system

Abstract

The system according to this embodiment aims to suggest sustainable fashion items based on the user's preferences and purchase history. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a recommendation unit, and a report generation unit. The collection unit collects user preferences and purchase history. The analysis unit analyzes the information collected by the collection unit and identifies sustainable materials and production methods. The recommendation unit makes personalized recommendations based on the information identified by the analysis unit. The report generation unit generates a carbon footprint report after the purchase of a product recommended by the recommendation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, sustainable fashion items have not been sufficiently proposed based on the user's preferences and purchase history, and there is room for improvement.

[0005] The system according to the embodiment aims to propose sustainable fashion items based on the user's preferences and purchase history.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a recommendation unit, and a report generation unit. The data collection unit collects user preferences and purchase history. The analysis unit analyzes the information collected by the data collection unit and identifies sustainable materials and production methods. The recommendation unit makes personalized recommendations based on the information identified by the analysis unit. The report generation unit generates a carbon footprint report after the purchase of a product recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest sustainable fashion items based on the user's preferences and purchase history. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The fashion item purchase suggestion platform according to an embodiment of the present invention is a system that uses AI to preferentially recommend products made from sustainable materials and production methods. This system suggests environmentally friendly fashion items based on the user's preferences and past purchase history. Specifically, it consists of the following steps: First, the system collects the user's past purchase history and preferences. Next, the AI ​​analyzes the collected information and learns the user's preferences and purchase history. The AI ​​identifies fashion items made from sustainable materials and production methods and preferentially recommends these items. Furthermore, the AI ​​provides personalized recommendations to the user. The AI ​​also generates a carbon footprint report after purchase. This system is designed for consumers particularly interested in sustainability, especially young people and environmentally conscious customers who value sustainability. As a result, the fashion item purchase suggestion platform can preferentially recommend sustainable fashion items based on the user's preferences and purchase history and generate a carbon footprint report after purchase.

[0029] The fashion item purchase suggestion platform according to this embodiment comprises a data collection unit, an analysis unit, a recommendation unit, and a report generation unit. The data collection unit collects user preferences and purchase history. The data collection unit can collect, for example, the user's past purchase history, survey results, browsing history, etc. The data collection unit can automatically obtain, for example, the history of products purchased online by the user via an API. The data collection unit can also scan and digitize receipts for products purchased by the user in stores. Furthermore, the data collection unit can obtain the user's purchase history from the official app or website of a specific brand. The analysis unit analyzes the information collected by the data collection unit and identifies sustainable materials and production methods. The analysis unit can, for example, identify sustainable materials and production methods, such as the use of renewable energy or organic materials, based on the collected information. The analysis unit can, for example, use AI to analyze the collected information and identify sustainable materials and production methods. The recommendation unit makes personalized recommendations based on the information identified by the analysis unit. The recommendation unit can, for example, recommend the most suitable fashion items to the user using a recommendation algorithm based on the user's past behavioral data. The recommendation unit can, for example, use AI to provide personalized recommendations based on the analysis results. The report generation unit generates a carbon footprint report after the purchase of the products recommended by the recommendation unit. The report generation unit can generate a carbon footprint report based, for example, the CO2 emission calculation method and the report format. The report generation unit can, for example, use AI to generate a post-purchase carbon footprint report. As a result, the fashion item purchase suggestion platform according to the embodiment can prioritize recommending sustainable fashion items based on the user's preferences and purchase history, and generate a post-purchase carbon footprint report.

[0030] The data collection unit collects user preferences and purchase history. For example, it can collect users' past purchase history, survey results, and browsing history. Specifically, it can automatically retrieve the history of products purchased online by users via API. This allows for a detailed understanding of what kinds of products users prefer to buy. The data collection unit can also scan and digitize receipts for products purchased in stores. This enables centralized management of both online and offline purchase history. Furthermore, the data collection unit can retrieve purchase history from official apps and websites of specific brands. This allows for a detailed understanding of users' preferences and purchasing trends for specific brands. The data collection unit centrally manages this data and builds customized databases for each user. This provides a foundation for detailed analysis of user preferences and purchase history, and for making optimal suggestions to individual users. Furthermore, the data collection unit securely manages user data and implements appropriate security measures to protect privacy. For example, it protects users' personal information by encrypting data and restricting access. This allows the data collection unit to gain user trust, collect data efficiently and effectively, and improve the overall system performance.

[0031] The analysis unit analyzes the information collected by the data collection unit to identify sustainable materials and production methods. For example, based on the collected information, the analysis unit can identify sustainable materials and production methods such as the use of renewable energy or organic materials. Specifically, it can use AI to analyze the collected information and identify sustainable materials and production methods. The AI ​​uses machine learning algorithms to extract patterns and trends from the collected data and identify sustainable materials and production methods. For example, it can analyze the materials and production methods of products that a user has purchased in the past to identify products produced using renewable energy or organic materials. Furthermore, based on the collected data, the analysis unit can analyze trends in sustainable fashion items and predict future demand. This allows the analysis unit to provide a foundation for suggesting sustainable fashion items to users. In addition, by collecting the latest information on sustainable materials and production methods and updating its database, the analysis unit can always perform analysis based on the latest information. This allows the analysis unit to contribute to environmental protection through the suggestion of sustainable fashion items.

