System

Abstract

An object of a system according to an embodiment is to enable a user to easily and optimally select daily clothes.SOLUTION: A system includes an interaction part, a history management part, a weather consideration part, a trend reflection part, and a learning part. The interaction unit proposes clothes while interacting with the user using the interactive AI. The history management unit manages a clothing history of the user. The weather consideration unit proposes clothes based on weather and temperature. The trend reflection unit reflects the trend and proposes clothes. The learning unit learns preferences and tendencies of the user and makes a proposal.SELECTED DRAWING: Figure 1

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 techniques have had the problem that users tend to be confused when choosing their daily outfits, making it difficult for them to choose the most suitable outfit.

[0005] The system according to the embodiment aims to enable a user to easily and optimally select daily outfits. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, a history management unit, a weather consideration unit, a trend reflection unit, and a learning unit. The dialogue unit suggests outfits while interacting with the user using interactive AI. The history management unit manages the user's outfit history. The weather consideration unit suggests outfits based on weather and temperature. The trend reflection unit suggests outfits by reflecting trends. The learning unit learns the user's preferences and tendencies and makes suggestions. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to easily and optimally select daily outfits. [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) The clothing selection support system according to the embodiment of the present invention is a system that uses an interactive AI to comprehensively support a user in selecting clothing. As a result, the clothing selection support system can comprehensively support a user in selecting clothing.

[0029] An outfit selection support system according to an embodiment includes a dialogue unit, a history management unit, a weather consideration unit, a trend reflection unit, and a learning unit. The dialogue unit suggests outfits while interacting with the user using interactive AI. For example, the dialogue unit grasps the user's needs by asking the user questions such as, "How are you feeling today?" and "Where are you planning to go today?" The dialogue unit can also suggest outfits by analyzing the user's tone and speed of voice to estimate the user's mood and physical condition for that day. The history management unit manages the user's outfit history. For example, the history management unit records when and what outfit the user wore to prevent the user from wearing the same outfit repeatedly. The history management unit can also analyze the user's past dialogue history, learn preferences for specific events and seasons, and reflect this in the suggestions. The weather consideration unit suggests outfits based on the weather and temperature. For example, the weather consideration unit considers the weather and temperature of the day and suggests weather-appropriate outfits, such as, "It's forecast to rain today, so I recommend a waterproof jacket." The weather consideration unit can also acquire weather and temperature data in real time and suggest clothing that can adapt to sudden weather changes. The trend reflection unit can suggest clothing that reflects trends. For example, the trend reflection unit can consider recent fashion trends and suggest outfits that reflect the latest trends, such as, "This season's trend color is green, so I recommend this shirt." The trend reflection unit can also collect the latest trend information from social media and fashion blogs and reflect it in suggestions in real time. The learning unit can learn the user's preferences and tendencies and make suggestions. For example, the learning unit can analyze the user's past selection data in detail, learn preferences for specific styles and colors, and reflect this in suggestions. The learning unit can also use an emotion estimation function to grasp the user's emotional state in real time and suggest clothing that evokes positive emotions. This allows the clothing selection support system according to the embodiment to comprehensively support the user's clothing selection. For example, it can suggest clothing that matches the user's mood and physical condition, thereby increasing user satisfaction.Also, choosing clothes appropriate for the weather and temperature will keep you comfortable, and incorporating the latest trends will give you a stylish impression. Furthermore, by learning the user's preferences and tendencies, more personalized suggestions will be possible, increasing user satisfaction.

[0030] The history management unit can analyze the user's past interaction history, learn preferences for specific events and seasons, and reflect the learned preferences in suggestions. For example, the history management unit analyzes the user's past interaction history to learn preferences for specific events and seasons. For example, it may suggest this year's Christmas outfit based on the outfit chosen for past Christmas seasons. The history management unit also extracts from the interaction history what kind of outfits the user prefers for specific events and seasons, and makes suggestions based on that information. For example, it may suggest outfits for this year's summer event based on the outfit chosen for past summer events. The history management unit also analyzes the user's interaction history to create patterns of preferences for specific events and seasons. For example, it may learn the colors and styles the user prefers in autumn from past data, and reflect these in suggestions. This makes it possible to make suggestions based on the user's past preferences.

