system

Abstract

The system according to this embodiment aims to analyze a user's past purchase history and preferences and suggest fashion items that are best suited to the individual. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects the user's past purchase history and preference data. The analysis unit analyzes the data collected by the collection unit and learns the user's preferences and tendencies. The suggestion unit suggests the most suitable fashion items for the individual based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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 prior art, there is a problem that it has not been possible to sufficiently utilize the user's past purchase history and preferences to propose the most suitable fashion items for an individual.

[0005] The system according to the embodiment aims to analyze the user's past purchase history and preferences and propose the most suitable fashion items for an individual.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects data on the user's past purchase history and preferences. The analysis unit analyzes the data collected by the data collection unit and learns the user's preferences and tendencies. The suggestion unit suggests the most suitable fashion items for the individual based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze a user's past purchase history and preferences and suggest fashion items that are best suited to the individual. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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 system according to an embodiment of the present invention is a system that analyzes a user's past purchase history, preferences, and local trend information to propose the most suitable fashion items for the individual. This AI agent system collects data on the user's past purchase history and preferences, and the AI ​​analyzes the collected data to learn the user's preferences and tendencies. Furthermore, it updates local trend information in real time and integrates this information to propose the most suitable fashion items for the individual. For example, the AI ​​agent system collects data on the user's past purchase history and preferences. For example, it can collect data such as the date and time of purchase, the purchased items, and the purchase amount. Next, the AI ​​agent system analyzes the collected data to learn the user's preferences and tendencies. For example, the AI ​​analyzes data such as color, style, and brand to learn the user's preferences. Furthermore, the AI ​​agent system updates local trend information in real time and reflects it in its suggestions. For example, it collects SNS posts and sales data to update local trend information. As a result, the AI ​​agent system can propose the most suitable fashion items for the user. This allows the user to reduce the time spent selecting fashion items and improve customer satisfaction. An increase in repeat purchase rates can also be expected. The target audience is men and women in their 20s to 40s who are interested in fashion, and the goal is to solve the problem of spending too much time choosing fashion items. By using generative AI to create user profiles and recommend items, the system saves time and improves customer satisfaction. This allows the AI ​​agent system to analyze the user's past purchase history and preference data to suggest the most suitable fashion items for each individual.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects data on the user's past purchase history and preferences. The data collection unit can collect data such as purchase date and time, purchased items, and purchase amount. The data collection unit obtains data from online shopping sites, for example. The data collection unit can also collect preference data entered by the user. For example, it can collect data such as the user's preferred colors, styles, and brands. The analysis unit analyzes the data collected by the data collection unit and learns the user's preferences and tendencies. The analysis unit analyzes the data using machine learning algorithms, for example. For example, the analysis unit learns the user's preferred colors and styles based on the user's purchase history. The analysis unit can also learn the user's preferred brands based on the user's preference data. The suggestion unit suggests the most suitable fashion items for the individual based on the analysis results obtained by the analysis unit. The suggestion unit selects the most suitable fashion items based on the user's preferences and tendencies, for example. The suggestion unit suggests items in the user's preferred colors and styles, for example. Furthermore, the suggestion function can also suggest items from brands that the user prefers. This allows the AI ​​agent system, according to the embodiment, to analyze the user's past purchase history and preference data to suggest the most suitable fashion items for the individual.

[0030] The data collection unit collects data on users' past purchase history and preferences. Specifically, the data collection unit can collect detailed data such as purchase date and time, purchased items, and purchase amount. This data is important for understanding users' purchasing behavior in detail. For example, the data collection unit can obtain data from online shopping sites via APIs. This allows for accurate collection of information on products that users have purchased in the past. The data collection unit can also collect preference data entered by users. For example, it can collect data on the user's preferred colors, styles, and brands. This data is important for understanding user preferences. The data collection unit can collect information entered by users in surveys and profile setting screens. Furthermore, the data collection unit can also collect behavioral data such as the user's browsing history and search history. This allows for understanding the products and categories that users are interested in. The data collection unit centrally manages this data and makes it accessible to the analysis and suggestion units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes data collected by the data collection unit to learn user preferences and trends. Specifically, the analysis unit uses machine learning algorithms to analyze data. For example, it learns user preferences for colors and styles based on user purchase history. Machine learning algorithms can find patterns in past data and predict user preferences. For example, it can use clustering algorithms to classify users into groups with similar preferences. The analysis unit can also learn user preferences for brands based on user preference data. For example, it can use collaborative filtering algorithms to compare data from other users and identify brands that are likely to interest the user. Furthermore, the analysis unit can analyze user behavior data to understand changes in user interests and concerns in real time. For example, it can analyze products recently searched for and categories viewed by users to identify their current interests. This allows the analysis unit to accurately understand user preferences and trends and provide useful information to the recommendation unit. In addition, the analysis unit can utilize past data and statistical information to predict long-term changes in preferences and trends. This allows the analysis unit to not only grasp the situation in real time but also respond to long-term changes in preferences, improving the reliability and accuracy of the entire system.

