system

Abstract

The system according to this embodiment aims to manage the user's wardrobe and suggest the optimal outfit. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a suggestion unit, a learning unit, and a recommendation unit. The collection unit collects information about the user's wardrobe. The analysis unit analyzes the information collected by the collection unit. The suggestion unit suggests daily outfits based on the information analyzed by the analysis unit. The learning unit learns the user's feedback and purchase history based on the outfits suggested by the suggestion unit. The recommendation unit recommends items to be purchased next based on the information learned by the learning unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems that it is laborious to manage a user's wardrobe and propose coordinates, and it is difficult to make an optimal proposal that reflects the user's preferences and feedback.

[0005] The system according to the embodiment aims to manage a user's wardrobe and propose an optimal coordinate.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, a learning unit, and a recommendation unit. The collection unit collects information about the user's wardrobe. The analysis unit analyzes the information collected by the collection unit. The suggestion unit suggests daily outfits based on the information analyzed by the analysis unit. The learning unit learns the user's feedback and purchase history based on the outfits suggested by the suggestion unit. The recommendation unit recommends items to be purchased next based on the information learned by the learning unit. [Effects of the Invention]

[0007] The system according to this embodiment can manage the user's wardrobe and suggest the optimal outfit. [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 communication I / F (Interface) with reference signs includes interfaces such as a communication processor and an antenna. 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 autonomous AI agent system according to an embodiment of the present invention is a system that manages a user's wardrobe and suggests daily outfits. This autonomous AI agent system collects information about the user's wardrobe, and the AI ​​analyzes that information. Next, based on information such as the user's style preferences, weather, and schedule, the AI ​​suggests daily outfits. Furthermore, it learns from the user's feedback and purchase history and recommends items that should be purchased next. This allows the user to save time spent choosing clothes every day and enjoy the evolution of their style. In addition, by having the AI ​​make suggestions tailored to the user's lifestyle, it can bring out the user's individuality and help them rediscover the joy of fashion. For example, the autonomous AI agent system includes a collection unit that collects information about the user's wardrobe. Next, it includes an analysis unit that analyzes the collected information. The analysis unit provides information to a suggestion unit that proposes daily outfits based on information such as the user's style preferences, weather, and schedule. Furthermore, it includes a learning unit that learns from the user's feedback and purchase history and provides information to a recommendation unit that recommends items that should be purchased next. As a result, the autonomous AI agent system can efficiently manage the user's wardrobe and suggest outfits.

[0029] The autonomous AI agent system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, a learning unit, and a recommendation unit. The collection unit collects information about the user's wardrobe. For example, the collection unit can collect information such as the type, color, size, and brand of items in the user's wardrobe. For example, the collection unit can take pictures of clothing owned by the user and extract item information using image analysis technology. The collection unit can also collect wardrobe information by analyzing receipts or online purchase history of items purchased by the user. Furthermore, the collection unit can also collect item information manually entered by the user. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses data mining technology to analyze the user's style preferences and trends. For example, the analysis unit analyzes the user's past outfit history to identify the user's preferences. The analysis unit also acquires weather forecast data and provides information for suggesting outfits according to the weather. Furthermore, the analysis unit acquires the user's schedule from a calendar application and provides information for suggesting outfits according to the schedule. The suggestion unit proposes daily outfits based on information analyzed by the analysis unit. The suggestion unit proposes the optimal outfit based on information such as the user's style preferences, weather, and schedule. For example, the suggestion unit proposes outfits considering the user's preferred colors and styles. The suggestion unit can also propose outfits suitable for the weather based on weather forecast data. Furthermore, the suggestion unit can propose outfits suitable for formal or casual occasions depending on the user's schedule. The learning unit learns from user feedback and purchase history based on the outfits proposed by the suggestion unit. For example, the learning unit collects feedback given by the user on the proposed outfits and incorporates it into the next suggestion. For example, the learning unit evaluates whether the user liked the proposed outfit and improves the next suggestion based on that evaluation. The learning unit can also analyze the user's purchase history and learn the user's preferred item trends.The recommendation unit recommends items that should be purchased next based on information learned by the learning unit. The recommendation unit recommends new items, for example, by considering the user's style preferences and trends. The recommendation unit also recommends items that are compatible with items the user has purchased in the past. Furthermore, the recommendation unit can also recommend items that the user is likely to like, based on user feedback. As a result, the autonomous AI agent system according to this embodiment can efficiently manage the user's wardrobe and suggest outfit combinations.

[0030] The data collection unit collects information about the user's wardrobe. For example, the unit can collect information such as the type, color, size, and brand of items in the user's wardrobe. Specifically, it takes photos of the clothes the user owns and extracts item information using image analysis technology. Image analysis technology includes object detection algorithms and image classification algorithms, which allow for accurate identification of the type, color, and design characteristics of the clothing. For example, the user uploads photos of their clothes to a cloud server, and by performing image analysis on the server, detailed information about the items is automatically extracted. The data collection unit can also collect wardrobe information by analyzing receipts and online purchase history of items purchased by the user. For receipt analysis, OCR (optical character recognition) technology is used to extract information such as the name, price, and purchase date of purchased items. For online purchase history analysis, web scraping technology is used to collect the user's purchase history data and store it in a database. Furthermore, the data collection unit can also collect item information manually entered by the user. Users can manually enter detailed information about items through a dedicated application and save it in the database. This allows the data collection unit to gather user wardrobe information in a variety of ways and build a comprehensive database.