[0031] The weather consideration unit acquires weather and temperature data in real time and can suggest clothing that can be adapted to sudden weather changes. The weather consideration unit, for example, acquires weather and temperature data in real time and suggests clothing that can be adapted to sudden weather changes. For example, a waterproof jacket is suggested in preparation for sudden rain. The weather consideration unit also acquires weather and temperature data in real time and suggests clothing that can be adapted to sudden weather changes. For example, if a sudden drop in temperature is forecast, warm clothing is suggested. The weather consideration unit also acquires weather and temperature data in real time and suggests clothing that can be adapted to sudden weather changes. For example, a windproof jacket is suggested on a windy day. This makes it possible to suggest clothing that can be adapted to sudden weather changes.

[0032] The trend reflection unit can collect the latest trend information from social media and fashion blogs and reflect it in proposals in real time. The trend reflection unit, for example, collects the latest trend information from social media and fashion blogs and reflects it in proposals in real time. For example, it identifies trends based on popular posts on Instagram and reflects it in proposals. The trend reflection unit also collects the latest trend information from social media and fashion blogs and reflects it in proposals in real time. For example, it identifies trends based on posts by fashion influencers and reflects it in proposals. The trend reflection unit also collects the latest trend information from social media and fashion blogs and reflects it in proposals in real time. For example, it identifies the latest fashion information based on trends on Twitter (registered trademark) and reflects it in proposals. This makes it possible to make proposals that reflect the latest trend information in real time.

[0033] The learning unit can analyze the user's past selection data in detail, learn preferences for specific styles and colors, and reflect these in the suggestions. The learning unit, for example, analyzes the user's past selection data in detail, learns preferences for specific styles and colors, and reflects these in the suggestions. For example, suggestions are made based on the colors and styles of clothing the user has selected in the past. The learning unit also analyzes the past selection data to learn the user's preferences. For example, suggestions are made based on clothes from brands and designers the user has selected in the past. The learning unit also analyzes the user's past selection data in detail, learns preferences for specific styles and colors, and reflects these in the suggestions. For example, suggestions are made based on seasonal clothing the user has selected in the past. This makes it possible to make suggestions based on the user's preferences.

[0034] The dialogue unit is able to grasp the user's hobbies and interests and propose themed outfits based on them. For example, the dialogue unit grasps the user's hobbies and interests through dialogue and proposes themed outfits based on them. For example, if the user is going to a music festival, it will propose outfits suitable for the festival. The dialogue unit also uses an interactive AI to learn the user's hobbies and interests and propose themed outfits based on them. For example, if the user likes outdoor activities, it will propose outfits suitable for outdoor activities. The dialogue unit also grasps the user's hobbies and interests through dialogue and proposes themed outfits based on them. For example, if the user likes watching movies, it will propose outfits suitable for going to the movie theater. This makes it possible to propose outfits based on the user's hobbies and interests.

[0035] The dialogue unit can suggest outfits that incorporate the opinions of the user's friends and family. The dialogue unit, for example, adds a function in which the dialogue AI suggests outfits that incorporate the opinions of the user's friends and family. For example, suggestions are made based on clothing recommended by friends. The dialogue unit also adds a function in which the dialogue AI collects the opinions of the user's friends and family and suggests outfits based on those opinions. For example, suggestions are made based on styles preferred by family members. The dialogue unit also adds a function in which the dialogue AI suggests outfits that incorporate the opinions of the user's friends and family. For example, suggestions are made based on colors and styles recommended by friends. This makes it possible to suggest outfits that reflect the opinions of the user's friends and family.

