system

Abstract

The system according to this embodiment aims to suggest fashion coordination suitable for a place you are visiting for the first time. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a reception unit, and a generation unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes fashion coordinates based on the analysis results obtained by the analysis unit. The reception unit registers the user's personal data. The generation unit makes individual proposals based on the personal data registered by the reception unit.

Description

Technical Field

[0006] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to propose a fashion coordination suitable for a place visited for the first time.

[0005] The system according to the embodiment aims to propose a fashion coordination suitable for a place visited for the first time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a reception unit, and a generation unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes fashion coordinates based on the analysis results obtained by the analysis unit. The reception unit registers the user's personal data. The generation unit makes individual proposals based on the personal data registered by the reception unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest fashion coordinates suitable for a place you are visiting for the first time. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The fashion coordination suggestion system according to an embodiment of the present invention is a system that solves the problem of what kind of clothing to wear when visiting a new destination for the first time. This system analyzes security camera footage data from all over the country to determine the fashion trends of each region. Next, it combines the acquired regional fashion base data with information such as the genre, price range, and dress code of each restaurant to generate recommended clothing information tailored to the location. For example, a stylish cafe in Omotesando might recommend a coordinated outfit using a smart cardigan, while an Italian restaurant in Shichirigahama might recommend a casual outfit using a work shirt. Furthermore, by registering personal data, the system can use generation AI technology to provide outing suggestions that are more tailored to the individual. For example, if a user who is 34 years old, male, 170cm tall, weighs 68kg, and likes Ron Herman as a brand is considering going to Shichirigahama Amalfi, the system will provide the most suitable fashion suggestion for that user. In this way, the fashion coordination suggestion system grasps the real fashion trends of each region and provides users with the most suitable fashion coordinate, creating an environment where they can go out with confidence in their fashion choices. It also provides new business value by promoting optimal purchasing and dining experiences using data. This allows the fashion coordination suggestion system to propose the most suitable fashion coordination for the user.

[0029] The fashion coordination suggestion system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, a reception unit, and a generation unit. The collection unit collects data. For example, the collection unit can collect security camera video data from across the country. The collection unit can also preferentially collect security camera video data from specific regions or stores. The collection unit can also preferentially collect data from specific time periods or event periods. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data and determine the fashion trends of the region. The analysis unit can also predict current trends by referring to past fashion trend data. The analysis unit can also determine fashion trends corresponding to specific events or seasons. The suggestion unit proposes fashion coordination based on the analysis results obtained by the analysis unit. For example, the suggestion unit can generate recommended clothing information tailored to a location by combining information such as the genre, price range, and dress code of each restaurant. The suggestion unit may also include a feedback unit that collects user feedback. The reception unit registers the user's personal data. The reception unit can, for example, select the optimal registration method by referring to the user's past fashion history. The generation unit makes individual suggestions based on the personal data registered by the reception unit. The generation unit can, for example, make outing suggestions that are more tailored to the user based on the user's personal data. The generation unit can also, for example, generate optimal suggestions by referring to the user's past fashion history. As a result, the fashion coordination suggestion system according to the embodiment can suggest the most suitable fashion coordination to the user.

[0030] The data collection unit collects data. For example, the data collection unit can collect security camera footage from across the country. Specifically, the data collection unit acquires video data in real time from security cameras installed in each region and transmits it to a central database. This allows for a broad understanding of fashion trends throughout the country. The data collection unit can also prioritize the collection of security camera footage from specific regions or stores. To analyze fashion trends in specific regions or stores in detail, the data collection unit prioritizes access to specific cameras and collects data. For example, by prioritizing the collection of camera footage from places where many people gather, such as busy downtown areas and shopping malls, the latest fashion trends can be quickly grasped. The data collection unit can also prioritize the collection of data from specific time periods or during events. Since fashion trends often differ from the norm during specific time periods or during events, the data collection unit prioritizes the collection of this data and provides it to the analysis unit. For example, by collecting data during weekends, holidays, or specific events (such as fashion shows or festivals), fashion trends under specific circumstances can be analyzed in detail. This allows the data collection unit to efficiently collect diverse data, improving the overall accuracy and reliability of the system.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data to determine the fashion trends of a region. Specifically, the analysis unit uses image recognition technology to analyze security camera footage and identify the clothing and accessories of people in the footage. AI-based image analysis algorithms extract features such as color, shape, and pattern, and analyze fashion trends based on this information. The analysis unit can also predict current trends by referring to past fashion trend data. Based on past data, it predicts seasonal trends and fashion trends for specific events, and by comparing them with current data, it grasps the latest trends. The analysis unit can also determine fashion trends corresponding to specific events or seasons. For example, it analyzes fashion trends specific to certain events or seasons, such as summer festivals or the winter Christmas season, to improve the accuracy of the information provided to users. In this way, the analysis unit can analyze the collected data from multiple angles and provide users with the latest and most appropriate fashion information. Furthermore, the analysis unit can continuously improve the accuracy of data analysis by utilizing AI-based machine learning algorithms. In this way, the analysis unit can always incorporate the latest technologies and improve the overall system performance.

[0032] The suggestion department proposes fashion coordinates based on the analysis results obtained by the analysis department. The suggestion department can generate location-specific clothing recommendations by combining information such as the genre, price range, and dress code of each restaurant. Specifically, the suggestion department proposes the optimal coordinate based on the user's destination and plans, based on fashion trend data provided by the analysis department. For example, it might suggest formal attire for a high-end restaurant and a relaxed style for a casual cafe. The suggestion department can also include a feedback department to collect user feedback. By providing feedback on the suggested coordinates, the suggestion department can more accurately understand the user's preferences and needs and reflect them in future suggestions. This allows the suggestion department to provide users with more personalized fashion coordinates. Furthermore, the suggestion department can utilize an AI-powered recommendation engine to make more accurate suggestions based on the user's past choices and feedback. This allows the suggestion department to improve user satisfaction and enhance the overall reliability of the system.

[0033] The reception desk registers the user's personal data. For example, the reception desk can select the optimal registration method by referring to the user's past fashion history. Specifically, the reception desk collects data on the clothes the user has worn and the items they have purchased in the past, and uses this to analyze the user's fashion style and preferences. Users can easily register their fashion history through a smartphone app or website. The reception desk also collects basic information such as the user's height, weight, age, and gender, and uses this data to provide more accurate fashion suggestions. Furthermore, the reception desk can collect detailed information such as the user's lifestyle, hobbies, and preferences for specific events and seasons. This allows the reception desk to collect data that meets the individual needs of the user and improve the overall personalization of the system.

[0034] The generation unit makes individual suggestions based on personal data registered by the reception unit. For example, the generation unit can make outing suggestions that are more tailored to the user based on their personal data. Specifically, the generation unit generates the optimal outfit for the user based on the user's past fashion history and current trend data. For example, it considers the colors and styles the user has preferred to wear in the past and suggests outfits that are appropriate for the current season and event. The generation unit can also generate optimal suggestions by referring to the user's past fashion history. Based on past data, it identifies the items and brands the user prefers and suggests outfits that combine them. Furthermore, the generation unit can utilize AI-based generative algorithms to learn the user's preferences and trends, enabling it to make more accurate suggestions. As a result, the generation unit can always provide users with the latest and most optimal fashion outfits, improving user satisfaction.

