In this way, the display unit can display highly relevant products based on the user's past purchase history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's purchase history data into a generation AI, which can then display highly relevant products.

[0073] The display unit can filter products based on the user's current areas of interest and trends when displaying the products. For example, the display unit can prioritize displaying products related to keywords recently searched by the user. For example, the display unit can also prioritize displaying products related to trends the user is following on social media. For example, the display unit can also prioritize displaying products related to articles recently viewed by the user. This allows the display unit to display products based on the user's areas of interest and trends. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's search history data into a generation AI and filter products using the generation AI.

[0074] The display unit can estimate the user's emotions and adjust the level of detail of the displayed product based on the estimated user's emotions. For example, if the user is in a hurry, the display unit can display only the main points of the product. For example, if the user is relaxed, the display unit can display a detailed description of the product. For example, if the user is excited, the display unit can display the product's visuals in an emphasized manner. This allows the display unit to adjust the level of detail of the product 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, 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 display unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the display unit can input the user's emotion data into the generation AI, and the generation AI can adjust the level of detail of the product.

[0075] When displaying, the display unit can prioritize displaying regionally limited products in consideration of the user's geographical location information. The display unit, for example, prioritizes displaying products from stores close to the user's current location. The display unit can also, for example, prioritize displaying products that are popular in the user's area. The display unit can also, for example, prioritize displaying products related to events being held in the user's area. This allows the display unit to display regionally limited products based on the user's geographical location information. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's geographical location data into a generation AI and display regionally limited products using the generation AI.

[0076] The display unit can analyze the user's social media activity and display related products when displaying the product. For example, the display unit can prioritize displaying products that the user has "liked" on social media. For example, the display unit can also prioritize displaying products introduced by influencers the user follows. For example, the display unit can also prioritize displaying products that are trending in groups the user participates in. This allows the display unit to display related products based on the user's social media activity. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's social media data into a generation AI and have the generation AI display related products.

[0077] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing information about products with a relaxing effect. For example, if the user is excited, the providing unit can also prioritize providing information about energetic products. For example, if the user is tired, the providing unit can also prioritize providing information about products with a relaxing effect. This allows the providing unit to determine the priority of information 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-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data into the generation AI, and the generation AI can determine the priority of information.

[0078] When providing the information, the providing unit can improve the accuracy of the information by referring to past data related to the product's life cycle. The providing unit, for example, provides information related to the durability of the product based on past data. The providing unit can also provide information related to the recyclability of the product based on past data. The providing unit can also provide information related to how to dispose of the product based on past data. This allows the providing unit to improve the accuracy of the information based on past data. 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 past data of the product into the generating AI, and the generating AI can improve the accuracy of the information.

[0079] The providing unit can provide different life cycle information for each product category at the time of providing. For example, in the case of electronic products, the providing unit provides information on recycling methods and disposal methods. For example, in the case of clothing, the providing unit can also provide information on durability and recyclability. For example, in the case of food, the providing unit can also provide information on expiration dates and disposal methods. This allows the providing unit to provide appropriate life cycle information for each product category. 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 into a generating AI, which can provide life cycle information for each category.

[0080] The providing unit can estimate the user's emotions and adjust the level of detail of the information to be provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide only the main points of the product. For example, if the user is relaxed, the providing unit can provide a detailed description of the product. For example, if the user is excited, the providing unit can provide an enhanced visual of the product. This allows the providing unit to adjust the level of detail of the information according to the user's emotions. 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 can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI, and the generation AI can adjust the level of detail of the information.

[0081] The providing unit can determine the priority of the life cycle information based on the purchase date of the product when providing the information. For example, immediately after purchase, the providing unit can prioritize providing information on how to use the product. For example, the providing unit can also provide information on how to maintain the product as the period of use increases. For example, the providing unit can also provide information on how to recycle or dispose of the product as the product approaches the end of its lifespan. This allows the providing unit to provide appropriate life cycle information based on the purchase date of the product. 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 data on the purchase date of the product into a generating AI, and the generating AI can determine the priority of the life cycle information.

