system

Abstract

The system according to this embodiment aims to recommend sustainable fashion items based on the user's preferences and past purchase history. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a recommendation unit, and an evolution unit. The collection unit collects the user's past purchase history and feedback. The analysis unit analyzes the data collected by the collection unit to understand the user's preferences. The recommendation unit recommends sustainable fashion items based on the analysis results obtained by the analysis unit. The evolution unit learns from the user's feedback on the items recommended by the recommendation unit to improve the accuracy of the recommendations.

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, it cannot be said that sustainable fashion items are appropriately recommended based on the user's preferences and past purchase history, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend sustainable fashion items based on the user's preferences and past 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 an evolution unit. The data collection unit collects the user's past purchase history and feedback. The analysis unit analyzes the data collected by the data collection unit to understand the user's preferences. The recommendation unit recommends sustainable fashion items based on the analysis results obtained by the analysis unit. The evolution unit learns from the user's feedback on the items recommended by the recommendation unit to improve the accuracy of the recommendations. [Effects of the Invention]

[0007] The system according to this embodiment can recommend sustainable fashion items based on the user's preferences and past 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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple 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 reception 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 reception 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) An AI agent according to an embodiment of the present invention is a system that recommends fashion items made from sustainable materials and production methods based on the user's preferences and past purchase history. This system uses a machine learning model to analyze the user's preferences. This model learns from the user's past purchase history and feedback to understand the user's preferences. Next, it recommends the most suitable sustainable fashion items to the user using a database of environmentally friendly products. This database contains products made from sustainable materials and production methods. Furthermore, it improves the accuracy of recommendations using a self-evolving algorithm that learns from user feedback. This allows users to easily find environmentally friendly products and promotes transformation in the fashion industry by providing sustainable options. The AI ​​agent also improves transparency between users and brands and sets new trends in sustainable fashion. The target audience is environmentally conscious young people aged 18 to 40 who are fashion-conscious and seek a sustainable lifestyle. This AI agent is expected to contribute to the expansion of the sustainable fashion market and raise awareness of reducing environmental impact. For example, the AI ​​agent collects the user's past purchase history and feedback and analyzes the user's preferences based on this. Next, the system recommends the most suitable fashion items from a database of environmentally friendly products. Furthermore, it learns from user feedback to improve the accuracy of its recommendations. This makes it easy for users to find environmentally friendly products. As a result, the AI ​​agent can recommend sustainable fashion items based on the user's preferences and past purchase history.

[0029] The AI ​​agent according to this embodiment comprises a data collection unit, an analysis unit, a recommendation unit, and an evolution unit. The data collection unit collects the user's past purchase history and feedback. For example, the data collection unit can collect information such as products the user has purchased in the past, the date and time of purchase, and the purchase price. The data collection unit can also collect feedback provided by the user. For example, it can collect evaluation scores, text comments, and survey results that the user has made about products. The analysis unit analyzes the data collected by the data collection unit to understand the user's preferences. For example, the analysis unit can use data mining techniques to analyze the user's purchase history and feedback and identify the user's preferences. The analysis unit can also use statistical analysis and machine learning algorithms to predict the user's preferences. For example, the analysis unit can extract the characteristics of fashion items that the user likes based on the user's past purchase history. The recommendation unit recommends sustainable fashion items based on the analysis results obtained by the analysis unit. For example, the recommendation unit can refer to a database of environmentally friendly products and select fashion items that match the user's preferences. The recommendation unit can also use recommendation algorithms to recommend the most suitable fashion items for the user. For example, the recommendation unit can recommend eco-friendly materials and recyclable products by considering the user's preferences and past purchase history. The evolution unit learns from user feedback on items recommended by the recommendation unit and improves the accuracy of recommendations. The evolution unit can, for example, collect ratings and comments made by users on recommended items and use this to improve the recommendation algorithm. Furthermore, the evolution unit can use a self-evolving algorithm to learn from user feedback and continuously improve the accuracy of recommendations. For example, the evolution unit can analyze user feedback and adjust the parameters of the recommendation algorithm to make more accurate recommendations. As a result, the AI ​​agent according to the embodiment can recommend sustainable fashion items based on the user's preferences and past purchase history.