[0032] The recommendation unit provides personalized recommendations based on information identified by the analysis unit. For example, the recommendation unit can recommend the most suitable fashion items to a user using a recommendation algorithm based on the user's past behavioral data. Specifically, it can use AI to provide personalized recommendations based on analysis results. The AI ​​analyzes the user's past purchase history, browsing history, survey results, etc., to understand the user's preferences and tastes. This allows it to recommend the most suitable fashion items to the user. For example, it can recommend products with similar characteristics based on the style, color, and brand of items the user has purchased in the past. Furthermore, the recommendation unit can make recommendations that consider sustainable materials and production methods. This allows it to propose environmentally friendly fashion items to users. The recommendation unit can collect user feedback and continuously improve its recommendation algorithm. For example, if a user purchases a recommended product, the recommendation algorithm can be adjusted based on that data to provide more accurate recommendations. This allows the recommendation department to consistently suggest the most suitable fashion items to users, thereby improving user satisfaction.

[0033] The report generation unit generates a carbon footprint report after a product recommended by the recommendation unit is purchased. The report generation unit can generate a carbon footprint report based on, for example, the method for calculating CO2 emissions and the report format. Specifically, it can use AI to generate a post-purchase carbon footprint report. The AI ​​calculates the CO2 emissions during the production and transportation processes of the product and calculates the carbon footprint. This allows users to understand the environmental impact of the products they purchase. Furthermore, the report generation unit can also suggest ways to reduce the carbon footprint to the user. For example, it can recommend purchasing products made from reusable materials or products produced using energy-efficient methods. This allows users to make environmentally conscious purchasing decisions. The report generation unit provides the generated report to the user, allowing them to use it as a reference to review their purchasing behavior. Furthermore, the report generation unit can manage the user's carbon footprint history and evaluate the long-term environmental impact. In this way, the report generation unit can promote environmentally conscious purchasing behavior to users and contribute to the realization of a sustainable society.

[0034] The data collection unit can analyze a user's past purchase history and select the optimal data collection method. For example, if a user frequently makes online purchases, the data collection unit can automatically retrieve online purchase history via an API. Furthermore, if a user prefers in-store purchases, the data collection unit can provide a receipt scanning function to digitize purchase history. Additionally, if a user prefers a specific brand, the data collection unit can retrieve purchase history from that brand's official app or website. This allows for efficient data collection by selecting the optimal method based on the user's purchase history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input user purchase history data into a generating AI and have the generating AI select the optimal data collection method.

[0035] The data collection unit can filter purchase history based on the user's current lifestyle and areas of interest. For example, if a user has started a new job, the data collection unit can prioritize collecting purchase history of business casual items. Similarly, if a user is planning a trip, the data collection unit can prioritize collecting purchase history of travel-related fashion items. Furthermore, if a user is interested in environmental protection, the data collection unit can prioritize collecting purchase history of eco-friendly items. This allows for the collection of more relevant information by filtering purchase history based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input user lifestyle data into a generating AI and have the generating AI perform the filtering.

[0036] The data collection unit can prioritize the collection of highly relevant purchase history by considering the user's geographical location information when collecting purchase history. For example, if the user lives in an urban area, the data collection unit can prioritize the collection of purchase history of fashion items popular in urban areas. Similarly, if the user lives in a cold region, the data collection unit can prioritize the collection of purchase history of cold-weather items. Furthermore, if the user lives by the sea, the data collection unit can prioritize the collection of purchase history of beachwear. This allows for the collection of more appropriate information by prioritizing the collection of highly relevant purchase history based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant purchase history.

[0037] The data collection unit can analyze a user's social media activity and collect relevant history when collecting purchase history. For example, if a user follows a specific brand on social media, the data collection unit can prioritize collecting purchase history of that brand. Similarly, if a user follows a specific fashion influencer on social media, the data collection unit can prioritize collecting purchase history of items recommended by that influencer. Furthermore, if a user frequently uses a specific hashtag on social media, the data collection unit can prioritize collecting purchase history of items related to that hashtag. This allows for the collection of more relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant history.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the product during the analysis. For example, the analysis unit can perform a detailed analysis for expensive products and provide it to the user. It can also perform a simplified analysis for products used daily and provide it to the user. Furthermore, the analysis unit can perform a special analysis for products for special events and provide it to the user. By adjusting the level of detail of the analysis based on the importance of the product, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the product category during analysis. For example, for clothing, the analysis unit can apply an analysis algorithm based on material and design. For accessories, the analysis unit can apply an analysis algorithm based on trends and style. Furthermore, for shoes, the analysis unit can apply an analysis algorithm based on comfort and durability. By applying different analysis algorithms depending on the product category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0040] The analysis unit can determine the priority of analysis based on the product submission date during the analysis process. For example, the analysis unit can prioritize the analysis of new products and provide the results to users. It can also quickly analyze products during sales periods and provide the results to users. Furthermore, it can analyze seasonal products according to the season and provide the results to users. By determining the priority of analysis based on the product submission date, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product submission date data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0041] The analysis unit can adjust the order of analysis based on the relevance of products during the analysis process. For example, the analysis unit can prioritize the analysis of products that are highly relevant to products the user has previously purchased. It can also prioritize the analysis of products in categories that the user has shown interest in. Furthermore, it can prioritize the analysis of products from brands that the user follows. By adjusting the order of analysis based on the relevance of products, the analysis unit can provide more appropriate analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0042] The recommendation unit can adjust the level of detail in its recommendations based on the importance of the products. For example, it can provide users with detailed recommendations for expensive products. It can also provide users with concise recommendations for products used daily. Furthermore, it can provide users with special recommendations for products for special events. By adjusting the level of detail in recommendations based on the importance of the products, it can provide more appropriate recommendations. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input product importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the recommendations.