[0082] When providing the information, the providing unit can improve the accuracy of the information by referring to related literature for the product. For example, the providing unit can provide information on durability by referring to research papers on the product. For example, the providing unit can also provide information on recyclability by referring to technical documentation for the product. For example, the providing unit can also provide information on disposal methods by referring to a product usage guide. This allows the providing unit to improve the accuracy of the information based on related literature. 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 related literature data for the product into a generation AI, which can improve the accuracy of the information.

[0083] The incentive unit can estimate the user's emotions and adjust the type of incentive based on the estimated user's emotions. For example, if the user is feeling stressed, the incentive unit can provide an incentive with a relaxing effect. For example, if the user is excited, the incentive unit can provide an energetic incentive. For example, if the user is tired, the incentive unit can provide an incentive with a relaxing effect. This allows the incentive unit to provide optimal incentives 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-mentioned processing in the incentive unit can be performed using AI, or can be performed without AI. For example, the incentive unit can input the user's emotion data into the generation AI and have the generation AI adjust the type of incentive.

[0084] When providing an incentive, the incentive unit can analyze the user's past purchasing behavior and select the optimal incentive. For example, the incentive unit can provide a discount on products in the same category as products the user has previously purchased. For example, the incentive unit can also provide points for products of the same brand as products that the user has previously given high ratings to. For example, the incentive unit can also provide incentives for complementary products to products the user has previously purchased. This allows the incentive unit to provide the optimal incentive based on the user's past purchasing behavior. Some or all of the above-mentioned processing in the incentive unit may be performed using, for example, AI, or may be performed without using AI. For example, the incentive unit can input the user's purchase history data into a generation AI, which can select the optimal incentive.

[0085] When providing an incentive, the incentive unit can adjust the amount of the incentive based on the user's current purchasing willingness. For example, if the user shows high purchasing willingness, the incentive unit can increase the amount of the incentive. For example, if the user shows low purchasing willingness, the incentive unit can also decrease the amount of the incentive. For example, if the user shows medium purchasing willingness, the incentive unit can also moderately adjust the amount of the incentive. This allows the incentive unit to adjust the amount of the incentive according to the user's purchasing willingness. Some or all of the above-mentioned processing in the incentive unit may be performed using, for example, AI, or may be performed without using AI. For example, the incentive unit can input the user's purchasing willingness data into a generation AI and have the generation AI adjust the amount of the incentive.

[0086] The incentive unit can estimate the user's emotions and adjust the timing of providing incentives based on the estimated user's emotions. For example, if the user is feeling stressed, the incentive unit can immediately provide an incentive with a relaxing effect. For example, if the user is excited, the incentive unit can immediately provide an energetic incentive. For example, if the user is tired, the incentive unit can immediately provide an incentive with a relaxing effect. This allows the incentive unit to provide incentives at optimal timing 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-mentioned processing in the incentive unit may be performed using AI, or may be performed without AI. For example, the incentive unit can input the user's emotion data into the generation AI and use the generation AI to adjust the timing of providing incentives.

[0087] When providing incentives, the incentive unit can provide region-specific incentives by taking into account the user's geographical location information. For example, the incentive unit can provide discount coupons that can be used at stores close to the user's current location. For example, the incentive unit can also provide incentives related to events held in the user's area. For example, the incentive unit can also provide incentives for products that are popular in the user's area. This allows the incentive unit to provide region-specific incentives based on the user's geographical location information. Some or all of the above-mentioned processing in the incentive unit may be performed using AI, for example, or may be performed without using AI. For example, the incentive unit can input the user's geographical location data into a generation AI, which can then provide region-specific incentives.

[0088] When providing an incentive, the incentive unit can analyze the user's social media activity and select the type of incentive. For example, the incentive unit can provide an incentive for a product that the user has "liked" on social media. For example, the incentive unit can also provide an incentive for a product introduced by an influencer the user follows. For example, the incentive unit can also provide an incentive for a product that is a hot topic in a group the user participates in. This allows the incentive unit to provide optimal incentives based on the user's social media activity. Some or all of the above-described processing in the incentive unit may be performed using, or without, AI. For example, the incentive unit can input the user's social media data into a generation AI, which can select the type of incentive.

