system

Abstract

The system according to this embodiment aims to suggest the most suitable clothing based on the user's daily activities, mood, and health condition. [Solution] The system according to the embodiment comprises a collection unit, a suggestion unit, a try-on unit, and a purchase unit. The collection unit collects information such as the user's daily activities, schedule, mood, and health status. The suggestion unit analyzes the information collected by the collection unit and suggests the most suitable clothing for the user. The try-on unit allows the user to try on the clothing suggested by the suggestion unit using augmented reality (AR). The purchase unit purchases or rents the clothing tried on by the try-on unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently done to propose optimal clothes based on the user's daily activities, mood, and health condition, and there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal clothes based on the user's daily activities, mood, and health condition.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a suggestion unit, a try-on unit, and a purchase unit. The data collection unit collects information such as the user's daily activities, schedule, mood, and health status. The suggestion unit analyzes the information collected by the data collection unit and suggests the most suitable clothing for the user. The try-on unit allows the user to try on the clothing suggested by the suggestion unit using augmented reality (AR). The purchase unit purchases or rents the clothing tried on by the try-on unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the most suitable clothing based on the user's daily activities, mood, and health condition. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​fashion ecosystem according to an embodiment of the present invention is a system that learns individual preferences, analyzes the user's daily activities, schedule, mood, and health in real time, and proposes the optimal outfit. This AI fashion ecosystem takes sustainability into consideration and collaborates with local recycling shops to suggest new combinations of existing clothing. The clothes suggested by the AI ​​can be tried on using augmented reality (AR), and if liked, can be immediately purchased or rented. This ecosystem is an idea that promotes sustainability throughout the fashion industry. For example, the generating AI collects information such as the user's daily activities, schedule, mood, and health. The generating AI analyzes this information and suggests the optimal clothing for the user. For example, if the user has plans to play sports, the generating AI will suggest clothing that allows for easy movement. It can also suggest relaxing clothes or clothes in cheerful colors depending on the user's mood and health. Furthermore, the generating AI takes sustainability into consideration and suggests new combinations of existing clothing. For example, it can suggest new outfits by combining clothes the user already owns, or it can collaborate with local recycling shops to suggest recycled clothing. This allows the user to effectively utilize the clothes they already own without having to buy new clothes. In addition, the clothes suggested by the AI ​​can be tried on using augmented reality (AR). Users can virtually try on suggested clothing using their smartphones or tablets and instantly purchase or rent what they like. This allows users to easily choose clothes from home without having to go to a physical store. This ecosystem not only promotes sustainability across the entire fashion industry but also provides users with a convenient and environmentally friendly option. For example, recycling unused clothing contributes to creating a more environmentally conscious society. The AI ​​fashion ecosystem collects information such as the user's daily activities, schedule, mood, and health status to suggest, try on, purchase, or rent the most suitable clothing.

[0029] The AI ​​fashion ecosystem according to this embodiment comprises a collection unit, a suggestion unit, a try-on unit, and a purchase unit. The collection unit collects information such as the user's daily activities, schedule, mood, and health status. The collection unit acquires data from, for example, the user's smartphone or wearable device. The collection unit can also acquire schedule and health status information from the user's calendar app or health management app. The collection unit can also analyze the content of the user's social media posts and messages in order to estimate the user's mood. The suggestion unit analyzes the information collected by the collection unit and suggests the most suitable clothes for the user. The suggestion unit selects clothes based on the user's preferences and activities, for example, using generative AI. The suggestion unit can also suggest relaxing clothes or clothes in cheerful colors depending on the user's mood and health status. The suggestion unit can also suggest new combinations of existing clothes. For example, the suggestion unit suggests a new outfit by combining clothes the user already owns. The suggestion unit can also suggest recycled clothes in cooperation with local recycling shops. The try-on unit tries on the clothes suggested by the suggestion unit using augmented reality (AR). The fitting unit allows users to virtually try on suggested clothing using their smartphone or tablet. The fitting unit can also simulate the fit of the clothing based on the user's body shape data. The fitting unit can save images of the clothing the user has tried on for later review. The purchasing unit purchases or rents the clothing tried on by the fitting unit. The purchasing unit can, for example, handle the process of the user purchasing their favorite clothing online. The purchasing unit can also handle the process of the user renting clothing. The purchasing unit can save the user's purchase history and use it to make future suggestions. As a result, the AI ​​fashion ecosystem according to this embodiment can collect information such as the user's daily activities, schedule, mood, and health status, and suggest, try on, purchase, or rent the most suitable clothing.

[0030] The data collection unit collects information such as the user's daily activities, schedule, mood, and health status. For example, it acquires data from the user's smartphone or wearable device. Specifically, it uses the smartphone's GPS function to track the user's movement history and understand their daily activity patterns. It can also acquire health information such as heart rate, steps, and sleep data from wearable devices. This allows for detailed monitoring of the user's health status and activity level. Furthermore, the data collection unit can acquire schedule and health status information from the user's calendar and health management apps. For example, it can acquire the user's schedule and event information from the calendar app to understand what activities are planned for a particular day. It can acquire data such as the user's weight, diet, and exercise records from the health management app to comprehensively evaluate their health status. The data collection unit can also analyze the content of the user's social media posts and messages to estimate their mood. For example, it can use natural language processing technology to analyze the sentiment in the user's posts and messages to estimate their current mood and psychological state. This allows for the suggestion of clothing that best suits the user's mood. The data collection unit centrally manages and updates this diverse data in real time, allowing it to always understand the user's current status.