[0036] The history management unit can analyze the frequency of use and combination patterns of specific clothing based on the clothing history and suggest the optimal rotation. The history management unit, for example, analyzes the clothing history and extracts the frequency of use and combination patterns of specific clothing. For example, it analyzes how many times a specific shirt is worn per week and suggests the optimal rotation. The history management unit also analyzes the frequency of use and combination patterns of specific clothing based on the clothing history and suggests the optimal rotation. For example, it suggests not wearing the same clothing consecutively. The history management unit also analyzes the clothing history and extracts the frequency of use and combination patterns of specific clothing and suggests the optimal rotation. For example, if a specific piece of clothing is worn frequently on a specific day of the week, it will suggest a different piece of clothing for that day. In this way, it is possible to analyze the frequency of use and combination patterns of specific clothing and suggest the optimal rotation.

[0037] The history management unit can extract trends in clothing suitable for specific events and seasons based on the clothing history and reflect them in the next proposal. The history management unit, for example, analyzes the clothing history and extracts trends in clothing suitable for specific events and seasons. For example, it learns from past data what clothing the user prefers in summer and reflects this in the next proposal. The history management unit also extracts trends in clothing suitable for specific events and seasons based on the clothing history and reflects this in the next proposal. For example, it learns from past data what colors and styles the user prefers in winter and reflects this in the proposal. The history management unit also analyzes the clothing history and extracts trends in clothing suitable for specific events and seasons and reflects this in the next proposal. For example, it learns from past data what clothing the user prefers for a specific event and reflects this in the proposal. This makes it possible to extract trends in clothing suitable for specific events and seasons and reflect this in the next proposal.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The clothing selection support system can further include a health management unit that monitors the user's health condition. For example, the health management unit can measure the user's heart rate and body temperature with a sensor and suggest clothing appropriate to the user's health condition. If the user's heart rate is high, the health management unit can suggest relaxing clothing, and if the user's body temperature is high, it can suggest cool clothing. The health management unit can also analyze the user's sleep data and suggest comfortable clothing if the user is sleep-deprived. This makes it possible to suggest optimal clothing appropriate to the user's health condition.

[0040] The clothing selection support system can further include an activity amount management unit that monitors the user's activity level. For example, the activity amount management unit tracks the user's number of steps and amount of exercise, and suggests clothing according to the amount of activity. If the amount of exercise is high, it can suggest clothing that is easy to move in, and if the number of steps is low, it can suggest clothing that is relaxing. The activity amount management unit can also learn the user's activity patterns and suggest clothing that is suitable for daily activities. This makes it possible to suggest optimal clothing according to the user's activity level.

[0041] The clothing selection support system can further include a diet management unit that manages the user's dietary data. For example, the diet management unit records the user's dietary details and suggests clothing that matches the dietary details. If the user has eaten a high-calorie meal, it can suggest clothing that is easy to move in, and if the user has eaten a light meal, it can suggest clothing that is relaxing. The diet management unit can also learn the user's eating patterns and suggest clothing that is appropriate for the dietary details. This makes it possible to suggest optimal clothing that matches the user's dietary details.

[0042] The clothing selection support system can further include a travel management unit that manages the user's travel plans. For example, the travel management unit can consider the climate and culture of the user's travel destination and suggest appropriate clothing. For example, it can suggest warm clothing if the travel destination is a cold region, and cool clothing if the travel destination is a tropical region. The travel management unit can also manage the user's travel schedule and suggest clothing suitable for activities during the trip. This makes it possible to suggest optimal clothing according to the user's travel plans.