[0043] The recommendation unit can apply different recommendation algorithms depending on the product category when making recommendations. For example, for clothing, the recommendation unit can apply a recommendation algorithm based on material and design. For accessories, the recommendation unit can apply a recommendation algorithm based on trends and style. Furthermore, for shoes, the recommendation unit can apply a recommendation algorithm based on comfort and durability. By applying different recommendation algorithms depending on the product category, more appropriate recommendations can be provided. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input product category data into a generating AI and have the generating AI execute the application of different recommendation algorithms.

[0044] The recommendation unit can determine the priority of recommendations based on the timing of product submission. For example, the recommendation unit can prioritize recommending new products to users. It can also quickly recommend products during sales periods to users. Furthermore, it can recommend seasonal products according to the season to users. By determining the priority of recommendations based on the timing of product submission, it is possible to provide more appropriate recommendations. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input product submission timing data into a generating AI and have the generating AI perform the determination of recommendation priorities.

[0045] The recommendation unit can adjust the order of recommendations based on the relevance of the products. For example, the recommendation unit can prioritize recommending products that are highly relevant to products the user has previously purchased. It can also prioritize recommending products in categories that the user has shown interest in. Furthermore, it can prioritize recommending products from brands that the user follows. By adjusting the order of recommendations based on the relevance of the products, the recommendation unit can provide more appropriate recommendations. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input product relevance data into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0046] The report generation unit can adjust the level of detail in a report based on the importance of the product during report generation. For example, the report generation unit can provide a detailed report to the user for expensive products. It can also provide a concise report to the user for products used on a daily basis. Furthermore, it can provide a special report to the user for products used for special events. By adjusting the level of detail in the report based on the importance of the product, a more appropriate report can be provided. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input product importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the report.

[0047] The report generation unit can apply different report generation algorithms depending on the product category when generating reports. For example, for clothing, the report generation unit can apply a report generation algorithm based on material and design. For accessories, it can apply a report generation algorithm based on trends and style. Furthermore, for shoes, it can apply a report generation algorithm based on comfort and durability. By applying different report generation algorithms depending on the product category, a more appropriate report can be provided. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input product category data into a generation AI and have the generation AI execute the application of different report generation algorithms.

[0048] The report generation unit can determine the priority of reports based on the product submission timing when generating reports. For example, the report generation unit can prioritize reporting on new products and provide them to users. It can also quickly report on products during sales periods and provide them to users. Furthermore, it can report on seasonal products according to the season and provide them to users. By determining the priority of reports based on the product submission timing, more appropriate reports can be provided. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input product submission timing data into a generation AI and have the generation AI perform the determination of report priorities.

[0049] The report generation unit can adjust the order of reports based on product relevance during report generation. For example, the report generation unit can prioritize reporting on products highly relevant to products the user has previously purchased. It can also prioritize reporting on products in categories the user has shown interest in. Furthermore, it can prioritize reporting on products from brands the user follows. By adjusting the order of reports based on product relevance, a more appropriate report can be provided. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input product relevance data into a generation AI and have the generation AI perform the adjustment of the report order.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can collect user preferences, purchase history, and health data. For example, if a user uses a fitness tracker, that data can be collected, and appropriate fashion items can be recommended based on the user's activity level and health condition. Furthermore, if a user has specific allergies, that information can be collected, and items made with allergy-friendly materials can be prioritized for recommendation. Additionally, if a user has specific health goals, items that align with those goals can be recommended. This allows for more personalized recommendations based on the user's health condition and goals.

[0052] The data collection unit can prioritize collecting highly relevant history by considering the user's geographical location. For example, if a user lives in an urban area, it can prioritize collecting purchase history of fashion items popular in the city. Similarly, if a user lives in a cold climate, it can prioritize collecting purchase history of cold-weather items. Furthermore, if a user lives by the sea, it can prioritize collecting purchase history of beachwear. This allows for the collection of more relevant information by prioritizing the collection of history based on the user's geographical location.

[0053] The data collection unit can analyze a user's social media activity and collect relevant history. For example, if a user follows a specific brand on social media, it can prioritize collecting purchase history of that brand. Similarly, if a user follows a specific fashion influencer on social media, it can prioritize collecting purchase history of items recommended by that influencer. Furthermore, if a user frequently uses a specific hashtag on social media, it can prioritize collecting purchase history of items related to that hashtag. This allows for the collection of more relevant information based on the user's social media activity.

[0054] The analysis unit can adjust the level of detail of the analysis based on the importance of the product. For example, a detailed analysis can be performed on expensive products and provided to the user. Conversely, a simpler analysis can be performed on products used daily and provided to the user. Furthermore, a special analysis can be performed on products for special events and provided to the user. In this way, by adjusting the level of detail of the analysis based on the importance of the product, more appropriate analysis results can be provided.

[0055] The recommendation system can apply different recommendation algorithms depending on the product category. For example, for clothing, a recommendation algorithm based on material and design can be applied. For accessories, a recommendation algorithm based on trends and style can be applied. Furthermore, for shoes, a recommendation algorithm based on comfort and durability can be applied. By applying different recommendation algorithms depending on the product category, the system can provide more appropriate recommendations.