[0031] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit uses data mining techniques to analyze users' style preferences and trends. Data mining techniques include clustering and association analysis, which allow for the identification of user preferences and behavioral patterns. For example, it analyzes a user's past outfit history to identify their preferred colors and styles. The analysis unit also acquires weather forecast data and provides information to suggest outfits appropriate for the weather. Weather forecast data is acquired in real time from an external weather information service, and the analysis unit uses this to suggest outfits suitable for the weather. Furthermore, the analysis unit acquires the user's schedule from a calendar application and provides information to suggest outfits appropriate for the schedule. The schedule information acquired from the calendar application includes the type of event, location, and time, and the analysis unit uses this to suggest appropriate outfits. For example, it suggests suits or dresses for formal events and relaxed styles for casual events. In this way, the analysis unit can provide the optimal outfit according to the user's preferences and circumstances.

[0032] The suggestion department proposes daily outfits based on information analyzed by the analysis department. For example, the suggestion department proposes the optimal outfit based on information such as the user's style preferences, weather, and schedule. Specifically, it proposes outfits considering the user's preferred colors and styles. The suggestion department also refers to the user's past outfit and purchase history to identify preferred items and styles. Furthermore, the suggestion department can propose outfits suitable for the weather based on weather forecast data. For example, it might suggest a waterproof jacket and boots on a rainy day, or a lighter outfit on a hot day. In addition, the suggestion department can propose outfits suitable for formal or casual occasions depending on the user's schedule. For example, it might suggest a suit and tie for a business meeting, or jeans and sneakers for a casual gathering with friends. The suggestion department can collect user feedback and continuously improve the accuracy of its suggestions. Based on the user's feedback on the suggested outfits, future suggestions can be tailored more closely to the user's preferences. This allows the suggestion department to provide optimal outfits that meet user needs and improve user satisfaction.

[0033] The learning unit learns from user feedback and purchase history based on the outfits suggested by the suggestion unit. For example, the learning unit collects user feedback on suggested outfits and incorporates it into future suggestions. Specifically, it evaluates whether the user liked the suggested outfit and uses that evaluation to improve future suggestions. The learning unit analyzes user feedback to identify the user's preferred items and styles. The learning unit can also analyze the user's purchase history to learn the user's preferred items. For example, it analyzes the brands, colors, and styles of items the user has purchased in the past to identify the user's preferences. The learning unit uses this information as a reference when the suggestion unit makes future outfit suggestions. Furthermore, the learning unit can improve the accuracy of suggestions by continuously collecting user feedback and purchase history and updating its learning model. This allows the learning unit to accurately understand user preferences and tendencies, helping the suggestion unit propose outfits that are more suitable for the user.

[0034] The recommendation unit recommends items that should be purchased next based on information learned by the learning unit. For example, the recommendation unit recommends new items considering the user's style preferences and trends. Specifically, it recommends items that go well with items the user has purchased in the past. For example, it might recommend pants or shirts that match a jacket the user has purchased. The recommendation unit can also recommend items that the user is likely to like based on user feedback. It recommends similar items based on items and brands that the user has previously given high ratings to. Furthermore, the recommendation unit can recommend items that are suitable for the user, taking into account the season and trends. For example, it might recommend warm coats and sweaters in winter, and light shirts and shorts in summer. The recommendation unit can improve the accuracy of its recommendations by continuously collecting user purchase history and feedback and updating its recommendation algorithm. This allows the recommendation unit to propose the most suitable items that meet the user's needs and improve user satisfaction.

[0035] The collection unit can analyze the user's past wardrobe information and select the optimal collection method. For example, the collection unit can prioritize collecting items that the user has frequently used in the past. For example, the collection unit can select different collection methods for each season based on the user's past wardrobe information. For example, the collection unit can analyze the user's past wardrobe information and adjust the collection method based on specific brands or styles. This allows the optimal collection method to be selected by analyzing past information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past wardrobe information into a generating AI and have the generating AI select the optimal collection method.

[0036] The data collection unit can filter wardrobe information based on the user's current fashion trends and areas of interest. For example, the data collection unit can prioritize collecting fashion items that the user is currently interested in. For example, the data collection unit can filter items of a specific style or color based on the user's current fashion trends. For example, the data collection unit can collect relevant fashion items based on the user's areas of interest. This allows information to be filtered based on current fashion trends 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 information on the user's current fashion trends and areas of interest into a generating AI and have the generating AI perform the filtering.