[0089] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user's emotions. For example, if the user is feeling stressed, the evaluation unit can emphasize the evaluation criteria for products with a relaxing effect. For example, if the user is excited, the evaluation unit can emphasize the evaluation criteria for energetic products. For example, if the user is tired, the evaluation unit can emphasize the evaluation criteria for products with a relaxing effect. This allows the evaluation unit to adjust the evaluation criteria 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-mentioned processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input the user's emotion data into the generation AI and have the generation AI adjust the evaluation criteria.

[0090] The evaluation unit can improve the accuracy of the evaluation by referring to past evaluation data of the product during the evaluation. The evaluation unit, for example, evaluates the durability of the product based on the past evaluation data. The evaluation unit can also evaluate the recyclability of the product based on the past evaluation data. The evaluation unit can also evaluate the disposal method of the product based on the past evaluation data. This allows the evaluation unit to improve the accuracy of the evaluation based on the past evaluation data. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input past evaluation data of the product into the generation AI, and the generation AI can improve the accuracy of the evaluation.

[0091] The evaluation unit can apply different evaluation criteria to each product category during evaluation. For example, in the case of electronic products, the evaluation unit applies evaluation criteria related to durability and recyclability. For example, in the case of clothing, the evaluation unit can also apply evaluation criteria related to durability and recyclability. For example, in the case of food, the evaluation unit can also apply evaluation criteria related to expiration dates and disposal methods. This allows the evaluation unit to apply appropriate evaluation criteria to each product category. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input product category data into a generation AI, and the generation AI can apply evaluation criteria for each category.

[0092] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is feeling stressed, the evaluation unit can display a simple, highly visible evaluation result. For example, if the user is excited, the evaluation unit can also display a detailed evaluation result. For example, if the user is tired, the evaluation unit can also display a simple, highly visible evaluation result. This allows the evaluation unit to adjust the display method of the evaluation results 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-mentioned processing in the evaluation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the evaluation unit can input the user's emotion data into the generation AI, and the generation AI can adjust the display method of the evaluation results.

[0093] During evaluation, the evaluation unit can determine the priority of evaluations based on the purchase date of the product. For example, immediately after purchase, the evaluation unit can prioritize evaluation of the product's initial quality. For example, the evaluation unit can also prioritize evaluation of the product's durability as the period of use increases. For example, the evaluation unit can also prioritize evaluation of the product's recyclability and disposal method as the product approaches the end of its lifespan. This allows the evaluation unit to perform appropriate evaluations based on the purchase date of the product. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the purchase date of the product into the generation AI, and the generation AI can determine the priority of evaluations.

[0094] During the evaluation, the evaluation unit can improve the accuracy of the evaluation by referring to literature related to the product. For example, the evaluation unit can evaluate durability by referring to research papers on the product. For example, the evaluation unit can also evaluate recyclability by referring to technical documentation on the product. For example, the evaluation unit can evaluate disposal methods by referring to a product usage guide. This allows the evaluation unit to improve the accuracy of the evaluation based on the related literature. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input literature data related to the product into a generation AI, which can improve the accuracy of the evaluation.

[0095] The point management unit can estimate the user's emotions and adjust the point awarding method based on the estimated user's emotions. For example, if the user is feeling stressed, the point management unit can award more points for purchasing products with a relaxing effect. For example, if the user is excited, the point management unit can award more points for purchasing energetic products. For example, if the user is tired, the point management unit can award more points for purchasing products with a relaxing effect. This allows the point management unit to provide an optimal point awarding method 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the point management unit can be performed using, for example, AI, or without AI. For example, the point management unit can input the user's emotion data into the generation AI and have the generation AI adjust the point awarding method.

[0096] During point management, the point management unit can analyze the user's past point usage history and select the optimal point awarding method. For example, the point management unit awards more points to product categories in which the user has used many points in the past. For example, the point management unit can also award more points to product categories that the user has given high ratings in the past. For example, the point management unit can also award more points to product categories that the user has frequently purchased in the past. This allows the point management unit to provide the optimal point awarding method based on the user's past point usage history. Some or all of the above-mentioned processing in the point management unit may be performed using, or without, AI, for example. For example, the point management unit can input the user's point usage history data into a generation AI, which can select the optimal point awarding method.