[0030] The data collection department collects users' past purchase history and feedback. Specifically, it can collect information such as products a user has purchased in the past, the date and time of purchase, and the purchase amount. This makes it possible to understand the patterns of users' purchasing behavior in detail. For example, if a user frequently purchases products from a particular brand or category, the data collection department can record this trend and use it for later analysis. The data collection department can also collect feedback provided by users. For example, it can collect evaluation scores, text comments, and survey results that users have made about products. This allows for an understanding of user satisfaction and dissatisfaction, providing foundational data for making better recommendations. Furthermore, the data collection department can also collect users' online behavior data. For example, by collecting product pages viewed on websites, products added to carts, and search history, it is possible to understand users' interests and preferences in more detail. This allows for the accumulation of data to predict users' potential needs and make more appropriate recommendations. The data collection department centrally manages this data and can collaborate with other departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and recommendation departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analytics department analyzes data collected by the data collection department to understand user preferences. Specifically, it can use data mining techniques to analyze users' purchase history and feedback to identify user preferences. For example, it can analyze the characteristics and rating scores of products that users have purchased in the past to extract trends in fashion items that users prefer. The analytics department can also predict user preferences using statistical analysis and machine learning algorithms. For example, it can extract the characteristics of fashion items that users prefer based on their past purchase history and use this to predict future purchase trends. Furthermore, the analytics department can analyze user feedback to understand user satisfaction and dissatisfaction. For example, it can analyze the rating scores and text comments that users have made about products to identify what aspects users are satisfied or dissatisfied with. This allows the analytics department to understand user preferences and needs in more detail and obtain foundational data for making more appropriate recommendations. In addition, the analytics department can utilize historical data and statistical information to analyze long-term trends and patterns. For example, it can predict purchasing trends in specific seasons or events based on past purchase data and formulate future marketing strategies. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0032] The recommendation department recommends sustainable fashion items based on the analysis results obtained by the analysis department. Specifically, it can refer to a database of environmentally friendly products and select fashion items that match the user's preferences. For example, by utilizing a database that includes eco-friendly materials and recyclable products, it can recommend the most suitable fashion items considering the user's preferences and past purchase history. The recommendation department can also recommend the most suitable fashion items to the user using recommendation algorithms. For example, it can use algorithms such as collaborative filtering and content-based filtering to recommend the most suitable fashion items based on the user's preferences and past purchase history. Furthermore, the recommendation department can improve the recommendation algorithm based on user feedback to enhance the accuracy of recommendations. For example, by collecting evaluations and comments made by users on recommended items and adjusting the parameters of the recommendation algorithm based on this, more accurate recommendations can be made. In this way, the recommendation department can recommend the most suitable fashion items that meet the user's preferences and needs, thereby improving user satisfaction. In addition, the recommendation department can continuously revise recommendation results based on real-time updated data to respond to the latest situation. For example, if new products are added or user preferences change, the recommendation department can immediately incorporate the new data and update the recommendation results. This allows the recommendation department to provide highly accurate recommendations based on the latest information at all times, and to support quick and appropriate responses.

[0033] The evolutionary unit learns from user feedback on items recommended by the recommendation unit to improve the accuracy of recommendations. Specifically, it can collect ratings and comments made by users on recommended items and use this to improve the recommendation algorithm. For example, if a user gives a high rating to a recommended item, the unit can analyze the characteristics of that item and increase the probability of recommending other items with similar characteristics. The evolutionary unit can also use a self-evolving algorithm to learn from user feedback and continuously improve the accuracy of recommendations. For example, by analyzing user feedback and adjusting the parameters of the recommendation algorithm, the evolutionary unit can make more accurate recommendations. This allows the evolutionary unit to recommend the most suitable fashion items according to the user's preferences and needs, thereby improving user satisfaction. Furthermore, based on user feedback, the evolutionary unit can identify areas for improvement in the recommendation algorithm and continuously evolve the algorithm. For example, if a user gives a low rating to a particular item, the unit can analyze the characteristics of that item and decrease the probability of recommending other items with similar characteristics. This allows the evolutionary unit to provide highly accurate recommendations based on user feedback and improve user satisfaction. Furthermore, the evolutionary component can continuously improve its recommendation algorithms based on real-time updated data, enabling it to adapt to the latest situations. For example, if new products are added or user preferences change, the evolutionary component can immediately incorporate the new data and update the recommendation algorithm. This allows the evolutionary component to always provide highly accurate recommendations based on the latest information, supporting quick and appropriate responses.

[0034] The data collection unit can analyze a user's past purchase history and select the optimal data collection method. For example, the data collection unit can identify the times of day when a user frequently makes purchases and collect data during those times. Furthermore, if a user tends to make purchases from a particular device, the data collection unit can prioritize data collection from that device. Additionally, if a user tends to purchase products from a particular category, the data collection unit can focus on collecting data from that category. 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 the user's 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 moves, the data collection unit can prioritize collecting purchase history related to the new address. It can also collect purchase history related to a new hobby if the user starts one. Furthermore, if a user plans to attend a specific event, the data collection unit can collect purchase history related to that event. This allows for the collection of highly relevant data by filtering it based on the user's lifestyle and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, 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 when collecting purchase history. For example, if the user is traveling, the data collection unit can prioritize the collection of purchase history at the travel destination. Furthermore, if the user lives in a specific region, the data collection unit can prioritize the collection of purchase history in that region. Additionally, if the user frequently makes purchases at a particular store, the data collection unit can prioritize the collection of purchase history at that store. This allows for the priority collection of highly relevant data by considering the user's geographical location. 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 location data into a generating AI and have the generating AI select highly relevant history.

[0037] The data collection unit can analyze the user's social media activity and collect relevant history when collecting purchase history. For example, the data collection unit can collect purchased items that the user has shared on social media. It can also collect products that the user has "liked" on social media. Furthermore, the data collection unit can collect products from brands that the user follows on social media. In this way, relevant data can be collected by analyzing 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 perform the collection of relevant history.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data, and a concise analysis on less important data. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can have a generating AI execute an algorithm to evaluate the importance of the data and adjust the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can select an appropriate analysis algorithm depending on the category of fashion items. It can also apply different analysis algorithms depending on the category of the user's purchase history. Furthermore, the analysis unit can select the optimal analysis algorithm based on the data category. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the data category. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data categories into a generating AI and have the generating AI select the optimal analysis algorithm.

[0040] The analysis department can prioritize analyses based on the data submission date. For example, it can prioritize analyzing recently submitted data. It can also postpone analyzing older data. Furthermore, the analysis department can adjust the analysis schedule based on the submission date. This allows for efficient analysis by prioritizing analyses based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can determine the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI determine the order of analysis.