[0037] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location when collecting wardrobe information. For example, if the user lives in a cold region, the data collection unit can prioritize collecting warm clothing. For example, if the user lives in an urban area, the data collection unit can prioritize collecting trendy items. For example, if the user is traveling, the data collection unit can collect items suitable for the climate of their travel destination. This allows for the collection of highly relevant information by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0038] The data collection unit can analyze the user's social media activity and collect relevant information when collecting wardrobe information. 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 items based on the styles of influencers that the user follows. For example, the data collection unit can collect items from brands that the user has shown interest in on social media. This allows for the analysis of social media activity and the collection of relevant 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 social media activity data into a generating AI and have the generating AI collect relevant information.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the wardrobe information during the analysis. For example, the analysis unit can perform a detailed analysis for important items. For example, the analysis unit can perform a simplified analysis for items that are not used often. For example, the analysis unit can perform an analysis with a level of detail appropriate to the season for items whose importance changes seasonally. This allows the level of detail of the analysis to be adjusted based on the importance of the wardrobe information. 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 wardrobe information importance 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 category of the wardrobe information during analysis. For example, the analysis unit can apply a casual analysis algorithm to casual items. For example, the analysis unit can apply a formal analysis algorithm to formal items. For example, the analysis unit can apply a sports analysis algorithm to sportswear. This allows different analysis algorithms to be applied depending on the category of the wardrobe information. 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 wardrobe information category data 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 when the wardrobe information was collected. For example, the analysis unit may prioritize the analysis of recently collected information. For example, the analysis unit may prioritize the analysis of information collected seasonally. For example, the analysis unit may prioritize the analysis of information related to the user's life events. This allows the analysis priority to be determined based on when the wardrobe information was collected. 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 may input wardrobe information collection timing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of wardrobe information during analysis. For example, the analysis unit may prioritize analyzing information related to the user's current style. For example, the analysis unit may prioritize analyzing information related to the user's past purchase history. For example, the analysis unit may prioritize analyzing information related to the user's schedule. This allows the order of analysis to be adjusted based on the relevance of wardrobe information. 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 wardrobe information relevance 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 coordination. For example, the proposal unit can provide detailed proposals for important events. For example, it can provide concise proposals for everyday coordination. For example, it can provide proposals with a level of detail appropriate for the season for coordinations whose importance changes seasonally. This allows the level of detail in proposals to be adjusted based on the importance of the coordination. 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 coordination importance data 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 category of the outfit when making suggestions. For example, the suggestion unit can apply a casual suggestion algorithm to a casual outfit. For example, the suggestion unit can apply a formal suggestion algorithm to a formal outfit. For example, the suggestion unit can apply a sports suggestion algorithm to a sports outfit. This allows different suggestion algorithms to be applied depending on the category of the outfit. 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 outfit category data into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0045] The proposal department can determine the priority of proposals based on the timing of their submission. For example, the proposal department may prioritize proposals related to recent events. For example, the proposal department may prioritize proposals submitted seasonally. For example, the proposal department may prioritize proposals related to the user's life events. This allows the proposal department to determine the priority of proposals based on the timing of their submission. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department may input data on the timing of outfit submissions 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 outfits when making suggestions. For example, the suggestion unit may prioritize suggesting outfits related to the user's current style. For example, the suggestion unit may prioritize suggesting outfits related to the user's past purchase history. For example, the suggestion unit may prioritize suggesting outfits related to the user's schedule. This allows the order of suggestions to be adjusted based on the relevance of the outfits. 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 outfit relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0047] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can adjust the parameters of the learning algorithm from past learning data. For example, the learning unit can analyze past learning data to improve the accuracy of the learning algorithm. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0048] The learning unit can weight the training data based on when the wardrobe information was collected during training. For example, the learning unit can assign higher weights to recently collected information. For example, the learning unit can assign different weights to information collected seasonally. For example, the learning unit can assign higher weights to information related to the user's life events. This allows for more accurate training by weighting the training data based on when the wardrobe information was collected. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input wardrobe information collection timing data into a generating AI and have the generating AI perform the weighting of the training data.

[0049] The recommendation unit can analyze the user's past purchase history to select the optimal recommendation method. For example, the recommendation unit can make recommendations based on items the user has purchased in the past. For example, the recommendation unit can recommend related items from the user's past purchase history. For example, the recommendation unit can analyze the user's purchase history and recommend items based on trends. In this way, the optimal recommendation method can be selected by analyzing the user's past purchase history. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's past purchase history data into a generating AI and have the generating AI select the optimal recommendation method.

[0050] The recommendation unit can customize its recommendation methods based on the user's current fashion trends. For example, the recommendation unit recommends relevant items based on the user's current fashion trends. The recommendation unit can, for example, provide customized recommendations tailored to the user's current style. The recommendation unit can adjust its recommendation methods based on the user's current areas of interest. This allows for the customization of recommendation methods based on the user's current fashion trends. Some or all of the above-described processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's current fashion trend data into a generating AI and have the generating AI perform the customization of recommendation methods.

[0051] The recommendation unit can select the optimal recommendation method by considering the user's geographical location information when making recommendations. For example, if the user lives in a cold region, the recommendation unit can recommend warm clothing. For example, if the user lives in an urban area, the recommendation unit can recommend trendy items. For example, if the user is traveling, the recommendation unit can recommend items suitable for the climate of their travel destination. This allows the recommendation unit to select the optimal recommendation method by considering the user's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal recommendation method.

[0052] The recommendation unit can analyze the user's social media activity and propose recommendation methods when making recommendations. For example, the recommendation unit can recommend items that the user has shared on social media. For example, the recommendation unit can recommend items based on the style of influencers that the user follows. For example, the recommendation unit can recommend items from brands that the user has shown interest in on social media. This allows the recommendation unit to analyze the user's social media activity and propose recommendation methods. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's social media activity data into a generating AI and have the generating AI propose recommendation methods.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The analysis unit can analyze the user's past outfit history and adjust the analysis algorithm according to changes in trends. For example, the analysis unit can identify changes in the user's style from the past outfit history. For example, the analysis unit can adjust the parameters of the analysis algorithm according to changes in trends. For example, the analysis unit can analyze the user's past outfit history and perform analysis based on trends. This makes it possible to adjust the analysis algorithm according to changes in trends by analyzing the past outfit history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past outfit history data into a generating AI and have the generating AI perform the adjustment of the analysis algorithm.

[0055] The suggestion unit can analyze past user feedback and adjust algorithms to improve the accuracy of suggestions. For example, the suggestion unit can identify user preferences from past feedback. For example, the suggestion unit can adjust the parameters of the suggestion algorithm based on the feedback. For example, the suggestion unit can analyze past user feedback and adjust algorithms to improve the accuracy of suggestions. This makes it possible to adjust algorithms to improve the accuracy of suggestions by analyzing past feedback. 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 past feedback data into a generating AI and have the generating AI perform the adjustment of the suggestion algorithm.

[0056] The learning unit can analyze the user's past purchase history and select training data. For example, the learning unit can identify the user's preferences from past purchase history. For example, the learning unit can select training data based on purchase history. For example, the learning unit can analyze the user's past purchase history and select training data. This makes it possible to select training data by analyzing past purchase history. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past purchase history data into a generating AI and have the generating AI perform the selection of training data.

[0057] The recommendation unit can analyze a user's past purchase history and adjust the algorithm to improve the accuracy of recommendations. For example, the recommendation unit can identify user preferences from past purchase history. The recommendation unit can adjust the parameters of the recommendation algorithm based on purchase history. The recommendation unit can analyze a user's past purchase history and adjust the algorithm to improve the accuracy of recommendations. This makes it possible to adjust the algorithm to improve recommendation accuracy by analyzing past purchase history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input past purchase history data into a generating AI and have the generating AI perform the adjustment of the recommendation algorithm.

