system

Abstract

The system according to this embodiment aims to suggest fashion coordinates that correspond to the user's emotional state. [Solution] The system according to the embodiment comprises an analysis unit, a suggestion unit, and a management unit. The analysis unit analyzes the user's emotional state. The suggestion unit suggests fashion coordination based on the emotional state analyzed by the analysis unit. The management unit manages the user's closet.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] Conventional technologies cannot propose fashion coordination according to the emotional state of a user, and there is room for improvement.

[0005] The system according to an embodiment aims to propose fashion coordination according to the emotional state of a user.

Means for Solving the Problems

[0006] The system according to an embodiment includes an analysis unit, a proposal unit, and a management unit. The analysis unit analyzes the emotional state of a user. The proposal unit proposes fashion coordination based on the emotional state analyzed by the analysis unit. The management unit manages the user's closet.

Effects of the Invention

[0007] The system according to this embodiment can suggest fashion coordinates that correspond to the user's emotional state. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the present invention, the emotion-synchronized fashion advisor, is a system that analyzes a user's emotional state in real time and proposes a fashion coordinate that corresponds to that emotion. To analyze the user's emotional state, the emotion-synchronized fashion advisor collects data such as the user's facial expressions, voice, and text input. For example, if a user speaks into their smartphone camera, the system analyzes the voice data to determine the user's emotional state. Similarly, if a user inputs "I'm not feeling well today" as text, the system analyzes the text data to determine the user's emotional state. Next, based on the analysis results, the system proposes a fashion coordinate that matches the user's emotion. For example, if the user inputs "I'm not feeling well today," the system suggests bright-colored clothing. If the user inputs "I want to relax," the system suggests clothing made of comfortable materials. Furthermore, the emotion-synchronized fashion advisor also has a function to manage the user's closet. It digitizes the items the user owns and selects appropriate items for them. For example, if the user inputs "I'm not feeling well today," the system selects and suggests bright-colored clothing from the user's closet. Furthermore, the system also includes features to support the purchase of missing items and new trendy items. For example, if a user enters "I want new trendy items," the system will suggest the latest fashion items and support their purchase. In this way, the emotion-synchronized fashion advisor enriches the user's fashion life by suggesting fashion coordinates that match the user's emotional state, managing their closet, and supporting their purchases.

[0029] The emotion-synchronized fashion advisor according to this embodiment comprises an analysis unit, a suggestion unit, and a management unit. The analysis unit analyzes the user's emotional state. The analysis unit collects data such as the user's facial expressions, voice, and text input, and analyzes the emotional state. For example, if the user speaks into the camera of a smartphone, the analysis unit analyzes the voice data and determines the user's emotional state. The analysis unit can also analyze text data if the user inputs "I'm not feeling well today" and determine the user's emotional state. The suggestion unit proposes a fashion coordinate based on the emotional state analyzed by the analysis unit. For example, if the user inputs "I'm not feeling well today," the suggestion unit will propose bright-colored clothes. The suggestion unit can also propose clothes made of comfortable materials if the user inputs "I want to relax." The suggestion unit can also propose seasonal fashion items according to the user's emotional state. The management unit manages the user's closet. For example, the management unit digitizes the items the user owns and selects appropriate items for them. For example, if a user inputs "I'm not feeling well today," the management unit will select and suggest bright-colored clothes from the user's closet. The management unit can also support the purchase of missing items or new trendy items. For example, if a user inputs "I want a new trendy item," the management unit will suggest the latest fashion items and support their purchase. Thus, the emotion-synchronized fashion advisor according to this embodiment can suggest fashion coordinates and manage the user's closet according to their emotional state.

[0030] The analysis unit analyzes the user's emotional state. For example, it collects data such as the user's facial expressions, voice, and text input to analyze their emotional state. Specifically, when a user speaks into the smartphone camera, the analysis unit analyzes the voice data to determine the user's emotional state. The analysis of voice data uses a combination of speech recognition and natural language processing technologies. Speech recognition converts the user's speech into text, and natural language processing extracts emotions from that text. For example, it analyzes the user's tone of voice, speaking speed, and word choice to determine whether the user is happy, sad, or stressed. Furthermore, if the user inputs text such as "I'm not feeling well today," the analysis unit can analyze that text data to determine the user's emotional state. The analysis of text data uses an emotion analysis algorithm to classify the input text into positive, negative, or neutral emotions. In addition, the analysis unit collects the user's facial expression data and analyzes emotions using facial expression recognition technology. For example, if a user smiles at the camera, a positive emotion can be determined from that expression. This allows the analysis unit to comprehensively analyze voice, text, and facial expression data, enabling it to determine the user's emotional state with high accuracy.