[0032] The suggestion department proposes the most suitable fashion items for each individual based on the analysis results obtained by the analysis department. Specifically, the suggestion department selects the most suitable fashion items based on the user's preferences and tendencies. For example, it may suggest items in colors and styles that the user likes. The suggestion department can identify items that the user is likely to be interested in based on the user's past purchase history and preference data. For example, it can suggest new products in colors and styles similar to items the user has purchased in the past. The suggestion department can also suggest items from brands that the user likes. For example, it can suggest new products from the same brand based on products the user has purchased in the past. Furthermore, the suggestion department can suggest items that match the user's current interests and concerns based on the user's behavioral data. For example, it can suggest related items based on products the user has recently searched for or categories they have viewed. The suggestion department can use means such as email and push notifications to notify the user of these suggestions. This allows the suggestion department to propose the most suitable fashion items to the user at the right time, increasing the user's purchasing intent. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the proposal department to consistently provide users with the best possible solutions and improve the overall system performance.

[0033] The suggestion department can update local trend information in real time and reflect it in its suggestions. For example, the suggestion department can collect SNS posts and sales data to update local trend information. For example, the suggestion department can make suggestions based on local fashion event information. It can also make suggestions based on new product information from popular local brands. Furthermore, the suggestion department can make suggestions based on seasonal local trend information. By updating local trend information in real time, it is possible to suggest more appropriate fashion items. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without AI. For example, in order to collect local trend information, the suggestion department can use generative AI to analyze SNS posts and extract trend information.

[0034] The proposal unit can make the user interface customizable. For example, the proposal unit can provide an interface that reflects the user's preferred colors and designs. For example, the proposal unit can allow the user to select a layout that is easy to use. The proposal unit can also customize the interface so that the user can easily access the information they need. This improves user convenience by making the user interface customizable. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input user preference data into a generating AI and have the generating AI perform the customization of the user interface.

[0035] The data collection unit can analyze the user's past purchase history and select the optimal data collection method. For example, the data collection unit may prioritize collecting items that the user frequently purchases. For example, if the user prefers a particular brand, the data collection unit may focus on collecting purchase history for that brand. The data collection unit can also select a seasonal data collection method if the user tends to purchase different items each season. In this way, the optimal data collection method can be selected by analyzing the user's past purchase history. 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 purchase history data into a generating AI and have the generating AI select the optimal data collection method.

[0036] The data collection unit can filter the collected purchase history based on the user's current lifestyle and areas of interest. For example, if a user has started a new job, the unit will prioritize collecting business casual items. If a user is planning a trip, the unit will collect items suitable for travel. The unit can also collect items related to a user's hobby if the user has one. This allows for the collection of more relevant data by filtering based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering.

[0037] The data collection unit can prioritize collecting highly relevant purchase history based on the user's geographical location information when collecting purchase history. For example, if the user lives in a specific region, the data collection unit will prioritize collecting items popular in that region. For example, if the user is traveling, the data collection unit will prioritize collecting purchase history from their travel destination. Furthermore, if the user is planning to move, the data collection unit can collect purchase history based on trend information in the new region. This allows for the collection of more appropriate data by prioritizing the collection of highly relevant purchase history based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant purchase history.

[0038] The data collection unit can analyze a user's social media activity and collect relevant history when collecting purchase history. For example, the data collection unit can collect items that the user has shared on social media. For example, the data collection unit can collect purchase history based on posts from brands or influencers that the user follows. The data collection unit can also collect items that the user has "liked" on social media. In this way, relevant history 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.