[0031] The suggestion department analyzes the information collected by the collection department and proposes the most suitable clothing for the user. For example, the suggestion department uses generative AI to select clothing based on the user's preferences and activities. The generative AI learns the user's past purchase history and preferred style to build a model for proposing the most suitable clothing for each individual user. Specifically, it selects appropriate styles, such as casual or formal wear, according to the user's activities and schedule. It can also suggest relaxing clothing or clothing in uplifting colors depending on the user's mood and health. For example, if a user is feeling stressed, it can suggest clothing made of soft materials with a relaxing effect and clothing in uplifting colors to improve their mood. The suggestion department can also suggest new combinations of existing clothing. For example, it can suggest new outfits by combining clothes the user already owns. This allows users to enjoy new styles using their existing clothes without having to buy new ones. Furthermore, the suggestion department can also collaborate with local recycling shops to suggest recycled clothing. This allows users to enjoy environmentally conscious fashion. The proposal department can increase user satisfaction by comprehensively analyzing information such as the user's preferences, activities, mood, and health condition, and then suggesting the most suitable clothing.

[0032] The fitting room allows users to virtually try on clothes suggested by the suggestion room using augmented reality (AR). For example, the fitting room uses the user's smartphone or tablet to virtually try on the suggested clothes. Specifically, it uses the smartphone or tablet's camera to capture the user's body shape and posture in real time and virtually overlays the suggested clothes onto the user's image. This allows users to virtually try on clothes without actually having to physically try them on. The fitting room can also simulate the fit of clothes based on the user's body shape data. For example, by inputting the user's height, weight, and body proportions, it can simulate how the suggested clothes will fit and select the optimal size. Furthermore, the fitting room can save images of the clothes the user has tried on for later review. This makes it easy for users to try on and compare multiple outfits. By utilizing AR technology, the fitting room can provide users with a realistic fitting experience, improving the convenience of online shopping.

[0033] The purchasing department buys or rents clothes that have been tried on by the fitting department. For example, the purchasing department handles the process of users purchasing clothes they like online. Specifically, it provides an interface for users to select their favorite items from the clothes they have tried on and proceed with the purchase. The purchasing department manages the user's payment and shipping information to support a smooth purchase process. The purchasing department can also handle the process of users renting clothes. For example, it can offer options to rent clothes for specific events or for a limited time, allowing users to use clothes only when needed. The purchasing department can also save the user's purchase history and use it to make future recommendations. For example, by learning the user's preferences and style based on past purchase history, it can reflect this in future recommendations to provide more personalized suggestions. In this way, the purchasing department can provide users with a smooth and convenient purchasing experience and increase user satisfaction.

[0034] The suggestion unit can propose new combinations of existing clothing. For example, the suggestion unit can suggest new outfits by combining clothes the user already owns. The suggestion unit can also suggest color and style combinations based on the user's preferences and activities. The suggestion unit can also suggest relaxing clothes or clothes in energizing colors depending on the user's mood and health. In this way, by suggesting new combinations of existing clothing, the user can make effective use of the clothes they already own without having to buy new clothes. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input data on the user's clothes into a generative AI and have the generative AI produce new outfit suggestions.

[0035] The suggestion unit can suggest recycled clothing in collaboration with local recycling shops. For example, the suggestion unit can access a database of local recycling shops to obtain information on recycled clothing. The suggestion unit can also suggest recycled clothing based on the user's preferences and activities. The suggestion unit can also suggest recycled clothing according to the user's mood and health condition. In this way, by collaborating with local recycling shops, recycled clothing is suggested and sustainability is promoted. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input recycling shop data into a generative AI and have the generative AI make suggestions for recycled clothing.

[0036] The data collection unit can analyze the user's past activity history and select the optimal information collection method. For example, the data collection unit can determine the priority of information collection based on activities the user has frequently performed in the past. The data collection unit can also collect information at specific time periods based on the user's past activity history. The data collection unit can also analyze the user's past activity history and select the most efficient information collection method. This allows the optimal information collection method to be selected by analyzing the user's past activity history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past activity history data into a generative AI and have the generative AI select the optimal information collection method.

[0037] The data collection unit can filter information based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to areas of interest that the user is currently interested in. The data collection unit can also filter necessary information based on the user's current living situation. The data collection unit can also collect highly relevant information based on the user's areas of interest. In this way, highly relevant information can be collected by filtering information based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input data on the user's current living situation and areas of interest into a generative AI and have the generative AI perform the information filtering.

[0038] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of information related to the user's current location. The data collection unit can also collect the most relevant information based on the user's geographical location information. The data collection unit can also prioritize the collection of event information related to the user's current location. This allows for the priority collection of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location data into a generative AI and have the generative AI perform the collection of highly relevant information.

[0039] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can prioritize collecting information that the user has shown interest in on social media. The data collection unit can also collect information that the user is interested in from the user's social media activity. The data collection unit can also analyze the content of the user's social media posts and collect relevant information. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI and have the generative AI perform the collection of relevant information.

[0040] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the clothing. For example, the suggestion unit will provide detailed suggestions for clothing for an important event. For everyday clothing, it can provide concise suggestions. For clothing for special occasions, it can provide special suggestions. By adjusting the level of detail in suggestions based on the importance of the clothing, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input clothing importance data into a generative AI and have the generative AI perform the adjustment of the level of detail in the suggestions.

[0041] The suggestion unit can apply different suggestion algorithms depending on the clothing category when making suggestions. For example, for casual clothing, the suggestion unit applies a casual suggestion algorithm. For formal clothing, the suggestion unit can also apply a formal suggestion algorithm. For sportswear, the suggestion unit can also apply a suggestion algorithm suitable for sports. By applying different suggestion algorithms depending on the clothing category, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input clothing category data into a generative AI and have the generative AI execute the application of different suggestion algorithms.

[0042] The proposal department can prioritize proposals based on the timing of clothing submission. For example, the proposal department will prioritize proposals for clothing intended for important events. For everyday clothing, the proposal department may postpone proposals. For clothing intended for special occasions, the proposal department may also prioritize proposals. This allows for more appropriate proposals by prioritizing proposals based on the timing of clothing submission. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input clothing submission timing data into a generative AI and have the generative AI determine the priority of proposals.

[0043] The suggestion unit can adjust the order of suggestions based on the relevance of the clothing items. For example, the suggestion unit may prioritize suggesting clothing related to the user's current activities. It may also prioritize suggesting clothing related to the user's schedule. It may also prioritize suggesting clothing related to the user's mood. By adjusting the order of suggestions based on the relevance of the clothing items, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input clothing relevance data into a generative AI and have the generative AI perform the adjustment of the suggestion order.