[0043] The clothing selection support system can further include a hobby reflection unit that reflects the user's hobbies and interests. For example, the hobby reflection unit can suggest themed outfits based on the user's hobbies and interests. If the user likes sports, it can suggest sportswear, and if the user is interested in art, it can suggest outfits suitable for art events. The hobby reflection unit can also learn the user's hobbies and interests and suggest outfits based on them. This makes it possible to suggest the most suitable outfits according to the user's hobbies and interests.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The dialogue unit uses conversational AI to suggest outfits while interacting with the user. For example, the dialogue unit grasps the user's needs by asking questions such as "How are you feeling today?" or "Where are you planning to go today?" The dialogue unit can also analyze the tone and speed of the user's voice to estimate their mood and physical condition for the day and suggest outfits accordingly. Step 2: The history management unit manages the user's clothing history. For example, the history management unit records when and what clothes the user wore to prevent the user from wearing the same outfit repeatedly. The history management unit can also analyze the user's past interaction history, learn preferences for specific events or seasons, and reflect these in the suggestions. Step 3: The weather consideration unit suggests clothing based on the weather and temperature. For example, the weather consideration unit considers the weather and temperature of the day and suggests clothing appropriate for the weather, such as "It's forecast to rain today, so I recommend a waterproof jacket." The weather consideration unit can also obtain weather and temperature data in real time and suggest clothing that can respond to sudden changes in weather. Step 4: The trend reflection unit suggests outfits that reflect the latest trends. For example, the trend reflection unit takes into account recent fashion trends and suggests outfits that reflect the latest trends, such as, "This season's trend color is green, so I recommend this shirt." The trend reflection unit can also collect the latest trend information from social media and fashion blogs and reflect it in its suggestions in real time. Step 5: The learning module learns the user's preferences and trends and makes suggestions based on that. For example, the learning module can analyze the user's past selection data in detail to learn preferences for specific styles and colors and reflect these in its suggestions. The learning module can also use emotion estimation to grasp the user's emotional state in real time and suggest clothing that evokes positive emotions.

[0046] (Example 2) The clothing selection support system according to the embodiment of the present invention is a system that uses an interactive AI to comprehensively support a user in selecting clothing. As a result, the clothing selection support system can comprehensively support a user in selecting clothing.

[0047] An outfit selection support system according to an embodiment includes a dialogue unit, a history management unit, a weather consideration unit, a trend reflection unit, and a learning unit. The dialogue unit suggests outfits while interacting with the user using interactive AI. For example, the dialogue unit grasps the user's needs by asking the user questions such as, "How are you feeling today?" and "Where are you planning to go today?" The dialogue unit can also suggest outfits by analyzing the user's tone and speed of voice to estimate the user's mood and physical condition for that day. The history management unit manages the user's outfit history. For example, the history management unit records when and what outfit the user wore to prevent the user from wearing the same outfit repeatedly. The history management unit can also analyze the user's past dialogue history, learn preferences for specific events and seasons, and reflect this in the suggestions. The weather consideration unit suggests outfits based on the weather and temperature. For example, the weather consideration unit considers the weather and temperature of the day and suggests weather-appropriate outfits, such as, "It's forecast to rain today, so I recommend a waterproof jacket." The weather consideration unit can also acquire weather and temperature data in real time and suggest clothing that can adapt to sudden weather changes. The trend reflection unit can suggest clothing that reflects trends. For example, the trend reflection unit can consider recent fashion trends and suggest outfits that reflect the latest trends, such as, "This season's trend color is green, so I recommend this shirt." The trend reflection unit can also collect the latest trend information from social media and fashion blogs and reflect it in suggestions in real time. The learning unit can learn the user's preferences and tendencies and make suggestions. For example, the learning unit can analyze the user's past selection data in detail, learn preferences for specific styles and colors, and reflect this in suggestions. The learning unit can also use an emotion estimation function to grasp the user's emotional state in real time and suggest clothing that evokes positive emotions. This allows the clothing selection support system according to the embodiment to comprehensively support the user's clothing selection. For example, it can suggest clothing that matches the user's mood and physical condition, thereby increasing user satisfaction.Also, choosing clothes appropriate for the weather and temperature will keep you comfortable, and incorporating the latest trends will give you a stylish impression. Furthermore, by learning the user's preferences and tendencies, more personalized suggestions will be possible, increasing user satisfaction.

[0048] The dialogue unit can analyze the tone and speed of the user's voice to estimate their mood and physical condition for the day and suggest clothing. For example, the dialogue unit analyzes the tone and speed of the user's voice when they speak to the conversational AI to estimate their mood and physical condition for the day. For example, if the user speaks in a low, slow voice, it may determine that the user is in a relaxed mood and suggest casual clothing. The dialogue unit also analyzes changes in the tone and speed of the voice in real time to estimate the user's emotional state. For example, if the user speaks in a high, fast voice, it may determine that the user is excited and suggest bright-colored clothing. The dialogue unit also compares the user's tone and speed with past data to estimate their mood and physical condition for the day. For example, it may make suggestions based on the clothing history of days when the user spoke in a similar voice tone and speed. This makes it possible to suggest clothing that matches the user's mood and physical condition.