[0039] 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 performs a detailed analysis on important data. For example, the analysis unit performs a concise analysis on less important data. The analysis unit can also perform a detailed analysis on data of high interest to the user. By adjusting the level of detail of the analysis based on the importance of the data, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of fashion items. For example, the analysis unit can apply different analysis algorithms depending on the brand. Furthermore, the analysis unit can also apply different analysis algorithms depending on the season. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0041] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. For example, the analysis unit may prioritize the analysis of data submitted by users within a specific period. Furthermore, if the user is in a hurry, the analysis unit may prioritize the analysis regardless of the submission date. This allows for the provision of more appropriate analysis results by determining the priority of analysis based on the data submission date. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may prioritize the analysis of data of high user interest. The analysis unit may also prioritize the analysis of data related to the user's past purchase history. By adjusting the order of analysis based on the relevance of the data, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0043] The proposal unit can adjust the level of detail in its proposals based on the importance of the products. For example, it can provide detailed proposals for important products, and concise proposals for less important products. It can also provide detailed proposals for products of high user interest. By adjusting the level of detail in proposals based on product importance, it can provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the products into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0044] The suggestion unit can apply different suggestion algorithms depending on the product category when making suggestions. For example, the suggestion unit can apply different suggestion algorithms depending on the category of fashion items. For example, the suggestion unit can apply different suggestion algorithms depending on the brand. Furthermore, the suggestion unit can also apply different suggestion algorithms depending on the season. By applying different suggestion algorithms depending on the product category, it is possible to provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the product category into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0045] The proposal department can prioritize proposals based on the timing of product submission. For example, it might prioritize recently arrived products. Or, it might suggest products based on the user's purchase history within a specific period. Furthermore, if the user is in a hurry, the proposal department can prioritize suggestions regardless of the submission timing. This allows for more appropriate suggestions to be provided by prioritizing proposals based on product submission timing. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department could input product submission timings into a generating AI and have the generating AI determine the priority of proposals.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion unit may prioritize suggesting highly relevant products. For example, the suggestion unit may prioritize suggesting products that are of high interest to the user. The suggestion unit may also prioritize suggesting products related to the user's past purchase history. By adjusting the order of suggestions based on the relevance of the products, it is possible to provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit may input the relevance of products into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0047] The suggestion department can update local trend information in real time and reflect it in its suggestions. For example, the suggestion department can make suggestions based on local fashion event information. For example, the suggestion department can make suggestions based on new product information from popular local brands. The suggestion department can also make suggestions based on seasonal local trend information. This allows for the suggestion of more appropriate fashion items by updating local trend information in real time. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without AI. For example, the suggestion department can use generative AI to analyze SNS posts and extract trend information in order to collect local trend information.

[0048] The proposal unit can make the user interface customizable. For example, the proposal unit can provide an interface that reflects the user's preferred colors and designs. For example, the proposal unit can allow the user to select a layout that is easy to use. The proposal unit can also customize the interface so that the user can easily access the information they need. This improves user convenience by making the user interface customizable. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input user preference data into a generating AI and have the generating AI perform the customization of the user interface.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit can collect not only the user's past purchase history and preference data, but also their health data. For example, it can collect data from the user's fitness tracker or smartwatch to understand their health status and activity level. This allows the data collection unit to suggest fashion items tailored to the user's health status. For instance, if the user leads an active lifestyle, it can suggest sportswear or activewear. If the user wants to relax, it can suggest comfortable casual wear. Furthermore, based on the user's health data, it can also suggest items suitable for seasonal health management.

[0051] The suggestion function not only updates local trend information in real time, but can also consider the fashion trends of the user's friends and family. For example, it collects the fashion styles of friends and family from the user's social media accounts and considers the likelihood that the user may be influenced by those styles. This allows the suggestion function to propose fashion items that harmonize with the style of the user's friends and family. For instance, if the user's friends prefer a particular brand, it will suggest items from that brand. It can also suggest items suitable for an event if the user's family is planning to attend one. Furthermore, it can even suggest seasonal fashion items based on the trend information of the user's friends and family.

