system

Abstract

The system according to the embodiment aims to suggest the most suitable clothes based on personal preferences and the clothes that a person already owns. [Solution] A system according to an embodiment includes an input unit, an analysis unit, a suggestion unit, an information acquisition unit, and an emergency suggestion unit. The input unit inputs information about a user's preferences and clothing. The analysis unit analyzes the information input by the input unit. The suggestion unit suggests clothing based on the information analyzed by the analysis unit. The information acquisition unit acquires information about weather and events. The emergency suggestion unit suggests clothing to accommodate changes in plans or emergencies.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately suggest the best clothes based on personal preferences and the clothes you own, so there is room for improvement.

[0005] The system according to the embodiment aims to suggest the most suitable clothes based on personal preferences and the clothes that a person already owns. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a suggestion unit, an information acquisition unit, and an emergency suggestion unit. The input unit inputs information about the user's preferences and clothing. The analysis unit analyzes the information input by the input unit. The suggestion unit suggests clothing based on the information analyzed by the analysis unit. The information acquisition unit acquires information about weather and events. The emergency suggestion unit suggests clothing to accommodate changes in plans or emergencies. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable clothes based on personal preferences and the clothes that a person already owns. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A clothing recommendation system according to an embodiment of the present invention utilizes a generative AI to suggest clothing that takes individual preferences into account. In this clothing recommendation system, a user inputs information about their preferences and the clothing they own, and the generative AI analyzes that information to suggest individually optimized clothing. For example, a user can input their preferred colors and styles, as well as photos of the clothing they own. The generative AI then suggests optimal clothing based on the user's preferences and clothing information. Furthermore, because the generative AI considers multiple information when making suggestions, it can also take into account factors such as weather and the type of event. Furthermore, as a response to emergencies, it can also suggest clothing appropriate for sudden changes in plans or emergencies. For example, if a user inputs, "I like casual styles and have a lot of blue clothing," the generative AI would suggest casual clothing and items that match blue clothing. If a user inputs, "It's expected to rain tomorrow, and I'm attending a friend's wedding," the generative AI would suggest formal clothing suitable for rainy days. Furthermore, if a user inputs, "I have a sudden work meeting," the generative AI would suggest business casual clothing. In this way, users can respond to sudden changes in plans. This system makes it easier for users to choose clothes because it suggests the best clothes based on their preferences and the clothes they own.It can also take into account multiple information and respond to emergencies, making it suitable for a variety of situations.This allows the clothing recommendation system to suggest the best clothes based on the user's preferences and information about the clothes they own.

[0029] The clothing recommendation system according to the embodiment includes an input unit, an analysis unit, a suggestion unit, an information acquisition unit, and an emergency suggestion unit. The input unit inputs information about a user's preferences and clothing items. The user's preferences include, but are not limited to, for example, color, style, and brand. The information about the clothing items includes, but is not limited to, for example, type, color, size, and brand of clothing. The analysis unit uses a generation AI to analyze the information input by the input unit. The analysis is performed based on, for example, the type of algorithm and the data to be analyzed, but is not limited to, for example. The suggestion unit uses the generation AI to suggest clothing items based on the information analyzed by the analysis unit. The suggestion is performed based on, for example, the suggestion format and the suggestion criteria, but is not limited to, for example. The information acquisition unit acquires weather and event information. The weather and event information includes, but is not limited to, for example, weather forecast data and an event calendar. The emergency suggestion unit uses the generation AI to suggest clothing items suitable for sudden changes in plans or emergencies. Emergencies include, but are not limited to, for example, sudden changes in plans or emergencies. As a result, the clothing recommendation system according to the embodiment can recommend the most suitable clothing based on the user's preferences and information about the clothing the user owns.

[0030] The input unit allows the user to input colors, styles, and photos of clothes they own. For example, the input unit allows the user to input photos of their preferred colors, styles, and clothes they own. For example, the user inputs, "I like casual styles and I have a lot of blue clothes." This information is input to the generation AI. This allows detailed information about the user's preferences and clothes they own. Some or all of the above-mentioned processing in the input unit may be performed using, or without, AI. For example, the input unit can input the information entered by the user to the generation AI, and the generation AI can analyze the information.

[0031] The analysis unit can perform analysis based on the user's past selection history and purchase history. The analysis unit, for example, performs analysis taking into account the user's past selection history and purchase history. For example, the analysis unit analyzes preference trends based on information about clothes the user has purchased in the past. The analysis unit can also suggest optimal clothes based on information about styles and colors the user has selected in the past. Furthermore, the analysis unit can analyze the user's past purchase history and suggest new clothes with similar trends. This enables more accurate analysis based on the user's past selection history and purchase history. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past selection history and purchase history into a generation AI, which can then analyze the information.

[0032] The suggestion unit can suggest clothes based on the user's preferences and information about the clothes the user owns. The suggestion unit suggests optimal clothes based on, for example, the user's preferences and information about the clothes the user owns. For example, if the user prefers a casual style, the suggestion unit can suggest casual clothes. Also, if the user has a lot of blue clothes, the suggestion unit can also suggest items that match blue clothes. In this way, optimal clothes can be suggested based on the user's preferences and information about the clothes the user owns. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input information about the user's preferences and the clothes the user owns into the generation AI, and the generation AI can suggest optimal clothes based on that information.

[0033] The information acquisition unit can acquire weather and event information. The information acquisition unit acquires, for example, weather and event information. Weather and event information includes, but is not limited to, weather forecast data and an event calendar. By acquiring the weather and event information, more appropriate clothing can be suggested. Some or all of the above-described processing in the information acquisition unit may be performed using AI, or may be performed without using AI. For example, the information acquisition unit inputs weather forecast data and an event calendar into the generation AI, and the generation AI can suggest clothing based on that information.

[0034] The emergency suggestion unit can suggest clothing appropriate for sudden changes in plans or emergencies. The emergency suggestion unit, for example, suggests clothing appropriate for sudden changes in plans or emergencies. For example, if a user inputs, "I have a sudden work meeting," the emergency suggestion unit can suggest business casual clothing. Also, if a user inputs, "I have suddenly decided to attend a friend's wedding," the emergency suggestion unit can also suggest formal clothing. This makes it possible to suggest clothing appropriate for sudden changes in plans or emergencies. Some or all of the above-mentioned processing in the emergency suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the emergency suggestion unit can input user input information into a generation AI, and the generation AI can suggest clothing appropriate for emergencies based on that information.