[0049] The history management unit can analyze the user's past interaction history, learn preferences for specific events and seasons, and reflect the learned preferences in suggestions. For example, the history management unit analyzes the user's past interaction history to learn preferences for specific events and seasons. For example, it may suggest this year's Christmas outfit based on the outfit chosen for past Christmas seasons. The history management unit also extracts from the interaction history what kind of outfits the user prefers for specific events and seasons, and makes suggestions based on that information. For example, it may suggest outfits for this year's summer event based on the outfit chosen for past summer events. The history management unit also analyzes the user's interaction history to create patterns of preferences for specific events and seasons. For example, it may learn the colors and styles the user prefers in autumn from past data, and reflect these in suggestions. This makes it possible to make suggestions based on the user's past preferences.

[0050] The weather consideration unit acquires weather and temperature data in real time and can suggest clothing that can be adapted to sudden weather changes. The weather consideration unit, for example, acquires weather and temperature data in real time and suggests clothing that can be adapted to sudden weather changes. For example, a waterproof jacket is suggested in preparation for sudden rain. The weather consideration unit also acquires weather and temperature data in real time and suggests clothing that can be adapted to sudden weather changes. For example, if a sudden drop in temperature is forecast, warm clothing is suggested. The weather consideration unit also acquires weather and temperature data in real time and suggests clothing that can be adapted to sudden weather changes. For example, a windproof jacket is suggested on a windy day. This makes it possible to suggest clothing that can be adapted to sudden weather changes.

[0051] The trend reflection unit can collect the latest trend information from social media and fashion blogs and reflect it in proposals in real time. The trend reflection unit, for example, collects the latest trend information from social media and fashion blogs and reflects it in proposals in real time. For example, it identifies trends based on popular posts on Instagram and reflects it in proposals. The trend reflection unit also collects the latest trend information from social media and fashion blogs and reflects it in proposals in real time. For example, it identifies trends based on posts by fashion influencers and reflects it in proposals. The trend reflection unit also collects the latest trend information from social media and fashion blogs and reflects it in proposals in real time. For example, it identifies the latest fashion information based on trends on Twitter (registered trademark) and reflects it in proposals. This makes it possible to make proposals that reflect the latest trend information in real time.

[0052] The learning unit can analyze the user's past selection data in detail, learn preferences for specific styles and colors, and reflect these in the suggestions. The learning unit, for example, analyzes the user's past selection data in detail, learns preferences for specific styles and colors, and reflects these in the suggestions. For example, suggestions are made based on the colors and styles of clothing the user has selected in the past. The learning unit also analyzes the past selection data to learn the user's preferences. For example, suggestions are made based on clothes from brands and designers the user has selected in the past. The learning unit also analyzes the user's past selection data in detail, learns preferences for specific styles and colors, and reflects these in the suggestions. For example, suggestions are made based on seasonal clothing the user has selected in the past. This makes it possible to make suggestions based on the user's preferences.

[0053] The dialogue unit is able to grasp the user's hobbies and interests and propose themed outfits based on them. For example, the dialogue unit grasps the user's hobbies and interests through dialogue and proposes themed outfits based on them. For example, if the user is going to a music festival, it will propose outfits suitable for the festival. The dialogue unit also uses an interactive AI to learn the user's hobbies and interests and propose themed outfits based on them. For example, if the user likes outdoor activities, it will propose outfits suitable for outdoor activities. The dialogue unit also grasps the user's hobbies and interests through dialogue and proposes themed outfits based on them. For example, if the user likes watching movies, it will propose outfits suitable for going to the movie theater. This makes it possible to propose outfits based on the user's hobbies and interests.