[0052] The data collection unit not only analyzes users' past purchase history to select the optimal collection method, but can also adjust the collection method based on users' life events. For example, if a user is getting married, the system can prioritize collecting purchase history of wedding-related items. If a user starts a new job, it can prioritize collecting purchase history of business casual items. Furthermore, if a user is planning to move, it can collect purchase history based on trend information in the new area. By adjusting the collection method based on users' life events, more relevant data can be collected.

[0053] The data collection unit can filter purchase history not only based on the user's current lifestyle and areas of interest, but also based on the user's future plans. For example, if a user is planning a future trip, the system can collect items based on trend information for their travel destination. If a user plans to attend a future event, the system can collect items suitable for that event. Furthermore, if a user plans to start a new hobby, the system can collect items related to that hobby. This allows for the collection of more relevant data by filtering based on the user's future plans.

[0054] The data collection unit not only prioritizes collecting highly relevant purchase history based on the user's geographical location, but can also adjust the collection method based on the user's travel patterns. For example, it can collect items based on trend information in places the user frequently visits. It can also collect items based on information about stores along the user's commute route. Furthermore, if the user is traveling, it can collect items based on trend information in their travel destination. By adjusting the collection method based on the user's travel patterns, it is possible to collect more relevant data.

[0055] The data collection unit can analyze not only users' social media activity but also their online shopping behavior when collecting purchase history. For example, it can collect items that users have viewed on online shopping sites. It can also collect items that users have added to their cart but did not purchase. Furthermore, it can collect items for which users have posted reviews. By analyzing users' online shopping behavior in this way, it is possible to collect more relevant data.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects data on the user's past purchase history and preferences. For example, it can collect data such as the date and time of purchase, the items purchased, and the purchase amount. The data collection unit obtains data from online shopping sites. It also collects preference data entered by the user, such as color, style, and brand. Step 2: The analysis unit analyzes the data collected by the collection unit to learn user preferences and trends. For example, it uses machine learning algorithms to analyze the data and learn the user's preferred colors and styles based on their purchase history. It can also learn the user's preferred brands based on their preference data. Step 3: The proposal department proposes the most suitable fashion items for the individual based on the analysis results obtained by the analysis department. For example, it selects the most suitable fashion items based on the user's preferences and tendencies, and proposes items in colors and styles that the user likes, as well as items from brands that the user likes.

[0058] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that analyzes a user's past purchase history, preferences, and local trend information to propose the most suitable fashion items for the individual. This AI agent system collects data on the user's past purchase history and preferences, and the AI ​​analyzes the collected data to learn the user's preferences and tendencies. Furthermore, it updates local trend information in real time and integrates this information to propose the most suitable fashion items for the individual. For example, the AI ​​agent system collects data on the user's past purchase history and preferences. For example, it can collect data such as the date and time of purchase, the purchased items, and the purchase amount. Next, the AI ​​agent system analyzes the collected data to learn the user's preferences and tendencies. For example, the AI ​​analyzes data such as color, style, and brand to learn the user's preferences. Furthermore, the AI ​​agent system updates local trend information in real time and reflects it in its suggestions. For example, it collects SNS posts and sales data to update local trend information. As a result, the AI ​​agent system can propose the most suitable fashion items for the user. This allows the user to reduce the time spent selecting fashion items and improve customer satisfaction. An increase in repeat purchase rates can also be expected. The target audience is men and women in their 20s to 40s who are interested in fashion, and the goal is to solve the problem of spending too much time choosing fashion items. By using generative AI to create user profiles and recommend items, the system saves time and improves customer satisfaction. This allows the AI ​​agent system to analyze the user's past purchase history and preference data to suggest the most suitable fashion items for each individual.