[0035] The suggestion function can generate appropriate attire recommendations for each location by combining information such as the genre, price range, and dress code of each restaurant. For example, it can generate attire recommendations based on genres such as Japanese, Western, and Chinese cuisine. It can also generate recommendations based on price ranges such as low-priced, mid-priced, and high-priced. Furthermore, it can generate recommendations based on dress code information such as casual, business casual, and formal. This enables specific fashion suggestions tailored to each location.

[0036] The data collection unit can collect security camera video data from across the country. The data collection unit can also prioritize the collection of security camera video data from specific areas or stores. The data collection unit can also prioritize the collection of data from specific time periods or during events. The data collection unit can prioritize the collection of data from specific time periods, such as weekends or holidays. The data collection unit can prioritize the collection of data from local events. The data collection unit can analyze pedestrian traffic during specific time periods and prioritize the collection of data from those times. This enables the collection of data over a wide area. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input security camera video data into a generating AI and have the generating AI perform the data collection.

[0037] The analysis unit can analyze the collected data and determine the fashion trends of the region. For example, the analysis unit can analyze the collected data and determine fashion trends such as popular colors, styles, and brands. For example, the analysis unit can predict current trends by referring to past fashion trend data. For example, the analysis unit can determine fashion trends corresponding to specific events or seasons. For example, the analysis unit can determine fashion trends corresponding to specific events. For example, the analysis unit can determine fashion trends corresponding to seasons. This makes it possible to understand fashion trends for each region. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the analysis of fashion trends.

[0038] The generation unit can provide outing suggestions that are more tailored to the user based on their personal data. For example, the generation unit can provide suggestions based on personal data such as the user's age, gender, and preferred style. For example, the generation unit can generate optimal suggestions by referring to the user's past fashion history. For example, the generation unit can provide suggestions based on the user's preferences and past history. For example, the generation unit can provide suggestions tailored to specific events or seasons based on the user's personal data. This enables individually optimized suggestions. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's personal data into a generation AI and have the generation AI execute individual suggestions.

[0039] The proposal unit may include an advertising unit that places advertisements for proposed fashion items on e-commerce sites. The proposal unit can, for example, place advertisements for proposed fashion items on specific e-commerce sites. The proposal unit can, for example, set the frequency at which proposed fashion items are placed. The proposal unit can, for example, place optimal advertisements by referring to the user's past purchase history when placing advertisements for proposed fashion items. The proposal unit can, for example, refer to the user's past purchase history and place advertisements based on their preferred style. The proposal unit can, for example, refer to the user's past purchase history and place advertisements according to the season or event. This allows the user to purchase the proposed fashion items on e-commerce sites. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input proposed fashion items into a generation AI and have the generation AI execute the advertisement placement.

[0040] The data collection unit can prioritize the collection of security camera video data during specific time periods or event periods. For example, the data collection unit can prioritize the collection of data during specific time periods such as weekends or holidays. For example, the data collection unit can prioritize the collection of data during local event periods. For example, the data collection unit can analyze the flow of people during specific time periods and prioritize the collection of data during those time periods. By prioritizing the collection of data during specific time periods or event periods, more detailed data can be obtained. 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 security camera video data into a generating AI and have the generating AI perform the collection of data during specific time periods or event periods.

[0041] The data collection unit can expand the types of data it collects and simultaneously collect weather information and traffic data. For example, the data collection unit can collect weather information and use it for analysis as a factor influencing fashion trends. For example, the data collection unit can collect traffic data and understand congestion levels in specific areas. For example, the data collection unit can combine weather information and traffic data to determine more detailed fashion trends. Thus, by collecting weather information and traffic data, more detailed fashion trends can be determined. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather information and traffic data into a generating AI and have the generating AI perform the data collection.

[0042] The data collection unit can prioritize the collection of data from specific regions or stores when collecting security camera video data. For example, the data collection unit can prioritize the collection of security camera video data from a specific region. For example, the data collection unit can prioritize the collection of security camera video data from a specific store. For example, the data collection unit can prioritize the collection of data from specific regions or stores in order to understand fashion trends in each region. By prioritizing the collection of data from specific regions or stores, it is possible to understand fashion trends in each region. 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 security camera video data into a generating AI and have the generating AI perform the collection of data from specific regions or stores.

[0043] The data collection unit can add user social media activity data to the data it collects. For example, the data collection unit can collect user social media activity data and use it to analyze fashion trends. For example, the data collection unit can collect user social media activity data and use it to make personalized fashion suggestions. For example, the data collection unit can collect user social media activity data and use it to understand fashion trends by region. This allows for a more detailed understanding of fashion trends by collecting user social media activity data. 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 user social media activity data into a generating AI and have the generating AI perform the data collection.

[0044] The analysis unit can predict current trends by referring to past fashion trend data during analysis. For example, the analysis unit can predict current fashion trends by referring to past fashion trend data. For example, the analysis unit can predict seasonal fashion trends by referring to past fashion trend data. For example, the analysis unit can predict fashion trends corresponding to specific events or seasons by referring to past fashion trend data. In this way, current fashion trends can be predicted by referring to past fashion trend data. 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 fashion trend data into a generating AI and have the generating AI perform a prediction of current trends.

[0045] The analysis unit can identify fashion trends corresponding to specific events or seasons during analysis. For example, the analysis unit can identify fashion trends corresponding to specific events. For example, the analysis unit can identify fashion trends corresponding to seasons. For example, the analysis unit can identify fashion trends corresponding to specific events or seasons and reflect them in the analysis results. This makes it possible to understand fashion trends corresponding to specific events or seasons. 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 data corresponding to specific events or seasons into a generating AI and have the generating AI perform the identification of fashion trends.

[0046] The analysis unit can perform analysis while considering the geographical distribution of the collected data. For example, the analysis unit can analyze fashion trends by region while considering the geographical distribution of the collected data. For example, the analysis unit can analyze fashion trends in a specific region while considering the geographical distribution of the collected data. For example, the analysis unit can grasp fashion trends by region while considering the geographical distribution of the collected data. In this way, by considering the geographical distribution of the collected data, it is possible to grasp fashion trends by region. 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 the geographical distribution of the collected data into a generating AI and have the generating AI perform the analysis.

[0047] The analysis unit can improve the accuracy of its analysis by referring to data from relevant fashion magazines and blogs during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to data from relevant fashion magazines. For example, the analysis unit can improve the accuracy of its analysis by referring to data from relevant fashion blogs. For example, the analysis unit can improve the accuracy of its analysis by referring to data from relevant fashion magazines and blogs. This allows the analysis to be improved by referring to data from relevant fashion magazines and blogs. 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 data from fashion magazines and blogs into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0048] The suggestion unit can make optimal suggestions by referring to the user's past fashion history. For example, the suggestion unit can refer to the user's past fashion history and make optimal suggestions. For example, the suggestion unit can refer to the user's past fashion history and make suggestions based on their preferred style. For example, the suggestion unit can refer to the user's past fashion history and make suggestions according to the season or event. In this way, optimal suggestions can be made by referring to the user's past fashion history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past fashion history into a generating AI and have the generating AI execute the optimal suggestion.

[0049] The suggestion unit can suggest fashion items that are appropriate for specific events or seasons. For example, the suggestion unit can suggest fashion items that are appropriate for specific events. For example, the suggestion unit can suggest fashion items that are appropriate for seasons. For example, the suggestion unit can suggest fashion items that are appropriate for specific events or seasons and make the best possible suggestions for the user. In this way, by suggesting fashion items that are appropriate for specific events or seasons, the suggestion unit can make the best possible suggestions for the user. 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 data appropriate for specific events or seasons into a generating AI and have the generating AI perform fashion item suggestions.