[0056] The report generation unit can prioritize reports based on when products are submitted. For example, new products can be reported on and provided to users as a priority. Products on sale can be reported on quickly and provided to users. Furthermore, seasonal products can be reported on according to the season and provided to users. In this way, by prioritizing reports based on when products are submitted, more appropriate reports can be provided.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects user preferences and purchase history. For example, it can collect the user's past purchase history, survey results, and browsing history. The data collection unit can automatically retrieve the history of products the user has purchased online via API. It can also scan and digitize receipts for products the user has purchased in stores. Furthermore, it can retrieve the purchase history from the user's official app or website for a specific brand. Step 2: The analysis unit analyzes the information collected by the collection unit to identify sustainable materials and production methods. For example, it can identify sustainable materials and production methods such as the use of renewable energy or organic materials. The analysis unit can use AI to analyze the collected information and identify sustainable materials and production methods. Step 3: The recommendation unit makes personalized recommendations based on the information identified by the analysis unit. For example, it can recommend the most suitable fashion items to the user using a recommendation algorithm based on the user's past behavior data. The recommendation unit can also make personalized recommendations based on the analysis results using AI. Step 4: The report generation unit generates a carbon footprint report after the purchase of a product recommended by the recommendation unit. For example, it can generate a carbon footprint report based on the CO2 emission calculation method and report format. The report generation unit can use AI to generate a post-purchase carbon footprint report.

[0059] (Example of form 2) The fashion item purchase suggestion platform according to an embodiment of the present invention is a system that uses AI to preferentially recommend products made from sustainable materials and production methods. This system suggests environmentally friendly fashion items based on the user's preferences and past purchase history. Specifically, it consists of the following steps: First, the system collects the user's past purchase history and preferences. Next, the AI ​​analyzes the collected information and learns the user's preferences and purchase history. The AI ​​identifies fashion items made from sustainable materials and production methods and preferentially recommends these items. Furthermore, the AI ​​provides personalized recommendations to the user. The AI ​​also generates a carbon footprint report after purchase. This system is designed for consumers particularly interested in sustainability, especially young people and environmentally conscious customers who value sustainability. As a result, the fashion item purchase suggestion platform can preferentially recommend sustainable fashion items based on the user's preferences and purchase history and generate a carbon footprint report after purchase.

[0060] The fashion item purchase suggestion platform according to this embodiment comprises a data collection unit, an analysis unit, a recommendation unit, and a report generation unit. The data collection unit collects user preferences and purchase history. The data collection unit can collect, for example, the user's past purchase history, survey results, browsing history, etc. The data collection unit can automatically obtain, for example, the history of products purchased online by the user via an API. The data collection unit can also scan and digitize receipts for products purchased by the user in stores. Furthermore, the data collection unit can obtain the user's purchase history from the official app or website of a specific brand. The analysis unit analyzes the information collected by the data collection unit and identifies sustainable materials and production methods. The analysis unit can, for example, identify sustainable materials and production methods, such as the use of renewable energy or organic materials, based on the collected information. The analysis unit can, for example, use AI to analyze the collected information and identify sustainable materials and production methods. The recommendation unit makes personalized recommendations based on the information identified by the analysis unit. The recommendation unit can, for example, recommend the most suitable fashion items to the user using a recommendation algorithm based on the user's past behavioral data. The recommendation unit can, for example, use AI to provide personalized recommendations based on the analysis results. The report generation unit generates a carbon footprint report after the purchase of the products recommended by the recommendation unit. The report generation unit can generate a carbon footprint report based, for example, the CO2 emission calculation method and the report format. The report generation unit can, for example, use AI to generate a post-purchase carbon footprint report. As a result, the fashion item purchase suggestion platform according to the embodiment can prioritize recommending sustainable fashion items based on the user's preferences and purchase history, and generate a post-purchase carbon footprint report.

[0061] The data collection unit collects user preferences and purchase history. For example, it can collect users' past purchase history, survey results, and browsing history. Specifically, it can automatically retrieve the history of products purchased online by users via API. This allows for a detailed understanding of what kinds of products users prefer to buy. The data collection unit can also scan and digitize receipts for products purchased in stores. This enables centralized management of both online and offline purchase history. Furthermore, the data collection unit can retrieve purchase history from official apps and websites of specific brands. This allows for a detailed understanding of users' preferences and purchasing trends for specific brands. The data collection unit centrally manages this data and builds customized databases for each user. This provides a foundation for detailed analysis of user preferences and purchase history, and for making optimal suggestions to individual users. Furthermore, the data collection unit securely manages user data and implements appropriate security measures to protect privacy. For example, it protects users' personal information by encrypting data and restricting access. This allows the data collection unit to gain user trust, collect data efficiently and effectively, and improve the overall system performance.

[0062] The analysis unit analyzes the information collected by the data collection unit to identify sustainable materials and production methods. For example, based on the collected information, the analysis unit can identify sustainable materials and production methods such as the use of renewable energy or organic materials. Specifically, it can use AI to analyze the collected information and identify sustainable materials and production methods. The AI ​​uses machine learning algorithms to extract patterns and trends from the collected data and identify sustainable materials and production methods. For example, it can analyze the materials and production methods of products that a user has purchased in the past to identify products produced using renewable energy or organic materials. Furthermore, based on the collected data, the analysis unit can analyze trends in sustainable fashion items and predict future demand. This allows the analysis unit to provide a foundation for suggesting sustainable fashion items to users. In addition, by collecting the latest information on sustainable materials and production methods and updating its database, the analysis unit can always perform analysis based on the latest information. This allows the analysis unit to contribute to environmental protection through the suggestion of sustainable fashion items.