[0031] The suggestion department proposes fashion coordinates based on the emotional state analyzed by the analysis department. Specifically, if a user inputs "I'm not feeling well today," it will suggest bright-colored clothing. The suggestion department selects the optimal coordinate based on the user's emotional state, taking into account color psychology and fashion trends. For example, for a user feeling unwell, it might suggest bright-colored clothing such as yellow or orange to lift their spirits. If a user inputs "I want to relax," it can also suggest clothing made of comfortable materials. For users who want to relax, it will suggest clothing made of soft cotton or linen to provide a comfortable wearing experience. Furthermore, the suggestion department can also suggest seasonal fashion items based on the user's emotional state. For example, it might suggest warm sweaters or coats in winter and shirts or dresses made of cool materials in summer. The suggestion department also considers the user's past preferences and purchase history to provide optimal suggestions for each individual user. In this way, the suggestion department can provide fashion coordinates that match the user's emotional state, increasing user satisfaction.

[0032] The management department manages the user's closet. Specifically, it digitizes the items the user owns and selects appropriate items for them. The management department registers information about the user's clothes and accessories in a database and manages attributes such as the color, material, season, and frequency of use of each item. For example, if a user enters "I'm not feeling well today," the management department will select bright-colored clothes from the database and provide them to the suggestion department. The management department can also support the purchase of missing items or new trendy items. For example, if a user enters "I want a new trendy item," the management department will suggest the latest fashion items and support the purchase. The management department integrates with online shopping sites to allow users to easily purchase items. Furthermore, the management department also supports the organization and maintenance of the user's closet. For example, it suggests changing items seasonally and assists with the disposal and recycling of unwanted items. In this way, the management department can efficiently manage the user's closet and always provide the latest fashion items.

[0033] The management department can digitize the user's closet and manage the items they own. For example, the management department can take photos of the items the user owns and create a database of them. For example, if the user inputs "I'm not feeling well today," the management department can select and suggest bright-colored clothes from the user's closet. For example, if the user inputs "I want a new trendy item," the management department can suggest the latest fashion items and support the purchase. This enables efficient closet management by digitizing the user's closet and managing the items they own. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the user's item data into a generating AI and have the generating AI manage the items.

[0034] The suggestion unit can support the purchase of missing items and new trend items. For example, if a user inputs "I want new trend items," the suggestion unit will suggest the latest fashion items and support their purchase. The suggestion unit can also identify items that are missing from the user's closet and support their purchase. The suggestion unit can also suggest missing items based on seasonal necessities or the user's past purchase history. In this way, it enriches the user's fashion life by supporting the purchase of missing items and new trend items. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's purchase history data into a generating AI and have the generating AI identify missing items.

[0035] The suggestion unit can refer to the user's past fashion history when making suggestions and propose the most suitable outfit. For example, the suggestion unit can propose the most suitable outfit based on items the user has worn in the past. For example, the suggestion unit can also propose an outfit suitable for a specific season based on the user's past fashion history. For example, the suggestion unit can analyze the user's past fashion history and propose an outfit based on trends. In this way, the optimal outfit can be proposed by referring to the user's past fashion history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's fashion history data into a generating AI and have the generating AI execute the optimal outfit suggestion.

[0036] The suggestion unit can adjust the outfit based on the user's current weather and temperature when making suggestions. For example, in rainy weather, the suggestion unit can suggest waterproof items. For example, in sunny weather, the suggestion unit can suggest breathable items. For example, in cold weather, the suggestion unit can suggest items with high heat retention. By adjusting the outfit based on the user's current weather and temperature, it becomes possible to make more appropriate fashion suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's weather data into a generating AI and have the generating AI perform the outfit adjustments.

[0037] The suggestion unit can suggest the most suitable fashion items by considering the user's geographical location information when making suggestions. For example, if the user is in an urban area, the suggestion unit will suggest urban fashion items. For example, if the user is in a resort area, the suggestion unit can also suggest resort fashion items. For example, if the user is in a cold region, the suggestion unit can also suggest fashion items with high heat retention. This makes it possible to suggest more appropriate fashion items by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of suggesting the most suitable fashion items.

[0038] The suggestion unit can analyze the user's social media activity and propose trend-based outfits when making suggestions. For example, the suggestion unit can analyze the user's social media trends and propose the optimal outfit. For example, the suggestion unit can also propose outfits by referencing the fashion of the user's friends on social media. For example, the suggestion unit can analyze the user's fashion-related posts on social media and propose trend-based outfits. This makes it possible to propose trend-based outfits by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media data into a generating AI and have the generating AI execute trend-based outfit suggestions.

[0039] The management unit can, during management, refer to the user's past usage history and propose the optimal item placement. For example, the management unit can propose the optimal placement based on items the user has frequently used in the past. For example, the management unit can also propose an item placement suitable for a specific season based on the user's past usage history. For example, the management unit can analyze the user's past usage history and propose an efficient item placement. In this way, the optimal item placement can be proposed by referring to the user's past usage history. Some or all of the above processes in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input user usage history data into a generating AI and have the generating AI execute a proposal for the optimal item placement.