[0050] The suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, the suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, the suggestion unit can make suggestions based on regional fashion trends by considering the user's geographical location information. For example, the suggestion unit can make optimal suggestions for a specific region by considering the user's geographical location information. In this way, by considering the user's geographical location information, suggestions based on regional fashion trends can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input the user's geographical location information into a generation AI and have the generation AI execute the optimal suggestion.

[0051] The suggestion unit can analyze the user's social media activity and suggest relevant fashion items when making suggestions. For example, the suggestion unit can analyze the user's social media activity and suggest relevant fashion items. For example, the suggestion unit can analyze the user's social media activity and make suggestions based on their preferred style. For example, the suggestion unit can analyze the user's social media activity and make suggestions based on trends. In this way, by analyzing the user's social media activity, it is possible to suggest relevant fashion items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI perform fashion item suggestions.

[0052] The reception desk can select the optimal registration method by referring to the user's past fashion history when registering personal data. For example, the reception desk can refer to the user's past fashion history and suggest the optimal registration method. For example, the reception desk can refer to the user's past fashion history and suggest a registration method based on the user's preferred style. For example, the reception desk can refer to the user's past fashion history and suggest a registration method that is appropriate for the season or event. In this way, the optimal registration method can be selected by referring to the user's past fashion history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past fashion history into a generating AI and have the generating AI execute the optimal registration method.

[0053] The reception desk can select the optimal registration method when registering personal data, taking into account the user's device information. For example, if the user is using a smartphone, the reception desk can provide a registration method adapted to the screen size. For example, if the user is using a tablet, the reception desk can provide a registration method optimized for a larger screen. For example, if the user is using a smartwatch, the reception desk can provide a concise and highly visible registration method. This allows the reception desk to select the optimal registration method by taking into account the user's device information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's device information into a generating AI and have the generating AI execute the optimal registration method.

[0054] The generation unit can generate optimal suggestions by referring to the user's past fashion history during the generation process. For example, the generation unit can refer to the user's past fashion history and generate optimal suggestions. For example, the generation unit can refer to the user's past fashion history and generate suggestions based on preferred styles. For example, the generation unit can refer to the user's past fashion history and generate suggestions according to the season or event. In this way, optimal suggestions can be generated by referring to the user's past fashion history. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past fashion history into a generation AI and have the generation AI execute optimal suggestions.

[0055] The generation unit can generate fashion items that correspond to specific events or seasons during the generation process. For example, the generation unit can generate fashion items that correspond to specific events. For example, the generation unit can generate fashion items that correspond to seasons. For example, the generation unit can generate fashion items that correspond to specific events or seasons and make the most suitable suggestions to the user. In this way, by generating fashion items that correspond to specific events or seasons, the generation unit can make the most suitable suggestions to the user. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data corresponding to specific events or seasons into a generation AI and have the generation AI execute the generation of fashion items.

[0056] The generation unit can generate optimal suggestions by considering the user's geographical location information during the generation process. For example, the generation unit can generate optimal suggestions by considering the user's geographical location information. For example, the generation unit can generate suggestions based on regional fashion trends by considering the user's geographical location information. For example, the generation unit can generate suggestions optimized for a specific region by considering the user's geographical location information. This makes it possible to generate suggestions based on regional fashion trends by considering the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI execute optimal suggestions.

[0057] The generation unit can analyze the user's social media activity during generation and generate relevant fashion items. For example, the generation unit can analyze the user's social media activity and generate relevant fashion items. For example, the generation unit can analyze the user's social media activity and generate suggestions based on preferred styles. For example, the generation unit can analyze the user's social media activity and generate suggestions based on trends. In this way, relevant fashion items can be generated by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation of fashion items.

[0058] The ad placement department can display the most suitable advertisements by referring to the user's past purchase history at the time of placement. For example, the ad placement department can display the most suitable advertisements by referring to the user's past purchase history. For example, the ad placement department can display advertisements based on the user's preferred style by referring to the user's past purchase history. For example, the ad placement department can display advertisements that are appropriate for the season or event by referring to the user's past purchase history. In this way, the most suitable advertisements can be displayed by referring to the user's past purchase history. Some or all of the above processes in the ad placement department may be performed using AI, for example, or without using AI. For example, the ad placement department can input the user's past purchase history into a generation AI and have the generation AI execute the most suitable advertisement.

[0059] The ad placement department can place the most suitable advertisements by considering the user's geographical location information at the time of placement. For example, the ad placement department can place the most suitable advertisements by considering the user's geographical location information. For example, the ad placement department can place advertisements based on regional fashion trends by considering the user's geographical location information. For example, the ad placement department can place advertisements that are optimal for a specific region by considering the user's geographical location information. This makes it possible to place advertisements based on regional fashion trends by considering the user's geographical location information. Some or all of the above processing in the ad placement department may be performed using AI, for example, or without using AI. For example, the ad placement department can input the user's geographical location information into a generating AI and have the generating AI execute the most suitable advertisement.

[0060] The feedback unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback unit can refer to the user's past feedback history and propose the optimal collection method. For example, the feedback unit can refer to the user's past feedback history and propose a collection method based on the user's preferred style. For example, the feedback unit can refer to the user's past feedback history and propose a collection method according to the season or event. In this way, the optimal collection method can be selected by referring to the user's past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past feedback history into a generating AI and have the generating AI execute the optimal collection method.

[0061] The feedback unit can select the optimal collection method when collecting feedback, taking into account the user's device information. For example, if the user is using a smartphone, the feedback unit can provide a collection method that matches the screen size. For example, if the user is using a tablet, the feedback unit can provide a collection method optimized for a larger screen. For example, if the user is using a smartwatch, the feedback unit can provide a concise and highly visible collection method. This allows the optimal collection method to be selected by considering the user's device information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's device information into a generating AI and have the generating AI execute the optimal collection method.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The data collection unit can collect user social media activity data and use it to analyze fashion trends. For example, it can analyze photos and comments posted by users on social media to understand current fashion trends. It can also collect posts from fashion influencers that users follow and analyze trends. Furthermore, it can collect the user's "likes" and shares on social media to identify their preferred style. By collecting social media activity data, it is possible to understand fashion trends in more detail and use this information for personalized fashion suggestions. 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 social media activity data into a generating AI and have the generating AI perform the data collection.

[0064] The suggestion unit can make optimal suggestions by taking into account the user's geographical location. For example, if the user is in a specific region, it can make suggestions based on the fashion trends of that region. If the user is traveling, it can also make suggestions based on the fashion trends of the destination. Furthermore, if the user is attending a specific event, it can make fashion suggestions that are best suited to the location of that event. In this way, by taking into account the user's geographical location, it is possible to make optimal suggestions based on regional fashion trends. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location into a generating AI and have the generating AI execute the optimal suggestion.

[0065] The data collection unit can collect weather information and traffic data and use it to analyze fashion trends. For example, it can collect weather information and make fashion suggestions suitable for rainy or sunny days. It can also collect traffic data and make fashion suggestions suitable for congested areas. Furthermore, by combining weather information and traffic data, more detailed fashion trends can be determined. As a result, by collecting weather information and traffic data, it is possible to grasp more detailed fashion trends and make optimal fashion suggestions. 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 weather information and traffic data into a generating AI and have the generating AI perform the data collection.