[0035] The input unit can analyze the user's past input history and suggest the optimal input method. For example, the input unit can automatically display as candidates the user's favorite colors and styles that they have frequently input in the past. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest the user's favorite colors and styles to use during a specific time period based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the input unit may be performed using AI, or may be performed without using AI. For example, the input unit can input the user's past input history into a generation AI, which can then suggest the optimal input method based on that data.

[0036] The input unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the input unit can prioritize inputting information about clothing related to that area. Furthermore, when the user is traveling, the input unit can prioritize inputting information about clothing related to the travel destination. Furthermore, when the user is attending a specific event, the input unit can prioritize inputting information about clothing related to the event. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the input unit may be performed using AI, or may be performed without using AI. For example, the input unit can input the user's geographical location information to a generation AI, and the generation AI can prioritize inputting highly relevant information based on that data.

[0037] The input unit can analyze the user's social media activity and input related information at the time of input. For example, the input unit can input preferred colors and styles based on photos and posts the user has shared on social media. The input unit can also input related clothing information based on information about brands and designers the user follows on social media. Furthermore, the input unit can also input related clothing information based on information about events the user plans to attend on social media. This allows for more appropriate suggestions by inputting related information based on the user's social media activity. Some or all of the above-described processing in the input unit may be performed using AI, or may be performed without using AI. For example, the input unit can input the user's social media activity data to the generation AI, which can then input related information based on that data.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past selection history and purchase history. The analysis unit, for example, analyzes preference trends based on information about clothes the user has purchased in the past. The analysis unit can also suggest optimal clothes based on information about styles and colors the user has selected in the past. Furthermore, the analysis unit can analyze the user's past purchase history and suggest new clothes with similar trends. This can improve the accuracy of the analysis based on the user's past selection history and purchase history. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past selection history and purchase history into the generation AI, and the generation AI can improve the accuracy of the analysis based on that data.

[0039] The analysis unit can perform analysis taking into account the user's current lifestyle and event information. For example, if the user plans to attend a specific event, the analysis unit can suggest clothes suitable for the event. Furthermore, if the user is in a specific lifestyle situation (e.g., work, travel), the analysis unit can also suggest clothes suitable for that situation. Furthermore, if the user is in a specific season or weather, the analysis unit can also suggest clothes suitable for that situation. This enables more appropriate analysis by taking into account the user's current lifestyle and event information. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's lifestyle and event information into the generation AI, and the generation AI can perform analysis based on that data.

[0040] The analysis unit can perform analysis taking into account the user's geographical location information. For example, if the user is in a specific region, the analysis unit can suggest clothing suitable for the climate and culture of that region. Furthermore, if the user is traveling, the analysis unit can suggest clothing suitable for the climate and culture of the travel destination. Furthermore, if the user is attending a specific event, the analysis unit can suggest clothing suitable for the location of the event. This enables more appropriate analysis by taking the user's geographical location information into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI, and the generation AI can perform analysis based on that data.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's social media activity. For example, the analysis unit analyzes preference trends based on photos and posts shared by the user on social media. The analysis unit can also suggest related clothing based on information about brands and designers the user follows on social media. Furthermore, the analysis unit can also suggest related clothing based on information about events the user plans to attend on social media. This can improve the accuracy of the analysis based on the user's social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's social media activity data into the generation AI, and the generation AI can perform analysis based on that data.

[0042] When making a suggestion, the suggestion unit can make optimal suggestions by referring to the user's past selection history and purchase history. For example, the suggestion unit can analyze the user's preference trends based on information about clothes the user has purchased in the past and suggest optimal clothes. The suggestion unit can also suggest related clothes based on information about styles and colors the user has selected in the past. Furthermore, the suggestion unit can analyze the user's past purchase history and suggest new clothes with similar trends. This enables more appropriate suggestions to be made based on the user's past selection history and purchase history. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the user's past selection history and purchase history into the generation AI, and the generation AI can make optimal suggestions based on that data.

[0043] When making a suggestion, the suggestion unit can take into consideration the user's current lifestyle situation and event information. For example, if the user plans to attend a specific event, the suggestion unit can suggest clothes suitable for the event. Furthermore, if the user is in a specific lifestyle situation (e.g., work, travel), the suggestion unit can also suggest clothes suitable for that situation. Furthermore, if the user is in a specific season or weather, the suggestion unit can also suggest clothes suitable for that situation. This enables more appropriate suggestions by taking into consideration the user's current lifestyle situation and event information. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the user's lifestyle situation and event information into the generation AI, and the generation AI can make suggestions based on that data.

[0044] When making a suggestion, the suggestion unit can make optimal suggestions by taking into account the user's geographical location information. For example, if the user is in a specific region, the suggestion unit can suggest clothes suitable for the climate and culture of that region. Also, if the user is traveling, the suggestion unit can suggest clothes suitable for the climate and culture of the travel destination. Furthermore, if the user is attending a specific event, the suggestion unit can suggest clothes suitable for the location of the event. This enables more appropriate suggestions by taking the user's geographical location information into consideration. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the user's geographical location information into the generation AI, and the generation AI can make suggestions based on that data.

[0045] When making suggestions, the suggestion unit can improve the accuracy of the suggestions by referring to the user's social media activity. For example, the suggestion unit can analyze preferences based on photos and posts shared by the user on social media and suggest optimal clothing. The suggestion unit can also suggest related clothing based on information about brands and designers the user follows on social media. Furthermore, the suggestion unit can also suggest related clothing based on information about events the user plans to attend on social media. This can improve the accuracy of suggestions based on the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the user's social media activity data into the generation AI, and the generation AI can make suggestions based on that data.

[0046] When acquiring information, the information acquisition unit can acquire optimal information by referring to the user's past information acquisition history. For example, the information acquisition unit acquires current weather information based on weather information acquired by the user in the past. The information acquisition unit can also acquire current event information based on event information acquired by the user in the past. Furthermore, the information acquisition unit can analyze the user's past information acquisition history and acquire the most relevant information. This makes it possible to acquire optimal information based on the user's past information acquisition history. Some or all of the above-described processing in the information acquisition unit may be performed using AI, or may be performed without using AI. For example, the information acquisition unit can input the user's past information acquisition history into the generation AI, and the generation AI can acquire optimal information based on that data.

