System

Abstract

An object of a system according to an embodiment is to make a specific purchase proposal based on clothes that a user has.SOLUTION: A system according to an embodiment includes a photographing unit, an information addition unit, an analysis unit, and a proposal generation unit. The photograph taking unit takes a photograph of clothes held by a user. The information adding unit adds detailed information to the photograph of the clothes taken by the photographing unit. The analysis unit analyzes the detailed information added by the information addition unit and interprets the user's preferences and purchasing habits. The proposal generation unit generates a specific purchase proposal based on the information interpreted by the analysis unit.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 technology has had the problem of making appropriate purchasing suggestions based on the clothes a user owns.

[0005] The system according to the embodiment aims to make specific purchase suggestions based on the clothes that the user owns. [Means for solving the problem]

[0006] The system according to the embodiment includes a photography unit, an information addition unit, an analysis unit, and a proposal generation unit. The photography unit takes a photo of clothes owned by a user. The information addition unit adds detailed information to the photo of the clothes taken by the photography unit. The analysis unit analyzes the detailed information added by the information addition unit and interprets the user's preferences and purchasing habits. The proposal generation unit generates specific purchasing proposals based on the information interpreted by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide specific purchase suggestions based on the clothes the user owns. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 purchase suggestion system according to an embodiment of the present invention is a system in which a user takes a photo of the clothes they own, a generation AI analyzes the photos, and makes specific purchase suggestions. This allows the purchase suggestion system to make specific purchase suggestions based on the user's preferences and purchasing habits.

[0029] A purchasing suggestion system according to an embodiment includes a photography unit, an information addition unit, an analysis unit, and a suggestion generation unit. The photography unit takes photos of clothes owned by a user. For example, a user takes photos of jackets, shirts, pants, etc. owned by the user and uploads them to the system, where the information is registered. The information addition unit adds detailed information to the photos of the clothes. For example, a user can add detailed text information, such as the brand name, store where the item was purchased, and the date of purchase, to the uploaded photos of the clothes. The analysis unit analyzes the added detailed information and interprets the user's preferences and purchasing habits. For example, a generation AI analyzes the uploaded photos of the clothes and the added detailed information to interpret the user's "preferences," "purchasing habits," and "available options (i.e., what is lacking)." The generation AI receives input in the form of prompts containing instructions on what the user wants the generation AI to do, and the generation AI performs analysis based on the prompts. The suggestion generation unit generates specific purchasing suggestions based on the interpreted information. For example, if a user explains their situation, such as "I want to buy more clothes that go well with the ones I already have," "I want clothes that are easy to move in in early summer," or "I'm confused because they said business casual," the AI ​​will make specific purchasing suggestions based on general fashion knowledge and information from various apparel and e-commerce sites. This allows the purchasing suggestion system to make specific purchasing suggestions based on the user's preferences and purchasing habits.

[0030] The photography unit can use a 360-degree camera to capture images from all directions and provide more detailed information to the system. For example, the photography unit can capture images of clothes owned by the user with a 360-degree camera and upload the images from all directions to the system. This allows the design and shape of the clothes to be understood in detail.

[0031] The photography unit can use a special filter to automatically recognize the material and texture of clothing and add material information. For example, the photography unit can use a special filter to automatically recognize the material and texture when taking a photo of clothing and upload that information to the system. For example, it can identify materials such as wool, cotton, and silk. This allows the automatic recognition of the material and texture of clothing and more accurate purchasing suggestions to be made.

[0032] The information addition unit can use voice input when the user inputs detailed information to enable the user to add information more quickly and accurately. The information addition unit adds information using voice input when the user inputs detailed information, for example. For example, the brand name, the store where the product was purchased, the date of purchase, etc. are input by voice. This allows the user to input detailed information quickly and accurately using voice input.

[0033] The information addition unit can add a function to automatically acquire information by scanning a barcode or QR code when entering a store name or brand name. The information addition unit can add a function to automatically acquire information by scanning a barcode or QR code when entering a store name or brand name, for example. For example, the information addition unit can scan a barcode or QR code using a smartphone camera. This allows the barcode or QR code to be scanned and information to be automatically acquired.

[0034] The analysis unit reflects clothing trends and fashions in real time when the generation AI performs analysis, and can provide the latest fashion information. For example, the analysis unit analyzes based on information from the latest fashion shows and magazines. This makes it possible to provide the latest fashion information.