[0063] The recommendation unit provides personalized recommendations based on information identified by the analysis unit. For example, the recommendation unit can recommend the most suitable fashion items to a user using a recommendation algorithm based on the user's past behavioral data. Specifically, it can use AI to provide personalized recommendations based on analysis results. The AI ​​analyzes the user's past purchase history, browsing history, survey results, etc., to understand the user's preferences and tastes. This allows it to recommend the most suitable fashion items to the user. For example, it can recommend products with similar characteristics based on the style, color, and brand of items the user has purchased in the past. Furthermore, the recommendation unit can make recommendations that consider sustainable materials and production methods. This allows it to propose environmentally friendly fashion items to users. The recommendation unit can collect user feedback and continuously improve its recommendation algorithm. For example, if a user purchases a recommended product, the recommendation algorithm can be adjusted based on that data to provide more accurate recommendations. This allows the recommendation department to consistently suggest the most suitable fashion items to users, thereby improving user satisfaction.

[0064] The report generation unit generates a carbon footprint report after a product recommended by the recommendation unit is purchased. The report generation unit can generate a carbon footprint report based on, for example, the method for calculating CO2 emissions and the report format. Specifically, it can use AI to generate a post-purchase carbon footprint report. The AI ​​calculates the CO2 emissions during the production and transportation processes of the product and calculates the carbon footprint. This allows users to understand the environmental impact of the products they purchase. Furthermore, the report generation unit can also suggest ways to reduce the carbon footprint to the user. For example, it can recommend purchasing products made from reusable materials or products produced using energy-efficient methods. This allows users to make environmentally conscious purchasing decisions. The report generation unit provides the generated report to the user, allowing them to use it as a reference to review their purchasing behavior. Furthermore, the report generation unit can manage the user's carbon footprint history and evaluate the long-term environmental impact. In this way, the report generation unit can promote environmentally conscious purchasing behavior to users and contribute to the realization of a sustainable society.

[0065] The data collection unit can estimate the user's emotions and adjust the timing of purchase history collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can temporarily delay the collection of purchase history and resume collection when the user is relaxed. If the user is excited, the data collection unit can immediately collect the purchase history and send it to the analysis unit in real time. Furthermore, if the user is tired, the data collection unit can postpone the collection of purchase history to the next day and collect it after the user has rested. This allows for information to be collected at a more appropriate time by adjusting the timing of purchase history collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0066] The data collection unit can analyze a user's past purchase history and select the optimal data collection method. For example, if a user frequently makes online purchases, the data collection unit can automatically retrieve online purchase history via an API. Furthermore, if a user prefers in-store purchases, the data collection unit can provide a receipt scanning function to digitize purchase history. Additionally, if a user prefers a specific brand, the data collection unit can retrieve purchase history from that brand's official app or website. This allows for efficient data collection by selecting the optimal method based on the user's purchase history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input user purchase history data into a generating AI and have the generating AI select the optimal data collection method.

[0067] The data collection unit can filter purchase history based on the user's current lifestyle and areas of interest. For example, if a user has started a new job, the data collection unit can prioritize collecting purchase history of business casual items. Similarly, if a user is planning a trip, the data collection unit can prioritize collecting purchase history of travel-related fashion items. Furthermore, if a user is interested in environmental protection, the data collection unit can prioritize collecting purchase history of eco-friendly items. This allows for the collection of more relevant information by filtering purchase history based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input user lifestyle data into a generating AI and have the generating AI perform the filtering.

[0068] The data collection unit can estimate the user's emotions and determine the priority of purchase history to collect based on the estimated emotions. For example, if the user is in a happy mood, the data collection unit can prioritize collecting purchase history of expensive items. If the user is in a sad mood, the data collection unit can prioritize collecting purchase history of comforting items. Furthermore, if the user is excited, the data collection unit can prioritize collecting purchase history of trending items. This allows for the collection of more relevant information by prioritizing purchase history according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of purchase history.

[0069] The data collection unit can prioritize the collection of highly relevant purchase history by considering the user's geographical location information when collecting purchase history. For example, if the user lives in an urban area, the data collection unit can prioritize the collection of purchase history of fashion items popular in urban areas. Similarly, if the user lives in a cold region, the data collection unit can prioritize the collection of purchase history of cold-weather items. Furthermore, if the user lives by the sea, the data collection unit can prioritize the collection of purchase history of beachwear. This allows for the collection of more appropriate information by prioritizing the collection of highly relevant purchase history based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant purchase history.

[0070] The data collection unit can analyze a user's social media activity and collect relevant history when collecting purchase history. For example, if a user follows a specific brand on social media, the data collection unit can prioritize collecting purchase history of that brand. Similarly, if a user follows a specific fashion influencer on social media, the data collection unit can prioritize collecting purchase history of items recommended by that influencer. Furthermore, if a user frequently uses a specific hashtag on social media, the data collection unit can prioritize collecting purchase history of items related to that hashtag. This allows for the collection of more relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant history.