[0047] When acquiring information, the information acquisition unit can acquire information taking into account the user's current living situation and event information. For example, if the user plans to participate in a specific event, the information acquisition unit acquires information related to the event. Furthermore, if the user is in a specific living situation (e.g., work, travel), the information acquisition unit can also acquire information related to that situation. Furthermore, if the user is in a specific season or weather, the information acquisition unit can also acquire information related to that situation. This makes it possible to acquire more appropriate information by taking into account the user's current living situation and event information. Some or all of the above-mentioned processing in the information acquisition unit may be performed using AI, or may be performed without using AI. For example, the information acquisition unit can input the user's living situation and event information into the generation AI, and the generation AI can acquire information based on that data.

[0048] When acquiring information, the information acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the information acquisition unit can prioritize acquiring weather information for that area. Furthermore, when the user is traveling, the information acquisition unit can also prioritize acquiring event information for the travel destination. Furthermore, when the user is participating in a specific event, the information acquisition unit can also prioritize acquiring information related to the location of the event. In this way, by taking the user's geographical location information into consideration, highly relevant information can be acquired preferentially. Some or all of the above-described processing in the information acquisition unit may be performed using AI, or may be performed without using AI. For example, the information acquisition unit can input the user's geographical location information to the generation AI, and the generation AI can prioritize acquiring highly relevant information based on that data.

[0049] When acquiring information, the information acquisition unit can acquire related information by referring to the user's social media activity. The information acquisition unit can acquire related information based on, for example, event information shared by the user on social media. The information acquisition unit can also acquire related information based on information about brands and designers the user follows on social media. Furthermore, the information acquisition unit can also acquire related information based on event information the user plans to attend on social media. This makes it possible to acquire related information based on the user's social media activity. Some or all of the above-described processing in the information acquisition unit may be performed using AI, or may be performed without using AI. For example, the information acquisition unit can input the user's social media activity data into the generation AI, and the generation AI can acquire related information based on that data.

[0050] The emergency suggestion unit can make optimal suggestions by referring to the user's past emergency response history when making an emergency suggestion. For example, the emergency suggestion unit can suggest optimal clothing based on information about clothing the user selected in past emergencies. The emergency suggestion unit can also analyze the user's past emergency response history and suggest clothing suitable for similar situations. Furthermore, the emergency suggestion unit can suggest related clothing based on information about styles and colors the user used in past emergencies. This enables optimal suggestions based on the user's past emergency response history. Some or all of the above-described processing in the emergency suggestion unit may be performed using or without the generation AI. For example, the emergency suggestion unit can input the user's past emergency response history into the generation AI, and the generation AI can make optimal suggestions based on that data.

[0051] The emergency suggestion unit can make emergency suggestions by taking into account the user's current living situation and event information. For example, if the user is attending a sudden meeting, the emergency suggestion unit can suggest clothes suitable for the meeting. Furthermore, if the user is leaving on a sudden trip, the emergency suggestion unit can also suggest clothes suitable for the trip. Furthermore, if the user is attending a sudden event, the emergency suggestion unit can also suggest clothes suitable for the event. This enables more appropriate suggestions by taking into account the user's current living situation and event information. Some or all of the above-mentioned processing in the emergency suggestion unit may be performed using or without the generation AI. For example, the emergency suggestion unit can input the user's living situation and event information into the generation AI, and the generation AI can make suggestions based on that data.

[0052] The emergency suggestion unit can make optimal suggestions in an emergency by taking into account the user's geographical location information. For example, if the user is in a specific area, the emergency suggestion unit can suggest clothes suitable for the climate and culture of that area. Furthermore, if the user is traveling, the emergency suggestion unit can also suggest clothes suitable for the climate and culture of the travel destination. Furthermore, if the user is attending a specific event, the emergency suggestion unit can also suggest clothes suitable for the location of the event. This enables more appropriate suggestions by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the emergency suggestion unit may be performed using or without the generation AI. For example, the emergency suggestion unit can input the user's geographical location information into the generation AI, and the generation AI can make suggestions based on that data.

[0053] The emergency suggestion unit can improve the accuracy of suggestions by referring to the user's social media activity when making emergency suggestions. For example, the emergency suggestion unit can suggest related clothing based on event information shared by the user on social media. The emergency suggestion unit can also suggest related clothing based on information about brands and designers the user follows on social media. Furthermore, the emergency suggestion unit can also suggest related clothing based on event information the user plans to attend on social media. This can improve the accuracy of suggestions based on the user's social media activity. Some or all of the above-mentioned processing in the emergency suggestion unit may be performed using or without the generation AI. For example, the emergency suggestion unit can input the user's social media activity data into the generation AI, and the generation AI can make suggestions based on that data.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The clothing suggestion system can also acquire information about the user's body type and reflect it in the suggestions. For example, information such as the user's height, weight, and body fat percentage is acquired by the input unit, and this information is analyzed by the analysis unit. This makes it possible to suggest clothing sizes and styles that are best suited to the user's body type. In addition, if the user inputs changes in their body type, the suggestion unit can also suggest clothes that correspond to those changes. Furthermore, if the user desires clothes that fit a specific body type, it is also possible to make suggestions based on that desire. This makes it possible to suggest clothes that are best suited to the user's body type, resulting in more satisfying suggestions.

[0056] The input unit can input information about the user's favorite brands and designers. For example, if the user likes a particular brand or designer, the information is input into the input unit and analyzed by the analysis unit. This allows the suggestion unit to preferentially suggest clothes from the user's favorite brands and designers. Also, if the user wants to try a new brand or designer, suggestions can be made based on that preference. Furthermore, if the user wants information about new products from a particular brand or designer, the acquisition unit can acquire that information and reflect it in the suggestions. This makes it possible to make suggestions based on the user's favorite brands and designers.

[0057] The clothing recommendation system can also obtain the user's budget information and reflect it in the suggestions. For example, it can suggest the most suitable clothes within the budget range set by the user. The user's budget information is input through the input unit, and the analysis unit analyzes the information. This allows the suggestion unit to suggest clothes that fit the user's budget. Also, if the user sets a budget for a specific event or season, the suggestion unit can make suggestions based on that budget. Furthermore, if the user exceeds their budget, it can also suggest alternatives. This makes it possible to make suggestions that fit the user's budget.