[0044] The fitting unit can provide optimal fitting displays by referring to the user's body shape data during fitting. For example, the fitting unit can display clothes of the optimal size based on the user's body shape data. The fitting unit can also adjust the fit based on the user's body shape data. The fitting unit can also provide optimal fitting displays by referring to the user's body shape data. In this way, it can provide optimal fitting displays by referring to the user's body shape data. Some or all of the above processing in the fitting unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the fitting unit can input the user's body shape data into a generative AI and have the generative AI perform the task of providing optimal fitting displays.

[0045] The fitting room unit can select the optimal fitting room display by referring to the user's past fitting room history during the fitting room process. For example, the fitting room unit selects the optimal fitting room display based on the user's past fitting room history. The fitting room unit can also reflect the user's preferred style from their past fitting room history. The fitting room unit can also provide the optimal fitting room display by referring to the user's past fitting room history. This allows the optimal fitting room display to be selected by referring to the user's past fitting room history. Some or all of the above processing in the fitting room unit may be performed using, for example, a generation AI, or without a generation AI. For example, the fitting room unit can input the user's past fitting room history data into a generation AI and have the generation AI select the optimal fitting room display.

[0046] The fitting room unit can provide an optimal fitting room display by considering the user's device information during the fitting process. For example, if the user is using a smartphone, the fitting room unit can provide a fitting room display that matches the screen size. If the user is using a tablet, the fitting room unit can also provide a fitting room display optimized for a larger screen. If the user is using a smartwatch, the fitting room unit can also provide a concise and highly visible fitting room display. In this way, the optimal fitting room display can be provided by considering the user's device information. Some or all of the above processing in the fitting room unit may be performed using, for example, a generative AI, or without a generative AI. For example, the fitting room unit can input the user's device information into a generative AI and have the generative AI perform the task of providing the optimal fitting room display.

[0047] The fitting room unit can analyze the user's social media activity during the fitting process and adjust how the clothes are displayed. For example, the fitting room unit can prioritize trying on clothes that the user has shown interest in on social media. The fitting room unit can also try on clothes that the user is interested in based on their social media activity. The fitting room unit can also analyze the content of the user's social media posts and try on related clothes. In this way, by analyzing the user's social media activity, it can try on relevant clothes. Some or all of the above processing in the fitting room unit may be performed using, for example, a generative AI, or without a generative AI. For example, the fitting room unit can input the user's social media activity data into a generative AI and have the generative AI adjust how the clothes are displayed.

[0048] The purchasing unit can select the optimal purchasing method by referring to the user's past purchase history at the time of purchase. For example, the purchasing unit can suggest the optimal purchasing method based on the user's past purchase history. The purchasing unit can also select a preferred purchasing method from the user's past purchase history. The purchasing unit can also provide the optimal purchasing method by referring to the user's past purchase history. In this way, the optimal purchasing method can be selected by referring to the user's past purchase history. Some or all of the above processing in the purchasing unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the purchasing unit can input the user's past purchase history data into a generative AI and have the generative AI perform the selection of the optimal purchasing method.

[0049] The purchasing unit can customize the means of purchase based on the user's current living situation at the time of purchase. For example, the purchasing unit can suggest the optimal means of purchase based on the user's current living situation. The purchasing unit can also customize the means of purchase considering the user's current living situation. The purchasing unit can also provide means of purchase according to the user's current living situation. By customizing the means of purchase based on the user's current living situation, a more appropriate means of purchase can be provided. Some or all of the above processing in the purchasing unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the purchasing unit can input the user's current living situation data into a generative AI and have the generative AI perform the customization of the means of purchase.

[0050] The purchasing unit can select the optimal purchase method at the time of purchase, taking into account the user's geographical location information. For example, the purchasing unit may suggest a purchase at a store related to the user's current location. The purchasing unit can also select the optimal purchase method based on the user's geographical location information. The purchasing unit can also suggest an online purchase related to the user's current location. This allows the optimal purchase method to be selected by considering the user's geographical location information. Some or all of the above processing in the purchasing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the purchasing unit can input the user's geographical location data into a generative AI and have the generative AI select the optimal purchase method.

[0051] The purchasing unit can analyze the user's social media activity and suggest purchasing options at the time of purchase. For example, the purchasing unit can prioritize purchasing items that the user has shown interest in on social media. The purchasing unit can also purchase items that the user is interested in based on their social media activity. The purchasing unit can also analyze the content of the user's social media posts and purchase related items. In this way, relevant items can be purchased by analyzing the user's social media activity. Some or all of the above processing in the purchasing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the purchasing unit can input the user's social media activity data into a generative AI and have the generative AI suggest purchasing options.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The data collection unit can analyze the user's past purchase history and select the optimal information collection method. For example, it can determine the priority of information collection based on the trends of items the user has purchased in the past. The data collection unit can also collect information from the user's past purchase history at specific time periods. The data collection unit can also analyze the user's past purchase history and select the most efficient information collection method. This allows the optimal information collection method to be selected by analyzing the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past purchase history data into a generative AI and have the generative AI select the optimal information collection method.

[0054] The data collection unit can determine the priority of information collection based on the user's current living situation and areas of interest. For example, it can prioritize the collection of information related to areas of interest that the user is currently interested in. The data collection unit can also filter the necessary information based on the user's current living situation. The data collection unit can also collect highly relevant information based on the user's areas of interest. In this way, highly relevant information can be collected by filtering information based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input data on the user's current living situation and areas of interest into a generative AI and have the generative AI perform the information filtering.

[0055] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, it can prioritize the collection of information related to the user's current location. The data collection unit can also collect the most relevant information based on the user's geographical location. The data collection unit can also prioritize the collection of event information related to the user's current location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location data into a generative AI and have the generative AI collect highly relevant information.