[0066] The suggestion unit can make optimal suggestions by referring to the user's past fashion history. For example, it can refer to items and outfits the user has purchased in the past and make suggestions based on their preferred style. If the user likes a particular brand, it can also suggest new items from that brand. Furthermore, it can refer to the user's past fashion history and make suggestions according to the season and events. In this way, it can make optimal suggestions by referring to the user's past fashion history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past fashion history into a generating AI and have the generating AI execute the optimal suggestions.

[0067] The analysis unit can perform analysis while considering the geographical distribution of the collected data. For example, it can analyze fashion trends by region while considering the geographical distribution of the collected data. It can also analyze fashion trends in a specific region and grasp styles and trends unique to that region. Furthermore, it can grasp fashion trends by region while considering the geographical distribution of the collected data. In this way, by considering the geographical distribution of the collected data, it is possible to grasp fashion trends by region. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the geographical distribution of the collected data into a generating AI and have the generating AI perform the analysis.

[0068] The suggestion unit can analyze a user's social media activity and suggest relevant fashion items. For example, it can analyze posts from fashion influencers that the user follows on social media and make suggestions based on trends. It can also analyze items that the user has "liked" or shared on social media and make suggestions based on their preferred style. Furthermore, it can analyze the user's social media activity and make suggestions tailored to specific events or seasons. In this way, by analyzing the user's social media activity, it is possible to suggest relevant fashion items. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI make fashion item suggestions.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The data collection unit collects data. For example, it can collect security camera footage data from across the country. It can also prioritize the collection of security camera footage data from specific areas or stores, or prioritize the collection of data from specific time periods or event periods. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can analyze the collected data to determine the fashion trends of a region. It can also predict current trends by referring to past fashion trend data, and it can determine fashion trends that correspond to specific events or seasons. Step 3: The suggestion unit proposes fashion coordinates based on the analysis results obtained by the analysis unit. For example, it can generate recommended clothing information tailored to a specific location by combining information such as the genre, price range, and dress code of each restaurant. It can also include a feedback unit to collect user feedback. Step 4: The reception desk registers the user's personal data. For example, it can select the most suitable registration method by referring to the user's past fashion history. Step 5: The generation unit makes individual suggestions based on the personal data registered by the reception unit. For example, it can make outing suggestions that are more tailored to the user based on their personal data. It can also generate optimal suggestions by referring to the user's past fashion history.

[0071] (Example of form 2) The fashion coordination suggestion system according to an embodiment of the present invention is a system that solves the problem of what kind of clothing to wear when visiting a new destination for the first time. This system analyzes security camera footage data from all over the country to determine the fashion trends of each region. Next, it combines the acquired regional fashion base data with information such as the genre, price range, and dress code of each restaurant to generate recommended clothing information tailored to the location. For example, a stylish cafe in Omotesando might recommend a coordinated outfit using a smart cardigan, while an Italian restaurant in Shichirigahama might recommend a casual outfit using a work shirt. Furthermore, by registering personal data, the system can use generation AI technology to provide outing suggestions that are more tailored to the individual. For example, if a user who is 34 years old, male, 170cm tall, weighs 68kg, and likes Ron Herman as a brand is considering going to Shichirigahama Amalfi, the system will provide the most suitable fashion suggestion for that user. In this way, the fashion coordination suggestion system grasps the real fashion trends of each region and provides users with the most suitable fashion coordinate, creating an environment where they can go out with confidence in their fashion choices. It also provides new business value by promoting optimal purchasing and dining experiences using data. This allows the fashion coordination suggestion system to propose the most suitable fashion coordination for the user.

[0072] The fashion coordination suggestion system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, a reception unit, and a generation unit. The collection unit collects data. For example, the collection unit can collect security camera video data from across the country. The collection unit can also preferentially collect security camera video data from specific regions or stores. The collection unit can also preferentially collect data from specific time periods or event periods. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data and determine the fashion trends of the region. The analysis unit can also predict current trends by referring to past fashion trend data. The analysis unit can also determine fashion trends corresponding to specific events or seasons. The suggestion unit proposes fashion coordination based on the analysis results obtained by the analysis unit. For example, the suggestion unit can generate recommended clothing information tailored to a location by combining information such as the genre, price range, and dress code of each restaurant. The suggestion unit may also include a feedback unit that collects user feedback. The reception unit registers the user's personal data. The reception unit can, for example, select the optimal registration method by referring to the user's past fashion history. The generation unit makes individual suggestions based on the personal data registered by the reception unit. The generation unit can, for example, make outing suggestions that are more tailored to the user based on the user's personal data. The generation unit can also, for example, generate optimal suggestions by referring to the user's past fashion history. As a result, the fashion coordination suggestion system according to the embodiment can suggest the most suitable fashion coordination to the user.

[0073] The data collection unit collects data. For example, the data collection unit can collect security camera footage from across the country. Specifically, the data collection unit acquires video data in real time from security cameras installed in each region and transmits it to a central database. This allows for a broad understanding of fashion trends throughout the country. The data collection unit can also prioritize the collection of security camera footage from specific regions or stores. To analyze fashion trends in specific regions or stores in detail, the data collection unit prioritizes access to specific cameras and collects data. For example, by prioritizing the collection of camera footage from places where many people gather, such as busy downtown areas and shopping malls, the latest fashion trends can be quickly grasped. The data collection unit can also prioritize the collection of data from specific time periods or during events. Since fashion trends often differ from the norm during specific time periods or during events, the data collection unit prioritizes the collection of this data and provides it to the analysis unit. For example, by collecting data during weekends, holidays, or specific events (such as fashion shows or festivals), fashion trends under specific circumstances can be analyzed in detail. This allows the data collection unit to efficiently collect diverse data, improving the overall accuracy and reliability of the system.

[0074] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data to determine the fashion trends of a region. Specifically, the analysis unit uses image recognition technology to analyze security camera footage and identify the clothing and accessories of people in the footage. AI-based image analysis algorithms extract features such as color, shape, and pattern, and analyze fashion trends based on this information. The analysis unit can also predict current trends by referring to past fashion trend data. Based on past data, it predicts seasonal trends and fashion trends for specific events, and by comparing them with current data, it grasps the latest trends. The analysis unit can also determine fashion trends corresponding to specific events or seasons. For example, it analyzes fashion trends specific to certain events or seasons, such as summer festivals or the winter Christmas season, to improve the accuracy of the information provided to users. In this way, the analysis unit can analyze the collected data from multiple angles and provide users with the latest and most appropriate fashion information. Furthermore, the analysis unit can continuously improve the accuracy of data analysis by utilizing AI-based machine learning algorithms. In this way, the analysis unit can always incorporate the latest technologies and improve the overall system performance.

[0075] The suggestion department proposes fashion coordinates based on the analysis results obtained by the analysis department. The suggestion department can generate location-specific clothing recommendations by combining information such as the genre, price range, and dress code of each restaurant. Specifically, the suggestion department proposes the optimal coordinate based on the user's destination and plans, based on fashion trend data provided by the analysis department. For example, it might suggest formal attire for a high-end restaurant and a relaxed style for a casual cafe. The suggestion department can also include a feedback department to collect user feedback. By providing feedback on the suggested coordinates, the suggestion department can more accurately understand the user's preferences and needs and reflect them in future suggestions. This allows the suggestion department to provide users with more personalized fashion coordinates. Furthermore, the suggestion department can utilize an AI-powered recommendation engine to make more accurate suggestions based on the user's past choices and feedback. This allows the suggestion department to improve user satisfaction and enhance the overall reliability of the system.