[0058] The input unit can input lifestyle information about the user. For example, if the user has an active lifestyle, the information is input into the input unit and analyzed by the analysis unit. This allows the suggestion unit to suggest clothes that suit the user's lifestyle. Also, if the user has a particular sport or hobby, suggestions can be made based on that information. Furthermore, if the user inputs changes in their lifestyle, it is also possible to suggest clothes that correspond to those changes. This makes it possible to make suggestions that suit the user's lifestyle.

[0059] The analysis unit can perform analysis taking into account the user's health condition. For example, if the user has a specific health condition (e.g., allergies, skin sensitivity), the analysis unit can analyze that information and the suggestion unit can suggest appropriate clothing. Also, if the user inputs changes in their health condition, it can suggest clothing that matches those changes. Furthermore, if the user has a specific health goal (e.g., diet, strength training), it can also suggest clothing that matches that goal. This makes it possible to make suggestions based on the user's health condition.

[0060] The suggestion unit can make suggestions taking into account the user's travel plans. For example, if the user inputs a specific travel destination and period, suggestions can be made based on that information. The suggestion unit can suggest clothes that are suitable for the climate and culture of the travel destination. It can also suggest clothes that correspond to the activities (e.g., hiking, beach) that the user plans to do during the trip. It can also suggest items that the user will need during the trip (e.g., cold weather gear, swimsuit). This makes it possible to make suggestions that correspond to the user's travel plans.

[0061] The information acquisition unit can acquire related information by referring to the user's social media activity. For example, related information can be acquired based on event information shared by the user on social media. The information acquisition unit can also acquire related information based on information about brands and designers that the user follows on social media. Furthermore, the information acquisition unit can also acquire related information based on event information that the user plans to attend on social media. In this way, related information can be acquired based on the user's social media activity.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The input unit inputs the user's preferences and information about the clothes they own. The user's preferences include color, style, brand, etc., and the information about the clothes they own includes type, color, size, brand, etc. Step 2: The analysis unit uses the generation AI to analyze the information input by the input unit. The analysis is performed based on the type of algorithm and the target data for analysis. Step 3: The suggestion unit uses the generation AI to suggest clothes based on the information analyzed by the analysis unit. The suggestions are made based on the suggestion format and suggestion criteria. Step 4: The information acquisition unit acquires weather and event information, including weather forecast data and an event calendar. Step 5: The emergency suggestion unit uses generative AI to suggest clothing suitable for sudden schedule changes or emergencies. Emergencies include sudden schedule changes and emergencies.

[0064] (Example 2) A clothing recommendation system according to an embodiment of the present invention utilizes a generative AI to suggest clothing that takes individual preferences into account. In this clothing recommendation system, a user inputs information about their preferences and the clothing they own, and the generative AI analyzes that information to suggest individually optimized clothing. For example, a user can input their preferred colors and styles, as well as photos of the clothing they own. The generative AI then suggests optimal clothing based on the user's preferences and clothing information. Furthermore, because the generative AI considers multiple information when making suggestions, it can also take into account factors such as weather and the type of event. Furthermore, as a response to emergencies, it can also suggest clothing appropriate for sudden changes in plans or emergencies. For example, if a user inputs, "I like casual styles and have a lot of blue clothing," the generative AI would suggest casual clothing and items that match blue clothing. If a user inputs, "It's expected to rain tomorrow, and I'm attending a friend's wedding," the generative AI would suggest formal clothing suitable for rainy days. Furthermore, if a user inputs, "I have a sudden work meeting," the generative AI would suggest business casual clothing. In this way, users can respond to sudden changes in plans. This system makes it easier for users to choose clothes because it suggests the best clothes based on their preferences and the clothes they own.It can also take into account multiple information and respond to emergencies, making it suitable for a variety of situations.This allows the clothing recommendation system to suggest the best clothes based on the user's preferences and information about the clothes they own.

[0065] The clothing recommendation system according to the embodiment includes an input unit, an analysis unit, a suggestion unit, an information acquisition unit, and an emergency suggestion unit. The input unit inputs information about a user's preferences and clothing items. The user's preferences include, but are not limited to, for example, color, style, and brand. The information about the clothing items includes, but is not limited to, for example, type, color, size, and brand of clothing. The analysis unit uses a generation AI to analyze the information input by the input unit. The analysis is performed based on, for example, the type of algorithm and the data to be analyzed, but is not limited to, for example. The suggestion unit uses the generation AI to suggest clothing items based on the information analyzed by the analysis unit. The suggestion is performed based on, for example, the suggestion format and the suggestion criteria, but is not limited to, for example. The information acquisition unit acquires weather and event information. The weather and event information includes, but is not limited to, for example, weather forecast data and an event calendar. The emergency suggestion unit uses the generation AI to suggest clothing items suitable for sudden changes in plans or emergencies. Emergencies include, but are not limited to, for example, sudden changes in plans or emergencies. As a result, the clothing recommendation system according to the embodiment can recommend the most suitable clothing based on the user's preferences and information about the clothing the user owns.

[0066] The input unit allows the user to input colors, styles, and photos of clothes they own. For example, the input unit allows the user to input photos of their preferred colors, styles, and clothes they own. For example, the user inputs, "I like casual styles and I have a lot of blue clothes." This information is input to the generation AI. This allows detailed information about the user's preferences and clothes they own. Some or all of the above-mentioned processing in the input unit may be performed using, or without, AI. For example, the input unit can input the information entered by the user to the generation AI, and the generation AI can analyze the information.

[0067] The analysis unit can perform analysis based on the user's past selection history and purchase history. The analysis unit, for example, performs analysis taking into account the user's past selection history and purchase history. For example, the analysis unit analyzes preference trends based on information about clothes the user has purchased in the past. The analysis unit can also suggest optimal clothes based on information about styles and colors the user has selected in the past. Furthermore, the analysis unit can analyze the user's past purchase history and suggest new clothes with similar trends. This enables more accurate analysis based on the user's past selection history and purchase history. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past selection history and purchase history into a generation AI, which can then analyze the information.

[0068] The suggestion unit can suggest clothes based on the user's preferences and information about the clothes the user owns. The suggestion unit suggests optimal clothes based on, for example, the user's preferences and information about the clothes the user owns. For example, if the user prefers a casual style, the suggestion unit can suggest casual clothes. Also, if the user has a lot of blue clothes, the suggestion unit can also suggest items that match blue clothes. In this way, optimal clothes can be suggested based on the user's preferences and information about the clothes the user owns. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input information about the user's preferences and the clothes the user owns into the generation AI, and the generation AI can suggest optimal clothes based on that information.