[0040] The management unit can customize how the closet is organized based on the user's current lifestyle during management. For example, if the user is busy, the management unit can arrange items for easy access. If the user is relaxed, the management unit can also provide an organization method that includes detailed information. If the user is moving, the management unit can also arrange items for easy movement. This allows for more efficient closet management by customizing the closet organization method based on the user's current lifestyle. Some or all of the above processes in the management unit may be performed using AI, for example, or not. For example, the management unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of the closet organization method.

[0041] The management unit can propose the optimal placement of items while considering the user's geographical location information. For example, if the user is in an urban area, the management unit will prioritize placing urban items. If the user is in a resort area, the management unit can also prioritize placing resort items. If the user is in a cold region, the management unit can also prioritize placing items with high heat retention. This allows for more appropriate item placement by considering the user's geographical location information. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's geographical location information into a generating AI and have the generating AI propose the optimal placement of items.

[0042] The management department can analyze users' social media activity during management and suggest the addition of trend-based items. For example, the management department can analyze the user's social media trends and suggest the most suitable items. For example, the management department can suggest items by referencing the fashion of the user's friends on social media. For example, the management department can analyze the user's fashion-related posts on social media and suggest trend-based items. This makes it possible to add trend-based items by analyzing the user's social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input user social media data into a generating AI and have the generating AI perform the task of suggesting the addition of trend-based items.

[0043] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0044] The suggestion function, when suggesting fashion coordinates based on the user's emotional state, can refer to the user's past fashion history to suggest the most suitable coordinate. For example, the suggestion function can suggest the most suitable coordinate based on items the user has worn in the past. For example, the suggestion function can suggest coordinates suitable for a specific season based on the user's past fashion history. For example, the suggestion function can analyze the user's past fashion history and suggest coordinates based on trends. In this way, by referring to the user's past fashion history, it can suggest the most suitable coordinate.

[0045] The management department can refer to the user's past usage history when managing a user's closet and suggest the optimal placement of items. For example, the management department can suggest the optimal placement based on items the user has frequently used in the past. For example, the management department can also suggest item placement suitable for a specific season based on the user's past usage history. For example, the management department can analyze the user's past usage history and suggest efficient item placement. In this way, by referring to the user's past usage history, the optimal placement of items can be suggested.

[0046] The suggestion function, when making fashion coordination suggestions based on the user's emotional state, can adjust the coordination based on the user's current weather and temperature. For example, the suggestion function can suggest waterproof items when it is raining. For example, it can suggest breathable items when it is sunny. For example, it can suggest warm items when it is cold. By adjusting the coordination based on the user's current weather and temperature, it becomes possible to make more appropriate fashion suggestions.

[0047] The suggestion function can analyze the user's social media activity and suggest trend-based outfits when making fashion coordination suggestions based on the user's emotional state. For example, the suggestion function can analyze the user's social media trends and suggest the most suitable outfit. For example, the suggestion function can suggest outfits by referencing the fashion of the user's friends on social media. For example, the suggestion function can analyze the user's fashion-related posts on social media and suggest trend-based outfits. In this way, by analyzing the user's social media activity, it becomes possible to suggest outfits that are based on trends.

[0048] The management department can customize how users organize their closets based on their current lifestyle. For example, if a user is busy, the department can arrange items for easy access. If a user is relaxed, the department can provide a more detailed organization method. If a user is moving, the department can arrange items for easy movement. By customizing closet organization based on the user's current lifestyle, more efficient closet management becomes possible.

[0049] The following briefly describes the processing flow for example form 1.

[0050] Step 1: The analysis unit analyzes the user's emotional state. The analysis unit collects data such as the user's facial expressions, voice, and text input, and analyzes their emotional state. For example, if a user speaks into their smartphone camera, the analysis unit analyzes that voice data to determine the user's emotional state. Also, if a user types "I'm not feeling well today" in text, the analysis unit can analyze that text data to determine the user's emotional state. Step 2: The suggestion unit proposes fashion coordinates based on the emotional state analyzed by the analysis unit. For example, if the user inputs "I'm not feeling well today," it will suggest bright-colored clothes. If the user inputs "I want to relax," it can also suggest clothes made of comfortable materials. Furthermore, it can also suggest fashion items appropriate for the season, depending on the user's emotional state. Step 3: The management department manages the user's closet. The management department digitizes the items the user owns and selects appropriate items for them. For example, if the user enters "I'm not feeling well today," the management department will select and suggest bright-colored clothes from the user's closet. The management department can also support the purchase of missing items or new trendy items. For example, if the user enters "I want a new trendy item," the management department will suggest the latest fashion items and support their purchase.