[0059] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects data on the user's past purchase history and preferences. The data collection unit can collect data such as purchase date and time, purchased items, and purchase amount. The data collection unit obtains data from online shopping sites, for example. The data collection unit can also collect preference data entered by the user. For example, it can collect data such as the user's preferred colors, styles, and brands. The analysis unit analyzes the data collected by the data collection unit and learns the user's preferences and tendencies. The analysis unit analyzes the data using machine learning algorithms, for example. For example, the analysis unit learns the user's preferred colors and styles based on the user's purchase history. The analysis unit can also learn the user's preferred brands based on the user's preference data. The suggestion unit suggests the most suitable fashion items for the individual based on the analysis results obtained by the analysis unit. The suggestion unit selects the most suitable fashion items based on the user's preferences and tendencies, for example. The suggestion unit suggests items in the user's preferred colors and styles, for example. Furthermore, the suggestion function can also suggest items from brands that the user prefers. This allows the AI ​​agent system, according to the embodiment, to analyze the user's past purchase history and preference data to suggest the most suitable fashion items for the individual.

[0060] The data collection unit collects data on users' past purchase history and preferences. Specifically, the data collection unit can collect detailed data such as purchase date and time, purchased items, and purchase amount. This data is important for understanding users' purchasing behavior in detail. For example, the data collection unit can obtain data from online shopping sites via APIs. This allows for accurate collection of information on products that users have purchased in the past. The data collection unit can also collect preference data entered by users. For example, it can collect data on the user's preferred colors, styles, and brands. This data is important for understanding user preferences. The data collection unit can collect information entered by users in surveys and profile setting screens. Furthermore, the data collection unit can also collect behavioral data such as the user's browsing history and search history. This allows for understanding the products and categories that users are interested in. The data collection unit centrally manages this data and makes it accessible to the analysis and suggestion units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0061] The analysis unit analyzes data collected by the data collection unit to learn user preferences and trends. Specifically, the analysis unit uses machine learning algorithms to analyze data. For example, it learns user preferences for colors and styles based on user purchase history. Machine learning algorithms can find patterns in past data and predict user preferences. For example, it can use clustering algorithms to classify users into groups with similar preferences. The analysis unit can also learn user preferences for brands based on user preference data. For example, it can use collaborative filtering algorithms to compare data from other users and identify brands that are likely to interest the user. Furthermore, the analysis unit can analyze user behavior data to understand changes in user interests and concerns in real time. For example, it can analyze products recently searched for and categories viewed by users to identify their current interests. This allows the analysis unit to accurately understand user preferences and trends and provide useful information to the recommendation unit. In addition, the analysis unit can utilize past data and statistical information to predict long-term changes in preferences and trends. This allows the analysis unit to not only grasp the situation in real time but also respond to long-term changes in preferences, improving the reliability and accuracy of the entire system.

[0062] The suggestion department proposes the most suitable fashion items for each individual based on the analysis results obtained by the analysis department. Specifically, the suggestion department selects the most suitable fashion items based on the user's preferences and tendencies. For example, it may suggest items in colors and styles that the user likes. The suggestion department can identify items that the user is likely to be interested in based on the user's past purchase history and preference data. For example, it can suggest new products in colors and styles similar to items the user has purchased in the past. The suggestion department can also suggest items from brands that the user likes. For example, it can suggest new products from the same brand based on products the user has purchased in the past. Furthermore, the suggestion department can suggest items that match the user's current interests and concerns based on the user's behavioral data. For example, it can suggest related items based on products the user has recently searched for or categories they have viewed. The suggestion department can use means such as email and push notifications to notify the user of these suggestions. This allows the suggestion department to propose the most suitable fashion items to the user at the right time, increasing the user's purchasing intent. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the proposal department to consistently provide users with the best possible solutions and improve the overall system performance.

[0063] The suggestion department can update local trend information in real time and reflect it in its suggestions. For example, the suggestion department can collect SNS posts and sales data to update local trend information. For example, the suggestion department can make suggestions based on local fashion event information. It can also make suggestions based on new product information from popular local brands. Furthermore, the suggestion department can make suggestions based on seasonal local trend information. By updating local trend information in real time, it is possible to suggest more appropriate fashion items. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without AI. For example, in order to collect local trend information, the suggestion department can use generative AI to analyze SNS posts and extract trend information.

[0064] The proposal unit can make the user interface customizable. For example, the proposal unit can provide an interface that reflects the user's preferred colors and designs. For example, the proposal unit can allow the user to select a layout that is easy to use. The proposal unit can also customize the interface so that the user can easily access the information they need. This improves user convenience by making the user interface customizable. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input user preference data into a generating AI and have the generating AI perform the customization of the user interface.