[0076] The reception desk registers the user's personal data. For example, the reception desk can select the optimal registration method by referring to the user's past fashion history. Specifically, the reception desk collects data on the clothes the user has worn and the items they have purchased in the past, and uses this to analyze the user's fashion style and preferences. Users can easily register their fashion history through a smartphone app or website. The reception desk also collects basic information such as the user's height, weight, age, and gender, and uses this data to provide more accurate fashion suggestions. Furthermore, the reception desk can collect detailed information such as the user's lifestyle, hobbies, and preferences for specific events and seasons. This allows the reception desk to collect data that meets the individual needs of the user and improve the overall personalization of the system.

[0077] The generation unit makes individual suggestions based on personal data registered by the reception unit. For example, the generation unit can make outing suggestions that are more tailored to the user based on their personal data. Specifically, the generation unit generates the optimal outfit for the user based on the user's past fashion history and current trend data. For example, it considers the colors and styles the user has preferred to wear in the past and suggests outfits that are appropriate for the current season and event. The generation unit can also generate optimal suggestions by referring to the user's past fashion history. Based on past data, it identifies the items and brands the user prefers and suggests outfits that combine them. Furthermore, the generation unit can utilize AI-based generative algorithms to learn the user's preferences and trends, enabling it to make more accurate suggestions. As a result, the generation unit can always provide users with the latest and most optimal fashion outfits, improving user satisfaction.

[0078] The suggestion function can generate appropriate attire recommendations for each location by combining information such as the genre, price range, and dress code of each restaurant. For example, it can generate attire recommendations based on genres such as Japanese, Western, and Chinese cuisine. It can also generate recommendations based on price ranges such as low-priced, mid-priced, and high-priced. Furthermore, it can generate recommendations based on dress code information such as casual, business casual, and formal. This enables specific fashion suggestions tailored to each location.

[0079] The data collection unit can collect security camera video data from across the country. The data collection unit can also prioritize the collection of security camera video data from specific areas or stores. The data collection unit can also prioritize the collection of data from specific time periods or during events. The data collection unit can prioritize the collection of data from specific time periods, such as weekends or holidays. The data collection unit can prioritize the collection of data from local events. The data collection unit can analyze pedestrian traffic during specific time periods and prioritize the collection of data from those times. This enables the collection of data over a wide area. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input security camera video data into a generating AI and have the generating AI perform the data collection.

[0080] The analysis unit can analyze the collected data and determine the fashion trends of the region. For example, the analysis unit can analyze the collected data and determine fashion trends such as popular colors, styles, and brands. For example, the analysis unit can predict current trends by referring to past fashion trend data. For example, the analysis unit can determine fashion trends corresponding to specific events or seasons. For example, the analysis unit can determine fashion trends corresponding to specific events. For example, the analysis unit can determine fashion trends corresponding to seasons. This makes it possible to understand fashion trends for each region. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the analysis of fashion trends.

[0081] The generation unit can provide outing suggestions that are more tailored to the user based on their personal data. For example, the generation unit can provide suggestions based on personal data such as the user's age, gender, and preferred style. For example, the generation unit can generate optimal suggestions by referring to the user's past fashion history. For example, the generation unit can provide suggestions based on the user's preferences and past history. For example, the generation unit can provide suggestions tailored to specific events or seasons based on the user's personal data. This enables individually optimized suggestions. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's personal data into a generation AI and have the generation AI execute individual suggestions.

[0082] The proposal unit may include an advertising unit that places advertisements for proposed fashion items on e-commerce sites. The proposal unit can, for example, place advertisements for proposed fashion items on specific e-commerce sites. The proposal unit can, for example, set the frequency at which proposed fashion items are placed. The proposal unit can, for example, place optimal advertisements by referring to the user's past purchase history when placing advertisements for proposed fashion items. The proposal unit can, for example, refer to the user's past purchase history and place advertisements based on their preferred style. The proposal unit can, for example, refer to the user's past purchase history and place advertisements according to the season or event. This allows the user to purchase the proposed fashion items on e-commerce sites. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input proposed fashion items into a generation AI and have the generation AI execute the advertisement placement.

[0083] The proposal unit may include a feedback unit for collecting user feedback. The proposal unit can collect user feedback through methods such as surveys, reviews, and comments. The proposal unit can collect user feedback to improve the accuracy of its suggestions. The proposal unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is stressed, the proposal unit can provide a simple interface and minimize the feedback collection procedure. For example, if the user is relaxed, the proposal unit can provide detailed feedback options and suggest a customizable collection method. For example, if the user is in a hurry, the proposal unit can prioritize voice input to quickly collect feedback. This allows for the collection of user feedback and improvement of the accuracy of its suggestions. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the proposal unit may be performed using AI or not using AI. For example, the proposal department can input user feedback data into the generation AI and have the generation AI collect the feedback.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can shorten the timing of data collection to collect data quickly. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above 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 emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0085] The data collection unit can prioritize the collection of security camera video data during specific time periods or event periods. For example, the data collection unit can prioritize the collection of data during specific time periods such as weekends or holidays. For example, the data collection unit can prioritize the collection of data during local event periods. For example, the data collection unit can analyze the flow of people during specific time periods and prioritize the collection of data during those time periods. By prioritizing the collection of data during specific time periods or event periods, more detailed data can be obtained. 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 security camera video data into a generating AI and have the generating AI perform the collection of data during specific time periods or event periods.

[0086] The data collection unit can expand the types of data it collects and simultaneously collect weather information and traffic data. For example, the data collection unit can collect weather information and use it for analysis as a factor influencing fashion trends. For example, the data collection unit can collect traffic data and understand congestion levels in specific areas. For example, the data collection unit can combine weather information and traffic data to determine more detailed fashion trends. Thus, by collecting weather information and traffic data, more detailed fashion trends can be determined. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather information and traffic data into a generating AI and have the generating AI perform the data collection.

[0087] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important data. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. In this way, important data can be prioritized by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of data priority.

[0088] The data collection unit can prioritize the collection of data from specific regions or stores when collecting security camera video data. For example, the data collection unit can prioritize the collection of security camera video data from a specific region. For example, the data collection unit can prioritize the collection of security camera video data from a specific store. For example, the data collection unit can prioritize the collection of data from specific regions or stores in order to understand fashion trends in each region. By prioritizing the collection of data from specific regions or stores, it is possible to understand fashion trends in each region. 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 security camera video data into a generating AI and have the generating AI perform the collection of data from specific regions or stores.

[0089] The data collection unit can add user social media activity data to the data it collects. For example, the data collection unit can collect user social media activity data and use it to analyze fashion trends. For example, the data collection unit can collect user social media activity data and use it to make personalized fashion suggestions. For example, the data collection unit can collect user social media activity data and use it to understand fashion trends by region. This allows for a more detailed understanding of fashion trends by collecting user social media activity data. 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 user social media activity data into a generating AI and have the generating AI perform the data collection.

[0090] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can simplify the analysis algorithm and provide analysis results quickly. For example, if the user is relaxed, the analysis unit can use a detailed analysis algorithm and provide highly accurate analysis results. For example, if the user is in a hurry, the analysis unit can speed up the analysis algorithm and provide analysis results quickly. In this way, by adjusting the analysis algorithm according to the user's emotions, it is possible to provide fast and highly accurate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the analysis algorithm.