[0058] The data collection unit can determine the priority of the information to collect, taking into account the user's geographical location. For example, if the user lives in a cold region, the data collection unit can prioritize collecting information on warm clothing. For example, if the user lives in an urban area, the data collection unit can prioritize collecting information on trendy items. For example, if the user is traveling, the data collection unit can prioritize collecting information on items suitable for the climate of their travel destination. By determining the priority of the information to collect, taking into account the user's geographical location, more appropriate information can be collected. 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 data into a generating AI and have the generating AI perform the task of determining the priority of the information to collect.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The collection unit collects information about the user's wardrobe. The collection unit can collect information such as the type, color, size, and brand of items in the user's wardrobe. For example, it can take photos of the clothes the user owns and extract item information using image analysis technology. It can also collect wardrobe information by analyzing receipts and online purchase history of items the user has purchased. Furthermore, it can collect item information that the user has manually entered. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses data mining techniques to analyze the user's style preferences and trends. For example, it analyzes the user's past outfit history to identify their preferences. It also acquires weather forecast data and provides information to suggest outfits appropriate for the weather. Furthermore, it acquires the user's schedule from a calendar application and provides information to suggest outfits appropriate for that schedule. Step 3: The suggestion unit proposes daily outfits based on the information analyzed by the analysis unit. The suggestion unit proposes the optimal outfit based on information such as the user's style preferences, weather, and schedule. For example, it proposes outfits that take into account the user's preferred colors and styles. It can also propose outfits suitable for the weather based on weather forecast data. Furthermore, it can propose outfits suitable for formal occasions or casual occasions depending on the user's schedule. Step 4: The learning unit learns from user feedback and purchase history based on the outfits suggested by the suggestion unit. The learning unit collects feedback from users on the suggested outfits and incorporates it into future suggestions. For example, it evaluates whether the user liked the suggested outfit and uses that evaluation to improve future suggestions. It can also analyze the user's purchase history and learn the trends in items the user prefers. Step 5: The recommendation unit recommends the next item to purchase based on the information learned by the learning unit. The recommendation unit recommends new items considering the user's style preferences and trends. For example, it may recommend items that are compatible with items the user has purchased in the past. It can also recommend items that the user is likely to like based on user feedback.

[0061] (Example of form 2) An autonomous AI agent system according to an embodiment of the present invention is a system that manages a user's wardrobe and suggests daily outfits. This autonomous AI agent system collects information about the user's wardrobe, and the AI ​​analyzes that information. Next, based on information such as the user's style preferences, weather, and schedule, the AI ​​suggests daily outfits. Furthermore, it learns from the user's feedback and purchase history and recommends items that should be purchased next. This allows the user to save time spent choosing clothes every day and enjoy the evolution of their style. In addition, by having the AI ​​make suggestions tailored to the user's lifestyle, it can bring out the user's individuality and help them rediscover the joy of fashion. For example, the autonomous AI agent system includes a collection unit that collects information about the user's wardrobe. Next, it includes an analysis unit that analyzes the collected information. The analysis unit provides information to a suggestion unit that proposes daily outfits based on information such as the user's style preferences, weather, and schedule. Furthermore, it includes a learning unit that learns from the user's feedback and purchase history and provides information to a recommendation unit that recommends items that should be purchased next. As a result, the autonomous AI agent system can efficiently manage the user's wardrobe and suggest outfits.

[0062] The autonomous AI agent system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, a learning unit, and a recommendation unit. The collection unit collects information about the user's wardrobe. For example, the collection unit can collect information such as the type, color, size, and brand of items in the user's wardrobe. For example, the collection unit can take pictures of clothing owned by the user and extract item information using image analysis technology. The collection unit can also collect wardrobe information by analyzing receipts or online purchase history of items purchased by the user. Furthermore, the collection unit can also collect item information manually entered by the user. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses data mining technology to analyze the user's style preferences and trends. For example, the analysis unit analyzes the user's past outfit history to identify the user's preferences. The analysis unit also acquires weather forecast data and provides information for suggesting outfits according to the weather. Furthermore, the analysis unit acquires the user's schedule from a calendar application and provides information for suggesting outfits according to the schedule. The suggestion unit proposes daily outfits based on information analyzed by the analysis unit. The suggestion unit proposes the optimal outfit based on information such as the user's style preferences, weather, and schedule. For example, the suggestion unit proposes outfits considering the user's preferred colors and styles. The suggestion unit can also propose outfits suitable for the weather based on weather forecast data. Furthermore, the suggestion unit can propose outfits suitable for formal or casual occasions depending on the user's schedule. The learning unit learns from user feedback and purchase history based on the outfits proposed by the suggestion unit. For example, the learning unit collects feedback given by the user on the proposed outfits and incorporates it into the next suggestion. For example, the learning unit evaluates whether the user liked the proposed outfit and improves the next suggestion based on that evaluation. The learning unit can also analyze the user's purchase history and learn the user's preferred item trends.The recommendation unit recommends items that should be purchased next based on information learned by the learning unit. The recommendation unit recommends new items, for example, by considering the user's style preferences and trends. The recommendation unit also recommends items that are compatible with items the user has purchased in the past. Furthermore, the recommendation unit can also recommend items that the user is likely to like, based on user feedback. As a result, the autonomous AI agent system according to this embodiment can efficiently manage the user's wardrobe and suggest outfit combinations.