[0035] The analysis unit can refer to information about past fashion events and exhibitions when the generation AI analyzes a user's purchasing history and preferences. For example, the analysis unit may refer to information about past fashion events and exhibitions when the generation AI analyzes a user's purchasing history and preferences. For example, the analysis may be based on information about exhibitions of a specific brand. By referencing information about past fashion events and exhibitions, more accurate analysis is possible.

[0036] The proposal generation unit can make optimal proposals by taking into consideration the user's budget and price range when the generation AI makes purchasing suggestions. For example, the proposal generation unit can make optimal proposals by taking into consideration the user's budget and price range when the generation AI makes purchasing suggestions. For example, it can suggest clothes that can be purchased within the budget set by the user. This makes it possible to make optimal purchasing suggestions by taking into consideration the user's budget and price range.

[0037] The proposal generation unit can suggest well-fitting items based on the user's body type and size information when the generation AI makes a purchasing suggestion. For example, when the generation AI makes a purchasing suggestion, the proposal generation unit can suggest well-fitting items based on the user's body type and size information. For example, it can suggest clothes that fit the user's height and weight. This makes it possible to suggest well-fitting items based on the user's body type and size information.

[0038] The proposal generation unit can take into account seasonal and weather information when the generation AI makes a purchasing suggestion and suggest appropriate items. For example, when the generation AI makes a purchasing suggestion, the proposal generation unit can take into account seasonal and weather information and suggest appropriate items. For example, in summer, the proposal generation unit can suggest clothes made of cool materials. This makes it possible to suggest appropriate items taking into account seasonal and weather information.

[0039] When the generation AI makes a purchasing suggestion, the suggestion generation unit can make a highly reliable suggestion by referring to the user's past purchase history and reviews. For example, when the generation AI makes a purchasing suggestion, the suggestion generation unit can make a highly reliable suggestion by referring to the user's past purchase history and reviews. For example, the suggestion generation unit can suggest clothes from a brand that the user has purchased in the past. In this way, by referring to the user's past purchase history and reviews, a highly reliable suggestion can be made.

[0040] The information addition unit can add a function to complement the input content by referring to past purchase history and reviews from other users when the user inputs detailed information. For example, the information addition unit can add a function to complement the input content by referring to past purchase history and reviews from other users when the user inputs detailed information. For example, the information addition unit can automatically input information about the same brand that was purchased in the past. This allows the user to complement the detailed information by referring to past purchase history and reviews from other users.

[0041] The information addition unit can add a function to automatically suggest similar clothes and related items based on the input information. The information addition unit can add a function to automatically suggest similar clothes and related items based on detailed information input by the user, for example, by suggesting other items from the same brand. This makes it possible to automatically suggest similar clothes and related items based on the input information.

[0042] When the generating AI performs analysis, the analysis unit incorporates fashion information from different cultures and regions, allowing it to make suggestions from a global perspective. For example, when the generating AI performs analysis, the analysis unit incorporates fashion information from different cultures and regions, allowing it to make suggestions from a global perspective. For example, the analysis may be based on fashion information from Europe and Asia. This allows it to make suggestions from a global perspective, incorporating fashion information from different cultures and regions.

[0043] When the generation AI performs an analysis, the analysis unit can make fashion suggestions based on the user's lifestyle and hobbies. For example, when the generation AI performs an analysis, the analysis unit can make fashion suggestions based on the user's lifestyle and hobbies. For example, functional clothing can be suggested to a user who likes the outdoors. This makes it possible to make fashion suggestions based on the user's lifestyle and hobbies.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The purchase suggestion system can also make suggestions based on the user's lifestyle. For example, if the user likes outdoor activities, it can suggest items made of durable materials and waterproof features. If the user mainly works in an office, it can suggest items suitable for business casual wear. Furthermore, if the user travels frequently, it can suggest items that are lightweight and easy to carry. This allows it to make specific purchase suggestions that match the user's lifestyle.

[0046] The purchase suggestion system can also analyze a user's past purchase history and make suggestions based on that history. For example, based on the brand and style of items the user has previously purchased, it can suggest items of the same brand or similar style. Also, based on items the user purchased in a particular season, it can suggest items suitable for the same season. Furthermore, based on items the user purchased for a specific event or occasion, it can suggest items suitable for a similar event or occasion. This allows for specific purchase suggestions to be made based on the user's past purchase history.