[0065] The data collection unit can estimate the user's emotions and adjust the timing of purchase history collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay collection until the user is relaxed. If the user is excited, the data collection unit can immediately collect purchase history and provide real-time suggestions. The data collection unit can also adjust the collection timing if the user is busy, collecting data during a calmer period. By adjusting the collection timing based on the user's emotions, data can be collected at a more appropriate time. 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.

[0066] The data collection unit can analyze the user's past purchase history and select the optimal data collection method. For example, the data collection unit may prioritize collecting items that the user frequently purchases. For example, if the user prefers a particular brand, the data collection unit may focus on collecting purchase history for that brand. The data collection unit can also select a seasonal data collection method if the user tends to purchase different items each season. In this way, the optimal data collection method can be selected by analyzing the user's past purchase history. 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 purchase history data into a generating AI and have the generating AI select the optimal data collection method.

[0067] The data collection unit can filter the collected purchase history based on the user's current lifestyle and areas of interest. For example, if a user has started a new job, the unit will prioritize collecting business casual items. If a user is planning a trip, the unit will collect items suitable for travel. The unit can also collect items related to a user's hobby if the user has one. This allows for the collection of more relevant data by filtering based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering.

[0068] The data collection unit can estimate the user's emotions and determine the priority of purchase history to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect the entire past purchase history. If the user is in a hurry, the data collection unit will prioritize collecting recent purchase history. The data collection unit can also focus on collecting specific brands or items if the user is excited. This allows for the collection of more relevant data by prioritizing purchase history based on 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 processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0069] The data collection unit can prioritize collecting highly relevant purchase history based on the user's geographical location information when collecting purchase history. For example, if the user lives in a specific region, the data collection unit will prioritize collecting items popular in that region. For example, if the user is traveling, the data collection unit will prioritize collecting purchase history from their travel destination. Furthermore, if the user is planning to move, the data collection unit can collect purchase history based on trend information in the new region. This allows for the collection of more appropriate data by prioritizing the collection of highly relevant purchase history based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant purchase history.

[0070] The data collection unit can analyze a user's social media activity and collect relevant history when collecting purchase history. For example, the data collection unit can collect items that the user has shared on social media. For example, the data collection unit can collect purchase history based on posts from brands or influencers that the user follows. The data collection unit can also collect items that the user has "liked" on social media. In this way, relevant history 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.

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

[0072] 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 performs a detailed analysis on important data. For example, the analysis unit performs a concise analysis on less important data. The analysis unit can also perform a detailed analysis on data of high interest to the user. By adjusting the level of detail of the analysis based on the importance of the data, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0073] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of fashion items. For example, the analysis unit can apply different analysis algorithms depending on the brand. Furthermore, the analysis unit can also apply different analysis algorithms depending on the season. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

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

[0075] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. For example, the analysis unit may prioritize the analysis of data submitted by users within a specific period. Furthermore, if the user is in a hurry, the analysis unit may prioritize the analysis regardless of the submission date. This allows for the provision of more appropriate analysis results by determining the priority of analysis based on the data submission date. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0076] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may prioritize the analysis of data of high user interest. The analysis unit may also prioritize the analysis of data related to the user's past purchase history. By adjusting the order of analysis based on the relevance of the data, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0077] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide concise suggestions. Furthermore, if the user is excited, the suggestion unit can provide visually appealing suggestions. This allows for the provision of more appropriate suggestions by adjusting the presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation based on those emotions.

[0078] The proposal unit can adjust the level of detail in its proposals based on the importance of the products. For example, it can provide detailed proposals for important products, and concise proposals for less important products. It can also provide detailed proposals for products of high user interest. By adjusting the level of detail in proposals based on product importance, it can provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the products into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0079] The suggestion unit can apply different suggestion algorithms depending on the product category when making suggestions. For example, the suggestion unit can apply different suggestion algorithms depending on the category of fashion items. For example, the suggestion unit can apply different suggestion algorithms depending on the brand. Furthermore, the suggestion unit can also apply different suggestion algorithms depending on the season. By applying different suggestion algorithms depending on the product category, it is possible to provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the product category into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0080] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit will provide short, concise suggestions. If the user is relaxed, the suggestion unit will provide detailed suggestions. The suggestion unit can also provide visually appealing suggestions if the user is excited. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be provided. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions based on the emotion.