[0056] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, it can prioritize collecting information that the user has shown interest in on social media. The data collection unit can also collect information that the user is interested in from their social media activity. The data collection unit can also analyze the content of the user's social media posts and collect relevant information. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI and have the generative AI collect relevant information.

[0057] The data collection unit can select the optimal data collection method by considering the user's device information when collecting information. For example, if the user is using a smartphone, it can provide a data collection method optimized for smartphones. The data collection unit can also provide a data collection method optimized for tablets if the user is using a tablet. The data collection unit can also provide a data collection method optimized for smartwatches if the user is using a smartwatch. This allows the optimal data collection method to be selected by considering the user's device information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's device information into a generative AI and have the generative AI select the optimal data collection method.

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

[0059] Step 1: The data collection unit collects information such as the user's daily activities, schedule, mood, and health status. The data collection unit obtains data from, for example, the user's smartphone or wearable device. The data collection unit can also obtain schedule and health status information from the user's calendar app or health management app. The data collection unit can also analyze the content of the user's social media posts and messages to estimate the user's mood. Step 2: The suggestion department analyzes the information collected by the collection department and suggests the most suitable clothing for the user. The suggestion department can, for example, use generative AI to select clothing based on the user's preferences and activities. The suggestion department can also suggest relaxing clothing or clothing in energizing colors depending on the user's mood and health condition. The suggestion department can also suggest new combinations of existing clothing. For example, the suggestion department can suggest new outfits by combining clothes the user already owns. The suggestion department can also collaborate with local recycling shops to suggest recycled clothing. Step 3: The fitting unit uses augmented reality (AR) to virtually try on the clothes suggested by the suggestion unit. The fitting unit uses, for example, the user's smartphone or tablet to virtually try on the suggested clothes. The fitting unit can also simulate the fit of the clothes based on the user's body shape data. The fitting unit can save images of the clothes the user tried on, allowing them to review them later. Step 4: The purchasing department purchases or rents the clothes that were tried on by the fitting department. For example, the purchasing department handles the process of the user purchasing clothes they like online. The purchasing department can also handle the process of the user renting clothes. The purchasing department can also save the user's purchase history and use it to make future recommendations.

[0060] (Example of form 2) The AI ​​fashion ecosystem according to an embodiment of the present invention is a system that learns individual preferences, analyzes the user's daily activities, schedule, mood, and health in real time, and proposes the optimal outfit. This AI fashion ecosystem takes sustainability into consideration and collaborates with local recycling shops to suggest new combinations of existing clothing. The clothes suggested by the AI ​​can be tried on using augmented reality (AR), and if liked, can be immediately purchased or rented. This ecosystem is an idea that promotes sustainability throughout the fashion industry. For example, the generating AI collects information such as the user's daily activities, schedule, mood, and health. The generating AI analyzes this information and suggests the optimal clothing for the user. For example, if the user has plans to play sports, the generating AI will suggest clothing that allows for easy movement. It can also suggest relaxing clothes or clothes in cheerful colors depending on the user's mood and health. Furthermore, the generating AI takes sustainability into consideration and suggests new combinations of existing clothing. For example, it can suggest new outfits by combining clothes the user already owns, or it can collaborate with local recycling shops to suggest recycled clothing. This allows the user to effectively utilize the clothes they already own without having to buy new clothes. In addition, the clothes suggested by the AI ​​can be tried on using augmented reality (AR). Users can virtually try on suggested clothing using their smartphones or tablets and instantly purchase or rent what they like. This allows users to easily choose clothes from home without having to go to a physical store. This ecosystem not only promotes sustainability across the entire fashion industry but also provides users with a convenient and environmentally friendly option. For example, recycling unused clothing contributes to creating a more environmentally conscious society. The AI ​​fashion ecosystem collects information such as the user's daily activities, schedule, mood, and health status to suggest, try on, purchase, or rent the most suitable clothing.

[0061] The AI ​​fashion ecosystem according to this embodiment comprises a collection unit, a suggestion unit, a try-on unit, and a purchase unit. The collection unit collects information such as the user's daily activities, schedule, mood, and health status. The collection unit acquires data from, for example, the user's smartphone or wearable device. The collection unit can also acquire schedule and health status information from the user's calendar app or health management app. The collection unit can also analyze the content of the user's social media posts and messages in order to estimate the user's mood. The suggestion unit analyzes the information collected by the collection unit and suggests the most suitable clothes for the user. The suggestion unit selects clothes based on the user's preferences and activities, for example, using generative AI. The suggestion unit can also suggest relaxing clothes or clothes in cheerful colors depending on the user's mood and health status. The suggestion unit can also suggest new combinations of existing clothes. For example, the suggestion unit suggests a new outfit by combining clothes the user already owns. The suggestion unit can also suggest recycled clothes in cooperation with local recycling shops. The try-on unit tries on the clothes suggested by the suggestion unit using augmented reality (AR). The fitting unit allows users to virtually try on suggested clothing using their smartphone or tablet. The fitting unit can also simulate the fit of the clothing based on the user's body shape data. The fitting unit can save images of the clothing the user has tried on for later review. The purchasing unit purchases or rents the clothing tried on by the fitting unit. The purchasing unit can, for example, handle the process of the user purchasing their favorite clothing online. The purchasing unit can also handle the process of the user renting clothing. The purchasing unit can save the user's purchase history and use it to make future suggestions. As a result, the AI ​​fashion ecosystem according to this embodiment can collect information such as the user's daily activities, schedule, mood, and health status, and suggest, try on, purchase, or rent the most suitable clothing.

[0062] The data collection unit collects information such as the user's daily activities, schedule, mood, and health status. For example, it acquires data from the user's smartphone or wearable device. Specifically, it uses the smartphone's GPS function to track the user's movement history and understand their daily activity patterns. It can also acquire health information such as heart rate, steps, and sleep data from wearable devices. This allows for detailed monitoring of the user's health status and activity level. Furthermore, the data collection unit can acquire schedule and health status information from the user's calendar and health management apps. For example, it can acquire the user's schedule and event information from the calendar app to understand what activities are planned for a particular day. It can acquire data such as the user's weight, diet, and exercise records from the health management app to comprehensively evaluate their health status. The data collection unit can also analyze the content of the user's social media posts and messages to estimate their mood. For example, it can use natural language processing technology to analyze the sentiment in the user's posts and messages to estimate their current mood and psychological state. This allows for the suggestion of clothing that best suits the user's mood. The data collection unit centrally manages and updates this diverse data in real time, allowing it to always understand the user's current status.