[0054] The dialogue unit can suggest outfits that incorporate the opinions of the user's friends and family. The dialogue unit, for example, adds a function in which the dialogue AI suggests outfits that incorporate the opinions of the user's friends and family. For example, suggestions are made based on clothing recommended by friends. The dialogue unit also adds a function in which the dialogue AI collects the opinions of the user's friends and family and suggests outfits based on those opinions. For example, suggestions are made based on styles preferred by family members. The dialogue unit also adds a function in which the dialogue AI suggests outfits that incorporate the opinions of the user's friends and family. For example, suggestions are made based on colors and styles recommended by friends. This makes it possible to suggest outfits that reflect the opinions of the user's friends and family.

[0055] The history management unit can analyze the frequency of use and combination patterns of specific clothing based on the clothing history and suggest the optimal rotation. The history management unit, for example, analyzes the clothing history and extracts the frequency of use and combination patterns of specific clothing. For example, it analyzes how many times a specific shirt is worn per week and suggests the optimal rotation. The history management unit also analyzes the frequency of use and combination patterns of specific clothing based on the clothing history and suggests the optimal rotation. For example, it suggests not wearing the same clothing consecutively. The history management unit also analyzes the clothing history and extracts the frequency of use and combination patterns of specific clothing and suggests the optimal rotation. For example, if a specific piece of clothing is worn frequently on a specific day of the week, it will suggest a different piece of clothing for that day. In this way, it is possible to analyze the frequency of use and combination patterns of specific clothing and suggest the optimal rotation.

[0056] The history management unit can extract trends in clothing suitable for specific events and seasons based on the clothing history and reflect them in the next proposal. The history management unit, for example, analyzes the clothing history and extracts trends in clothing suitable for specific events and seasons. For example, it learns from past data what clothing the user prefers in summer and reflects this in the next proposal. The history management unit also extracts trends in clothing suitable for specific events and seasons based on the clothing history and reflects this in the next proposal. For example, it learns from past data what colors and styles the user prefers in winter and reflects this in the proposal. The history management unit also analyzes the clothing history and extracts trends in clothing suitable for specific events and seasons and reflects this in the next proposal. For example, it learns from past data what clothing the user prefers for a specific event and reflects this in the proposal. This makes it possible to extract trends in clothing suitable for specific events and seasons and reflect this in the next proposal.

[0057] The weather consideration unit can use the emotion estimation function to analyze the user's emotional response to the weather and suggest clothing that prioritizes comfort. The weather consideration unit, for example, uses the emotion estimation function to analyze the user's emotional response to the weather and suggest clothing that prioritizes comfort. For example, if the user feels uncomfortable on a rainy day, highly waterproof clothing is suggested. The weather consideration unit can also analyze the user's emotional response to the weather using the emotion estimation function and suggest clothing that prioritizes comfort. For example, if the user feels uncomfortable on a cold day, warm clothing is suggested. The weather consideration unit can also use the emotion estimation function to analyze the user's emotional response to the weather and suggest clothing that prioritizes comfort. For example, if the user feels uncomfortable on a hot day, cool clothing is suggested. In this way, the user's emotional response to the weather can be analyzed and clothing that prioritizes comfort can be suggested.

[0058] The trend reflection unit can use the emotion estimation function to analyze the user's emotional reaction to trends and preferentially suggest trends that elicit a positive reaction. The trend reflection unit, for example, uses the emotion estimation function to analyze the user's emotional reaction to trends and preferentially suggest trends that elicit a positive reaction. For example, if a user feels positive about a specific trend, the trend reflection unit preferentially suggests that trend. The trend reflection unit also analyzes the user's emotional reaction to trends using the emotion estimation function and preferentially suggests trends that elicit a positive reaction. For example, if a user feels positive about a specific trend color, the trend reflection unit suggests outfits that include that color. The trend reflection unit also uses the emotion estimation function to analyze the user's emotional reaction to trends and preferentially suggest trends that elicit a positive reaction. For example, if a user feels positive about a specific trend style, the trend reflection unit preferentially suggests that style. In this way, the user's emotional reaction to trends can be analyzed and trends that elicit a positive reaction can be preferentially suggested.