[0097] During point management, the point management unit can adjust the amount of points awarded based on the user's current purchasing willingness. For example, if the user shows a high purchasing willingness, the point management unit can increase the amount of points awarded. For example, if the user shows a low purchasing willingness, the point management unit can also decrease the amount of points awarded. For example, if the user shows a medium purchasing willingness, the point management unit can also moderately adjust the amount of points awarded. This allows the point management unit to adjust the amount of points awarded according to the user's purchasing willingness. Some or all of the above-described processing in the point management unit may be performed using, for example, AI, or may be performed without using AI. For example, the point management unit can input the user's purchasing willingness data into a generation AI, and the generation AI can adjust the amount of points awarded.

[0098] The point management unit can estimate the user's emotions and adjust how points are used based on the estimated user's emotions. For example, if the user is feeling stressed, the point management unit can allow the user to use points to purchase products with a relaxing effect. For example, if the user is excited, the point management unit can also allow the user to use points to purchase energetic products. For example, if the user is tired, the point management unit can also allow the user to use points to purchase products with a relaxing effect. This allows the point management unit to provide an optimal point usage method 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-mentioned processing in the point management unit can be performed using, for example, AI, or without AI. For example, the point management unit can input the user's emotion data into the generation AI, and the generation AI can adjust how points are used.

[0099] The point management unit can provide area-specific points by taking into account the user's geographical location information when managing points. The point management unit can, for example, provide points that can be used at stores close to the user's current location. The point management unit can also provide points related to events held in the user's area, for example. The point management unit can also provide points for popular products in the user's area, for example. This allows the point management unit to provide area-specific points based on the user's geographical location information. Some or all of the above-mentioned processing in the point management unit may be performed using AI, for example, or may be performed without using AI. For example, the point management unit can input the user's geographical location data into a generation AI, which can provide area-specific points.

[0100] During point management, the point management unit can analyze the user's social media activity and select a point awarding method. For example, the point management unit provides points for products that the user has "liked" on social media. For example, the point management unit can also provide points for products introduced by influencers the user follows. For example, the point management unit can also provide points for products that are popular in groups the user participates in. This allows the point management unit to provide an optimal point awarding method based on the user's social media activity. Some or all of the above-described processing in the point management unit may be performed using, for example, AI, or may be performed without using AI. For example, the point management unit can input the user's social media data into a generation AI, which can select a point awarding method. === Hard Collateral 1-1 === Each of the multiple elements including the display unit, provision unit, incentive unit, evaluation unit, and point management unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the display unit is realized by the control unit 46A of the smart device 14 and displays product information. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides information on post-purchase life cycle management (LCM). The incentive unit is realized by the control unit 46A of the smart device 14 and provides incentives. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and sets evaluation criteria for SDGs / ESG. The point management unit is realized by the control unit 46A of the smart device 14 and manages the point system. === Hard Collateral 1-2 === Each of the multiple elements, including the display unit, provision unit, incentive unit, evaluation unit, and point management unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the display unit is realized by the control unit 46A of the smart glasses 214 and displays product information. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides information regarding post-purchase life cycle management (LCM). The incentive unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides incentives. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets evaluation criteria for SDGs / ESG. The point management unit is realized, for example, by the control unit 46A of the smart glasses 214 and manages the point system. === Hard Collateral 1-3 === Each of the multiple elements including the display unit, provision unit, incentive unit, evaluation unit, and point management unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the display unit is realized by the control unit 46A of the headset type terminal 314 and displays product information. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides information regarding post-purchase life cycle management (LCM). The incentive unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides incentives. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets evaluation criteria for SDGs / ESG. The point management unit is realized, for example, by the control unit 46A of the headset type terminal 314 and manages the point system. === Hard Collateral 1-4 === Each of the multiple elements including the display unit, provision unit, incentive unit, evaluation unit, and point management unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the display unit is realized by the control unit 46A of the robot 414 and displays product information. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides information regarding post-purchase life cycle management (LCM). The incentive unit is realized, for example, by the control unit 46A of the robot 414 and provides incentives. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets evaluation criteria for SDGs / ESG. The point management unit is realized, for example, by the control unit 46A of the robot 414 and manages the point system.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The purchasing platform system may further include a recommendation unit that analyzes a user's purchasing history and recommends products based on the user's preferences. The recommendation unit, for example, preferentially recommends products in the same category as products previously purchased by the user. The recommendation unit may also recommend products from the same brand as products previously rated highly by the user. The recommendation unit may also recommend complementary products to products previously purchased by the user. This allows the recommendation unit to recommend highly relevant products based on the user's purchasing history. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit may input the user's purchasing history data into a generation AI, which may then recommend highly relevant products.