[0063] The suggestion department analyzes the information collected by the collection department and proposes the most suitable clothing for the user. For example, the suggestion department uses generative AI to select clothing based on the user's preferences and activities. The generative AI learns the user's past purchase history and preferred style to build a model for proposing the most suitable clothing for each individual user. Specifically, it selects appropriate styles, such as casual or formal wear, according to the user's activities and schedule. It can also suggest relaxing clothing or clothing in uplifting colors depending on the user's mood and health. For example, if a user is feeling stressed, it can suggest clothing made of soft materials with a relaxing effect and clothing in uplifting colors to improve their mood. The suggestion department can also suggest new combinations of existing clothing. For example, it can suggest new outfits by combining clothes the user already owns. This allows users to enjoy new styles using their existing clothes without having to buy new ones. Furthermore, the suggestion department can also collaborate with local recycling shops to suggest recycled clothing. This allows users to enjoy environmentally conscious fashion. The proposal department can increase user satisfaction by comprehensively analyzing information such as the user's preferences, activities, mood, and health condition, and then suggesting the most suitable clothing.

[0064] The fitting room allows users to virtually try on clothes suggested by the suggestion room using augmented reality (AR). For example, the fitting room uses the user's smartphone or tablet to virtually try on the suggested clothes. Specifically, it uses the smartphone or tablet's camera to capture the user's body shape and posture in real time and virtually overlays the suggested clothes onto the user's image. This allows users to virtually try on clothes without actually having to physically try them on. The fitting room can also simulate the fit of clothes based on the user's body shape data. For example, by inputting the user's height, weight, and body proportions, it can simulate how the suggested clothes will fit and select the optimal size. Furthermore, the fitting room can save images of the clothes the user has tried on for later review. This makes it easy for users to try on and compare multiple outfits. By utilizing AR technology, the fitting room can provide users with a realistic fitting experience, improving the convenience of online shopping.

[0065] The purchasing department buys or rents clothes that have been tried on by the fitting department. For example, the purchasing department handles the process of users purchasing clothes they like online. Specifically, it provides an interface for users to select their favorite items from the clothes they have tried on and proceed with the purchase. The purchasing department manages the user's payment and shipping information to support a smooth purchase process. The purchasing department can also handle the process of users renting clothes. For example, it can offer options to rent clothes for specific events or for a limited time, allowing users to use clothes only when needed. The purchasing department can also save the user's purchase history and use it to make future recommendations. For example, by learning the user's preferences and style based on past purchase history, it can reflect this in future recommendations to provide more personalized suggestions. In this way, the purchasing department can provide users with a smooth and convenient purchasing experience and increase user satisfaction.

[0066] The suggestion unit can propose new combinations of existing clothing. For example, the suggestion unit can suggest new outfits by combining clothes the user already owns. The suggestion unit can also suggest color and style combinations based on the user's preferences and activities. The suggestion unit can also suggest relaxing clothes or clothes in energizing colors depending on the user's mood and health. In this way, by suggesting new combinations of existing clothing, the user can make effective use of the clothes they already own without having to buy new clothes. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input data on the user's clothes into a generative AI and have the generative AI produce new outfit suggestions.

[0067] The suggestion unit can suggest recycled clothing in collaboration with local recycling shops. For example, the suggestion unit can access a database of local recycling shops to obtain information on recycled clothing. The suggestion unit can also suggest recycled clothing based on the user's preferences and activities. The suggestion unit can also suggest recycled clothing according to the user's mood and health condition. In this way, by collaborating with local recycling shops, recycled clothing is suggested and sustainability is promoted. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input recycling shop data into a generative AI and have the generative AI make suggestions for recycled clothing.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is feeling stressed, the data collection unit can collect information during times when the user is relaxed. If the user is busy, the data collection unit can also collect information during their free time. If the user is relaxed, the data collection unit can also collect information in real time. This allows for information to be collected at a more appropriate time by adjusting the timing of information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using a generative 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 information collection.

[0069] The data collection unit can analyze the user's past activity history and select the optimal information collection method. For example, the data collection unit can determine the priority of information collection based on activities the user has frequently performed in the past. The data collection unit can also collect information at specific time periods based on the user's past activity history. The data collection unit can also analyze the user's past activity history and select the most efficient information collection method. This allows the optimal information collection method to be selected by analyzing the user's past activity history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past activity history data into a generative AI and have the generative AI select the optimal information collection method.

[0070] The data collection unit can filter information based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to areas of interest that the user is currently interested in. The data collection unit can also filter necessary information based on the user's current living situation. The data collection unit can also collect highly relevant information based on the user's areas of interest. In this way, highly relevant information can be collected by filtering information based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input data on the user's current living situation and areas of interest into a generative AI and have the generative AI perform the information filtering.

[0071] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information that helps them relax. If the user is busy, the data collection unit can also prioritize collecting important information. If the user is relaxed, the data collection unit can also prioritize collecting interesting information. This allows for the collection of more relevant information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0072] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of information related to the user's current location. The data collection unit can also collect the most relevant information based on the user's geographical location information. The data collection unit can also prioritize the collection of event information related to the user's current location. This allows for the priority collection of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location data into a generative AI and have the generative AI perform the collection of highly relevant information.

[0073] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can prioritize collecting information that the user has shown interest in on social media. The data collection unit can also collect information that the user is interested in from the user's social media activity. The data collection unit can also analyze the content of the user's social media posts and collect relevant information. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI and have the generative AI perform the collection of relevant information.

[0074] 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 offer relaxing suggestions. If the user is busy, the suggestion unit can offer concise suggestions. If the user is relaxed, the suggestion unit can offer detailed suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using a generative 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.