[0059] The learning unit can use the emotion estimation function to grasp the user's emotional state in real time and suggest clothing that will elicit positive emotions. For example, the learning unit can use the emotion estimation function to analyze the user's emotional state in real time and suggest clothing that will elicit positive emotions. For example, if the user is depressed, it can suggest bright-colored clothing. The learning unit can also monitor the user's emotional state in real time and suggest clothing that will elicit positive emotions. For example, if the user is feeling stressed, it can suggest clothing that will help them relax. The learning unit can also use the emotion estimation function to analyze the user's emotional state and suggest clothing that will elicit positive emotions. For example, if the user is nervous, it can suggest casual clothing that will help them relax. In this way, the user's emotional state can be grasped in real time and clothing that will elicit positive emotions can be suggested.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The clothing selection support system can further include a health management unit that monitors the user's health condition. For example, the health management unit can measure the user's heart rate and body temperature with a sensor and suggest clothing appropriate to the user's health condition. If the user's heart rate is high, the health management unit can suggest relaxing clothing, and if the user's body temperature is high, it can suggest cool clothing. The health management unit can also analyze the user's sleep data and suggest comfortable clothing if the user is sleep-deprived. This makes it possible to suggest optimal clothing appropriate to the user's health condition.

[0062] The clothing selection support system can further include an activity amount management unit that monitors the user's activity level. For example, the activity amount management unit tracks the user's number of steps and amount of exercise, and suggests clothing according to the amount of activity. If the amount of exercise is high, it can suggest clothing that is easy to move in, and if the number of steps is low, it can suggest clothing that is relaxing. The activity amount management unit can also learn the user's activity patterns and suggest clothing that is suitable for daily activities. This makes it possible to suggest optimal clothing according to the user's activity level.

[0063] The clothing selection support system can further include a diet management unit that manages the user's dietary data. For example, the diet management unit records the user's dietary details and suggests clothing that matches the dietary details. If the user has eaten a high-calorie meal, it can suggest clothing that is easy to move in, and if the user has eaten a light meal, it can suggest clothing that is relaxing. The diet management unit can also learn the user's eating patterns and suggest clothing that is appropriate for the dietary details. This makes it possible to suggest optimal clothing that matches the user's dietary details.

[0064] The clothing selection support system can further include a travel management unit that manages the user's travel plans. For example, the travel management unit can consider the climate and culture of the user's travel destination and suggest appropriate clothing. For example, it can suggest warm clothing if the travel destination is a cold region, and cool clothing if the travel destination is a tropical region. The travel management unit can also manage the user's travel schedule and suggest clothing suitable for activities during the trip. This makes it possible to suggest optimal clothing according to the user's travel plans.

[0065] The clothing selection support system can further include a hobby reflection unit that reflects the user's hobbies and interests. For example, the hobby reflection unit can suggest themed outfits based on the user's hobbies and interests. If the user likes sports, it can suggest sportswear, and if the user is interested in art, it can suggest outfits suitable for art events. The hobby reflection unit can also learn the user's hobbies and interests and suggest outfits based on them. This makes it possible to suggest the most suitable outfits according to the user's hobbies and interests.

[0066] The dialogue unit can estimate the user's emotions and, based on the estimated emotions, suggest music to improve the user's mood. For example, if the user is feeling down, it can suggest uplifting music, and if the user wants to relax, it can suggest calming music. The dialogue unit can also analyze the user's emotional state in real time and suggest appropriate music. This makes it possible to suggest optimal music according to the user's emotional state.

[0067] The dialogue unit can estimate the user's emotions and, based on the estimated emotions, suggest activities to improve the user's mood. For example, if the user is feeling stressed, it can suggest relaxing activities, and if the user is feeling low, it can suggest energetic activities. The dialogue unit can also analyze the user's emotional state in real time and suggest appropriate activities. This makes it possible to suggest optimal activities according to the user's emotional state.

[0068] The dialogue unit can estimate the user's emotions and, based on the estimated emotions, suggest movies and dramas to improve the user's mood. For example, if the user is feeling down, it can suggest a comedy movie, or if the user wants to relax, it can suggest a soothing drama. The dialogue unit can also analyze the user's emotional state in real time and suggest appropriate movies and dramas. This makes it possible to suggest the most appropriate entertainment according to the user's emotional state.