[0069] The information acquisition unit can acquire weather and event information. The information acquisition unit acquires, for example, weather and event information. Weather and event information includes, but is not limited to, weather forecast data and an event calendar. By acquiring the weather and event information, more appropriate clothing can be suggested. Some or all of the above-described processing in the information acquisition unit may be performed using AI, or may be performed without using AI. For example, the information acquisition unit inputs weather forecast data and an event calendar into the generation AI, and the generation AI can suggest clothing based on that information.

[0070] The emergency suggestion unit can suggest clothing appropriate for sudden changes in plans or emergencies. The emergency suggestion unit, for example, suggests clothing appropriate for sudden changes in plans or emergencies. For example, if a user inputs, "I have a sudden work meeting," the emergency suggestion unit can suggest business casual clothing. Also, if a user inputs, "I have suddenly decided to attend a friend's wedding," the emergency suggestion unit can also suggest formal clothing. This makes it possible to suggest clothing appropriate for sudden changes in plans or emergencies. Some or all of the above-mentioned processing in the emergency suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the emergency suggestion unit can input user input information into a generation AI, and the generation AI can suggest clothing appropriate for emergencies based on that information.

[0071] The input unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated user emotions. For example, when the user is stressed, the input unit can provide a simple interface and minimize input steps. Furthermore, when the user is relaxed, the input unit can provide detailed input options and suggest customizable input methods. Furthermore, when the user is in a hurry, the input unit can prioritize voice input and enable the user to quickly enter information about their preferences and clothing. This allows for a more comfortable input experience by adjusting the display method of the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using AI, or may be performed without AI. For example, the input unit can input the user's emotion data into a generation AI, which can then adjust the display method of the input interface based on the data.

[0072] The input unit can analyze the user's past input history and suggest the optimal input method. For example, the input unit can automatically display as candidates the user's favorite colors and styles that they have frequently input in the past. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest the user's favorite colors and styles to use during a specific time period based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the input unit may be performed using AI, or may be performed without using AI. For example, the input unit can input the user's past input history into a generation AI, which can then suggest the optimal input method based on that data.

[0073] The input unit can filter input content based on the user's current mood and physical condition. For example, when the user is tired, the input unit provides simple, highly visible input options. Furthermore, when the user is in good spirits, the input unit can provide detailed input options and suggest customizable input methods. Furthermore, when the user is in a hurry, the input unit can prioritize voice input, allowing the user to quickly enter information about their preferences and clothing. This allows for more appropriate input by filtering the input content according to the user's mood and physical condition. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using AI, or may be performed without AI. For example, the input unit can input the user's mood and physical condition data into a generation AI, which can then filter the input content based on that data.

[0074] The input unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, if the user is feeling stressed, the input unit can prompt the user to input important information first. The input unit can also prompt the user to input detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the input unit can prompt the user to input the most important information first. This allows the user to prioritize input of important information by determining the priority of input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the input unit can be performed using AI, or can be performed without AI. For example, the input unit can input the user's emotion data into a generation AI, which can then prioritize input content based on the data.

[0075] The input unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the input unit can prioritize inputting information about clothing related to that area. Furthermore, when the user is traveling, the input unit can prioritize inputting information about clothing related to the travel destination. Furthermore, when the user is attending a specific event, the input unit can prioritize inputting information about clothing related to the event. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the input unit may be performed using AI, or may be performed without using AI. For example, the input unit can input the user's geographical location information to a generation AI, and the generation AI can prioritize inputting highly relevant information based on that data.

[0076] The input unit can analyze the user's social media activity and input related information at the time of input. For example, the input unit can input preferred colors and styles based on photos and posts the user has shared on social media. The input unit can also input related clothing information based on information about brands and designers the user follows on social media. Furthermore, the input unit can also input related clothing information based on information about events the user plans to attend on social media. This allows for more appropriate suggestions by inputting related information based on the user's social media activity. Some or all of the above-described processing in the input unit may be performed using AI, or may be performed without using AI. For example, the input unit can input the user's social media activity data to the generation AI, which can then input related information based on that data.

[0077] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and suggest the most appropriate outfit. If the user is in a hurry, the analysis unit can also perform a quick analysis and suggest outfits based on the most important information. If the user is feeling stressed, the analysis unit can perform a simple analysis and suggest outfits that will reduce stress. This allows for more appropriate analysis by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using the generation AI, or can be performed without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the analysis algorithm based on the data.

[0078] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past selection history and purchase history. The analysis unit, for example, analyzes preference trends based on information about clothes the user has purchased in the past. The analysis unit can also suggest optimal clothes based on information about styles and colors the user has selected in the past. Furthermore, the analysis unit can analyze the user's past purchase history and suggest new clothes with similar trends. This can improve the accuracy of the analysis based on the user's past selection history and purchase history. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past selection history and purchase history into the generation AI, and the generation AI can improve the accuracy of the analysis based on that data.

[0079] The analysis unit can perform analysis taking into account the user's current lifestyle and event information. For example, if the user plans to attend a specific event, the analysis unit can suggest clothes suitable for the event. Furthermore, if the user is in a specific lifestyle situation (e.g., work, travel), the analysis unit can also suggest clothes suitable for that situation. Furthermore, if the user is in a specific season or weather, the analysis unit can also suggest clothes suitable for that situation. This enables more appropriate analysis by taking into account the user's current lifestyle and event information. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's lifestyle and event information into the generation AI, and the generation AI can perform analysis based on that data.

[0080] 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 relaxed, the analysis unit can display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can display concise analysis results that focus on the main points. Furthermore, if the user is feeling stressed, the analysis unit can provide a display method that visually reduces stress. This allows for more appropriate display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using the generation AI, or can be performed without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, and the generation AI can adjust the display method of the analysis results based on the data.

[0081] The analysis unit can perform analysis taking into account the user's geographical location information. For example, if the user is in a specific region, the analysis unit can suggest clothing suitable for the climate and culture of that region. Furthermore, if the user is traveling, the analysis unit can suggest clothing suitable for the climate and culture of the travel destination. Furthermore, if the user is attending a specific event, the analysis unit can suggest clothing suitable for the location of the event. This enables more appropriate analysis by taking the user's geographical location information into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI, and the generation AI can perform analysis based on that data.