[0047] The purchasing suggestion system can also suggest items that fit well based on the user's body type and size information. For example, it can suggest items with the optimal size and cut based on the user's height, weight, and body type characteristics. Also, if the user is accustomed to the sizing of a particular brand, it can preferentially suggest items from that brand. Furthermore, if the user has a particular body type concern, it can suggest items designed to cover that concern. This allows for specific purchasing suggestions to be made based on the user's body type and size information.

[0048] The purchase suggestion system can further consider the user's budget and price range to make optimal suggestions. For example, it can suggest items that can be purchased within the budget set by the user. Also, if the user prefers items in a specific price range, it can preferentially suggest items in that price range. Furthermore, if the user prefers items from a specific brand, it can suggest items from that brand that fit the user's budget. This makes it possible to make specific purchase suggestions that take the user's budget and price range into consideration.

[0049] The purchase suggestion system can further analyze a user's purchasing history and preferences and make suggestions based on that information. For example, based on the brand and style of items the user has previously purchased, it can suggest items of the same brand or similar style. Also, based on items the user purchased in a particular season, it can suggest items suitable for the same season. Furthermore, based on items the user purchased for a specific event or occasion, it can suggest items suitable for a similar event or occasion. This allows for specific purchase suggestions to be made based on the user's purchasing history and preferences.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The photography unit takes photos of the clothes the user owns. For example, the user takes photos of their jacket, shirt, pants, etc. and uploads them to the system, where the information is registered. Step 2: The information addition unit adds detailed information to the photograph of the clothes. For example, a user can add detailed information such as the brand name, the store where the clothes were purchased, and the date of purchase in text form to the photograph of the clothes they uploaded. Step 3: The analysis unit analyzes the added detailed information and interprets the user's preferences and purchasing habits. For example, the generation AI analyzes the uploaded photos of clothes and the added detailed information to interpret the user's "preferences," "purchasing habits," and "available options (= what is lacking)." The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI performs analysis based on that prompt. Step 4: The proposal generator generates specific purchasing suggestions based on the interpreted information. For example, by providing information such as "I want to buy more clothes that go well with the ones I already have," "I want clothes that are easy to move in in early summer," or "I was told business casual is a problem," the AI ​​generator will make specific purchasing suggestions based on general fashion knowledge and information from various apparel and e-commerce sites.

[0052] (Example 2) The purchase suggestion system according to an embodiment of the present invention is a system in which a user takes a photo of the clothes they own, a generation AI analyzes the photos, and makes specific purchase suggestions. This allows the purchase suggestion system to make specific purchase suggestions based on the user's preferences and purchasing habits.

[0053] A purchasing suggestion system according to an embodiment includes a photography unit, an information addition unit, an analysis unit, and a suggestion generation unit. The photography unit takes photos of clothes owned by a user. For example, a user takes photos of jackets, shirts, pants, etc. owned by the user and uploads them to the system, where the information is registered. The information addition unit adds detailed information to the photos of the clothes. For example, a user can add detailed text information, such as the brand name, store where the item was purchased, and the date of purchase, to the uploaded photos of the clothes. The analysis unit analyzes the added detailed information and interprets the user's preferences and purchasing habits. For example, a generation AI analyzes the uploaded photos of the clothes and the added detailed information to interpret the user's "preferences," "purchasing habits," and "available options (i.e., what is lacking)." The generation AI receives input in the form of prompts containing instructions on what the user wants the generation AI to do, and the generation AI performs analysis based on the prompts. The suggestion generation unit generates specific purchasing suggestions based on the interpreted information. For example, if a user explains their situation, such as "I want to buy more clothes that go well with the ones I already have," "I want clothes that are easy to move in in early summer," or "I'm confused because they said business casual," the AI ​​will make specific purchasing suggestions based on general fashion knowledge and information from various apparel and e-commerce sites. This allows the purchasing suggestion system to make specific purchasing suggestions based on the user's preferences and purchasing habits.

[0054] The photography unit can use a 360-degree camera to capture images from all directions and provide more detailed information to the system. For example, the photography unit can capture images of clothes owned by the user with a 360-degree camera and upload the images from all directions to the system. This allows the design and shape of the clothes to be understood in detail.

[0055] The photography unit can use a special filter to automatically recognize the material and texture of clothing and add material information. For example, the photography unit can use a special filter to automatically recognize the material and texture when taking a photo of clothing and upload that information to the system. For example, it can identify materials such as wool, cotton, and silk. This allows the automatic recognition of the material and texture of clothing and more accurate purchasing suggestions to be made.