[0069] The dialogue unit can estimate the user's emotions and, based on the estimated emotions, suggest meals to improve the user's mood. For example, if the user is tired, it can suggest a nutritious meal, and if the user wants to relax, it can suggest a light meal. The dialogue unit can also analyze the user's emotional state in real time and suggest an appropriate meal. This makes it possible to suggest the optimal meal according to the user's emotional state.

[0070] The dialogue unit can estimate the user's emotions and, based on the estimated emotions, suggest reading that will improve the user's mood. For example, if the user is feeling down, it can suggest an uplifting book, or if the user wants to relax, it can suggest a calming book. The dialogue unit can also analyze the user's emotional state in real time and suggest appropriate reading. This makes it possible to suggest optimal reading that matches the user's emotional state.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The dialogue unit uses conversational AI to suggest outfits while interacting with the user. For example, the dialogue unit grasps the user's needs by asking questions such as "How are you feeling today?" or "Where are you planning to go today?" The dialogue unit can also analyze the tone and speed of the user's voice to estimate their mood and physical condition for the day and suggest outfits accordingly. Step 2: The history management unit manages the user's clothing history. For example, the history management unit records when and what clothes the user wore to prevent the user from wearing the same outfit repeatedly. The history management unit can also analyze the user's past interaction history, learn preferences for specific events or seasons, and reflect these in the suggestions. Step 3: The weather consideration unit suggests clothing based on the weather and temperature. For example, the weather consideration unit considers the weather and temperature of the day and suggests clothing appropriate for the weather, such as "It's forecast to rain today, so I recommend a waterproof jacket." The weather consideration unit can also obtain weather and temperature data in real time and suggest clothing that can respond to sudden changes in weather. Step 4: The trend reflection unit suggests outfits that reflect the latest trends. For example, the trend reflection unit takes into account recent fashion trends and suggests outfits that reflect the latest trends, such as, "This season's trend color is green, so I recommend this shirt." The trend reflection unit can also collect the latest trend information from social media and fashion blogs and reflect it in its suggestions in real time. Step 5: The learning module learns the user's preferences and trends and makes suggestions based on that. For example, the learning module can analyze the user's past selection data in detail to learn preferences for specific styles and colors and reflect these in its suggestions. The learning module can also use emotion estimation to grasp the user's emotional state in real time and suggest clothing that evokes positive emotions.

[0073] 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.

[0074] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0099] 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.

[0100] 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.

[0101] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0102] 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.

[0103] 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.

[0104] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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, in order to avoid confusion and to 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.

[0139] 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. [Explanation of symbols]

[0140] 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. A dialogue section that uses conversational AI to suggest outfits to users, and a history management unit that manages the user's clothing history; A weather consideration department that suggests clothing based on the weather and temperature; The trend reflection department proposes clothing that reflects the latest trends, a learning unit that learns the preferences and tendencies of the user and makes suggestions. A system characterized by:

2. The dialogue unit Analyze the tone and speed of the user's voice to estimate their mood and physical condition for the day and suggest outfits.

2. The system of claim 1.

3. The history management unit Analyzing the user's past interaction history to learn preferences for specific events and seasons and reflect them in the recommendations 2. The system of claim 1.

4. The weather consideration unit The weather and temperature data is acquired in real time, and clothing suggestions are made to accommodate sudden weather changes.

2. The system of claim 1.

5. The trend reflection unit Gather the latest trend information from social media and fashion blogs and incorporate it into proposals in real time 2. The system of claim 1.

6. The learning unit Deeply analyze the user's past selection data to learn and incorporate specific style and color preferences into recommendations 2. The system of claim 1.

7. The dialogue unit Identifying the user's hobbies and interests and proposing themed outfits based on those interests 2. The system of claim 1.

8. The dialogue unit Proposes outfits incorporating the opinions of the user's friends and family 2. The system of claim 1.

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