[0103] The purchasing platform system may further include an emotion recommendation unit that estimates a user's emotion and recommends products based on the estimated user emotion. For example, if the user is feeling stressed, the emotion recommendation unit may recommend products with a relaxing effect. For example, if the user is excited, the emotion recommendation unit may recommend energetic products. For example, if the user is tired, the emotion recommendation unit may recommend products with a relaxing effect. This allows the emotion recommendation unit to recommend optimal products according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the emotion recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the emotion recommendation unit may input user emotion data into the generation AI, which may then recommend products.

[0104] The purchasing platform system may further include a region recommendation unit that recommends region-exclusive products taking into account the user's geographical location information. The region recommendation unit, for example, recommends products from stores close to the user's current location. The region recommendation unit may also recommend products that are popular in the user's region. The region recommendation unit may also recommend products related to events held in the user's region. This allows the region recommendation unit to recommend region-exclusive products based on the user's geographical location information. Some or all of the above-described processing in the region recommendation unit may be performed using, or without, AI, for example. For example, the region recommendation unit may input the user's geographical location data into a generation AI, which may then recommend region-exclusive products.

[0105] The purchasing platform system may further include a social recommendation unit that analyzes the user's social media activity and recommends related products. The social recommendation unit, for example, recommends products that the user has "liked" on social media. The social recommendation unit may also recommend products introduced by influencers the user follows. The social recommendation unit may also recommend products that are trending in groups the user participates in. This allows the social recommendation unit to recommend related products based on the user's social media activity. Some or all of the above-described processing in the social recommendation unit may be performed using, or without, AI. For example, the social recommendation unit may input the user's social media data into a generation AI, which then recommends related products.

[0106] The purchasing platform system may further include an emotion incentive unit that estimates a user's emotion and adjusts the type of incentive based on the estimated user emotion. For example, if the user is feeling stressed, the emotion incentive unit may provide an incentive with a relaxing effect. For example, if the user is excited, the emotion incentive unit may provide an energetic incentive. For example, if the user is tired, the emotion incentive unit may provide an incentive with a relaxing effect. This allows the emotion incentive unit to provide optimal incentives according to the user's emotion. 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 emotion incentive unit may be performed using, for example, AI, or without AI. For example, the emotion incentive unit may input the user's emotion data into the generation AI, and the generation AI may adjust the type of incentive.

[0107] The purchasing platform system may further include a purchasing motivation incentive unit that estimates a user's purchasing motivation and adjusts the amount of incentive based on the estimated purchasing motivation. For example, the purchasing motivation incentive unit may increase the amount of incentive if the user indicates high purchasing motivation. For example, the purchasing motivation incentive unit may decrease the amount of incentive if the user indicates low purchasing motivation. For example, the purchasing motivation incentive unit may moderately adjust the amount of incentive if the user indicates medium purchasing motivation. This allows the purchasing motivation incentive unit to adjust the amount of incentive according to the user's purchasing motivation. Some or all of the above-described processing in the purchasing motivation incentive unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing motivation incentive unit may input the user's purchasing motivation data into a generation AI, which may adjust the amount of incentive.