[0082] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's social media activity. For example, the analysis unit analyzes preference trends based on photos and posts shared by the user on social media. The analysis unit can also suggest related clothing based on information about brands and designers the user follows on social media. Furthermore, the analysis unit can also suggest related clothing based on information about events the user plans to attend on social media. This can improve the accuracy of the analysis based on the user's social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's social media activity data into the generation AI, and the generation AI can perform analysis based on that data.

[0083] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can also provide concise suggestions that focus on the main points. Furthermore, if the user is feeling stressed, the suggestion unit can also provide suggestions that visually reduce stress. This enables more appropriate suggestions by adjusting the way suggestions are expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the suggestion unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI, and the generation AI can adjust the way suggestions are expressed based on the data.

[0084] When making a suggestion, the suggestion unit can make optimal suggestions by referring to the user's past selection history and purchase history. For example, the suggestion unit can analyze the user's preference trends based on information about clothes the user has purchased in the past and suggest optimal clothes. The suggestion unit can also suggest related clothes based on information about styles and colors the user has selected in the past. Furthermore, the suggestion unit can analyze the user's past purchase history and suggest new clothes with similar trends. This enables more appropriate suggestions to be made based on the user's past selection history and purchase history. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the user's past selection history and purchase history into the generation AI, and the generation AI can make optimal suggestions based on that data.

[0085] When making a suggestion, the suggestion unit can take into consideration the user's current lifestyle situation and event information. For example, if the user plans to attend a specific event, the suggestion unit can suggest clothes suitable for the event. Furthermore, if the user is in a specific lifestyle situation (e.g., work, travel), the suggestion unit can also suggest clothes suitable for that situation. Furthermore, if the user is in a specific season or weather, the suggestion unit can also suggest clothes suitable for that situation. This enables more appropriate suggestions by taking into consideration the user's current lifestyle situation and event information. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the user's lifestyle situation and event information into the generation AI, and the generation AI can make suggestions based on that data.

[0086] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can prioritize detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can prioritize the most important suggestions. Furthermore, if the user is feeling stressed, the suggestion unit can prioritize suggestions to reduce stress. Thus, by prioritizing suggestions based on the user's emotions, important suggestions can be prioritized. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using the generation AI, or can be performed without the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI, which can then prioritize suggestions based on the data.

[0087] When making a suggestion, the suggestion unit can make optimal suggestions by taking into account the user's geographical location information. For example, if the user is in a specific region, the suggestion unit can suggest clothes suitable for the climate and culture of that region. Also, if the user is traveling, the suggestion unit can suggest clothes suitable for the climate and culture of the travel destination. Furthermore, if the user is attending a specific event, the suggestion unit can suggest clothes suitable for the location of the event. This enables more appropriate suggestions by taking the user's geographical location information into consideration. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the user's geographical location information into the generation AI, and the generation AI can make suggestions based on that data.

[0088] When making suggestions, the suggestion unit can improve the accuracy of the suggestions by referring to the user's social media activity. For example, the suggestion unit can analyze preferences based on photos and posts shared by the user on social media and suggest optimal clothing. The suggestion unit can also suggest related clothing based on information about brands and designers the user follows on social media. Furthermore, the suggestion unit can also suggest related clothing based on information about events the user plans to attend on social media. This can improve the accuracy of suggestions based on the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the user's social media activity data into the generation AI, and the generation AI can make suggestions based on that data.

[0089] The information acquisition unit can estimate the user's emotions and adjust the timing of information acquisition based on the estimated user's emotions. For example, when the user is relaxed, the information acquisition unit can adjust the timing of acquiring detailed information. Furthermore, when the user is in a hurry, the information acquisition unit can quickly acquire necessary information. Furthermore, when the user is feeling stressed, the information acquisition unit can prioritize acquiring information that will reduce stress. This allows more appropriate information to be acquired by adjusting the timing of information acquisition according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the information acquisition unit may be performed using AI or without AI. For example, the information acquisition unit can input user's emotion data into the generation AI, and the generation AI can adjust the timing of information acquisition based on the data.

[0090] When acquiring information, the information acquisition unit can acquire optimal information by referring to the user's past information acquisition history. For example, the information acquisition unit acquires current weather information based on weather information acquired by the user in the past. The information acquisition unit can also acquire current event information based on event information acquired by the user in the past. Furthermore, the information acquisition unit can analyze the user's past information acquisition history and acquire the most relevant information. This makes it possible to acquire optimal information based on the user's past information acquisition history. Some or all of the above-described processing in the information acquisition unit may be performed using AI, or may be performed without using AI. For example, the information acquisition unit can input the user's past information acquisition history into the generation AI, and the generation AI can acquire optimal information based on that data.

[0091] When acquiring information, the information acquisition unit can acquire information taking into account the user's current living situation and event information. For example, if the user plans to participate in a specific event, the information acquisition unit acquires information related to the event. Furthermore, if the user is in a specific living situation (e.g., work, travel), the information acquisition unit can also acquire information related to that situation. Furthermore, if the user is in a specific season or weather, the information acquisition unit can also acquire information related to that situation. This makes it possible to acquire more appropriate information by taking into account the user's current living situation and event information. Some or all of the above-mentioned processing in the information acquisition unit may be performed using AI, or may be performed without using AI. For example, the information acquisition unit can input the user's living situation and event information into the generation AI, and the generation AI can acquire information based on that data.

[0092] The information acquisition unit can estimate the user's emotions and determine the priority of information to be acquired based on the estimated user's emotions. For example, when the user is relaxed, the information acquisition unit can prioritize acquiring detailed information. Furthermore, when the user is in a hurry, the information acquisition unit can prioritize acquiring the most important information. Furthermore, when the user is feeling stressed, the information acquisition unit can prioritize acquiring information that will reduce stress. Thus, by determining the priority of information to be acquired according to the user's emotions, important information can be acquired preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information acquisition unit may be performed using AI, or may be performed without AI. For example, the information acquisition unit can input the user's emotion data into the generation AI and determine the priority of information to be acquired by the generation AI based on the data.

[0093] When acquiring information, the information acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the information acquisition unit can prioritize acquiring weather information for that area. Furthermore, when the user is traveling, the information acquisition unit can also prioritize acquiring event information for the travel destination. Furthermore, when the user is participating in a specific event, the information acquisition unit can also prioritize acquiring information related to the location of the event. In this way, by taking the user's geographical location information into consideration, highly relevant information can be acquired preferentially. Some or all of the above-described processing in the information acquisition unit may be performed using AI, or may be performed without using AI. For example, the information acquisition unit can input the user's geographical location information to the generation AI, and the generation AI can prioritize acquiring highly relevant information based on that data.