[0075] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the clothing. For example, the suggestion unit will provide detailed suggestions for clothing for an important event. For everyday clothing, it can provide concise suggestions. For clothing for special occasions, it can provide special suggestions. By adjusting the level of detail in suggestions based on the importance of the clothing, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input clothing importance data into a generative AI and have the generative AI perform the adjustment of the level of detail in the suggestions.

[0076] The suggestion unit can apply different suggestion algorithms depending on the clothing category when making suggestions. For example, for casual clothing, the suggestion unit applies a casual suggestion algorithm. For formal clothing, the suggestion unit can also apply a formal suggestion algorithm. For sportswear, the suggestion unit can also apply a suggestion algorithm suitable for sports. By applying different suggestion algorithms depending on the clothing category, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input clothing category data into a generative AI and have the generative AI execute the application of different suggestion algorithms.

[0077] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will make short suggestions. If the user is busy, the suggestion unit may also make concise suggestions. If the user is relaxed, the suggestion unit may also make detailed suggestions. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be made. 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 processing described above in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0078] The proposal department can prioritize proposals based on the timing of clothing submission. For example, the proposal department will prioritize proposals for clothing intended for important events. For everyday clothing, the proposal department may postpone proposals. For clothing intended for special occasions, the proposal department may also prioritize proposals. This allows for more appropriate proposals by prioritizing proposals based on the timing of clothing submission. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input clothing submission timing data into a generative AI and have the generative AI determine the priority of proposals.

[0079] The suggestion unit can adjust the order of suggestions based on the relevance of the clothing items. For example, the suggestion unit may prioritize suggesting clothing related to the user's current activities. It may also prioritize suggesting clothing related to the user's schedule. It may also prioritize suggesting clothing related to the user's mood. By adjusting the order of suggestions based on the relevance of the clothing items, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input clothing relevance data into a generative AI and have the generative AI perform the adjustment of the suggestion order.

[0080] The fitting room unit can estimate the user's emotions and adjust the display method of the fitting room based on the estimated emotions. For example, if the user is feeling stressed, the fitting room unit can provide a simple display method. If the user is busy, the fitting room unit can also provide a quick display method. If the user is relaxed, the fitting room unit can also provide a detailed display method. This allows for more appropriate fitting by adjusting the display method of the fitting room based on 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 fitting room unit may be performed using a generative AI, or not using a generative AI. For example, the fitting room unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the fitting room.

[0081] The fitting unit can provide optimal fitting displays by referring to the user's body shape data during fitting. For example, the fitting unit can display clothes of the optimal size based on the user's body shape data. The fitting unit can also adjust the fit based on the user's body shape data. The fitting unit can also provide optimal fitting displays by referring to the user's body shape data. In this way, it can provide optimal fitting displays by referring to the user's body shape data. Some or all of the above processing in the fitting unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the fitting unit can input the user's body shape data into a generative AI and have the generative AI perform the task of providing optimal fitting displays.

[0082] The fitting room unit can select the optimal fitting room display by referring to the user's past fitting room history during the fitting room process. For example, the fitting room unit selects the optimal fitting room display based on the user's past fitting room history. The fitting room unit can also reflect the user's preferred style from their past fitting room history. The fitting room unit can also provide the optimal fitting room display by referring to the user's past fitting room history. This allows the optimal fitting room display to be selected by referring to the user's past fitting room history. Some or all of the above processing in the fitting room unit may be performed using, for example, a generation AI, or without a generation AI. For example, the fitting room unit can input the user's past fitting room history data into a generation AI and have the generation AI select the optimal fitting room display.

[0083] The fitting room unit can estimate the user's emotions and determine the priority of clothing to try on based on the estimated emotions. For example, if the user is feeling stressed, the fitting room unit will prioritize trying on relaxing clothes. If the user is busy, the fitting room unit can also prioritize trying on important clothes. If the user is relaxed, the fitting room unit can also prioritize trying on interesting clothes. This allows for more appropriate clothing try-ons by determining the priority of clothing try-ons based on 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 fitting room unit may be performed using a generative AI, or not. For example, the fitting room unit can input user emotion data into a generative AI and have the generative AI determine the priority of clothing try-ons.

[0084] The fitting room unit can provide an optimal fitting room display by considering the user's device information during the fitting process. For example, if the user is using a smartphone, the fitting room unit can provide a fitting room display that matches the screen size. If the user is using a tablet, the fitting room unit can also provide a fitting room display optimized for a larger screen. If the user is using a smartwatch, the fitting room unit can also provide a concise and highly visible fitting room display. In this way, the optimal fitting room display can be provided by considering the user's device information. Some or all of the above processing in the fitting room unit may be performed using, for example, a generative AI, or without a generative AI. For example, the fitting room unit can input the user's device information into a generative AI and have the generative AI perform the task of providing the optimal fitting room display.

[0085] The fitting room unit can analyze the user's social media activity during the fitting process and adjust how the clothes are displayed. For example, the fitting room unit can prioritize trying on clothes that the user has shown interest in on social media. The fitting room unit can also try on clothes that the user is interested in based on their social media activity. The fitting room unit can also analyze the content of the user's social media posts and try on related clothes. In this way, by analyzing the user's social media activity, it can try on relevant clothes. Some or all of the above processing in the fitting room unit may be performed using, for example, a generative AI, or without a generative AI. For example, the fitting room unit can input the user's social media activity data into a generative AI and have the generative AI adjust how the clothes are displayed.

[0086] The purchasing unit can estimate the user's emotions and adjust the timing of purchases based on those emotions. For example, if the user is feeling stressed, the purchasing unit can encourage them to purchase during a time when they can relax. If the user is busy, the purchasing unit can also encourage them to purchase during their free time. If the user is relaxed, the purchasing unit can even encourage them to purchase in real time. This allows for more appropriate timing of purchases by adjusting the timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 purchasing unit may be performed using or without a generative AI. For example, the purchasing unit can input user emotion data into a generative AI and have the generative AI adjust the timing of purchases.