[0071] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide analysis results using visually stimulating graphics. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0072] The analysis unit can adjust the level of detail of the analysis based on the importance of the product during the analysis. For example, the analysis unit can perform a detailed analysis for expensive products and provide it to the user. It can also perform a simplified analysis for products used daily and provide it to the user. Furthermore, the analysis unit can perform a special analysis for products for special events and provide it to the user. By adjusting the level of detail of the analysis based on the importance of the product, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0073] The analysis unit can apply different analysis algorithms depending on the product category during analysis. For example, for clothing, the analysis unit can apply an analysis algorithm based on material and design. For accessories, the analysis unit can apply an analysis algorithm based on trends and style. Furthermore, for shoes, the analysis unit can apply an analysis algorithm based on comfort and durability. By applying different analysis algorithms depending on the product category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0074] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a longer analysis with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0075] The analysis unit can determine the priority of analysis based on the product submission date during the analysis process. For example, the analysis unit can prioritize the analysis of new products and provide the results to users. It can also quickly analyze products during sales periods and provide the results to users. Furthermore, it can analyze seasonal products according to the season and provide the results to users. By determining the priority of analysis based on the product submission date, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product submission date data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0076] The analysis unit can adjust the order of analysis based on the relevance of products during the analysis process. For example, the analysis unit can prioritize the analysis of products that are highly relevant to products the user has previously purchased. It can also prioritize the analysis of products in categories that the user has shown interest in. Furthermore, it can prioritize the analysis of products from brands that the user follows. By adjusting the order of analysis based on the relevance of products, the analysis unit can provide more appropriate analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0077] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, the recommendation unit can provide recommendations that include detailed explanations. If the user is in a hurry, the recommendation unit can provide concise recommendations that get straight to the point. Furthermore, if the user is excited, the recommendation unit can provide recommendations using visually stimulating graphics. By adjusting the way recommendations are presented according to the user's emotions, more appropriate recommendations can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, or not using AI. For example, the recommendation unit can input user emotion data into the generative AI and have the generative AI adjust the way recommendations are presented.

[0078] The recommendation unit can adjust the level of detail in its recommendations based on the importance of the products. For example, it can provide users with detailed recommendations for expensive products. It can also provide users with concise recommendations for products used daily. Furthermore, it can provide users with special recommendations for products for special events. By adjusting the level of detail in recommendations based on the importance of the products, it can provide more appropriate recommendations. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input product importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the recommendations.

[0079] The recommendation unit can apply different recommendation algorithms depending on the product category when making recommendations. For example, for clothing, the recommendation unit can apply a recommendation algorithm based on material and design. For accessories, the recommendation unit can apply a recommendation algorithm based on trends and style. Furthermore, for shoes, the recommendation unit can apply a recommendation algorithm based on comfort and durability. By applying different recommendation algorithms depending on the product category, more appropriate recommendations can be provided. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input product category data into a generating AI and have the generating AI execute the application of different recommendation algorithms.

[0080] The recommendation unit can estimate the user's emotions and adjust the length of recommendations based on the estimated emotions. For example, if the user is in a hurry, the recommendation unit can provide short, concise recommendations. If the user is relaxed, the recommendation unit can provide longer recommendations with detailed explanations. Furthermore, if the user is excited, the recommendation unit can provide recommendations with visually stimulating effects. By adjusting the length of recommendations according to the user's emotions, more appropriate recommendations can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input user emotion data into the generative AI and have the generative AI adjust the length of the recommendations.

[0081] The recommendation unit can determine the priority of recommendations based on the timing of product submission. For example, the recommendation unit can prioritize recommending new products to users. It can also quickly recommend products during sales periods to users. Furthermore, it can recommend seasonal products according to the season to users. By determining the priority of recommendations based on the timing of product submission, it is possible to provide more appropriate recommendations. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input product submission timing data into a generating AI and have the generating AI perform the determination of recommendation priorities.

[0082] The recommendation unit can adjust the order of recommendations based on the relevance of the products. For example, the recommendation unit can prioritize recommending products that are highly relevant to products the user has previously purchased. It can also prioritize recommending products in categories that the user has shown interest in. Furthermore, it can prioritize recommending products from brands that the user follows. By adjusting the order of recommendations based on the relevance of the products, the recommendation unit can provide more appropriate recommendations. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input product relevance data into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0083] The report generation unit can estimate the user's emotions and adjust the report's presentation based on the estimated emotions. For example, if the user is relaxed, the report generation unit can provide a detailed report. If the user is in a hurry, it can provide a concise report that gets straight to the point. Furthermore, if the user is excited, the report generation unit can provide a report using visually stimulating graphics. In this way, by adjusting the report's presentation according to the user's emotions, a more appropriate report can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the report generation unit may be performed using AI, or not using AI. For example, the report generation unit can input user emotion data into the generative AI and have the generative AI adjust the report's presentation.

[0084] The report generation unit can adjust the level of detail in a report based on the importance of the product during report generation. For example, the report generation unit can provide a detailed report to the user for expensive products. It can also provide a concise report to the user for products used on a daily basis. Furthermore, it can provide a special report to the user for products used for special events. By adjusting the level of detail in the report based on the importance of the product, a more appropriate report can be provided. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input product importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the report.