[0056] The photography unit can use the emotion estimation function to analyze the emotion the user is feeling when taking a photo and provide a photography guide based on that emotion. For example, the photography unit can use the emotion estimation function to analyze the emotion the user is feeling when taking a photo of clothes and provide a photography guide based on that emotion. For example, if the user is nervous, the photography unit can advise the user to relax. This makes it possible to provide appropriate photography guide based on the user's emotion.

[0057] The information addition unit can use voice input when the user inputs detailed information to enable the user to add information more quickly and accurately. The information addition unit adds information using voice input when the user inputs detailed information, for example. For example, the brand name, the store where the product was purchased, the date of purchase, etc. are input by voice. This allows the user to input detailed information quickly and accurately using voice input.

[0058] The information addition unit can add a function to automatically acquire information by scanning a barcode or QR code when entering a store name or brand name. The information addition unit can add a function to automatically acquire information by scanning a barcode or QR code when entering a store name or brand name, for example. For example, the information addition unit can scan a barcode or QR code using a smartphone camera. This allows the barcode or QR code to be scanned and information to be automatically acquired.

[0059] The information addition unit can use the emotion estimation function to analyze the emotion of the user regarding the information input by the user and provide input assistance based on the emotion. For example, the information addition unit can use the emotion estimation function to analyze the emotion of the user regarding the information input by the user and provide input assistance based on the emotion. For example, if the user is having trouble, input guidance is provided. This makes it possible to provide appropriate input assistance based on the user's emotion.

[0060] The analysis unit reflects clothing trends and fashions in real time when the generation AI performs analysis, and can provide the latest fashion information. For example, the analysis unit analyzes based on information from the latest fashion shows and magazines. This makes it possible to provide the latest fashion information.

[0061] The analysis unit can refer to information about past fashion events and exhibitions when the generation AI analyzes a user's purchasing history and preferences. For example, the analysis unit may refer to information about past fashion events and exhibitions when the generation AI analyzes a user's purchasing history and preferences. For example, the analysis may be based on information about exhibitions of a specific brand. By referencing information about past fashion events and exhibitions, more accurate analysis is possible.

[0062] The analysis unit can use the emotion estimation function to analyze the user's emotion and customize purchasing suggestions based on the emotion. For example, the analysis unit can use the emotion estimation function to analyze the user's emotion and customize purchasing suggestions based on the emotion. For example, if the user is happy, bright colored clothes can be suggested. This allows purchasing suggestions to be customized based on the user's emotion.

[0063] The proposal generation unit can make optimal proposals by taking into consideration the user's budget and price range when the generation AI makes purchasing suggestions. For example, the proposal generation unit can make optimal proposals by taking into consideration the user's budget and price range when the generation AI makes purchasing suggestions. For example, it can suggest clothes that can be purchased within the budget set by the user. This makes it possible to make optimal purchasing suggestions by taking into consideration the user's budget and price range.

[0064] The proposal generation unit can suggest well-fitting items based on the user's body type and size information when the generation AI makes a purchasing suggestion. For example, when the generation AI makes a purchasing suggestion, the proposal generation unit can suggest well-fitting items based on the user's body type and size information. For example, it can suggest clothes that fit the user's height and weight. This makes it possible to suggest well-fitting items based on the user's body type and size information.

[0065] The proposal generation unit can take into account seasonal and weather information when the generation AI makes a purchasing suggestion and suggest appropriate items. For example, when the generation AI makes a purchasing suggestion, the proposal generation unit can take into account seasonal and weather information and suggest appropriate items. For example, in summer, the proposal generation unit can suggest clothes made of cool materials. This makes it possible to suggest appropriate items taking into account seasonal and weather information.

[0066] When the generation AI makes a purchasing suggestion, the suggestion generation unit can make a highly reliable suggestion by referring to the user's past purchase history and reviews. For example, when the generation AI makes a purchasing suggestion, the suggestion generation unit can make a highly reliable suggestion by referring to the user's past purchase history and reviews. For example, the suggestion generation unit can suggest clothes from a brand that the user has purchased in the past. In this way, by referring to the user's past purchase history and reviews, a highly reliable suggestion can be made.