[0042] The recommendation system can adjust the level of detail in recommendations based on the importance of the products. For example, it can provide detailed recommendations for important products, and concise recommendations for less important products. Furthermore, the recommendation system can prioritize recommendations based on product importance. This allows for efficient recommendations by adjusting the level of detail based on product importance. Some or all of the above processes in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can have a generating AI execute an algorithm to evaluate product importance and adjust the level of detail in the recommendations.

[0043] The recommendation unit can apply different recommendation algorithms depending on the product category during the recommendation process. For example, the recommendation unit can select an appropriate recommendation algorithm based on the category of fashion items. It can also apply different recommendation algorithms based on the category of the user's purchase history. Furthermore, the recommendation unit can select the optimal recommendation algorithm based on the product category. This improves the accuracy of recommendations by applying the most suitable recommendation algorithm for each product category. Some or all of the above-described processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the product category into a generating AI and have the generating AI select the optimal recommendation algorithm.

[0044] The recommendation department can determine the priority of recommendations based on the product submission date. For example, the recommendation department can prioritize recently released products. It can also postpone older products. Furthermore, the recommendation department can adjust the recommendation schedule based on the submission date. This allows for efficient recommendations by prioritizing recommendations based on the product submission date. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not. For example, the recommendation department can input the product submission date into a generating AI and have the generating AI determine the recommendation priority.

[0045] The recommendation unit can adjust the order of recommendations based on the relevance of the products during the recommendation process. For example, the recommendation unit can prioritize recommending highly relevant products. It can also postpone recommending less relevant products. Furthermore, the recommendation unit can determine the order of recommendations based on the relevance of the products. This allows for efficient recommendations by adjusting the order of recommendations based on the relevance of the products. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input the relevance of the products into a generating AI and have the generating AI determine the order of recommendations.

[0046] The evolution unit can analyze past user feedback during evolution to select the optimal evolution method. For example, the evolution unit can select the optimal evolution method based on past user feedback. Furthermore, the evolution unit can extract areas for improvement from past user feedback and select an evolution method from that. In addition, the evolution unit can analyze past user feedback to determine evolution priorities. This allows for the selection of the optimal evolution method by analyzing past user feedback. Some or all of the above processes in the evolution unit may be performed using AI, for example, or without AI. For example, the evolution unit can input user feedback data into a generation AI and have the generation AI select the optimal evolution method.

[0047] The evolution unit can customize the evolution process based on the user's current living situation. For example, if the user moves, the evolution unit can select an evolution method related to the new address. It can also select an evolution method related to a new hobby if the user starts one. Furthermore, if the user plans to attend a specific event, the evolution unit can select an evolution method related to that event. This allows for more appropriate evolution by customizing the evolution process based on the user's current living situation. Some or all of the above-described processes in the evolution unit may be performed using AI, for example, or without AI. For example, the evolution unit can input user living situation data into a generating AI and have the generating AI customize the evolution process.

[0048] The evolution unit can select the optimal evolution method during evolution, taking into account the user's geographical location information. For example, if the user lives in an urban area, the evolution unit can select an evolution method suitable for urban areas. Similarly, if the user lives in a suburban area, the evolution unit can select an evolution method suitable for suburban areas. Furthermore, if the user frequently visits a particular region, the evolution unit can select an evolution method suitable for that region. This allows the evolution unit to select the optimal evolution method by considering the user's geographical location information. Some or all of the above-described processes in the evolution unit may be performed using AI, for example, or without AI. For example, the evolution unit can input the user's location data into a generation AI and have the generation AI select the optimal evolution method.

[0049] The evolution unit can analyze the user's social media activity during evolution and propose methods for evolution. For example, the evolution unit can propose methods for evolution based on feedback shared by the user on social media. It can also propose methods for evolution based on content that the user "likes" on social media. Furthermore, the evolution unit can propose methods for evolution based on information about brands that the user follows on social media. In this way, by analyzing the user's social media activity, it is possible to propose the optimal method for evolution. Some or all of the above processing in the evolution unit may be performed using AI, for example, or without AI. For example, the evolution unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of methods for evolution.

[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 consider the user's health data when collecting their past purchase history and feedback. For example, if a user uses a health management app, the unit can collect that data and analyze their purchase history based on their health status. The data collection unit can also collect the user's exercise data to understand their purchase trends after exercise. Furthermore, the data collection unit can collect the user's dietary data and analyze their purchase history based on their diet. By collecting data based on the user's health status, more personalized recommendations become possible.

[0052] The data collection unit can analyze a user's past purchase history and prioritize collecting purchases related to specific seasons or events. For example, the unit can collect items a user has purchased during the Christmas season to understand Christmas-related purchasing trends. It can also collect items a user has purchased during summer vacation to understand summer vacation-related purchasing trends. Furthermore, it can collect items a user has purchased on their birthday to understand birthday-related purchasing trends. By prioritizing the collection of data related to specific seasons or events, more relevant data can be obtained.

[0053] The data collection unit can consider external factors that influence user purchasing behavior when collecting purchase history. For example, the data collection unit can collect weather data and analyze the impact of weather on user purchasing behavior. It can also collect economic indicator data and analyze the impact of economic conditions on user purchasing behavior. Furthermore, the data collection unit can collect social trend data and analyze the impact of social trends on user purchasing behavior. By considering external factors, more accurate data collection becomes possible.