[0094] When acquiring information, the information acquisition unit can acquire related information by referring to the user's social media activity. The information acquisition unit can acquire related information based on, for example, event information shared by the user on social media. The information acquisition unit can also acquire related information based on information about brands and designers the user follows on social media. Furthermore, the information acquisition unit can also acquire related information based on event information the user plans to attend on social media. This makes it possible to acquire related information based on the user's social media activity. Some or all of the above-described processing in the information acquisition unit may be performed using AI, or may be performed without using AI. For example, the information acquisition unit can input the user's social media activity data into the generation AI, and the generation AI can acquire related information based on that data.

[0095] The emergency suggestion unit can estimate the user's emotions and adjust the emergency suggestion method based on the estimated user's emotions. For example, if the user is nervous, the emergency suggestion unit can provide a calm suggestion method. Furthermore, if the user is relaxed, the emergency suggestion unit can also provide a detailed suggestion method. Furthermore, if the user is in a hurry, the emergency suggestion unit can also provide a quick and concise suggestion method. This enables more appropriate suggestions by adjusting the emergency suggestion method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the emergency suggestion unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the emergency suggestion unit can input the user's emotion data into the generation AI, and the generation AI can adjust the emergency suggestion method based on the data.

[0096] The emergency suggestion unit can make optimal suggestions by referring to the user's past emergency response history when making an emergency suggestion. For example, the emergency suggestion unit can suggest optimal clothing based on information about clothing the user selected in past emergencies. The emergency suggestion unit can also analyze the user's past emergency response history and suggest clothing suitable for similar situations. Furthermore, the emergency suggestion unit can suggest related clothing based on information about styles and colors the user used in past emergencies. This enables optimal suggestions based on the user's past emergency response history. Some or all of the above-described processing in the emergency suggestion unit may be performed using or without the generation AI. For example, the emergency suggestion unit can input the user's past emergency response history into the generation AI, and the generation AI can make optimal suggestions based on that data.

[0097] The emergency suggestion unit can make emergency suggestions by taking into account the user's current living situation and event information. For example, if the user is attending a sudden meeting, the emergency suggestion unit can suggest clothes suitable for the meeting. Furthermore, if the user is leaving on a sudden trip, the emergency suggestion unit can also suggest clothes suitable for the trip. Furthermore, if the user is attending a sudden event, the emergency suggestion unit can also suggest clothes suitable for the event. This enables more appropriate suggestions by taking into account the user's current living situation and event information. Some or all of the above-mentioned processing in the emergency suggestion unit may be performed using or without the generation AI. For example, the emergency suggestion unit can input the user's living situation and event information into the generation AI, and the generation AI can make suggestions based on that data.

[0098] The emergency suggestion unit can estimate the user's emotions and prioritize emergency suggestions based on the estimated user emotions. For example, if the user is nervous, the emergency suggestion unit can prioritize the most important suggestions. Furthermore, if the user is relaxed, the emergency suggestion unit can prioritize detailed suggestions. Furthermore, if the user is in a hurry, the emergency suggestion unit can prioritize quick and concise suggestions. Thus, by prioritizing emergency suggestions according to the user's emotions, important suggestions can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the emergency suggestion unit can be performed using the generation AI, or can be performed without the generation AI. For example, the emergency suggestion unit can input the user's emotion data into the generation AI, which can then prioritize emergency suggestions based on the data.

[0099] The emergency suggestion unit can make optimal suggestions in an emergency by taking into account the user's geographical location information. For example, if the user is in a specific area, the emergency suggestion unit can suggest clothes suitable for the climate and culture of that area. Furthermore, if the user is traveling, the emergency suggestion unit can also suggest clothes suitable for the climate and culture of the travel destination. Furthermore, if the user is attending a specific event, the emergency suggestion unit can also suggest clothes suitable for the location of the event. This enables more appropriate suggestions by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the emergency suggestion unit may be performed using or without the generation AI. For example, the emergency suggestion unit can input the user's geographical location information into the generation AI, and the generation AI can make suggestions based on that data.

[0100] The emergency suggestion unit can improve the accuracy of suggestions by referring to the user's social media activity when making emergency suggestions. For example, the emergency suggestion unit can suggest related clothing based on event information shared by the user on social media. The emergency suggestion unit can also suggest related clothing based on information about brands and designers the user follows on social media. Furthermore, the emergency suggestion unit can also suggest related clothing based on event information the user plans to attend on social media. This can improve the accuracy of suggestions based on the user's social media activity. Some or all of the above-mentioned processing in the emergency suggestion unit may be performed using or without the generation AI. For example, the emergency suggestion unit can input the user's social media activity data into the generation AI, and the generation AI can make suggestions based on that data. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, analysis unit, suggestion unit, information acquisition unit, and emergency suggestion unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14 and inputs information about the user's preferences and clothing. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests clothing based on the analyzed information. The information acquisition unit, for example, acquires weather and event information via the communication I / F 44 of the smart device 14. The emergency suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests clothing appropriate for sudden schedule changes or emergencies. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, analysis unit, suggestion unit, information acquisition unit, and emergency suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the smart glasses 214 and inputs information about the user's preferences and clothing. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests clothing based on the analyzed information. The information acquisition unit, for example, acquires weather and event information via the communication I / F 44 of the smart glasses 214. The emergency suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests clothing appropriate for sudden schedule changes or emergencies. === Hard Collateral 1-3 === Each of the multiple elements including the above-described input unit, analysis unit, suggestion unit, information acquisition unit, and emergency suggestion unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset-type terminal 314 and inputs information about the user's preferences and clothing. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests clothing based on the analyzed information. The information acquisition unit, for example, acquires weather and event information via the communication I / F 44 of the headset-type terminal 314. The emergency suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests clothing appropriate for sudden schedule changes or emergencies. === Hard Collateral 1-4 === Each of the multiple elements including the input unit, analysis unit, suggestion unit, information acquisition unit, and emergency suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414 and inputs information about the user's preferences and clothing. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generative AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests clothing based on the analyzed information. The information acquisition unit, for example, acquires weather and event information via the communication I / F 44 of the robot 414. The emergency suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests clothing appropriate for sudden schedule changes or emergencies.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The clothing suggestion system can also acquire information about the user's body type and reflect it in the suggestions. For example, information such as the user's height, weight, and body fat percentage is acquired by the input unit, and this information is analyzed by the analysis unit. This makes it possible to suggest clothing sizes and styles that are best suited to the user's body type. In addition, if the user inputs changes in their body type, the suggestion unit can also suggest clothes that correspond to those changes. Furthermore, if the user desires clothes that fit a specific body type, it is also possible to make suggestions based on that desire. This makes it possible to suggest clothes that are best suited to the user's body type, resulting in more satisfying suggestions.