[0051] (Example of form 2) An embodiment of the present invention, the emotion-synchronized fashion advisor, is a system that analyzes a user's emotional state in real time and proposes a fashion coordinate that corresponds to that emotion. To analyze the user's emotional state, the emotion-synchronized fashion advisor collects data such as the user's facial expressions, voice, and text input. For example, if a user speaks into their smartphone camera, the system analyzes the voice data to determine the user's emotional state. Similarly, if a user inputs "I'm not feeling well today" as text, the system analyzes the text data to determine the user's emotional state. Next, based on the analysis results, the system proposes a fashion coordinate that matches the user's emotion. For example, if the user inputs "I'm not feeling well today," the system suggests bright-colored clothing. If the user inputs "I want to relax," the system suggests clothing made of comfortable materials. Furthermore, the emotion-synchronized fashion advisor also has a function to manage the user's closet. It digitizes the items the user owns and selects appropriate items for them. For example, if the user inputs "I'm not feeling well today," the system selects and suggests bright-colored clothing from the user's closet. Furthermore, the system also includes features to support the purchase of missing items and new trendy items. For example, if a user enters "I want new trendy items," the system will suggest the latest fashion items and support their purchase. In this way, the emotion-synchronized fashion advisor enriches the user's fashion life by suggesting fashion coordinates that match the user's emotional state, managing their closet, and supporting their purchases.

[0052] The emotion-synchronized fashion advisor according to this embodiment comprises an analysis unit, a suggestion unit, and a management unit. The analysis unit analyzes the user's emotional state. The analysis unit collects data such as the user's facial expressions, voice, and text input, and analyzes the emotional state. For example, if the user speaks into the camera of a smartphone, the analysis unit analyzes the voice data and determines the user's emotional state. The analysis unit can also analyze text data if the user inputs "I'm not feeling well today" and determine the user's emotional state. The suggestion unit proposes a fashion coordinate based on the emotional state analyzed by the analysis unit. For example, if the user inputs "I'm not feeling well today," the suggestion unit will propose bright-colored clothes. The suggestion unit can also propose clothes made of comfortable materials if the user inputs "I want to relax." The suggestion unit can also propose seasonal fashion items according to the user's emotional state. The management unit manages the user's closet. For example, the management unit digitizes the items the user owns and selects appropriate items for them. For example, if a user inputs "I'm not feeling well today," the management unit will select and suggest bright-colored clothes from the user's closet. The management unit can also support the purchase of missing items or new trendy items. For example, if a user inputs "I want a new trendy item," the management unit will suggest the latest fashion items and support their purchase. Thus, the emotion-synchronized fashion advisor according to this embodiment can suggest fashion coordinates and manage the user's closet according to their emotional state.

[0053] The analysis unit analyzes the user's emotional state. For example, it collects data such as the user's facial expressions, voice, and text input to analyze their emotional state. Specifically, when a user speaks into the smartphone camera, the analysis unit analyzes the voice data to determine the user's emotional state. The analysis of voice data uses a combination of speech recognition and natural language processing technologies. Speech recognition converts the user's speech into text, and natural language processing extracts emotions from that text. For example, it analyzes the user's tone of voice, speaking speed, and word choice to determine whether the user is happy, sad, or stressed. Furthermore, if the user inputs text such as "I'm not feeling well today," the analysis unit can analyze that text data to determine the user's emotional state. The analysis of text data uses an emotion analysis algorithm to classify the input text into positive, negative, or neutral emotions. In addition, the analysis unit collects the user's facial expression data and analyzes emotions using facial expression recognition technology. For example, if a user smiles at the camera, a positive emotion can be determined from that expression. This allows the analysis unit to comprehensively analyze voice, text, and facial expression data, enabling it to determine the user's emotional state with high accuracy.

[0054] The suggestion department proposes fashion coordinates based on the emotional state analyzed by the analysis department. Specifically, if a user inputs "I'm not feeling well today," it will suggest bright-colored clothing. The suggestion department selects the optimal coordinate based on the user's emotional state, taking into account color psychology and fashion trends. For example, for a user feeling unwell, it might suggest bright-colored clothing such as yellow or orange to lift their spirits. If a user inputs "I want to relax," it can also suggest clothing made of comfortable materials. For users who want to relax, it will suggest clothing made of soft cotton or linen to provide a comfortable wearing experience. Furthermore, the suggestion department can also suggest seasonal fashion items based on the user's emotional state. For example, it might suggest warm sweaters or coats in winter and shirts or dresses made of cool materials in summer. The suggestion department also considers the user's past preferences and purchase history to provide optimal suggestions for each individual user. In this way, the suggestion department can provide fashion coordinates that match the user's emotional state, increasing user satisfaction.

[0055] The management department manages the user's closet. Specifically, it digitizes the items the user owns and selects appropriate items for them. The management department registers information about the user's clothes and accessories in a database and manages attributes such as the color, material, season, and frequency of use of each item. For example, if a user enters "I'm not feeling well today," the management department will select bright-colored clothes from the database and provide them to the suggestion department. The management department can also support the purchase of missing items or new trendy items. For example, if a user enters "I want a new trendy item," the management department will suggest the latest fashion items and support the purchase. The management department integrates with online shopping sites to allow users to easily purchase items. Furthermore, the management department also supports the organization and maintenance of the user's closet. For example, it suggests changing items seasonally and assists with the disposal and recycling of unwanted items. In this way, the management department can efficiently manage the user's closet and always provide the latest fashion items.