[0054] The data collection unit can consider advertising data that influences user purchasing behavior when collecting purchase history. For example, the data collection unit can collect data on ads that users have clicked on in the past and analyze the impact those ads had on the user's purchasing behavior. It can also collect data on ads that users have viewed in the past and analyze the impact those ads had on the user's purchasing behavior. Furthermore, the data collection unit can collect data on ads that users have shared in the past and analyze the impact those ads had on the user's purchasing behavior. By considering advertising data, more accurate data collection becomes possible.

[0055] The data collection unit can consider data from friends and family that influence the user's purchasing behavior when collecting purchase history. For example, the data collection unit can collect items that the user's friends and family have purchased in the past and analyze their influence. It can also collect items that the user's friends and family have rated in the past and analyze their influence. Furthermore, the data collection unit can collect items that the user's friends and family have shared in the past and analyze their influence. This allows for more accurate data collection by considering data from friends and family.

[0056] The analysis department can consider psychological factors that influence user purchasing behavior during analysis. For example, the analysis department can analyze and consider the impact of stress levels on user purchasing behavior. It can also analyze and consider the impact of motivation on user purchasing behavior. Furthermore, it can analyze and consider the impact of self-efficacy on user purchasing behavior. This allows for more accurate analysis by considering psychological factors.

[0057] The analysis department can consider cultural factors that influence user purchasing behavior during analysis. For example, the analysis department can analyze the cultural background that influences user purchasing behavior and consider its impact. Furthermore, the analysis department can analyze religious beliefs that influence user purchasing behavior and consider their impact. In addition, the analysis department can analyze social norms that influence user purchasing behavior and consider their impact. This allows for more accurate analysis by considering cultural factors.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data collection unit collects the user's past purchase history and feedback. For example, it collects information such as the products the user has purchased in the past, the date and time of purchase, and the purchase price. It also collects feedback provided by the user, such as rating scores, text comments, and survey results. Step 2: The analysis department analyzes the data collected by the data collection department to understand user preferences. For example, they use data mining techniques, statistical analysis, and machine learning algorithms to analyze users' purchase history and feedback to identify and predict user preferences. Step 3: The recommendation department recommends sustainable fashion items based on the analysis results obtained by the analysis department. For example, it refers to a database of environmentally friendly products, selects fashion items that match the user's preferences, and uses a recommendation algorithm to recommend the most suitable items. Step 4: The evolution unit learns from user feedback on items recommended by the recommendation unit and improves the accuracy of recommendations. For example, it collects ratings and comments made by users on recommended items, uses this to improve the recommendation algorithm, and continuously improves the accuracy of recommendations using a self-evolving algorithm.

[0060] (Example of form 2) An AI agent according to an embodiment of the present invention is a system that recommends fashion items made from sustainable materials and production methods based on the user's preferences and past purchase history. This system uses a machine learning model to analyze the user's preferences. This model learns from the user's past purchase history and feedback to understand the user's preferences. Next, it recommends the most suitable sustainable fashion items to the user using a database of environmentally friendly products. This database contains products made from sustainable materials and production methods. Furthermore, it improves the accuracy of recommendations using a self-evolving algorithm that learns from user feedback. This allows users to easily find environmentally friendly products and promotes transformation in the fashion industry by providing sustainable options. The AI ​​agent also improves transparency between users and brands and sets new trends in sustainable fashion. The target audience is environmentally conscious young people aged 18 to 40 who are fashion-conscious and seek a sustainable lifestyle. This AI agent is expected to contribute to the expansion of the sustainable fashion market and raise awareness of reducing environmental impact. For example, the AI ​​agent collects the user's past purchase history and feedback and analyzes the user's preferences based on this. Next, the system recommends the most suitable fashion items from a database of environmentally friendly products. Furthermore, it learns from user feedback to improve the accuracy of its recommendations. This makes it easy for users to find environmentally friendly products. As a result, the AI ​​agent can recommend sustainable fashion items based on the user's preferences and past purchase history.

[0061] The AI ​​agent according to this embodiment comprises a data collection unit, an analysis unit, a recommendation unit, and an evolution unit. The data collection unit collects the user's past purchase history and feedback. For example, the data collection unit can collect information such as products the user has purchased in the past, the date and time of purchase, and the purchase price. The data collection unit can also collect feedback provided by the user. For example, it can collect evaluation scores, text comments, and survey results that the user has made about products. The analysis unit analyzes the data collected by the data collection unit to understand the user's preferences. For example, the analysis unit can use data mining techniques to analyze the user's purchase history and feedback and identify the user's preferences. The analysis unit can also use statistical analysis and machine learning algorithms to predict the user's preferences. For example, the analysis unit can extract the characteristics of fashion items that the user likes based on the user's past purchase history. The recommendation unit recommends sustainable fashion items based on the analysis results obtained by the analysis unit. For example, the recommendation unit can refer to a database of environmentally friendly products and select fashion items that match the user's preferences. The recommendation unit can also use recommendation algorithms to recommend the most suitable fashion items for the user. For example, the recommendation unit can recommend eco-friendly materials and recyclable products by considering the user's preferences and past purchase history. The evolution unit learns from user feedback on items recommended by the recommendation unit and improves the accuracy of recommendations. The evolution unit can, for example, collect ratings and comments made by users on recommended items and use this to improve the recommendation algorithm. Furthermore, the evolution unit can use a self-evolving algorithm to learn from user feedback and continuously improve the accuracy of recommendations. For example, the evolution unit can analyze user feedback and adjust the parameters of the recommendation algorithm to make more accurate recommendations. As a result, the AI ​​agent according to the embodiment can recommend sustainable fashion items based on the user's preferences and past purchase history.