[0063] The data collection unit collects information about the user's wardrobe. For example, the unit can collect information such as the type, color, size, and brand of items in the user's wardrobe. Specifically, it takes photos of the clothes the user owns and extracts item information using image analysis technology. Image analysis technology includes object detection algorithms and image classification algorithms, which allow for accurate identification of the type, color, and design characteristics of the clothing. For example, the user uploads photos of their clothes to a cloud server, and by performing image analysis on the server, detailed information about the items is automatically extracted. The data collection unit can also collect wardrobe information by analyzing receipts and online purchase history of items purchased by the user. For receipt analysis, OCR (optical character recognition) technology is used to extract information such as the name, price, and purchase date of purchased items. For online purchase history analysis, web scraping technology is used to collect the user's purchase history data and store it in a database. Furthermore, the data collection unit can also collect item information manually entered by the user. Users can manually enter detailed information about items through a dedicated application and save it in the database. This allows the data collection unit to gather user wardrobe information in a variety of ways and build a comprehensive database.

[0064] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit uses data mining techniques to analyze users' style preferences and trends. Data mining techniques include clustering and association analysis, which allow for the identification of user preferences and behavioral patterns. For example, it analyzes a user's past outfit history to identify their preferred colors and styles. The analysis unit also acquires weather forecast data and provides information to suggest outfits appropriate for the weather. Weather forecast data is acquired in real time from an external weather information service, and the analysis unit uses this to suggest outfits suitable for the weather. Furthermore, the analysis unit acquires the user's schedule from a calendar application and provides information to suggest outfits appropriate for the schedule. The schedule information acquired from the calendar application includes the type of event, location, and time, and the analysis unit uses this to suggest appropriate outfits. For example, it suggests suits or dresses for formal events and relaxed styles for casual events. In this way, the analysis unit can provide the optimal outfit according to the user's preferences and circumstances.

[0065] The suggestion department proposes daily outfits based on information analyzed by the analysis department. For example, the suggestion department proposes the optimal outfit based on information such as the user's style preferences, weather, and schedule. Specifically, it proposes outfits considering the user's preferred colors and styles. The suggestion department also refers to the user's past outfit and purchase history to identify preferred items and styles. Furthermore, the suggestion department can propose outfits suitable for the weather based on weather forecast data. For example, it might suggest a waterproof jacket and boots on a rainy day, or a lighter outfit on a hot day. In addition, the suggestion department can propose outfits suitable for formal or casual occasions depending on the user's schedule. For example, it might suggest a suit and tie for a business meeting, or jeans and sneakers for a casual gathering with friends. The suggestion department can collect user feedback and continuously improve the accuracy of its suggestions. Based on the user's feedback on the suggested outfits, future suggestions can be tailored more closely to the user's preferences. This allows the suggestion department to provide optimal outfits that meet user needs and improve user satisfaction.

[0066] The learning unit learns from user feedback and purchase history based on the outfits suggested by the suggestion unit. For example, the learning unit collects user feedback on suggested outfits and incorporates it into future suggestions. Specifically, it evaluates whether the user liked the suggested outfit and uses that evaluation to improve future suggestions. The learning unit analyzes user feedback to identify the user's preferred items and styles. The learning unit can also analyze the user's purchase history to learn the user's preferred items. For example, it analyzes the brands, colors, and styles of items the user has purchased in the past to identify the user's preferences. The learning unit uses this information as a reference when the suggestion unit makes future outfit suggestions. Furthermore, the learning unit can improve the accuracy of suggestions by continuously collecting user feedback and purchase history and updating its learning model. This allows the learning unit to accurately understand user preferences and tendencies, helping the suggestion unit propose outfits that are more suitable for the user.

[0067] The recommendation unit recommends items that should be purchased next based on information learned by the learning unit. For example, the recommendation unit recommends new items considering the user's style preferences and trends. Specifically, it recommends items that go well with items the user has purchased in the past. For example, it might recommend pants or shirts that match a jacket the user has purchased. The recommendation unit can also recommend items that the user is likely to like based on user feedback. It recommends similar items based on items and brands that the user has previously given high ratings to. Furthermore, the recommendation unit can recommend items that are suitable for the user, taking into account the season and trends. For example, it might recommend warm coats and sweaters in winter, and light shirts and shorts in summer. The recommendation unit can improve the accuracy of its recommendations by continuously collecting user purchase history and feedback and updating its recommendation algorithm. This allows the recommendation unit to propose the most suitable items that meet the user's needs and improve user satisfaction.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of wardrobe information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect the information when the user is relaxed. For example, if the user is relaxed, the data collection unit can immediately collect wardrobe information. For example, if the user is busy, the data collection unit can adjust the collection timing to match the user's schedule. By adjusting the collection timing according to the user's emotions, information 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. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0069] The collection unit can analyze the user's past wardrobe information and select the optimal collection method. For example, the collection unit can prioritize collecting items that the user has frequently used in the past. For example, the collection unit can select different collection methods for each season based on the user's past wardrobe information. For example, the collection unit can analyze the user's past wardrobe information and adjust the collection method based on specific brands or styles. This allows the optimal collection method to be selected by analyzing past information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past wardrobe information into a generating AI and have the generating AI select the optimal collection method.

[0070] The data collection unit can filter wardrobe information based on the user's current fashion trends and areas of interest. For example, the data collection unit can prioritize collecting fashion items that the user is currently interested in. For example, the data collection unit can filter items of a specific style or color based on the user's current fashion trends. For example, the data collection unit can collect relevant fashion items based on the user's areas of interest. This allows information to be filtered based on current fashion trends 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 information on the user's current fashion trends and areas of interest into a generating AI and have the generating AI perform the filtering.