[0056] The analysis unit can collect data such as the user's facial expressions, voice, and text input, and analyze their emotional state. For example, the analysis unit can capture the user's facial expressions with a camera and analyze their emotional state using facial recognition technology. The analysis unit can also record the user's voice and analyze their emotional state using speech recognition technology. For example, if the user inputs "I'm not feeling well today" in text, the analysis unit can analyze that text data using natural language processing technology and determine the emotional state. By collecting diverse data on the user and analyzing their emotional state, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform the emotional state analysis.

[0057] The suggestion unit can propose fashion coordinates that match the user's emotions based on the analysis results. For example, if the user inputs "I'm not feeling well today," the suggestion unit will suggest bright-colored clothes. For example, if the user inputs "I want to relax," the suggestion unit can also suggest clothes made of comfortable materials. For example, the suggestion unit can also suggest fashion items appropriate for the season, depending on the user's emotional state. This improves user satisfaction by suggesting fashion coordinates that match the user's emotions. 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 the analysis results into a generative AI and have the generative AI execute a fashion coordinate suggestion that matches the user's emotions.

[0058] The management department can digitize the user's closet and manage the items they own. For example, the management department can take photos of the items the user owns and create a database of them. For example, if the user inputs "I'm not feeling well today," the management department can select and suggest bright-colored clothes from the user's closet. For example, if the user inputs "I want a new trendy item," the management department can suggest the latest fashion items and support the purchase. This enables efficient closet management by digitizing the user's closet and managing the items they own. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the user's item data into a generating AI and have the generating AI manage the items.

[0059] The suggestion unit can support the purchase of missing items and new trend items. For example, if a user inputs "I want new trend items," the suggestion unit will suggest the latest fashion items and support their purchase. The suggestion unit can also identify items that are missing from the user's closet and support their purchase. The suggestion unit can also suggest missing items based on seasonal necessities or the user's past purchase history. In this way, it enriches the user's fashion life by supporting the purchase of missing items and new trend items. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's purchase history data into a generating AI and have the generating AI identify missing items.

[0060] The analysis unit can estimate the user's emotions and dynamically adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit prioritizes analyzing facial expression data to improve the accuracy of emotion analysis. For example, if the user is relaxed, the analysis unit can also prioritize voice data and adjust the accuracy of emotion analysis. For example, if the user is excited, the analysis unit can analyze text input data in detail and dynamically adjust the accuracy of emotion analysis. This allows for more accurate emotion analysis by dynamically adjusting the accuracy of the analysis 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the analysis accuracy.

[0061] The analysis unit can refer to the user's past emotional data and analyze patterns of emotional change. For example, the analysis unit can collect the user's emotional data for the past month and analyze patterns of emotional change. The analysis unit can also compare the user's emotional data before and after a specific event and analyze patterns of emotional change. The analysis unit can also analyze the user's weekly emotional data and identify patterns of emotional change. This allows for more accurate emotional analysis by referring to the user's past emotional data and analyzing patterns of emotional change. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's past emotional data into a generative AI and have the generative AI perform the analysis of emotional change patterns.

[0062] The analysis unit can improve the accuracy of emotion analysis by additionally collecting the user's physiological data (heart rate, skin electrical response, etc.) during analysis. For example, the analysis unit can collect the user's heart rate data to improve the accuracy of emotion analysis. The analysis unit can also collect the user's skin electrical response data to improve the accuracy of emotion analysis. The analysis unit can also collect the user's breathing pattern data to improve the accuracy of emotion analysis. In this way, the accuracy of emotion analysis can be improved by additionally collecting the user's physiological data. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's physiological data into the generative AI and have the generative AI perform the improvement of the accuracy of emotion analysis.

[0063] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is excited, the analysis unit can also provide a visually stimulating display method. By adjusting the display method of the analysis results based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0064] The analysis unit can analyze the emotional state by considering the user's geographical location information during analysis. For example, if the user is in a specific location, the analysis unit can analyze the emotional state by referring to past emotional data for that location. For example, if the user is traveling, the analysis unit can also analyze the emotional state by considering the geographical location information of the travel destination. For example, if the user is at home, the analysis unit can also analyze the emotional state based on the geographical location information of the home. This makes it possible to analyze the emotional state more accurately by considering the user's geographical location information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into the generative AI and have the generative AI perform the emotional state analysis.

[0065] The analysis unit can analyze the user's social media activity during analysis and supplement their emotional state. For example, the analysis unit can analyze the content of the user's social media posts and supplement their emotional state. The analysis unit can also analyze the user's comments and reactions on social media and supplement their emotional state. The analysis unit can also analyze the user's interactions with friends on social media and supplement their emotional state. This allows for more accurate sentiment analysis by supplementing the emotional state through analysis of the user's social media activity. Sentiment estimation is achieved using a sentiment estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media data into a generative AI and have the generative AI perform the emotional state supplementation.