[0087] The purchasing unit can select the optimal purchasing method by referring to the user's past purchase history at the time of purchase. For example, the purchasing unit can suggest the optimal purchasing method based on the user's past purchase history. The purchasing unit can also select a preferred purchasing method from the user's past purchase history. The purchasing unit can also provide the optimal purchasing method by referring to the user's past purchase history. In this way, the optimal purchasing method can be selected by referring to the user's past purchase history. Some or all of the above processing in the purchasing unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the purchasing unit can input the user's past purchase history data into a generative AI and have the generative AI perform the selection of the optimal purchasing method.

[0088] The purchasing unit can customize the means of purchase based on the user's current living situation at the time of purchase. For example, the purchasing unit can suggest the optimal means of purchase based on the user's current living situation. The purchasing unit can also customize the means of purchase considering the user's current living situation. The purchasing unit can also provide means of purchase according to the user's current living situation. By customizing the means of purchase based on the user's current living situation, a more appropriate means of purchase can be provided. Some or all of the above processing in the purchasing unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the purchasing unit can input the user's current living situation data into a generative AI and have the generative AI perform the customization of the means of purchase.

[0089] The purchasing unit can estimate the user's emotions and determine purchase priorities based on those estimated emotions. For example, if the user is stressed, the purchasing unit may prioritize purchasing items that promote relaxation. If the user is busy, the purchasing unit may also prioritize purchasing important items. If the user is relaxed, the purchasing unit may also prioritize purchasing interesting items. This allows for the purchase of more appropriate items by prioritizing purchases based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the purchasing unit may be performed using or without a generative AI. For example, the purchasing unit can input user emotion data into a generative AI and have the generative AI determine purchase priorities.

[0090] The purchasing unit can select the optimal purchase method at the time of purchase, taking into account the user's geographical location information. For example, the purchasing unit may suggest a purchase at a store related to the user's current location. The purchasing unit can also select the optimal purchase method based on the user's geographical location information. The purchasing unit can also suggest an online purchase related to the user's current location. This allows the optimal purchase method to be selected by considering the user's geographical location information. Some or all of the above processing in the purchasing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the purchasing unit can input the user's geographical location data into a generative AI and have the generative AI select the optimal purchase method.

[0091] The purchasing unit can analyze the user's social media activity and suggest purchasing options at the time of purchase. For example, the purchasing unit can prioritize purchasing items that the user has shown interest in on social media. The purchasing unit can also purchase items that the user is interested in based on their social media activity. The purchasing unit can also analyze the content of the user's social media posts and purchase related items. In this way, relevant items can be purchased by analyzing the user's social media activity. Some or all of the above processing in the purchasing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the purchasing unit can input the user's social media activity data into a generative AI and have the generative AI suggest purchasing options.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is feeling stressed, it can suggest relaxing clothes and colors. If the user is feeling down, it can suggest clothes in cheerful colors and designs. Furthermore, if the user is nervous about a particular event, it can suggest clothing appropriate for that event. By adjusting the content of suggestions based on the user's emotions, more appropriate suggestions can be made. 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 suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the content of its suggestions.

[0094] The data collection unit can analyze the user's past purchase history and select the optimal information collection method. For example, it can determine the priority of information collection based on the trends of items the user has purchased in the past. The data collection unit can also collect information from the user's past purchase history at specific time periods. The data collection unit can also analyze the user's past purchase history and select the most efficient information collection method. This allows the optimal information collection method to be selected by analyzing the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past purchase history data into a generative AI and have the generative AI select the optimal information collection method.

[0095] 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, suggestions can be made during a time when they can relax. If the user is busy, suggestions can be made during their free time. If the user is relaxed, suggestions can be made in real time. By adjusting the timing of suggestions based on the user's emotions, suggestions can be made at a more appropriate time. 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 suggestion unit may be performed using a generative 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.

[0096] The data collection unit can determine the priority of information collection based on the user's current living situation and areas of interest. For example, it can prioritize the collection of information related to areas of interest that the user is currently interested in. The data collection unit can also filter the necessary information based on the user's current living situation. The data collection unit can also collect highly relevant information based on the user's areas of interest. In this way, highly relevant information can be collected by filtering information based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input data on the user's current living situation and areas of interest into a generative AI and have the generative AI perform the information filtering.

[0097] 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 offer relaxing suggestions. If the user is busy, it can offer concise suggestions. If the user is relaxed, it can offer detailed suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. 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 a generative 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.

[0098] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, it can prioritize the collection of information related to the user's current location. The data collection unit can also collect the most relevant information based on the user's geographical location. The data collection unit can also prioritize the collection of event information related to the user's current location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location data into a generative AI and have the generative AI collect highly relevant information.

[0099] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is stressed, it can make a short suggestion. If the user is busy, it can also make a concise suggestion. If the user is relaxed, it can also make a detailed suggestion. By adjusting the length of the suggestion based on the user's emotions, more appropriate suggestions can be made. 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 processing described above in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestion.

[0100] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, it can prioritize collecting information that the user has shown interest in on social media. The data collection unit can also collect information that the user is interested in from their social media activity. The data collection unit can also analyze the content of the user's social media posts and collect relevant information. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI and have the generative AI collect relevant information.

[0101] The fitting room unit can estimate the user's emotions and adjust the display method of the fitting room based on the estimated emotions. For example, if the user is stressed, a simple display method can be provided. If the user is busy, a quick display method can be provided. If the user is relaxed, a detailed display method can be provided. This allows for more appropriate fitting by adjusting the display method of the fitting room based on 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 fitting room unit may be performed using a generative AI, or not using a generative AI. For example, the fitting room unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the fitting room.