[0085] The report generation unit can apply different report generation algorithms depending on the product category when generating reports. For example, for clothing, the report generation unit can apply a report generation algorithm based on material and design. For accessories, it can apply a report generation algorithm based on trends and style. Furthermore, for shoes, it can apply a report generation algorithm based on comfort and durability. By applying different report generation algorithms depending on the product category, a more appropriate report can be provided. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input product category data into a generation AI and have the generation AI execute the application of different report generation algorithms.

[0086] The report generation unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is in a hurry, the report generation unit can provide a short, concise report. If the user is relaxed, the report generation unit can provide a longer report with detailed explanations. Furthermore, if the user is excited, the report generation unit can provide a report with visually stimulating effects. By adjusting the length of the report according to the user's emotions, a more appropriate report can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the report generation unit may be performed using AI or not. For example, the report generation unit can input user emotion data into the generative AI and have the generative AI adjust the length of the report.

[0087] The report generation unit can determine the priority of reports based on the product submission timing when generating reports. For example, the report generation unit can prioritize reporting on new products and provide them to users. It can also quickly report on products during sales periods and provide them to users. Furthermore, it can report on seasonal products according to the season and provide them to users. By determining the priority of reports based on the product submission timing, more appropriate reports can be provided. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input product submission timing data into a generation AI and have the generation AI perform the determination of report priorities.

[0088] The report generation unit can adjust the order of reports based on product relevance during report generation. For example, the report generation unit can prioritize reporting on products highly relevant to products the user has previously purchased. It can also prioritize reporting on products in categories the user has shown interest in. Furthermore, it can prioritize reporting on products from brands the user follows. By adjusting the order of reports based on product relevance, a more appropriate report can be provided. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can input product relevance data into a generation AI and have the generation AI perform the adjustment of the report order.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The data collection unit can collect user preferences, purchase history, and health data. For example, if a user uses a fitness tracker, that data can be collected, and appropriate fashion items can be recommended based on the user's activity level and health condition. Furthermore, if a user has specific allergies, that information can be collected, and items made with allergy-friendly materials can be prioritized for recommendation. Additionally, if a user has specific health goals, items that align with those goals can be recommended. This allows for more personalized recommendations based on the user's health condition and goals.

[0091] The data collection unit can estimate the user's emotions and adjust the timing of purchase history collection based on those emotions. For example, if the user is stressed, the collection of purchase history can be temporarily delayed and resumed when the user is relaxed. If the user is excited, the purchase history can be collected immediately and sent to the analysis unit in real time. Furthermore, if the user is tired, the collection of purchase history can be postponed until the next day, after the user has rested. By adjusting the timing of purchase history collection according to the user's emotions, information can be collected at a more appropriate time.

[0092] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, it can provide analysis results using visually stimulating graphics. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide more appropriate analysis results.

[0093] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, it can provide recommendations with detailed explanations. If the user is in a hurry, it can provide concise recommendations that get straight to the point. Furthermore, if the user is excited, it can provide recommendations using visually stimulating graphics. By adjusting the way recommendations are presented according to the user's emotions, the system can provide more appropriate recommendations.

[0094] The report generation unit can estimate the user's emotions and adjust the report's presentation based on those emotions. For example, if the user is relaxed, it can provide a detailed report. If the user is in a hurry, it can provide a concise report that gets straight to the point. Furthermore, if the user is excited, it can provide a report using visually stimulating graphics. By adjusting the report's presentation according to the user's emotions, it can provide a more appropriate report.

[0095] The data collection unit can prioritize collecting highly relevant history by considering the user's geographical location. For example, if a user lives in an urban area, it can prioritize collecting purchase history of fashion items popular in the city. Similarly, if a user lives in a cold climate, it can prioritize collecting purchase history of cold-weather items. Furthermore, if a user lives by the sea, it can prioritize collecting purchase history of beachwear. This allows for the collection of more relevant information by prioritizing the collection of history based on the user's geographical location.

[0096] The data collection unit can analyze a user's social media activity and collect relevant history. For example, if a user follows a specific brand on social media, it can prioritize collecting purchase history of that brand. Similarly, if a user follows a specific fashion influencer on social media, it can prioritize collecting purchase history of items recommended by that influencer. Furthermore, if a user frequently uses a specific hashtag on social media, it can prioritize collecting purchase history of items related to that hashtag. This allows for the collection of more relevant information based on the user's social media activity.

[0097] The analysis unit can adjust the level of detail of the analysis based on the importance of the product. For example, a detailed analysis can be performed on expensive products and provided to the user. Conversely, a simpler analysis can be performed on products used daily and provided to the user. Furthermore, a special analysis can be performed on products for special events and provided to the user. In this way, by adjusting the level of detail of the analysis based on the importance of the product, more appropriate analysis results can be provided.

[0098] The recommendation system can apply different recommendation algorithms depending on the product category. For example, for clothing, a recommendation algorithm based on material and design can be applied. For accessories, a recommendation algorithm based on trends and style can be applied. Furthermore, for shoes, a recommendation algorithm based on comfort and durability can be applied. By applying different recommendation algorithms depending on the product category, the system can provide more appropriate recommendations.