[0081] The proposal department can prioritize proposals based on the timing of product submission. For example, it might prioritize recently arrived products. Or, it might suggest products based on the user's purchase history within a specific period. Furthermore, if the user is in a hurry, the proposal department can prioritize suggestions regardless of the submission timing. This allows for more appropriate suggestions to be provided by prioritizing proposals based on product submission timing. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department could input product submission timings into a generating AI and have the generating AI determine the priority of proposals.

[0082] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion unit may prioritize suggesting highly relevant products. For example, the suggestion unit may prioritize suggesting products that are of high interest to the user. The suggestion unit may also prioritize suggesting products related to the user's past purchase history. By adjusting the order of suggestions based on the relevance of the products, it is possible to provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit may input the relevance of products into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0083] The suggestion department can update local trend information in real time and reflect it in its suggestions. For example, the suggestion department can make suggestions based on local fashion event information. For example, the suggestion department can make suggestions based on new product information from popular local brands. The suggestion department can also make suggestions based on seasonal local trend information. This allows for the suggestion of more appropriate fashion items by updating local trend information in real time. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without AI. For example, the suggestion department can use generative AI to analyze SNS posts and extract trend information in order to collect local trend information.

[0084] The proposal unit can make the user interface customizable. For example, the proposal unit can provide an interface that reflects the user's preferred colors and designs. For example, the proposal unit can allow the user to select a layout that is easy to use. The proposal unit can also customize the interface so that the user can easily access the information they need. This improves user convenience by making the user interface customizable. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input user preference data into a generating AI and have the generating AI perform the customization of the user interface.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The data collection unit can collect not only the user's past purchase history and preference data, but also their health data. For example, it can collect data from the user's fitness tracker or smartwatch to understand their health status and activity level. This allows the data collection unit to suggest fashion items tailored to the user's health status. For instance, if the user leads an active lifestyle, it can suggest sportswear or activewear. If the user wants to relax, it can suggest comfortable casual wear. Furthermore, based on the user's health data, it can also suggest items suitable for seasonal health management.

[0087] The suggestion function not only updates local trend information in real time, but can also consider the fashion trends of the user's friends and family. For example, it collects the fashion styles of friends and family from the user's social media accounts and considers the likelihood that the user may be influenced by those styles. This allows the suggestion function to propose fashion items that harmonize with the style of the user's friends and family. For instance, if the user's friends prefer a particular brand, it will suggest items from that brand. It can also suggest items suitable for an event if the user's family is planning to attend one. Furthermore, it can even suggest seasonal fashion items based on the trend information of the user's friends and family.

[0088] The proposed solution not only allows for customizable user interfaces but also enables dynamic changes to the interface design based on the user's emotions. For example, if a user is stressed, the interface colors can be changed to calming colors to create a relaxing design. If the user is excited, the design can be changed to a bright and lively one. Furthermore, if the user is tired, the design can be changed to a simple and easy-to-read one. This allows for improved user convenience and satisfaction by providing an interface that responds to the user's emotions.

[0089] The data collection unit can estimate the user's emotions and adjust the timing of purchase history collection based on those emotions. It can also change the type of data collected based on the user's emotions. For example, if a user is stressed, it can prioritize collecting purchase history of relaxing items. If a user is excited, it can prioritize collecting purchase history of active items. Similarly, if a user is tired, it can prioritize collecting purchase history of comforting items. This allows for the collection of more relevant data by changing the type of data collected based on the user's emotions.

[0090] The data collection unit not only analyzes users' past purchase history to select the optimal collection method, but can also adjust the collection method based on users' life events. For example, if a user is getting married, the system can prioritize collecting purchase history of wedding-related items. If a user starts a new job, it can prioritize collecting purchase history of business casual items. Furthermore, if a user is planning to move, it can collect purchase history based on trend information in the new area. By adjusting the collection method based on users' life events, more relevant data can be collected.