[0108] The purchasing platform system may further include an emotion detail adjustment unit that estimates a user's emotion and adjusts the level of detail of the displayed product based on the estimated user emotion. For example, if the user is in a hurry, the emotion detail adjustment unit may display only the main points of the product. For example, if the user is relaxed, the emotion detail adjustment unit may display a detailed description of the product. For example, if the user is excited, the emotion detail adjustment unit may display the product visually with emphasis. This allows the emotion detail adjustment unit to adjust the level of detail of the product according to the user's emotion. 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 emotion detail adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the emotion detail adjustment unit may input the user's emotion data into the generation AI, which may then adjust the level of detail of the product.

[0109] The purchasing platform system may further include a history incentive unit that analyzes a user's purchasing history and provides incentives based on the user's preferences. The history incentive unit may, for example, provide discounts on products in the same category as products previously purchased by the user. The history incentive unit may, for example, provide points for products of the same brand as products previously rated by the user. The history incentive unit may, for example, provide incentives for complementary products to products previously purchased by the user. This allows the history incentive unit to provide optimal incentives based on the user's purchasing history. Some or all of the above-described processing in the history incentive unit may be performed using, or without, AI. For example, the history incentive unit may input the user's purchasing history data into a generation AI, which may then provide optimal incentives.

[0110] The purchasing platform system may further include an emotion evaluation display unit that estimates the user's emotion and adjusts the display method of the evaluation result based on the estimated user emotion. For example, if the user is feeling stressed, the emotion evaluation display unit may display a simple, highly visible evaluation result. For example, if the user is excited, the emotion evaluation display unit may also display a detailed evaluation result. For example, if the user is tired, the emotion evaluation display unit may also display a simple, highly visible evaluation result. This allows the emotion evaluation display unit to adjust the display method of the evaluation result according to the user's emotion. 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 emotion evaluation display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the emotion evaluation display unit may input the user's emotion data into the generation AI, and the generation AI may adjust the display method of the evaluation result.

[0111] The purchasing platform system may further include a history point management unit that analyzes a user's purchase history and adjusts the point awarding method based on the user's preferences. The history point management unit, for example, awards more points to product categories in which the user has used many points in the past. The history point management unit may also award more points to product categories in which the user has given high ratings in the past. The history point management unit may also award more points to product categories in which the user has frequently purchased in the past. This allows the history point management unit to provide an optimal point awarding method based on the user's purchase history. Some or all of the above-described processing in the history point management unit may be performed using, for example, AI, or may be performed without using AI. For example, the history point management unit may input the user's purchase history data into a generation AI, which may then provide an optimal point awarding method.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The display unit displays product information. Product information includes price, specifications, reviews, SDG / ESG ratings, etc. The display unit displays a list of products offered by multiple suppliers, and displays the price of each product, customer comments and reviews, and SDG / ESG ratings. For example, information about products made from environmentally friendly materials is displayed. Step 2: The providing unit provides information about post-purchase life cycle management (LCM) based on the information displayed by the display unit. The providing unit provides information such as the durability, recyclability, and disposal method of the product. For example, when the user selects a product, the durability, recyclability, and disposal method of the product are displayed. Step 3: The incentive unit provides incentives based on the information provided by the provision unit. For example, the incentive unit provides points when an environmentally friendly product is purchased. These points can be used as a discount for the next purchase. In addition, additional points can be provided when the user posts a review of the product.

[0114] 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.

[0115] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 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 display unit that displays product information; a providing unit that provides information regarding post-purchase life cycle management based on the information displayed by the display unit; an incentive unit that provides an incentive based on the information provided by the providing unit. A system characterized by:

2. Equipping an evaluation department that sets SDG / ESG evaluation criteria 2. The system of claim 1.

3. Equipped with a point management department that manages the point system 2. The system of claim 1.

4. Equipping the review department with an evaluation department to evaluate the quality of reviews 2. The system of claim 1.

5. The display unit Displays the price of each product, customer comments and reviews, and SDG / ESG ratings.

2. The system of claim 1.

6. The providing unit Providing information about the durability or recyclability of the product and how to dispose of it 2. The system of claim 1.

7. The display unit Infer user sentiment and adjust the order of displayed products based on the inferred sentiment 2. The system of claim 1.

8. The display unit When displaying, the system analyzes the user's past purchase history and prioritizes displaying highly relevant products.

2. The system of claim 1.

Images