[0062] The data collection department collects users' past purchase history and feedback. Specifically, it can collect information such as products a user has purchased in the past, the date and time of purchase, and the purchase amount. This makes it possible to understand the patterns of users' purchasing behavior in detail. For example, if a user frequently purchases products from a particular brand or category, the data collection department can record this trend and use it for later analysis. The data collection department can also collect feedback provided by users. For example, it can collect evaluation scores, text comments, and survey results that users have made about products. This allows for an understanding of user satisfaction and dissatisfaction, providing foundational data for making better recommendations. Furthermore, the data collection department can also collect users' online behavior data. For example, by collecting product pages viewed on websites, products added to carts, and search history, it is possible to understand users' interests and preferences in more detail. This allows for the accumulation of data to predict users' potential needs and make more appropriate recommendations. The data collection department centrally manages this data and can collaborate with other departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and recommendation departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0063] The analytics department analyzes data collected by the data collection department to understand user preferences. Specifically, it can use data mining techniques to analyze users' purchase history and feedback to identify user preferences. For example, it can analyze the characteristics and rating scores of products that users have purchased in the past to extract trends in fashion items that users prefer. The analytics department can also predict user preferences using statistical analysis and machine learning algorithms. For example, it can extract the characteristics of fashion items that users prefer based on their past purchase history and use this to predict future purchase trends. Furthermore, the analytics department can analyze user feedback to understand user satisfaction and dissatisfaction. For example, it can analyze the rating scores and text comments that users have made about products to identify what aspects users are satisfied or dissatisfied with. This allows the analytics department to understand user preferences and needs in more detail and obtain foundational data for making more appropriate recommendations. In addition, the analytics department can utilize historical data and statistical information to analyze long-term trends and patterns. For example, it can predict purchasing trends in specific seasons or events based on past purchase data and formulate future marketing strategies. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0064] The recommendation department recommends sustainable fashion items based on the analysis results obtained by the analysis department. Specifically, it can refer to a database of environmentally friendly products and select fashion items that match the user's preferences. For example, by utilizing a database that includes eco-friendly materials and recyclable products, it can recommend the most suitable fashion items considering the user's preferences and past purchase history. The recommendation department can also recommend the most suitable fashion items to the user using recommendation algorithms. For example, it can use algorithms such as collaborative filtering and content-based filtering to recommend the most suitable fashion items based on the user's preferences and past purchase history. Furthermore, the recommendation department can improve the recommendation algorithm based on user feedback to enhance the accuracy of recommendations. For example, by collecting evaluations and comments made by users on recommended items and adjusting the parameters of the recommendation algorithm based on this, more accurate recommendations can be made. In this way, the recommendation department can recommend the most suitable fashion items that meet the user's preferences and needs, thereby improving user satisfaction. In addition, the recommendation department can continuously revise recommendation results based on real-time updated data to respond to the latest situation. For example, if new products are added or user preferences change, the recommendation department can immediately incorporate the new data and update the recommendation results. This allows the recommendation department to provide highly accurate recommendations based on the latest information at all times, and to support quick and appropriate responses.

[0065] The evolutionary unit learns from user feedback on items recommended by the recommendation unit to improve the accuracy of recommendations. Specifically, it can collect ratings and comments made by users on recommended items and use this to improve the recommendation algorithm. For example, if a user gives a high rating to a recommended item, the unit can analyze the characteristics of that item and increase the probability of recommending other items with similar characteristics. The evolutionary unit can also use a self-evolving algorithm to learn from user feedback and continuously improve the accuracy of recommendations. For example, by analyzing user feedback and adjusting the parameters of the recommendation algorithm, the evolutionary unit can make more accurate recommendations. This allows the evolutionary unit to recommend the most suitable fashion items according to the user's preferences and needs, thereby improving user satisfaction. Furthermore, based on user feedback, the evolutionary unit can identify areas for improvement in the recommendation algorithm and continuously evolve the algorithm. For example, if a user gives a low rating to a particular item, the unit can analyze the characteristics of that item and decrease the probability of recommending other items with similar characteristics. This allows the evolutionary unit to provide highly accurate recommendations based on user feedback and improve user satisfaction. Furthermore, the evolutionary component can continuously improve its recommendation algorithms based on real-time updated data, enabling it to adapt to the latest situations. For example, if new products are added or user preferences change, the evolutionary component can immediately incorporate the new data and update the recommendation algorithm. This allows the evolutionary component to always provide highly accurate recommendations based on the latest information, supporting quick and appropriate responses.

[0066] 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 delay collection until the user is relaxed. Alternatively, if the user is excited, the data collection unit can immediately collect purchase history and perform real-time analysis. Furthermore, if the user is tired, the data collection unit can adjust the collection timing until the user has rested. This allows for data collection at a more appropriate time by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0067] The data collection unit can analyze a user's past purchase history and select the optimal data collection method. For example, the data collection unit can identify the times of day when a user frequently makes purchases and collect data during those times. Furthermore, if a user tends to make purchases from a particular device, the data collection unit can prioritize data collection from that device. Additionally, if a user tends to purchase products from a particular category, the data collection unit can focus on collecting data from that category. 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 the user's purchase history data into a generating AI and have the generating AI select the optimal data collection method.