[0071] The data collection unit can estimate the user's emotions and determine the priority of wardrobe information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting items that promote relaxation. If the user is relaxed, the data collection unit may prioritize collecting trendy items. If the user is busy, the data collection unit may prioritize collecting practical items. This allows the information to be prioritized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0072] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location when collecting wardrobe information. For example, if the user lives in a cold region, the data collection unit can prioritize collecting warm clothing. For example, if the user lives in an urban area, the data collection unit can prioritize collecting trendy items. For example, if the user is traveling, the data collection unit can collect items suitable for the climate of their travel destination. This allows for the collection of highly relevant information by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0073] The data collection unit can analyze the user's social media activity and collect relevant information when collecting wardrobe information. 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 items based on the styles of influencers that the user follows. For example, the data collection unit can collect items from brands that the user has shown interest in on social media. This allows for the analysis of social media activity and the collection of relevant 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 social media activity data into a generating AI and have the generating AI collect relevant information.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. For example, if the user is excited, the analysis unit can provide visually appealing analysis results. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the wardrobe information during the analysis. For example, the analysis unit can perform a detailed analysis for important items. For example, the analysis unit can perform a simplified analysis for items that are not used often. For example, the analysis unit can perform an analysis with a level of detail appropriate to the season for items whose importance changes seasonally. This allows the level of detail of the analysis to be adjusted based on the importance of the wardrobe information. 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 wardrobe information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the category of the wardrobe information during analysis. For example, the analysis unit can apply a casual analysis algorithm to casual items. For example, the analysis unit can apply a formal analysis algorithm to formal items. For example, the analysis unit can apply a sports analysis algorithm to sportswear. This allows different analysis algorithms to be applied depending on the category of the wardrobe information. 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 wardrobe information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0077] 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. If the user is excited, the analysis unit can perform a visually appealing analysis. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0078] The analysis unit can determine the priority of analysis based on when the wardrobe information was collected. For example, the analysis unit may prioritize the analysis of recently collected information. For example, the analysis unit may prioritize the analysis of information collected seasonally. For example, the analysis unit may prioritize the analysis of information related to the user's life events. This allows the analysis priority to be determined based on when the wardrobe information was collected. 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 may input wardrobe information collection timing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0079] The analysis unit can adjust the order of analysis based on the relevance of wardrobe information during analysis. For example, the analysis unit may prioritize analyzing information related to the user's current style. For example, the analysis unit may prioritize analyzing information related to the user's past purchase history. For example, the analysis unit may prioritize analyzing information related to the user's schedule. This allows the order of analysis to be adjusted based on the relevance of wardrobe information. 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 wardrobe information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0080] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. If the user is excited, the suggestion unit can provide visually appealing suggestions. This allows the suggestion unit to adjust its presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0081] The proposal unit can adjust the level of detail in its proposals based on the importance of the coordination. For example, the proposal unit can provide detailed proposals for important events. For example, it can provide concise proposals for everyday coordination. For example, it can provide proposals with a level of detail appropriate for the season for coordinations whose importance changes seasonally. This allows the level of detail in proposals to be adjusted based on the importance of the coordination. 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 coordination importance data into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0082] The suggestion unit can apply different suggestion algorithms depending on the category of the outfit when making suggestions. For example, the suggestion unit can apply a casual suggestion algorithm to a casual outfit. For example, the suggestion unit can apply a formal suggestion algorithm to a formal outfit. For example, the suggestion unit can apply a sports suggestion algorithm to a sports outfit. This allows different suggestion algorithms to be applied depending on the category of the outfit. 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 outfit category data into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0083] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can make a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can make a detailed suggestion. If the user is excited, the suggestion unit can make a visually appealing suggestion. This allows the length of the suggestion to be adjusted according to the user's emotions. 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0084] The proposal department can determine the priority of proposals based on the timing of their submission. For example, the proposal department may prioritize proposals related to recent events. For example, the proposal department may prioritize proposals submitted seasonally. For example, the proposal department may prioritize proposals related to the user's life events. This allows the proposal department to determine the priority of proposals based on the timing of their submission. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department may input data on the timing of outfit submissions into a generating AI and have the generating AI determine the priority of proposals.

[0085] The suggestion unit can adjust the order of suggestions based on the relevance of the outfits when making suggestions. For example, the suggestion unit may prioritize suggesting outfits related to the user's current style. For example, the suggestion unit may prioritize suggesting outfits related to the user's past purchase history. For example, the suggestion unit may prioritize suggesting outfits related to the user's schedule. This allows the order of suggestions to be adjusted based on the relevance of the outfits. 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 outfit relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0086] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit can select detailed training data. For example, if the user is in a hurry, the learning unit can select concise training data. For example, if the user is excited, the learning unit can select visually appealing training data. This allows for the use of more appropriate data for training by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0087] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can adjust the parameters of the learning algorithm from past learning data. For example, the learning unit can analyze past learning data to improve the accuracy of the learning algorithm. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0088] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit may learn more frequently when the user is relaxed. For example, it may reduce the learning frequency when the user is busy. For example, it may increase the learning frequency when the user is excited. By adjusting the learning frequency according to the user's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0089] The learning unit can weight the training data based on when the wardrobe information was collected during training. For example, the learning unit can assign higher weights to recently collected information. For example, the learning unit can assign different weights to information collected seasonally. For example, the learning unit can assign higher weights to information related to the user's life events. This allows for more accurate training by weighting the training data based on when the wardrobe information was collected. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input wardrobe information collection timing data into a generating AI and have the generating AI perform the weighting of the training data.

[0090] The recommendation unit can estimate the user's emotions and adjust its recommendation method based on the estimated emotions. For example, if the user is relaxed, the recommendation unit can provide detailed recommendations. If the user is in a hurry, the recommendation unit can provide concise recommendations. If the user is excited, the recommendation unit can provide visually appealing recommendations. By adjusting the recommendation method according to the user's emotions, more appropriate recommendations become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, or not using AI. For example, the recommendation unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0091] The recommendation unit can analyze the user's past purchase history to select the optimal recommendation method. For example, the recommendation unit can make recommendations based on items the user has purchased in the past. For example, the recommendation unit can recommend related items from the user's past purchase history. For example, the recommendation unit can analyze the user's purchase history and recommend items based on trends. In this way, the optimal recommendation method can be selected by analyzing the user's past purchase history. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's past purchase history data into a generating AI and have the generating AI select the optimal recommendation method.