[0066] 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 will present simple and highly visible suggestions. If the user is relaxed, the suggestion unit may present suggestions containing detailed information. If the user is excited, the suggestion unit may present visually stimulating suggestions. By adjusting the way suggestions are presented based on the user's emotions, suggestions that are easy for the user to understand 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 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.

[0067] The suggestion unit can refer to the user's past fashion history when making suggestions and propose the most suitable outfit. For example, the suggestion unit can propose the most suitable outfit based on items the user has worn in the past. For example, the suggestion unit can also propose an outfit suitable for a specific season based on the user's past fashion history. For example, the suggestion unit can analyze the user's past fashion history and propose an outfit based on trends. In this way, the optimal outfit can be proposed by referring to the user's past fashion history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's fashion history data into a generating AI and have the generating AI execute the optimal outfit suggestion.

[0068] The suggestion unit can adjust the outfit based on the user's current weather and temperature when making suggestions. For example, in rainy weather, the suggestion unit can suggest waterproof items. For example, in sunny weather, the suggestion unit can suggest breathable items. For example, in cold weather, the suggestion unit can suggest items with high heat retention. By adjusting the outfit based on the user's current weather and temperature, it becomes possible to make more appropriate fashion suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's weather data into a generating AI and have the generating AI perform the outfit adjustments.

[0069] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize suggesting items that promote relaxation. If the user is relaxed, the suggestion unit may also prioritize suggesting trendy items. If the user is excited, the suggestion unit may also prioritize suggesting visually stimulating items. By prioritizing suggestions based on the user's emotions, more appropriate fashion suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0070] The suggestion unit can suggest the most suitable fashion items by considering the user's geographical location information when making suggestions. For example, if the user is in an urban area, the suggestion unit will suggest urban fashion items. For example, if the user is in a resort area, the suggestion unit can also suggest resort fashion items. For example, if the user is in a cold region, the suggestion unit can also suggest fashion items with high heat retention. This makes it possible to suggest more appropriate fashion items by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of suggesting the most suitable fashion items.

[0071] The suggestion unit can analyze the user's social media activity and propose trend-based outfits when making suggestions. For example, the suggestion unit can analyze the user's social media trends and propose the optimal outfit. For example, the suggestion unit can also propose outfits by referencing the fashion of the user's friends on social media. For example, the suggestion unit can analyze the user's fashion-related posts on social media and propose trend-based outfits. This makes it possible to propose trend-based outfits by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media data into a generating AI and have the generating AI execute trend-based outfit suggestions.

[0072] The management unit can estimate the user's emotions and adjust the closet management method based on the estimated emotions. For example, if the user is stressed, the management unit can provide a simple and highly visible closet management method. For example, if the user is relaxed, the management unit can also provide a closet management method that includes detailed information. For example, if the user is excited, the management unit can also provide a visually stimulating closet management method. This allows for more appropriate closet management by adjusting the closet management method 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 management unit may be performed using AI, for example, or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI adjust the closet management method.

[0073] The management unit can, during management, refer to the user's past usage history and propose the optimal item placement. For example, the management unit can propose the optimal placement based on items the user has frequently used in the past. For example, the management unit can also propose an item placement suitable for a specific season based on the user's past usage history. For example, the management unit can analyze the user's past usage history and propose an efficient item placement. In this way, the optimal item placement can be proposed by referring to the user's past usage history. Some or all of the above processes in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input user usage history data into a generating AI and have the generating AI execute a proposal for the optimal item placement.

[0074] The management unit can customize how the closet is organized based on the user's current lifestyle during management. For example, if the user is busy, the management unit can arrange items for easy access. If the user is relaxed, the management unit can also provide an organization method that includes detailed information. If the user is moving, the management unit can also arrange items for easy movement. This allows for more efficient closet management by customizing the closet organization method based on the user's current lifestyle. Some or all of the above processes in the management unit may be performed using AI, for example, or not. For example, the management unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of the closet organization method.

[0075] The management unit can estimate the user's emotions and determine the priority of closet organization based on the estimated emotions. For example, if the user is stressed, the management unit will prioritize organizing items that promote relaxation. If the user is relaxed, the management unit may also prioritize organizing trendy items. If the user is excited, the management unit may also prioritize organizing visually stimulating items. This allows for more appropriate closet management by determining the priority of closet organization 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 processing in the management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI and have the generative AI determine the priority of closet organization.

[0076] The management unit can propose the optimal placement of items while considering the user's geographical location information. For example, if the user is in an urban area, the management unit will prioritize placing urban items. If the user is in a resort area, the management unit can also prioritize placing resort items. If the user is in a cold region, the management unit can also prioritize placing items with high heat retention. This allows for more appropriate item placement by considering the user's geographical location information. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's geographical location information into a generating AI and have the generating AI propose the optimal placement of items.