[0091] The analysis unit can predict current trends by referring to past fashion trend data during analysis. For example, the analysis unit can predict current fashion trends by referring to past fashion trend data. For example, the analysis unit can predict seasonal fashion trends by referring to past fashion trend data. For example, the analysis unit can predict fashion trends corresponding to specific events or seasons by referring to past fashion trend data. In this way, current fashion trends can be predicted by referring to past fashion trend data. 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 fashion trend data into a generating AI and have the generating AI perform a prediction of current trends.

[0092] The analysis unit can identify fashion trends corresponding to specific events or seasons during analysis. For example, the analysis unit can identify fashion trends corresponding to specific events. For example, the analysis unit can identify fashion trends corresponding to seasons. For example, the analysis unit can identify fashion trends corresponding to specific events or seasons and reflect them in the analysis results. This makes it possible to understand fashion trends corresponding to specific events or seasons. 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 data corresponding to specific events or seasons into a generating AI and have the generating AI perform the identification of fashion trends.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a highly visible display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0094] The analysis unit can perform analysis while considering the geographical distribution of the collected data. For example, the analysis unit can analyze fashion trends by region while considering the geographical distribution of the collected data. For example, the analysis unit can analyze fashion trends in a specific region while considering the geographical distribution of the collected data. For example, the analysis unit can grasp fashion trends by region while considering the geographical distribution of the collected data. In this way, by considering the geographical distribution of the collected data, it is possible to grasp fashion trends by region. 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 the geographical distribution of the collected data into a generating AI and have the generating AI perform the analysis.

[0095] The analysis unit can improve the accuracy of its analysis by referring to data from relevant fashion magazines and blogs during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to data from relevant fashion magazines. For example, the analysis unit can improve the accuracy of its analysis by referring to data from relevant fashion blogs. For example, the analysis unit can improve the accuracy of its analysis by referring to data from relevant fashion magazines and blogs. This allows the analysis to be improved by referring to data from relevant fashion magazines and blogs. 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 data from fashion magazines and blogs into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0096] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide suggestions that include detailed information. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, highly easily understandable suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the way suggestions are presented.

[0097] The suggestion unit can make optimal suggestions by referring to the user's past fashion history. For example, the suggestion unit can refer to the user's past fashion history and make optimal suggestions. For example, the suggestion unit can refer to the user's past fashion history and make suggestions based on their preferred style. For example, the suggestion unit can refer to the user's past fashion history and make suggestions according to the season or event. In this way, optimal suggestions can be made by referring to the user's past fashion history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past fashion history into a generating AI and have the generating AI execute the optimal suggestion.

[0098] The suggestion unit can suggest fashion items that are appropriate for specific events or seasons. For example, the suggestion unit can suggest fashion items that are appropriate for specific events. For example, the suggestion unit can suggest fashion items that are appropriate for seasons. For example, the suggestion unit can suggest fashion items that are appropriate for specific events or seasons and make the best possible suggestions for the user. In this way, by suggesting fashion items that are appropriate for specific events or seasons, the suggestion unit can make the best possible suggestions for the user. 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 data appropriate for specific events or seasons into a generating AI and have the generating AI perform fashion item suggestions.

[0099] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can prioritize important suggestions. For example, if the user is relaxed, the suggestion unit can prioritize detailed suggestions. For example, if the user is in a hurry, the suggestion unit can provide suggestions quickly. In this way, by determining the priority of suggestions according to the user's emotions, important suggestions can be prioritized. 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 emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0100] The suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, the suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, the suggestion unit can make suggestions based on regional fashion trends by considering the user's geographical location information. For example, the suggestion unit can make optimal suggestions for a specific region by considering the user's geographical location information. In this way, by considering the user's geographical location information, suggestions based on regional fashion trends can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input the user's geographical location information into a generation AI and have the generation AI execute the optimal suggestion.

[0101] The suggestion unit can analyze the user's social media activity and suggest relevant fashion items when making suggestions. For example, the suggestion unit can analyze the user's social media activity and suggest relevant fashion items. For example, the suggestion unit can analyze the user's social media activity and make suggestions based on their preferred style. For example, the suggestion unit can analyze the user's social media activity and make suggestions based on trends. In this way, by analyzing the user's social media activity, it is possible to suggest relevant fashion items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI perform fashion item suggestions.

[0102] The reception desk can estimate the user's emotions and adjust the method of registering personal data based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick registration of personal data. This reduces the burden on the user by adjusting the method of registering personal data 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 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 reception desk may be performed using AI or not. For example, the reception desk can input the user's emotion data into a generative AI and have the generative AI adjust the registration method.

[0103] The reception desk can select the optimal registration method by referring to the user's past fashion history when registering personal data. For example, the reception desk can refer to the user's past fashion history and suggest the optimal registration method. For example, the reception desk can refer to the user's past fashion history and suggest a registration method based on the user's preferred style. For example, the reception desk can refer to the user's past fashion history and suggest a registration method that is appropriate for the season or event. In this way, the optimal registration method can be selected by referring to the user's past fashion history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past fashion history into a generating AI and have the generating AI execute the optimal registration method.

[0104] The reception unit can estimate the user's emotions and prioritize personal data based on the estimated emotions. For example, if the user is stressed, the reception unit can prioritize registering only important data. For example, if the user is relaxed, the reception unit can prioritize registering detailed data. For example, if the user is in a hurry, the reception unit can prioritize registering data that can be registered quickly. This allows for the priority registration of important data by prioritizing personal data 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 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 reception unit may be performed using AI or not. For example, the reception unit can input the user's emotion data into a generative AI and have the generative AI determine the data prioritization.

[0105] The reception desk can select the optimal registration method when registering personal data, taking into account the user's device information. For example, if the user is using a smartphone, the reception desk can provide a registration method adapted to the screen size. For example, if the user is using a tablet, the reception desk can provide a registration method optimized for a larger screen. For example, if the user is using a smartwatch, the reception desk can provide a concise and highly visible registration method. This allows the reception desk to select the optimal registration method by taking into account the user's device information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's device information into a generating AI and have the generating AI execute the optimal registration method.

[0106] The generation unit can estimate the user's emotions and adjust the way the generated suggestions are presented based on the estimated emotions. For example, if the user is stressed, the generation unit can provide simple and highly visible suggestions. If the user is relaxed, the generation unit can provide suggestions that include detailed information. If the user is in a hurry, the generation unit can provide concise suggestions. By adjusting the presentation of suggestions according to the user's emotions, highly visible suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the presentation of the suggestions.

[0107] The generation unit can generate optimal suggestions by referring to the user's past fashion history during the generation process. For example, the generation unit can refer to the user's past fashion history and generate optimal suggestions. For example, the generation unit can refer to the user's past fashion history and generate suggestions based on preferred styles. For example, the generation unit can refer to the user's past fashion history and generate suggestions according to the season or event. In this way, optimal suggestions can be generated by referring to the user's past fashion history. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past fashion history into a generation AI and have the generation AI execute optimal suggestions.

[0108] The generation unit can generate fashion items that correspond to specific events or seasons during the generation process. For example, the generation unit can generate fashion items that correspond to specific events. For example, the generation unit can generate fashion items that correspond to seasons. For example, the generation unit can generate fashion items that correspond to specific events or seasons and make the most suitable suggestions to the user. In this way, by generating fashion items that correspond to specific events or seasons, the generation unit can make the most suitable suggestions to the user. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data corresponding to specific events or seasons into a generation AI and have the generation AI execute the generation of fashion items.