[0102] The data collection unit can select the optimal data collection method by considering the user's device information when collecting information. For example, if the user is using a smartphone, it can provide a data collection method optimized for smartphones. The data collection unit can also provide a data collection method optimized for tablets if the user is using a tablet. The data collection unit can also provide a data collection method optimized for smartwatches if the user is using a smartwatch. This allows the optimal data collection method to be selected by considering the user's device information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's device information into a generative AI and have the generative AI select the optimal data collection method.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The data collection unit collects information such as the user's daily activities, schedule, mood, and health status. The data collection unit obtains data from, for example, the user's smartphone or wearable device. The data collection unit can also obtain schedule and health status information from the user's calendar app or health management app. The data collection unit can also analyze the content of the user's social media posts and messages to estimate the user's mood. Step 2: The suggestion department analyzes the information collected by the collection department and suggests the most suitable clothing for the user. The suggestion department can, for example, use generative AI to select clothing based on the user's preferences and activities. The suggestion department can also suggest relaxing clothing or clothing in energizing colors depending on the user's mood and health condition. The suggestion department can also suggest new combinations of existing clothing. For example, the suggestion department can suggest new outfits by combining clothes the user already owns. The suggestion department can also collaborate with local recycling shops to suggest recycled clothing. Step 3: The fitting unit uses augmented reality (AR) to virtually try on the clothes suggested by the suggestion unit. The fitting unit uses, for example, the user's smartphone or tablet to virtually try on the suggested clothes. The fitting unit can also simulate the fit of the clothes based on the user's body shape data. The fitting unit can save images of the clothes the user tried on, allowing them to review them later. Step 4: The purchasing department purchases or rents the clothes that were tried on by the fitting department. For example, the purchasing department handles the process of the user purchasing clothes they like online. The purchasing department can also handle the process of the user renting clothes. The purchasing department can also save the user's purchase history and use it to make future recommendations.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] Each of the multiple elements described above, including the data collection unit, suggestion unit, try-on unit, and purchase unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart device 14 and collects information such as the user's daily activities, schedule, mood, and health status using the smart device 14's receiving device 38 and camera 42. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to suggest the most suitable clothing for the user. The try-on unit is implemented by the control unit 46A of the smart device 14 and allows the user to try on the suggested clothing using augmented reality. The purchase unit is implemented by the control unit 46A of the smart device 14 and handles the procedure for purchasing or renting the tried-on clothing. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the data collection unit, suggestion unit, try-on unit, and purchase unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart glasses 214 and uses the microphone 238 and camera 42 of the smart glasses 214 to collect information such as the user's daily activities, schedule, mood, and health status. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to suggest the most suitable clothing for the user. The try-on unit is implemented by the control unit 46A of the smart glasses 214 and allows the user to try on the suggested clothing using augmented reality. The purchase unit is implemented by the control unit 46A of the smart glasses 214 and handles the procedure for purchasing or renting the tried-on clothing. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the data collection unit, suggestion unit, try-on unit, and purchase unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the headset terminal 314 and uses the microphone 238 and camera 42 of the headset terminal 314 to collect information such as the user's daily activities, schedule, mood, and health status. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to suggest the most suitable clothing for the user. The try-on unit is implemented by the control unit 46A of the headset terminal 314 and allows the user to try on the suggested clothing using augmented reality. The purchase unit is implemented by the control unit 46A of the headset terminal 314 and handles the procedure for purchasing or renting the tried-on clothing. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the data collection unit, suggestion unit, try-on unit, and purchase unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the robot 414 and uses the robot 414's microphone 238 and camera 42 to collect information such as the user's daily activities, schedule, mood, and health status. The suggestion unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to suggest the most suitable clothing for the user. The try-on unit is implemented by, for example, the control unit 46A of the robot 414 and allows the user to try on the suggested clothing using augmented reality. The purchase unit is implemented by, for example, the control unit 46A of the robot 414 and handles the procedure for purchasing or renting the clothing that was tried on. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] (Note 1) A collection unit that collects information such as the user's daily activities, schedule, mood, and health status, The information collected by the aforementioned collection unit is analyzed, and the suggestion unit proposes the most suitable clothing for the user. A fitting unit that allows users to try on clothes proposed by the aforementioned proposal unit using AR, The system includes a purchase section where customers can purchase or rent clothes that have been tried on in the fitting section. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Proposing new combinations of existing clothing items. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We collaborate with local recycling shops to offer recycled clothing. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Analyze the user's past activity history and select the optimal method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the clothing. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the clothing category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the clothing samples will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the clothing. The system described in Appendix 1, characterized by the features described herein. (Note 16) The fitting area is, The system estimates the user's emotions and adjusts how the try-on display is based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The fitting area is, During the try-on process, the system provides optimal fitting suggestions based on the user's body shape data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The fitting area is, During the try-on process, the system selects the most suitable try-on display by referring to the user's past try-on history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The fitting area is, The system estimates the user's emotions and determines the priority of try-on based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The fitting area is, When trying on clothes, the system provides an optimal fitting display that takes into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The fitting area is, During the try-on process, we analyze the user's social media activity and adjust how the try-on items are displayed. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned purchasing department, It estimates the user's emotions and adjusts the timing of purchases based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned purchasing department, When a purchase is made, the system selects the most suitable purchase method by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned purchasing department, At the time of purchase, the purchase method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned purchasing department, It estimates user emotions and determines purchase priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned purchasing department, When making a purchase, the system will select the most suitable purchase method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned purchasing department, When a user makes a purchase, we analyze their social media activity and suggest ways to make that purchase. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects information such as the user's daily activities, schedule, mood, and health status, The information collected by the aforementioned collection unit is analyzed, and the suggestion unit proposes the most suitable clothing for the user. A fitting unit that allows users to try on clothes proposed by the aforementioned proposal unit using AR, The system includes a purchase section where customers can purchase or rent clothes that have been tried on in the fitting section. A system characterized by the following features.

2. The aforementioned proposal section is, Proposing new combinations of existing clothing items. The system according to feature 1.

3. The aforementioned proposal section is, We collaborate with local recycling shops to offer recycled clothing. The system according to feature 1.

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

5. The aforementioned collection unit is Analyze the user's past activity history and select the optimal method for collecting information. The system according to feature 1.

6. The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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

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

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