[0077] The management department can analyze users' social media activity during management and suggest the addition of trend-based items. For example, the management department can analyze the user's social media trends and suggest the most suitable items. For example, the management department can suggest items by referencing the fashion of the user's friends on social media. For example, the management department can analyze the user's fashion-related posts on social media and suggest trend-based items. This makes it possible to add trend-based items by analyzing the user's social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input user social media data into a generating AI and have the generating AI perform the task of suggesting the addition of trend-based items.

[0078] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0079] The analysis unit can analyze a user's emotional state by referring to the user's past emotional data and analyzing patterns of emotional change. For example, the analysis unit can collect emotional data from the user over the past month and analyze patterns of emotional change. The analysis unit can also compare emotional data from a user before and after a specific event and analyze patterns of emotional change. For example, the analysis unit can analyze a user's weekly emotional data and identify patterns of emotional change. This allows for more accurate emotional analysis by referring to the user's past emotional data and analyzing patterns of emotional change.

[0080] The suggestion function, when suggesting fashion coordinates based on the user's emotional state, can refer to the user's past fashion history to suggest the most suitable coordinate. For example, the suggestion function can suggest the most suitable coordinate based on items the user has worn in the past. For example, the suggestion function can suggest coordinates suitable for a specific season based on the user's past fashion history. For example, the suggestion function can analyze the user's past fashion history and suggest coordinates based on trends. In this way, by referring to the user's past fashion history, it can suggest the most suitable coordinate.

[0081] The management department can refer to the user's past usage history when managing a user's closet and suggest the optimal placement of items. For example, the management department can suggest the optimal placement based on items the user has frequently used in the past. For example, the management department can also suggest item placement suitable for a specific season based on the user's past usage history. For example, the management department can analyze the user's past usage history and suggest efficient item placement. In this way, by referring to the user's past usage history, the optimal placement of items can be suggested.

[0082] The analysis unit can improve the accuracy of emotion analysis by collecting additional physiological data from the user (such as heart rate and skin electrical response) when analyzing the user's emotional state. For example, the analysis unit can collect the user's heart rate data to improve the accuracy of emotion analysis. The analysis unit can also collect the user's skin electrical response data to improve the accuracy of emotion analysis. The analysis unit can also collect the user's breathing pattern data to improve the accuracy of emotion analysis. In this way, the accuracy of emotion analysis can be improved by collecting additional physiological data from the user.

[0083] The suggestion function, when making fashion coordination suggestions based on the user's emotional state, can adjust the coordination based on the user's current weather and temperature. For example, the suggestion function can suggest waterproof items when it is raining. For example, it can suggest breathable items when it is sunny. For example, it can suggest warm items when it is cold. By adjusting the coordination based on the user's current weather and temperature, it becomes possible to make more appropriate fashion suggestions.

[0084] The analysis unit can analyze a user's emotional state by considering the user's geographical location. For example, if the user is in a specific location, the analysis unit can analyze the emotional state by referring to past emotional data for that location. For example, if the user is traveling, the analysis unit can also analyze the emotional state by considering the geographical location of the travel destination. For example, if the user is at home, the analysis unit can also analyze the emotional state based on the geographical location of the home. This allows for a more accurate analysis of the emotional state by considering the user's geographical location.

[0085] The suggestion function can analyze the user's social media activity and suggest trend-based outfits when making fashion coordination suggestions based on the user's emotional state. For example, the suggestion function can analyze the user's social media trends and suggest the most suitable outfit. For example, the suggestion function can suggest outfits by referencing the fashion of the user's friends on social media. For example, the suggestion function can analyze the user's fashion-related posts on social media and suggest trend-based outfits. In this way, by analyzing the user's social media activity, it becomes possible to suggest outfits that are based on trends.

[0086] The management unit can estimate the user's emotional state when managing the user's closet and adjust the closet management method based on the estimated emotion. For example, if the user is stressed, the management unit can provide a simple and highly visible closet management method. For example, if the user is relaxed, the management unit can provide a closet management method that includes detailed information. For example, if the user is excited, the management unit can provide a closet management method that is visually stimulating. By adjusting the closet management method based on the user's emotion, more appropriate closet management becomes possible.

[0087] The suggestion function can prioritize fashion coordination suggestions based on the user's emotional state. For example, if the user is stressed, the suggestion function will prioritize suggesting items that promote relaxation. If the user is relaxed, the suggestion function may also prioritize suggesting trendy items. If the user is excited, the suggestion function may also prioritize suggesting visually stimulating items. By prioritizing suggestions based on the user's emotions, more appropriate fashion suggestions can be made.

[0088] The management department can customize how users organize their closets based on their current lifestyle. For example, if a user is busy, the department can arrange items for easy access. If a user is relaxed, the department can provide a more detailed organization method. If a user is moving, the department can arrange items for easy movement. By customizing closet organization based on the user's current lifestyle, more efficient closet management becomes possible.

[0089] The following briefly describes the processing flow for example form 2.