[0103] The input unit can input information about the user's favorite brands and designers. For example, if the user likes a particular brand or designer, the information is input into the input unit and analyzed by the analysis unit. This allows the suggestion unit to preferentially suggest clothes from the user's favorite brands and designers. Also, if the user wants to try a new brand or designer, suggestions can be made based on that preference. Furthermore, if the user wants information about new products from a particular brand or designer, the acquisition unit can acquire that information and reflect it in the suggestions. This makes it possible to make suggestions based on the user's favorite brands and designers.

[0104] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis can be performed to suggest the most suitable clothes. If the user is in a hurry, a quick analysis can be performed to suggest clothes based on the most important information. Furthermore, if the user is feeling stressed, a simple analysis can be performed to suggest clothes that will reduce stress. This allows for more appropriate analysis by adjusting the analysis algorithm according to the user's emotions.

[0105] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is relaxed, detailed suggestions can be made. If the user is in a hurry, concise suggestions that focus on the main points can be made. Furthermore, if the user is feeling stressed, suggestions that visually reduce stress can be made. In this way, more appropriate suggestions can be made by adjusting the way suggestions are expressed according to the user's emotions.

[0106] The information acquisition unit can estimate the user's emotions and adjust the timing of information acquisition based on the estimated user emotions. For example, if the user is relaxed, the timing of acquiring detailed information can be adjusted. Also, if the user is in a hurry, the necessary information can be acquired quickly. Furthermore, if the user is feeling stressed, information that will reduce stress can be acquired preferentially. In this way, by adjusting the timing of information acquisition according to the user's emotions, more appropriate information can be acquired.

[0107] The clothing recommendation system can also obtain the user's budget information and reflect it in the suggestions. For example, it can suggest the most suitable clothes within the budget range set by the user. The user's budget information is input through the input unit, and the analysis unit analyzes the information. This allows the suggestion unit to suggest clothes that fit the user's budget. Also, if the user sets a budget for a specific event or season, the suggestion unit can make suggestions based on that budget. Furthermore, if the user exceeds their budget, it can also suggest alternatives. This makes it possible to make suggestions that fit the user's budget.

[0108] The input unit can input lifestyle information about the user. For example, if the user has an active lifestyle, the information is input into the input unit and analyzed by the analysis unit. This allows the suggestion unit to suggest clothes that suit the user's lifestyle. Also, if the user has a particular sport or hobby, suggestions can be made based on that information. Furthermore, if the user inputs changes in their lifestyle, it is also possible to suggest clothes that correspond to those changes. This makes it possible to make suggestions that suit the user's lifestyle.

[0109] The analysis unit can perform analysis taking into account the user's health condition. For example, if the user has a specific health condition (e.g., allergies, skin sensitivity), the analysis unit can analyze that information and the suggestion unit can suggest appropriate clothing. Also, if the user inputs changes in their health condition, it can suggest clothing that matches those changes. Furthermore, if the user has a specific health goal (e.g., diet, strength training), it can also suggest clothing that matches that goal. This makes it possible to make suggestions based on the user's health condition.

[0110] The suggestion unit can make suggestions taking into account the user's travel plans. For example, if the user inputs a specific travel destination and period, suggestions can be made based on that information. The suggestion unit can suggest clothes that are suitable for the climate and culture of the travel destination. It can also suggest clothes that correspond to the activities (e.g., hiking, beach) that the user plans to do during the trip. It can also suggest items that the user will need during the trip (e.g., cold weather gear, swimsuit). This makes it possible to make suggestions that correspond to the user's travel plans.

[0111] The information acquisition unit can acquire related information by referring to the user's social media activity. For example, related information can be acquired based on event information shared by the user on social media. The information acquisition unit can also acquire related information based on information about brands and designers that the user follows on social media. Furthermore, the information acquisition unit can also acquire related information based on event information that the user plans to attend on social media. In this way, related information can be acquired based on the user's social media activity.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The input unit inputs the user's preferences and information about the clothes they own. The user's preferences include color, style, brand, etc., and the information about the clothes they own includes type, color, size, brand, etc. Step 2: The analysis unit uses the generation AI to analyze the information input by the input unit. The analysis is performed based on the type of algorithm and the target data for analysis. Step 3: The suggestion unit uses the generation AI to suggest clothes based on the information analyzed by the analysis unit. The suggestions are made based on the suggestion format and suggestion criteria. Step 4: The information acquisition unit acquires weather and event information, including weather forecast data and an event calendar. Step 5: The emergency suggestion unit uses generative AI to suggest clothing suitable for sudden schedule changes or emergencies. Emergencies include sudden schedule changes and emergencies.

[0114] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a 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.

[0152] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] 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.

[0177] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0178] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0179] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0183] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input unit for inputting information about the user's preferences and clothing; an analysis unit that analyzes the information input by the input unit; a suggestion unit that suggests clothes based on the information analyzed by the analysis unit; an information acquisition unit that acquires weather and event information; An emergency suggestion section that suggests clothes to wear in the event of a change in plans or an emergency. A system characterized by:

2. The input unit Enter the user's color, style, and photos of the clothes they own 2. The system of claim 1.

3. The analysis unit Conduct analysis based on the user's past selection and purchase history 2. The system of claim 1.

4. The proposal unit Suggesting clothes based on the user's preferences and clothing information 2. The system of claim 1.

5. The information acquisition unit Get weather and event information 2. The system of claim 1.

6. The emergency proposal unit Suggest clothing for changes in plans or emergencies 2. The system of claim 1.

7. The input unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The input unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

9. The input unit As you type, filter your input based on your current mood or state of health 2. The system of claim 1.

10. The input unit Estimate the user's emotions and prioritize input content based on the estimated user emotions.

2. The system of claim 1.

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