[0099] The report generation unit can prioritize reports based on when products are submitted. For example, new products can be reported on and provided to users as a priority. Products on sale can be reported on quickly and provided to users. Furthermore, seasonal products can be reported on according to the season and provided to users. In this way, by prioritizing reports based on when products are submitted, more appropriate reports can be provided.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The data collection unit collects user preferences and purchase history. For example, it can collect the user's past purchase history, survey results, and browsing history. The data collection unit can automatically retrieve the history of products the user has purchased online via API. It can also scan and digitize receipts for products the user has purchased in stores. Furthermore, it can retrieve the purchase history from the user's official app or website for a specific brand. Step 2: The analysis unit analyzes the information collected by the collection unit to identify sustainable materials and production methods. For example, it can identify sustainable materials and production methods such as the use of renewable energy or organic materials. The analysis unit can use AI to analyze the collected information and identify sustainable materials and production methods. Step 3: The recommendation unit makes personalized recommendations based on the information identified by the analysis unit. For example, it can recommend the most suitable fashion items to the user using a recommendation algorithm based on the user's past behavior data. The recommendation unit can also make personalized recommendations based on the analysis results using AI. Step 4: The report generation unit generates a carbon footprint report after the purchase of a product recommended by the recommendation unit. For example, it can generate a carbon footprint report based on the CO2 emission calculation method and report format. The report generation unit can use AI to generate a post-purchase carbon footprint report.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0104] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, and report generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user preferences and purchase history using the control unit 46A of the smart device 14 and analyzes them using the identification processing unit 290 of the data processing unit 12. The analysis unit identifies sustainable materials and production methods using the identification processing unit 290 of the data processing unit 12. The recommendation unit provides personalized recommendations using the control unit 46A of the smart device 14. The report generation unit generates a carbon footprint report using the identification processing unit 290 of the data processing unit 12. The data collection unit can estimate the user's emotions and adjust the timing of purchase history collection based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0112] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0113] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0114] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0115] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0116] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, and report generation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user preferences and purchase history by the control unit 46A of the smart glasses 214 and analyzes them by the identification processing unit 290 of the data processing unit 12. The analysis unit identifies sustainable materials and production methods by the identification processing unit 290 of the data processing unit 12. The recommendation unit provides personalized recommendations by the control unit 46A of the smart glasses 214. The report generation unit generates a carbon footprint report by the identification processing unit 290 of the data processing unit 12. The data collection unit can estimate the user's emotions and adjust the timing of purchase history collection based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0129] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, and report generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user preferences and purchase history using the control unit 46A of the headset terminal 314 and analyzes them using the identification unit 290 of the data processing unit 12. The analysis unit identifies sustainable materials and production methods using the identification unit 290 of the data processing unit 12. The recommendation unit provides personalized recommendations using the control unit 46A of the headset terminal 314. The report generation unit generates a carbon footprint report using the identification unit 290 of the data processing unit 12. The data collection unit can estimate the user's emotions and adjust the timing of purchase history collection based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0146] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0149] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, and report generation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user preferences and purchase history by the control unit 46A of the robot 414 and analyzes them by the identification processing unit 290 of the data processing unit 12. The analysis unit identifies sustainable materials and production methods by the identification processing unit 290 of the data processing unit 12. The recommendation unit provides personalized recommendations by the control unit 46A of the robot 414. The report generation unit generates a carbon footprint report by the identification processing unit 290 of the data processing unit 12. The data collection unit can estimate the user's emotions and adjust the timing of purchase history collection based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0155] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0157] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0158] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0159] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0163] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0164] 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.

[0165] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0166] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0167] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0168] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0170] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0171] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0172] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0173] (Note 1) A data collection unit that collects user preferences and purchase history, An analysis unit analyzes the information collected by the aforementioned collection unit to identify sustainable materials and production methods, A recommendation unit that provides personalized recommendations based on the information identified by the analysis unit, The system includes a report generation unit that generates a carbon footprint report after the purchase of a product recommended by the recommendation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of purchase history collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past purchase history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting purchase history, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and determines the priority of purchase history to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting purchase history, the system prioritizes collecting highly relevant history based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting purchase history, the system analyzes the user's social media activity and collects relevant history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the timing of product submission. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 14) The recommendation unit is, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The recommendation unit is, When making recommendations, adjust the level of detail based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 16) The recommendation unit is, When making recommendations, different recommendation algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The recommendation unit is, It estimates the user's emotions and adjusts the length of recommendations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The recommendation unit is, When making recommendations, the priority of recommendations is determined based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The recommendation unit is, When making recommendations, the order of recommendations is adjusted based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 20) The report generation unit, It estimates user sentiment and adjusts the way reports are presented based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The report generation unit, When generating reports, adjust the level of detail in the report based on the importance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 22) The report generation unit, When generating reports, different report generation algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The report generation unit, It estimates the user's sentiment and adjusts the length of the report based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The report generation unit, When generating reports, prioritize reports based on when the products were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The report generation unit, When generating reports, adjust the order of reports based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user preferences and purchase history, An analysis unit analyzes the information collected by the aforementioned collection unit to identify sustainable materials and production methods, A recommendation unit that provides personalized recommendations based on the information identified by the analysis unit, The system includes a report generation unit that generates a carbon footprint report after the purchase of a product recommended by the recommendation unit. A system characterized by the following features.

2. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of purchase history collection based on those estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze the user's past purchase history and select the optimal data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting purchase history, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and determines the priority of purchase history to collect based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting purchase history, the system prioritizes collecting highly relevant history based on the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting purchase history, the system analyzes the user's social media activity and collects relevant history. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

Images