[0092] The recommendation unit can customize its recommendation methods based on the user's current fashion trends. For example, the recommendation unit recommends relevant items based on the user's current fashion trends. The recommendation unit can, for example, provide customized recommendations tailored to the user's current style. The recommendation unit can adjust its recommendation methods based on the user's current areas of interest. This allows for the customization of recommendation methods based on the user's current fashion trends. Some or all of the above-described processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's current fashion trend data into a generating AI and have the generating AI perform the customization of recommendation methods.

[0093] The recommendation unit can estimate the user's emotions and determine the priority of recommendations based on the estimated emotions. For example, if the user is relaxed, the recommendation unit may prioritize detailed recommendations. If the user is in a hurry, the recommendation unit may prioritize concise recommendations. If the user is excited, the recommendation unit may prioritize visually appealing recommendations. By determining the priority of recommendations according to the user's emotions, more appropriate recommendations become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI or not using AI. For example, the recommendation unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0094] The recommendation unit can select the optimal recommendation method by considering the user's geographical location information when making recommendations. For example, if the user lives in a cold region, the recommendation unit can recommend warm clothing. For example, if the user lives in an urban area, the recommendation unit can recommend trendy items. For example, if the user is traveling, the recommendation unit can recommend items suitable for the climate of their travel destination. This allows the recommendation unit to select the optimal recommendation method by considering the user's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal recommendation method.

[0095] The recommendation unit can analyze the user's social media activity and propose recommendation methods when making recommendations. For example, the recommendation unit can recommend items that the user has shared on social media. For example, the recommendation unit can recommend items based on the style of influencers that the user follows. For example, the recommendation unit can recommend items from brands that the user has shown interest in on social media. This allows the recommendation unit to analyze the user's social media activity and propose recommendation methods. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's social media activity data into a generating AI and have the generating AI propose recommendation methods.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can make a suggestion immediately. For example, if the user is busy, the suggestion unit can delay the timing of the suggestion. For example, if the user is stressed, the suggestion unit can adjust the timing of the suggestion so that the user receives the suggestion in a relaxed state. In this way, by adjusting the timing of suggestions according to the user's emotions, suggestions can be made at a more appropriate time. 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0098] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit may prioritize a detailed analysis. For example, if the user is in a hurry, the analysis unit may prioritize a concise analysis. For example, if the user is excited, the analysis unit may prioritize a visually appealing analysis. This allows for more appropriate analysis by determining the priority of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0099] The learning unit can estimate the user's emotions and adjust the timing of learning based on the estimated emotions. For example, if the user is relaxed, the learning unit can start learning immediately. For example, if the user is busy, the learning unit can delay the timing of learning. For example, if the user is stressed, the learning unit can adjust the timing of learning so that the user can learn in a relaxed state. In this way, by adjusting the timing of learning according to the user's emotions, learning can be performed at a more appropriate time. 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 learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0100] The recommendation unit can estimate the user's emotions and adjust the timing of recommendations based on the estimated emotions. For example, if the user is relaxed, the recommendation unit can make recommendations immediately. For example, if the user is busy, the recommendation unit can delay the timing of recommendations. For example, if the user is stressed, the recommendation unit can adjust the timing of recommendations so that the user receives them in a relaxed state. In this way, by adjusting the timing of recommendations according to the user's emotions, recommendations can be made at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0101] The data collection unit can estimate the user's emotions and adjust the type of information it collects based on the estimated emotions. For example, if the user is relaxed, the data collection unit can collect detailed information. If the user is busy, for example, the data collection unit can collect concise information. If the user is stressed, for example, the data collection unit can adjust the type of information it collects so that the user can collect information in a relaxed state. This allows for the collection of more appropriate information by adjusting the type of information collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0102] The analysis unit can analyze the user's past outfit history and adjust the analysis algorithm according to changes in trends. For example, the analysis unit can identify changes in the user's style from the past outfit history. For example, the analysis unit can adjust the parameters of the analysis algorithm according to changes in trends. For example, the analysis unit can analyze the user's past outfit history and perform analysis based on trends. This makes it possible to adjust the analysis algorithm according to changes in trends by analyzing the past outfit history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past outfit history data into a generating AI and have the generating AI perform the adjustment of the analysis algorithm.

[0103] The suggestion unit can analyze past user feedback and adjust algorithms to improve the accuracy of suggestions. For example, the suggestion unit can identify user preferences from past feedback. For example, the suggestion unit can adjust the parameters of the suggestion algorithm based on the feedback. For example, the suggestion unit can analyze past user feedback and adjust algorithms to improve the accuracy of suggestions. This makes it possible to adjust algorithms to improve the accuracy of suggestions by analyzing past feedback. 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 past feedback data into a generating AI and have the generating AI perform the adjustment of the suggestion algorithm.

[0104] The learning unit can analyze the user's past purchase history and select training data. For example, the learning unit can identify the user's preferences from past purchase history. For example, the learning unit can select training data based on purchase history. For example, the learning unit can analyze the user's past purchase history and select training data. This makes it possible to select training data by analyzing past purchase history. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past purchase history data into a generating AI and have the generating AI perform the selection of training data.

[0105] The recommendation unit can analyze a user's past purchase history and adjust the algorithm to improve the accuracy of recommendations. For example, the recommendation unit can identify user preferences from past purchase history. The recommendation unit can adjust the parameters of the recommendation algorithm based on purchase history. The recommendation unit can analyze a user's past purchase history and adjust the algorithm to improve the accuracy of recommendations. This makes it possible to adjust the algorithm to improve recommendation accuracy by analyzing past purchase history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input past purchase history data into a generating AI and have the generating AI perform the adjustment of the recommendation algorithm.