[0090] Step 1: The analysis unit analyzes the user's emotional state. The analysis unit collects data such as the user's facial expressions, voice, and text input, and analyzes their emotional state. For example, if a user speaks into their smartphone camera, the analysis unit analyzes that voice data to determine the user's emotional state. Also, if a user types "I'm not feeling well today" in text, the analysis unit can analyze that text data to determine the user's emotional state. Step 2: The suggestion unit proposes fashion coordinates based on the emotional state analyzed by the analysis unit. For example, if the user inputs "I'm not feeling well today," it will suggest bright-colored clothes. If the user inputs "I want to relax," it can also suggest clothes made of comfortable materials. Furthermore, it can also suggest fashion items appropriate for the season, depending on the user's emotional state. Step 3: The management department manages the user's closet. The management department digitizes the items the user owns and selects appropriate items for them. For example, if the user enters "I'm not feeling well today," the management department will select and suggest bright-colored clothes from the user's closet. The management department can also support the purchase of missing items or new trendy items. For example, if the user enters "I want a new trendy item," the management department will suggest the latest fashion items and support their purchase.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] Each of the multiple elements described above, including the analysis unit, proposal unit, and management unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit collects the user's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14, and analyzes the emotional state using the control unit 46A. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes fashion coordination based on the user's emotions using the emotion identification model 59. The management unit manages the user's closet using the control unit 46A of the smart device 14 and selects and proposes appropriate items. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0095] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the analysis unit, suggestion unit, and management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit collects the user's facial expressions and voice using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A analyzes the emotional state. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and suggests fashion coordination based on the user's emotions using the emotion identification model 59. The management unit manages the user's closet using the control unit 46A of the smart glasses 214 and selects and suggests appropriate items. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0111] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the analysis unit, proposal unit, and management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit collects the user's facial expressions and voice using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A analyzes the emotional state. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes fashion coordination based on the user's emotions using the emotion identification model 59. The management unit manages the user's closet using the control unit 46A of the headset terminal 314 and selects and proposes appropriate items. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the analysis unit, proposal unit, and management unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 238 of the robot 414 to collect the user's facial expressions and voice, and the control unit 46A analyzes the emotional state. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses the emotion identification model 59 to propose fashion coordination based on the user's emotions. The management unit manages the user's closet using the control unit 46A of the robot 414 and selects and proposes appropriate items. 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] (Note 1) An analysis unit that analyzes the user's emotional state, A proposal unit that proposes fashion coordination based on the emotional state analyzed by the analysis unit, It includes a management unit that manages the user's closet. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The system collects data such as the user's facial expressions, voice, and text input, and analyzes their emotional state. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on the analysis results, we suggest fashion coordinates that match the user's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, Digitizing the user's closet and managing their belongings. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Supports the purchase of items you're missing or new trendy items. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, It estimates the user's emotions and dynamically adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It analyzes patterns of emotional change by referencing the user's past emotional data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, During analysis, additional physiological data from the user is collected to improve the accuracy of emotion analysis. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, the user's emotional state is analyzed while taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, the user's social media activity is analyzed to supplement their emotional state. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) The aforementioned proposal section is, When making suggestions, the system refers to the user's past fashion history to propose the most suitable outfit. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making suggestions, the outfit is adjusted based on the user's current weather and temperature. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making suggestions, we take the user's geographical location into consideration to propose the most suitable fashion items. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and suggest coordinated outfits based on current trends. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned management department, It estimates the user's emotions and adjusts the closet management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, During management, the system refers to the user's past usage history and suggests the optimal placement of items. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, During management, the system customizes how the closet is organized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, It estimates the user's emotions and determines the priority of closet organization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, During management, the system suggests the optimal placement of items, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, During management, we analyze users' social media activity and suggest adding items based on trends. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0163] 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. An analysis unit that analyzes the user's emotional state, A proposal unit that proposes fashion coordination based on the emotional state analyzed by the analysis unit, It includes a management unit that manages the user's closet. A system characterized by the following features.

2. The aforementioned analysis unit, The system collects data such as the user's facial expressions, voice, and text input, and analyzes their emotional state. The system according to feature 1.

3. The aforementioned proposal section is, Based on the analysis results, we suggest fashion coordinates that match the user's emotions. The system according to feature 1.

4. The aforementioned management department, Digitizing the user's closet and managing their belongings. The system according to feature 1.

5. The aforementioned proposal section is, Supports the purchase of items you're missing or new trendy items. The system according to feature 1.

6. The aforementioned analysis unit, It estimates the user's emotions and dynamically adjusts the accuracy of the analysis based on the estimated user emotions. The system according to feature 1.

7. The aforementioned analysis unit, It analyzes patterns of emotional change by referencing the user's past emotional data. The system according to feature 1.

8. The aforementioned analysis unit, During analysis, additional physiological data from the user is collected to improve the accuracy of emotion analysis. The system according to feature 1.

Images