[0068] The data collection unit can filter purchase history based on the user's current lifestyle and areas of interest. For example, if a user moves, the data collection unit can prioritize collecting purchase history related to the new address. It can also collect purchase history related to a new hobby if the user starts one. Furthermore, if a user plans to attend a specific event, the data collection unit can collect purchase history related to that event. This allows for the collection of highly relevant data by filtering it based on the user's lifestyle and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user lifestyle data into a generating AI and have the generating AI perform the filtering.

[0069] 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 excited, the data collection unit can prioritize collecting recent purchase history. If the user is relaxed, the data collection unit can also collect detailed past purchase history. Furthermore, if the user is stressed, the data collection unit can postpone collecting less important purchase history. This allows for the priority collection of important data by determining the priority of purchase history to collect 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 voice data into a generative AI and have the generative AI perform emotion estimation.

[0070] The data collection unit can prioritize the collection of highly relevant purchase history by considering the user's geographical location when collecting purchase history. For example, if the user is traveling, the data collection unit can prioritize the collection of purchase history at the travel destination. Furthermore, if the user lives in a specific region, the data collection unit can prioritize the collection of purchase history in that region. Additionally, if the user frequently makes purchases at a particular store, the data collection unit can prioritize the collection of purchase history at that store. This allows for the priority collection of highly relevant data by considering the user's geographical location. 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 location data into a generating AI and have the generating AI select highly relevant history.

[0071] The data collection unit can analyze the user's social media activity and collect relevant history when collecting purchase history. For example, the data collection unit can collect purchased items that the user has shared on social media. It can also collect products that the user has "liked" on social media. Furthermore, the data collection unit can collect products from brands that the user follows on social media. In this way, relevant data can be collected by analyzing 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 perform the collection of relevant history.

[0072] 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. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis results can be made easier for the user to understand. 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 or not using AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data, and a concise analysis on less important data. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can have a generating AI execute an algorithm to evaluate the importance of the data and adjust the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can select an appropriate analysis algorithm depending on the category of fashion items. It can also apply different analysis algorithms depending on the category of the user's purchase history. Furthermore, the analysis unit can select the optimal analysis algorithm based on the data category. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the data category. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data categories into a generating AI and have the generating AI select the optimal analysis algorithm.

[0075] 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 detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually engaging analysis. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with an analysis result of an appropriate length. 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 or not using AI. For example, the analysis unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0076] The analysis department can prioritize analyses based on the data submission date. For example, it can prioritize analyzing recently submitted data. It can also postpone analyzing older data. Furthermore, the analysis department can adjust the analysis schedule based on the submission date. This allows for efficient analysis by prioritizing analyses based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0077] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can determine the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI determine the order of analysis.

[0078] 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, the recommendation system can provide detailed recommendations. If the user is in a hurry, it can provide concise recommendations. Furthermore, if the user is excited, it can provide visually appealing recommendations. By adjusting the way recommendations are presented according to the user's emotions, the system can provide recommendations that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0079] The recommendation system can adjust the level of detail in recommendations based on the importance of the products. For example, it can provide detailed recommendations for important products, and concise recommendations for less important products. Furthermore, the recommendation system can prioritize recommendations based on product importance. This allows for efficient recommendations by adjusting the level of detail based on product importance. Some or all of the above processes in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can have a generating AI execute an algorithm to evaluate product importance and adjust the level of detail in the recommendations.

[0080] The recommendation unit can apply different recommendation algorithms depending on the product category during the recommendation process. For example, the recommendation unit can select an appropriate recommendation algorithm based on the category of fashion items. It can also apply different recommendation algorithms based on the category of the user's purchase history. Furthermore, the recommendation unit can select the optimal recommendation algorithm based on the product category. This improves the accuracy of recommendations by applying the most suitable recommendation algorithm for each product category. Some or all of the above-described processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the product category into a generating AI and have the generating AI select the optimal recommendation algorithm.

[0081] The recommendation section 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 section can provide short, concise recommendations. If the user is relaxed, it can provide detailed recommendations. Furthermore, if the user is excited, it can provide visually appealing recommendations. By adjusting the length of recommendations according to the user's emotions, the recommendation section can provide recommendations of an appropriate length for the user. 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 section may be performed using AI or not. For example, the recommendation section can input user voice data into a generative AI and have the generative AI perform emotion estimation.

[0082] The recommendation department can determine the priority of recommendations based on the product submission date. For example, the recommendation department can prioritize recently released products. It can also postpone older products. Furthermore, the recommendation department can adjust the recommendation schedule based on the submission date. This allows for efficient recommendations by prioritizing recommendations based on the product submission date. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not. For example, the recommendation department can input the product submission date into a generating AI and have the generating AI determine the recommendation priority.

[0083] The recommendation unit can adjust the order of recommendations based on the relevance of the products during the recommendation process. For example, the recommendation unit can prioritize recommending highly relevant products. It can also postpone recommending less relevant products. Furthermore, the recommendation unit can determine the order of recommendations based on the relevance of the products. This allows for efficient recommendations by adjusting the order of recommendations based on the relevance of the products. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input the relevance of the products into a generating AI and have the generating AI determine the order of recommendations.

[0084] The evolution unit can estimate the user's emotions and adjust the evolution method based on the estimated emotions. For example, if the user is relaxed, the evolution unit can collect detailed feedback and adjust the evolution method. If the user is in a hurry, the evolution unit can collect concise feedback and adjust the evolution method. Furthermore, if the user is excited, the evolution unit can collect visually appealing feedback and adjust the evolution method. This allows for more appropriate evolution by adjusting the evolution method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evolution unit may be performed using AI or not using AI. For example, the evolution unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0085] The evolution unit can analyze past user feedback during evolution to select the optimal evolution method. For example, the evolution unit can select the optimal evolution method based on past user feedback. Furthermore, the evolution unit can extract areas for improvement from past user feedback and select an evolution method from that. In addition, the evolution unit can analyze past user feedback to determine evolution priorities. This allows for the selection of the optimal evolution method by analyzing past user feedback. Some or all of the above processes in the evolution unit may be performed using AI, for example, or without AI. For example, the evolution unit can input user feedback data into a generation AI and have the generation AI select the optimal evolution method.