[0109] The generation unit can estimate the user's emotions and determine the priority of suggestions to generate based on the estimated emotions. For example, if the user is stressed, the generation unit can prioritize important suggestions. For example, if the user is relaxed, the generation unit can prioritize detailed suggestions. For example, if the user is in a hurry, the generation unit can provide suggestions quickly. This allows for prioritizing important suggestions by determining the priority of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI determine the priority of suggestions.

[0110] The generation unit can generate optimal suggestions by considering the user's geographical location information during the generation process. For example, the generation unit can generate optimal suggestions by considering the user's geographical location information. For example, the generation unit can generate suggestions based on regional fashion trends by considering the user's geographical location information. For example, the generation unit can generate suggestions optimized for a specific region by considering the user's geographical location information. This makes it possible to generate suggestions based on regional fashion trends by considering the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI execute optimal suggestions.

[0111] The generation unit can analyze the user's social media activity during generation and generate relevant fashion items. For example, the generation unit can analyze the user's social media activity and generate relevant fashion items. For example, the generation unit can analyze the user's social media activity and generate suggestions based on preferred styles. For example, the generation unit can analyze the user's social media activity and generate suggestions based on trends. In this way, relevant fashion items can be generated by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation of fashion items.

[0112] The ad placement department can estimate the user's emotions and adjust the presentation of the ads based on those emotions. For example, if the user is stressed, the department can present a simple, highly visible ad. If the user is relaxed, the department can present an ad containing detailed information. If the user is in a hurry, the department can present a concise ad. By adjusting the presentation of ads according to the user's emotions, highly visible ads can be presented. Emotion estimation is achieved using an emotion estimation function, such as 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 ad placement department may be performed using AI or not. For example, the ad placement department can input user emotion data into a generative AI and have the generative AI adjust the presentation of the ads.

[0113] The ad placement department can display the most suitable advertisements by referring to the user's past purchase history at the time of placement. For example, the ad placement department can display the most suitable advertisements by referring to the user's past purchase history. For example, the ad placement department can display advertisements based on the user's preferred style by referring to the user's past purchase history. For example, the ad placement department can display advertisements that are appropriate for the season or event by referring to the user's past purchase history. In this way, the most suitable advertisements can be displayed by referring to the user's past purchase history. Some or all of the above processes in the ad placement department may be performed using AI, for example, or without using AI. For example, the ad placement department can input the user's past purchase history into a generation AI and have the generation AI execute the most suitable advertisement.

[0114] The ad placement department can estimate the user's emotions and determine the priority of the ads to be displayed based on the estimated user emotions. For example, if the user is stressed, the ad placement department can prioritize important ads. For example, if the user is relaxed, the ad placement department can prioritize detailed ads. For example, if the user is in a hurry, the ad placement department can quickly display ads. In this way, important ads can be prioritized by determining the priority of ads 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 ad placement department may be performed using AI or not using AI. For example, the ad placement department can input user emotion data into a generative AI and have the generative AI perform the determination of ad prioritization.

[0115] The ad placement department can place the most suitable advertisements by considering the user's geographical location information at the time of placement. For example, the ad placement department can place the most suitable advertisements by considering the user's geographical location information. For example, the ad placement department can place advertisements based on regional fashion trends by considering the user's geographical location information. For example, the ad placement department can place advertisements that are optimal for a specific region by considering the user's geographical location information. This makes it possible to place advertisements based on regional fashion trends by considering the user's geographical location information. Some or all of the above processing in the ad placement department may be performed using AI, for example, or without using AI. For example, the ad placement department can input the user's geographical location information into a generating AI and have the generating AI execute the most suitable advertisement.

[0116] The feedback unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is stressed, the feedback unit can provide a simple interface and minimize the feedback collection procedure. For example, if the user is relaxed, the feedback unit can provide detailed feedback options and suggest a customizable collection method. For example, if the user is in a hurry, the feedback unit can prioritize voice input to quickly collect feedback. This reduces the user's burden by adjusting the feedback collection method 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 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 feedback unit may be performed using AI or not. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI adjust the collection method.

[0117] The feedback unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback unit can refer to the user's past feedback history and propose the optimal collection method. For example, the feedback unit can refer to the user's past feedback history and propose a collection method based on the user's preferred style. For example, the feedback unit can refer to the user's past feedback history and propose a collection method according to the season or event. In this way, the optimal collection method can be selected by referring to the user's past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past feedback history into a generating AI and have the generating AI execute the optimal collection method.

[0118] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit can prioritize collecting important feedback. For example, if the user is relaxed, the feedback unit can prioritize collecting detailed feedback. For example, if the user is in a hurry, the feedback unit can quickly collect feedback. This allows for the priority collection of important feedback by determining the priority of feedback 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 feedback unit may be performed using AI or not using AI. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI determine the priority of feedback.

[0119] The feedback unit can select the optimal collection method when collecting feedback, taking into account the user's device information. For example, if the user is using a smartphone, the feedback unit can provide a collection method that matches the screen size. For example, if the user is using a tablet, the feedback unit can provide a collection method optimized for a larger screen. For example, if the user is using a smartwatch, the feedback unit can provide a concise and highly visible collection method. This allows the optimal collection method to be selected by considering the user's device information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's device information into a generating AI and have the generating AI execute the optimal collection method.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the frequency of suggestions can be reduced to lessen the user's burden. If the user is relaxed, the frequency of suggestions can be increased, and more detailed suggestions can be provided. If the user is in a hurry, suggestions can be provided quickly. In this way, by adjusting the timing of suggestions according to the user's emotions, the user's burden can be reduced and optimal suggestions can be provided. Emotion estimation is achieved 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the timing of suggestions.

[0122] The data collection unit can collect user social media activity data and use it to analyze fashion trends. For example, it can analyze photos and comments posted by users on social media to understand current fashion trends. It can also collect posts from fashion influencers that users follow and analyze trends. Furthermore, it can collect the user's "likes" and shares on social media to identify their preferred style. By collecting social media activity data, it is possible to understand fashion trends in more detail and use this information for personalized fashion suggestions. 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 social media activity data into a generating AI and have the generating AI perform the data collection.

[0123] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method of the analysis results according to the user's emotions, a highly visible display is possible. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. 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 emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0124] The suggestion unit can make optimal suggestions by taking into account the user's geographical location. For example, if the user is in a specific region, it can make suggestions based on the fashion trends of that region. If the user is traveling, it can also make suggestions based on the fashion trends of the destination. Furthermore, if the user is attending a specific event, it can make fashion suggestions that are best suited to the location of that event. In this way, by taking into account the user's geographical location, it is possible to make optimal suggestions based on regional fashion trends. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location into a generating AI and have the generating AI execute the optimal suggestion.

[0125] The data collection unit can collect weather information and traffic data and use it to analyze fashion trends. For example, it can collect weather information and make fashion suggestions suitable for rainy or sunny days. It can also collect traffic data and make fashion suggestions suitable for congested areas. Furthermore, by combining weather information and traffic data, more detailed fashion trends can be determined. As a result, by collecting weather information and traffic data, it is possible to grasp more detailed fashion trends and make optimal fashion suggestions. 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 weather information and traffic data into a generating AI and have the generating AI perform the data collection.