[0106] The data collection unit can determine the priority of the information to collect, taking into account the user's geographical location. For example, if the user lives in a cold region, the data collection unit can prioritize collecting information on warm clothing. For example, if the user lives in an urban area, the data collection unit can prioritize collecting information on trendy items. For example, if the user is traveling, the data collection unit can prioritize collecting information on items suitable for the climate of their travel destination. By determining the priority of the information to collect, taking into account the user's geographical location, more appropriate information can be collected. 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 data into a generating AI and have the generating AI perform the task of determining the priority of the information to collect.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The collection unit collects information about the user's wardrobe. The collection unit can collect information such as the type, color, size, and brand of items in the user's wardrobe. For example, it can take photos of the clothes the user owns and extract item information using image analysis technology. It can also collect wardrobe information by analyzing receipts and online purchase history of items the user has purchased. Furthermore, it can collect item information that the user has manually entered. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses data mining techniques to analyze the user's style preferences and trends. For example, it analyzes the user's past outfit history to identify their preferences. It also acquires weather forecast data and provides information to suggest outfits appropriate for the weather. Furthermore, it acquires the user's schedule from a calendar application and provides information to suggest outfits appropriate for that schedule. Step 3: The suggestion unit proposes daily outfits based on the information analyzed by the analysis unit. The suggestion unit proposes the optimal outfit based on information such as the user's style preferences, weather, and schedule. For example, it proposes outfits that take into account the user's preferred colors and styles. It can also propose outfits suitable for the weather based on weather forecast data. Furthermore, it can propose outfits suitable for formal occasions or casual occasions depending on the user's schedule. Step 4: The learning unit learns from user feedback and purchase history based on the outfits suggested by the suggestion unit. The learning unit collects feedback from users on the suggested outfits and incorporates it into future suggestions. For example, it evaluates whether the user liked the suggested outfit and uses that evaluation to improve future suggestions. It can also analyze the user's purchase history and learn the trends in items the user prefers. Step 5: The recommendation unit recommends the next item to purchase based on the information learned by the learning unit. The recommendation unit recommends new items considering the user's style preferences and trends. For example, it may recommend items that are compatible with items the user has purchased in the past. It can also recommend items that the user is likely to like based on user feedback.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, learning unit, and recommendation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart device 14 to take pictures of items in the user's wardrobe, and the control unit 46A performs image analysis. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to identify the user's style preferences and trends. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes daily outfits based on the analysis results. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12, and learns the user's feedback and purchase history. The recommendation unit is implemented in the specific processing unit 290 of the data processing unit 12, and recommends items to be purchased next based on the learning results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, learning unit, and recommendation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart glasses 214 to take pictures of items in the user's wardrobe, and the control unit 46A performs image analysis. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, and analyzes the collected information to identify the user's style preferences and trends. The suggestion unit is implemented in the identification processing unit 290 of the data processing unit 12, and suggests daily outfits based on the analysis results. The learning unit is implemented in the identification processing unit 290 of the data processing unit 12, and learns the user's feedback and purchase history. The recommendation unit is implemented in the identification processing unit 290 of the data processing unit 12, and recommends items to be purchased next based on the learning results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, learning unit, and recommendation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 of the headset terminal 314 to take pictures of items in the user's wardrobe, and the control unit 46A performs image analysis. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected information to identify the user's style preferences and trends. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes daily outfits based on the analysis results. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and learns the user's feedback and purchase history. The recommendation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and recommends items to be purchased next based on the learning results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, learning unit, and recommendation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 of the robot 414 to take pictures of items in the user's wardrobe, and the control unit 46A performs image analysis. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to identify the user's style preferences and trends. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes daily outfits based on the analysis results. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12, and learns the user's feedback and purchase history. The recommendation unit is implemented in the specific processing unit 290 of the data processing unit 12, and recommends items to be purchased next based on the learning results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A collection unit that collects information about the user's wardrobe, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the information analyzed by the aforementioned analysis unit, a proposal unit proposes daily coordination, A learning unit learns user feedback and purchase history based on the coordination proposed by the aforementioned proposal unit, The system includes a recommendation unit that recommends the next item to be purchased based on the information learned by the learning unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of wardrobe information collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past wardrobe information and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting wardrobe information, filtering is performed based on the user's current fashion trends and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the wardrobe information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting wardrobe information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting wardrobe information, we analyze the user's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the wardrobe information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of wardrobe information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the wardrobe information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of wardrobe information. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the coordination. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the coordination category. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of their submission. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making proposals, adjust the order of suggestions based on their relevance to the overall coordination. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned learning unit, During training, the training data is weighted based on when the wardrobe information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The recommendation unit is, It estimates the user's emotions and adjusts the recommendation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The recommendation unit is, When making recommendations, the system analyzes the user's past purchase history to select the most suitable recommendation method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The recommendation unit is, When making recommendations, customize the recommendation method based on the user's current fashion trends. The system described in Appendix 1, characterized by the features described herein. (Note 27) The recommendation unit is, It estimates the user's emotions and determines the priority of recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The recommendation unit is, When making recommendations, the system selects the optimal recommendation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The recommendation unit is, When making recommendations, the system analyzes the user's social media activity to suggest appropriate recommendation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects information about the user's wardrobe, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the information analyzed by the aforementioned analysis unit, a proposal unit proposes daily coordination, A learning unit learns user feedback and purchase history based on the coordination proposed by the aforementioned proposal unit, The system includes a recommendation unit that recommends the next item to be purchased based on the information learned by the learning unit. A system characterized by the following features.

2. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of wardrobe information collection based on those estimated emotions. The system according to feature 1.

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

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

5. The aforementioned collection unit is It estimates the user's emotions and prioritizes the wardrobe information to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting wardrobe information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting wardrobe information, we analyze the user's social media activity and collect relevant information. The system according to feature 1.

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

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

10. The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of wardrobe information. The system according to feature 1.

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