[0086] The evolution unit can customize the evolution process based on the user's current living situation. For example, if the user moves, the evolution unit can select an evolution method related to the new address. It can also select an evolution method related to a new hobby if the user starts one. Furthermore, if the user plans to attend a specific event, the evolution unit can select an evolution method related to that event. This allows for more appropriate evolution by customizing the evolution process based on the user's current living situation. Some or all of the above-described processes in the evolution unit may be performed using AI, for example, or without AI. For example, the evolution unit can input user living situation data into a generating AI and have the generating AI customize the evolution process.

[0087] The evolution unit can estimate the user's emotions and determine evolutionary priorities based on the estimated emotions. For example, if the user is excited, the evolution unit can immediately increase the evolutionary priority. If the user is relaxed, the evolutionary unit can set the evolutionary priority to normal. Furthermore, if the user is stressed, the evolutionary unit can set the evolutionary priority to low. This allows for more appropriate evolution by determining evolutionary priorities 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 evolutionary unit may be performed using AI, or not using AI. For example, the evolutionary unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0088] The evolution unit can select the optimal evolution method during evolution, taking into account the user's geographical location information. For example, if the user lives in an urban area, the evolution unit can select an evolution method suitable for urban areas. Similarly, if the user lives in a suburban area, the evolution unit can select an evolution method suitable for suburban areas. Furthermore, if the user frequently visits a particular region, the evolution unit can select an evolution method suitable for that region. This allows the evolution unit to select the optimal evolution method by considering the user's geographical location information. Some or all of the above-described processes in the evolution unit may be performed using AI, for example, or without AI. For example, the evolution unit can input the user's location data into a generation AI and have the generation AI select the optimal evolution method.

[0089] The evolution unit can analyze the user's social media activity during evolution and propose methods for evolution. For example, the evolution unit can propose methods for evolution based on feedback shared by the user on social media. It can also propose methods for evolution based on content that the user "likes" on social media. Furthermore, the evolution unit can propose methods for evolution based on information about brands that the user follows on social media. In this way, by analyzing the user's social media activity, it is possible to propose the optimal method for evolution. Some or all of the above processing in the evolution unit may be performed using AI, for example, or without AI. For example, the evolution unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of methods for evolution.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The data collection unit can consider the user's health data when collecting their past purchase history and feedback. For example, if a user uses a health management app, the unit can collect that data and analyze their purchase history based on their health status. The data collection unit can also collect the user's exercise data to understand their purchase trends after exercise. Furthermore, the data collection unit can collect the user's dietary data and analyze their purchase history based on their diet. By collecting data based on the user's health status, more personalized recommendations become possible.

[0092] The data collection unit can estimate the user's emotions and customize how purchase history is collected based on those estimated emotions. For example, if a user is sad, the unit can prioritize collecting items purchased to lift their spirits. If a user is happy, the unit can collect items purchased to maintain that feeling. Furthermore, if a user is feeling anxious, the unit can collect items purchased to gain a sense of security. By customizing the collection method according to the user's emotions, more relevant data can be collected.

[0093] The data collection unit can analyze a user's past purchase history and prioritize collecting purchases related to specific seasons or events. For example, the unit can collect items a user has purchased during the Christmas season to understand Christmas-related purchasing trends. It can also collect items a user has purchased during summer vacation to understand summer vacation-related purchasing trends. Furthermore, it can collect items a user has purchased on their birthday to understand birthday-related purchasing trends. By prioritizing the collection of data related to specific seasons or events, more relevant data can be obtained.

[0094] The data collection unit can consider external factors that influence user purchasing behavior when collecting purchase history. For example, the data collection unit can collect weather data and analyze the impact of weather on user purchasing behavior. It can also collect economic indicator data and analyze the impact of economic conditions on user purchasing behavior. Furthermore, the data collection unit can collect social trend data and analyze the impact of social trends on user purchasing behavior. By considering external factors, more accurate data collection becomes possible.

[0095] The data collection unit can estimate the user's emotions and select the types of data to collect based on those estimated emotions. For example, if the user is stressed, the unit can prioritize collecting items the user purchased to relieve stress. If the user is happy, the unit can also collect items the user purchased to maintain that feeling. Furthermore, if the user is tired, the unit can collect items the user purchased to relax. By selecting the types of data to collect according to the user's emotions, more appropriate data can be collected.

[0096] The data collection unit can consider advertising data that influences user purchasing behavior when collecting purchase history. For example, the data collection unit can collect data on ads that users have clicked on in the past and analyze the impact those ads had on the user's purchasing behavior. It can also collect data on ads that users have viewed in the past and analyze the impact those ads had on the user's purchasing behavior. Furthermore, the data collection unit can collect data on ads that users have shared in the past and analyze the impact those ads had on the user's purchasing behavior. By considering advertising data, more accurate data collection becomes possible.