[0126] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, it can present simple and highly visible suggestions. If the user is relaxed, it can present suggestions that include detailed information. If the user is in a hurry, it can present suggestions that get straight to the point. By adjusting the way suggestions are presented according to the user's emotions, highly visible suggestions become possible. Emotion estimation is achieved 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0127] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. If the user is relaxed, the frequency of data collection can be increased to collect more detailed data. If the user is in a hurry, the timing of data collection can be shortened to collect data quickly. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0128] The suggestion unit can make optimal suggestions by referring to the user's past fashion history. For example, it can refer to items and outfits the user has purchased in the past and make suggestions based on their preferred style. If the user likes a particular brand, it can also suggest new items from that brand. Furthermore, it can refer to the user's past fashion history and make suggestions according to the season and events. In this way, it can make optimal suggestions by referring to the user's past fashion history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past fashion history into a generating AI and have the generating AI execute the optimal suggestions.

[0129] The analysis unit can perform analysis while considering the geographical distribution of the collected data. For example, it can analyze fashion trends by region while considering the geographical distribution of the collected data. It can also analyze fashion trends in a specific region and grasp styles and trends unique to that region. Furthermore, it can grasp fashion trends by region while considering the geographical distribution of the collected data. In this way, by considering the geographical distribution of the collected data, it is possible to grasp fashion trends by region. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the geographical distribution of the collected data into a generating AI and have the generating AI perform the analysis.

[0130] The suggestion unit can analyze a user's social media activity and suggest relevant fashion items. For example, it can analyze posts from fashion influencers that the user follows on social media and make suggestions based on trends. It can also analyze items that the user has "liked" or shared on social media and make suggestions based on their preferred style. Furthermore, it can analyze the user's social media activity and make suggestions tailored to specific events or seasons. In this way, by analyzing the user's social media activity, it is possible to suggest relevant fashion items. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI make fashion item suggestions.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The data collection unit collects data. For example, it can collect security camera footage data from across the country. It can also prioritize the collection of security camera footage data from specific areas or stores, or prioritize the collection of data from specific time periods or event periods. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can analyze the collected data to determine the fashion trends of a region. It can also predict current trends by referring to past fashion trend data, and it can determine fashion trends that correspond to specific events or seasons. Step 3: The suggestion unit proposes fashion coordinates based on the analysis results obtained by the analysis unit. For example, it can generate recommended clothing information tailored to a specific location by combining information such as the genre, price range, and dress code of each restaurant. It can also include a feedback unit to collect user feedback. Step 4: The reception desk registers the user's personal data. For example, it can refer to the user's past fashion history to select the most suitable registration method. Step 5: The generation unit makes individual suggestions based on the personal data registered by the reception unit. For example, it can make outing suggestions that are more tailored to the user based on their personal data. It can also generate optimal suggestions by referring to the user's past fashion history.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, reception unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects security camera video data using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to determine local fashion trends. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes fashion coordination based on the analysis results. The reception unit registers the user's personal data using the reception device 38 of the smart device 14, for example. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, and makes individual suggestions based on the registered personal data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, reception unit, and generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects security camera video data using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to determine local fashion trends. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes fashion coordination based on the analysis results. The reception unit registers the user's personal data using the microphone 238 of the smart glasses 214. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and makes individual suggestions based on the registered personal data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, reception unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects security camera video data using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to determine local fashion trends. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes fashion coordination based on the analysis results. The reception unit registers the user's personal data using the microphone 238 of the headset terminal 314. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, and makes individual proposals based on the registered personal data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, reception unit, and generation unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects security camera video data using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to determine local fashion trends. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes fashion coordination based on the analysis results. The reception unit registers the user's personal data using the microphone 238 of the robot 414. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and makes individual suggestions based on the registered personal data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes fashion coordination, A reception area where users' personal data is registered, The system includes a generation unit that makes individual suggestions based on personal data registered by the reception unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, By combining information such as the genre, price range, and dress code of each restaurant, we generate recommended attire information tailored to each location. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Collecting security camera footage data from across the country. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The collected data is analyzed to determine the fashion trends of that region. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Based on the user's personal data, we provide outing suggestions that are more tailored to their individual needs. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, It includes a department for posting proposed fashion items on e-commerce sites. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, It includes a feedback unit for collecting user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting security camera footage, prioritize the collection of data from specific time periods or event durations. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Expand the types of data collected to include weather information and traffic data simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting security camera footage, prioritize the collection of data from specific areas or stores. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Add user social media activity data to the data we collect. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, past fashion trend data is referenced to predict current trends. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, fashion trends corresponding to specific events or seasons are identified. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the geographical distribution of the collected data will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, we refer to data from relevant fashion magazines and blogs to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) 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 21) The aforementioned proposal section is, When making suggestions, the system refers to the user's past fashion history to provide the most suitable recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, suggest fashion items that are appropriate for specific events or seasons. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, we take the user's geographical location into consideration to provide the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making suggestions, we analyze the user's social media activity and suggest relevant fashion items. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reception unit is We estimate the user's emotions and adjust the method of registering personal data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reception unit is When registering personal data, the system selects the most suitable registration method by referring to the user's past fashion history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reception unit is It estimates the user's emotions and prioritizes personal data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reception unit is When registering personal data, the optimal registration method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is It estimates the user's emotions and adjusts how suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The generating unit is During generation, the system references the user's past fashion history to generate optimal suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The generating unit is During generation, fashion items are generated that are appropriate for specific events or seasons. The system described in Appendix 1, characterized by the features described herein. (Note 33) The generating unit is It estimates the user's emotions and determines the priority of suggestions to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The generating unit is During generation, the system considers the user's geographical location to generate optimal suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The generating unit is During generation, the system analyzes the user's social media activity to generate relevant fashion items. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned submission department is, We estimate user emotions and adjust the way ads are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned submission department is, When placing an ad, the system displays the most relevant ads by referencing the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned submission department is, It estimates user sentiment and determines the priority of ads to display based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned submission department is, When placing an ad, the system takes into account the user's geographical location to display the most suitable ad. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned feedback unit is We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned feedback unit is When collecting feedback, the system selects the optimal collection method by referring to the user's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned feedback unit is When collecting feedback, the optimal collection method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes fashion coordination, A reception area where users' personal data is registered, The system includes a generation unit that makes individual suggestions based on personal data registered by the reception unit. A system characterized by the following features.

2. The aforementioned proposal section is, By combining information such as the genre, price range, and dress code of each restaurant, we generate recommended attire information tailored to each location. The system according to feature 1.

3. The aforementioned collection unit is Collecting security camera footage data from across the country. The system according to feature 1.

4. The aforementioned analysis unit, The collected data is analyzed to determine the fashion trends of that region. The system according to feature 1.

5. The generating unit is Based on the user's personal data, we provide outing suggestions that are more tailored to their individual needs. The system according to feature 1.

6. The aforementioned proposal section is, It includes a department for posting proposed fashion items on e-commerce sites. The system according to feature 1.

7. The aforementioned proposal section is, It includes a feedback unit for collecting user feedback. The system according to feature 1.

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

9. The aforementioned collection unit is When collecting security camera footage, prioritize the collection of data from specific time periods or event durations. The system according to feature 1.

10. The aforementioned collection unit is Expand the types of data collected to include weather information and traffic data simultaneously. The system according to feature 1.

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