[0091] The data collection unit can filter purchase history not only based on the user's current lifestyle and areas of interest, but also based on the user's future plans. For example, if a user is planning a future trip, the system can collect items based on trend information for their travel destination. If a user plans to attend a future event, the system can collect items suitable for that event. Furthermore, if a user plans to start a new hobby, the system can collect items related to that hobby. This allows for the collection of more relevant data by filtering based on the user's future plans.

[0092] The data collection unit not only estimates the user's emotions and prioritizes the purchase history to collect based on those emotions, but it can also adjust the scope of data collected based on the user's emotions. For example, if the user is relaxed, a wide range of data may be collected. If the user is in a hurry, recent data may be prioritized. If the user is excited, it may be possible to focus on collecting data from specific brands or items. This allows for the collection of more relevant data by adjusting the scope of data collected based on the user's emotions.

[0093] The data collection unit not only prioritizes collecting highly relevant purchase history based on the user's geographical location, but can also adjust the collection method based on the user's travel patterns. For example, it can collect items based on trend information in places the user frequently visits. It can also collect items based on information about stores along the user's commute route. Furthermore, if the user is traveling, it can collect items based on trend information in their travel destination. By adjusting the collection method based on the user's travel patterns, it is possible to collect more relevant data.

[0094] The data collection unit can analyze not only users' social media activity but also their online shopping behavior when collecting purchase history. For example, it can collect items that users have viewed on online shopping sites. It can also collect items that users have added to their cart but did not purchase. Furthermore, it can collect items for which users have posted reviews. By analyzing users' online shopping behavior in this way, it is possible to collect more relevant data.

[0095] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. It can also adjust the timing of the analysis based on the user's emotions. For example, if the user is relaxed, it can adjust the timing of providing detailed analysis results. If the user is in a hurry, it can provide concise analysis results immediately. Furthermore, if the user is excited, it can adjust the timing of providing visually appealing analysis results. By adjusting the timing of the analysis based on the user's emotions, more appropriate analysis results can be provided.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The data collection unit collects data on the user's past purchase history and preferences. For example, it can collect data such as the date and time of purchase, the items purchased, and the purchase amount. The data collection unit obtains data from online shopping sites. It also collects preference data entered by the user, such as color, style, and brand. Step 2: The analysis unit analyzes the data collected by the collection unit to learn user preferences and trends. For example, it uses machine learning algorithms to analyze the data and learn the user's preferred colors and styles based on their purchase history. It can also learn the user's preferred brands based on their preference data. Step 3: The proposal department proposes the most suitable fashion items for the individual based on the analysis results obtained by the analysis department. For example, it selects the most suitable fashion items based on the user's preferences and tendencies, and proposes items in colors and styles that the user likes, as well as items from brands that the user likes.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit described above, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects the user's past purchase history and preference data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to learn the user's preferences and tendencies. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and suggests the most suitable fashion items for the individual based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion 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 preference data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to learn the user's preferences and tendencies. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and suggests the most suitable fashion items for the individual based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion 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 preference data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to learn the user's preferences and tendencies. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and suggests the most suitable fashion items for the individual based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion 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 data on the user's past purchase history and preferences. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to learn the user's preferences and tendencies. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and suggests the most suitable fashion items for the individual based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) A data collection unit that collects the user's past purchase history and preference data, The analysis unit analyzes the data collected by the aforementioned collection unit and learns the user's preferences and tendencies, The system includes a suggestion unit that proposes the most suitable fashion items for an individual based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We update local trend information in real time and incorporate it into our proposals. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Make the user interface customizable. The system described in Appendix 1, characterized by the features described herein. (Note 4) 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 5) 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 6) 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 7) 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 8) The aforementioned collection unit is When collecting purchase history, the system prioritizes collecting highly relevant history based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, 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 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, 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 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the products are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, We update local trend information in real time and incorporate it into our proposals. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, Make the user interface customizable. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects the user's past purchase history and preference data, The analysis unit analyzes the data collected by the aforementioned collection unit and learns the user's preferences and tendencies, The system includes a suggestion unit that proposes the most suitable fashion items for an individual based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned proposal section is, We update local trend information in real time and incorporate it into our proposals. The system according to feature 1.

3. The aforementioned proposal section is, Make the user interface customizable. The system according to feature 1.

4. 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.

5. 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.

6. 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.

7. 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.

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

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