[0097] The data collection unit can consider data from friends and family that influence the user's purchasing behavior when collecting purchase history. For example, the data collection unit can collect items that the user's friends and family have purchased in the past and analyze their influence. It can also collect items that the user's friends and family have rated in the past and analyze their influence. Furthermore, the data collection unit can collect items that the user's friends and family have shared in the past and analyze their influence. This allows for more accurate data collection by considering data from friends and family.

[0098] The analysis unit can estimate the user's emotions and adjust the visualization method of the analysis based on those emotions. For example, if the user is relaxed, the analysis results can be visualized using detailed graphs and charts. If the user is in a hurry, the analysis results can be visualized using simple icons and symbols. Furthermore, if the user is excited, the analysis results can be visualized using colorful visuals. By adjusting the visualization method of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0099] The analysis department can consider psychological factors that influence user purchasing behavior during analysis. For example, the analysis department can analyze and consider the impact of stress levels on user purchasing behavior. It can also analyze and consider the impact of motivation on user purchasing behavior. Furthermore, it can analyze and consider the impact of self-efficacy on user purchasing behavior. This allows for more accurate analysis by considering psychological factors.

[0100] The analysis department can consider cultural factors that influence user purchasing behavior during analysis. For example, the analysis department can analyze the cultural background that influences user purchasing behavior and consider its impact. Furthermore, the analysis department can analyze religious beliefs that influence user purchasing behavior and consider their impact. In addition, the analysis department can analyze social norms that influence user purchasing behavior and consider their impact. This allows for more accurate analysis by considering cultural factors.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The data collection unit collects the user's past purchase history and feedback. For example, it collects information such as the products the user has purchased in the past, the date and time of purchase, and the purchase price. It also collects feedback provided by the user, such as rating scores, text comments, and survey results. Step 2: The analysis department analyzes the data collected by the data collection department to understand user preferences. For example, they use data mining techniques, statistical analysis, and machine learning algorithms to analyze users' purchase history and feedback to identify and predict user preferences. Step 3: The recommendation department recommends sustainable fashion items based on the analysis results obtained by the analysis department. For example, it refers to a database of environmentally friendly products, selects fashion items that match the user's preferences, and uses a recommendation algorithm to recommend the most suitable items. Step 4: The evolution unit learns from user feedback on items recommended by the recommendation unit and improves the accuracy of recommendations. For example, it collects ratings and comments made by users on recommended items, uses this to improve the recommendation algorithm, and continuously improves the accuracy of recommendations using a self-evolving algorithm.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, and evolution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects the user's past purchase history and feedback. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to understand the user's preferences. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends the most suitable fashion items to the user by referring to a database of environmentally friendly products. The evolution unit is implemented by the identification processing unit 290 of the data processing unit 12 and learns from user feedback to improve the accuracy of recommendations. 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.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, and evolution unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the user's past purchase history and feedback. The analysis unit is implemented by the identification unit 290 of the data processing unit 12 and analyzes the collected data to understand the user's preferences. The recommendation unit is implemented by the identification unit 290 of the data processing unit 12 and recommends the most suitable fashion items to the user by referring to a database of environmentally friendly products. The evolution unit is implemented by the identification unit 290 of the data processing unit 12 and learns from user feedback to improve the accuracy of recommendations. 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.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, and evolution unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the user's past purchase history and feedback. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to understand the user's preferences. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends the most suitable fashion items to the user by referring to a database of environmentally friendly products. The evolution unit is implemented by the identification processing unit 290 of the data processing unit 12 and learns from user feedback to improve the accuracy of recommendations. 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.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, and evolution unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects the user's past purchase history and feedback. The analysis unit is implemented by the identification unit 290 of the data processing unit 12 and analyzes the collected data to understand the user's preferences. The recommendation unit is implemented by the identification unit 290 of the data processing unit 12 and recommends the most suitable fashion items to the user by referring to a database of environmentally friendly products. The evolution unit is implemented by the identification unit 290 of the data processing unit 12 and learns from user feedback to improve the accuracy of recommendations. 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) A collection unit that collects users' past purchase history and feedback, The data collected by the aforementioned collection unit is analyzed by an analysis unit to understand user preferences, Based on the analysis results obtained by the aforementioned analysis department, a recommendation department recommends sustainable fashion items, The system includes an evolution unit that learns user feedback on items recommended by the recommendation unit and improves the accuracy of recommendations. 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 by considering 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 is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is 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 is During analysis, prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned recommendation department, 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 aforementioned recommendation department, 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 aforementioned recommendation department, 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 aforementioned recommendation department, It estimates the user's sentiment and adjusts the length of recommendations based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recommendation department, When making a recommendation, we will prioritize recommendations based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recommendation department, 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 aforementioned evolutionary section is It estimates user emotions and adjusts the evolution method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned evolutionary section is During evolution, we analyze past user feedback to select the optimal evolution method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned evolutionary section is During evolution, the means of evolution are customized based on the user's current life circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned evolutionary section is It estimates user emotions and determines evolutionary priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned evolutionary section is During evolution, the optimal evolution method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned evolutionary section is During evolution, we analyze users' social media activity and propose methods for evolution. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 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 collection unit that collects users' past purchase history and feedback, The data collected by the aforementioned collection unit is analyzed by an analysis unit to understand user preferences, Based on the analysis results obtained by the aforementioned analysis department, a recommendation department recommends sustainable fashion items, The system includes an evolution unit that learns user feedback on items recommended by the recommendation unit and improves the accuracy of recommendations. 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 by considering 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 is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system according to feature 1.

10. The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system according to feature 1.

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