[0067] The suggestion generation unit can use the emotion estimation function to analyze the user's emotion and provide recommended purchasing suggestions to other users based on the emotion. The suggestion generation unit can, for example, use the emotion estimation function to analyze the user's emotion and provide recommended purchasing suggestions to other users based on the emotion. For example, if the user is happy, items from the same brand can be suggested. This makes it possible to provide recommended purchasing suggestions to other users based on the user's emotion.

[0068] The information addition unit can add a function to complement the input content by referring to past purchase history and reviews from other users when the user inputs detailed information. For example, the information addition unit can add a function to complement the input content by referring to past purchase history and reviews from other users when the user inputs detailed information. For example, the information addition unit can automatically input information about the same brand that was purchased in the past. This allows the user to complement the detailed information by referring to past purchase history and reviews from other users.

[0069] The information addition unit can add a function to automatically suggest similar clothes and related items based on the input information. The information addition unit can add a function to automatically suggest similar clothes and related items based on detailed information input by the user, for example, by suggesting other items from the same brand. This makes it possible to automatically suggest similar clothes and related items based on the input information.

[0070] The information addition unit can use the emotion estimation function to analyze the emotion of the user regarding the information input by the user and provide recommended information to other users based on the emotion. For example, the information addition unit can use the emotion estimation function to analyze the emotion of the user regarding the information input by the user and provide recommended information to other users based on the emotion. For example, if the user is satisfied, information about the same brand can be provided. In this way, recommended information can be provided to other users based on the user's emotion.

[0071] When the generating AI performs analysis, the analysis unit incorporates fashion information from different cultures and regions, allowing it to make suggestions from a global perspective. For example, when the generating AI performs analysis, the analysis unit incorporates fashion information from different cultures and regions, allowing it to make suggestions from a global perspective. For example, the analysis may be based on fashion information from Europe and Asia. This allows it to make suggestions from a global perspective, incorporating fashion information from different cultures and regions.

[0072] When the generation AI performs an analysis, the analysis unit can make fashion suggestions based on the user's lifestyle and hobbies. For example, when the generation AI performs an analysis, the analysis unit can make fashion suggestions based on the user's lifestyle and hobbies. For example, functional clothing can be suggested to a user who likes the outdoors. This makes it possible to make fashion suggestions based on the user's lifestyle and hobbies.

[0073] The analysis unit can use the emotion estimation function to analyze the user's emotions and provide recommended fashion information to other users based on those emotions. The analysis unit, for example, can use the emotion estimation function to analyze the user's emotions and provide recommended fashion information to other users based on those emotions. For example, if the user is happy, information about the same brand can be provided. This makes it possible to provide recommended fashion information to other users based on the user's emotions.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The purchase suggestion system can also make suggestions based on the user's lifestyle. For example, if the user likes outdoor activities, it can suggest items made of durable materials and waterproof features. If the user mainly works in an office, it can suggest items suitable for business casual wear. Furthermore, if the user travels frequently, it can suggest items that are lightweight and easy to carry. This allows it to make specific purchase suggestions that match the user's lifestyle.

[0076] The purchase suggestion system can further analyze the user's emotions and customize purchase suggestions based on those emotions. For example, if the user is feeling stressed, items with relaxing materials and designs can be suggested. If the user is happy, items with bright colors and fun designs can be suggested. Furthermore, if the user is tired, items that emphasize comfort can be suggested. In this way, purchase suggestions can be customized based on the user's emotions.

[0077] The purchase suggestion system can also analyze a user's past purchase history and make suggestions based on that history. For example, based on the brand and style of items the user has previously purchased, it can suggest items of the same brand or similar style. Also, based on items the user purchased in a particular season, it can suggest items suitable for the same season. Furthermore, based on items the user purchased for a specific event or occasion, it can suggest items suitable for a similar event or occasion. This allows for specific purchase suggestions to be made based on the user's past purchase history.

[0078] The purchase suggestion system can further analyze the user's emotions and provide recommended purchase suggestions to other users based on the emotions. For example, if a user is very satisfied with a particular item, the system can recommend that item to other users. Also, if a user is dissatisfied with a particular item, the system can advise other users to avoid that item. Furthermore, if a user has strong emotions toward a particular brand, the system can recommend other items from that brand to other users. This makes it possible to provide recommended purchase suggestions to other users based on the user's emotions.

[0079] The purchasing suggestion system can also suggest items that fit well based on the user's body type and size information. For example, it can suggest items with the optimal size and cut based on the user's height, weight, and body type characteristics. Also, if the user is accustomed to the sizing of a particular brand, it can preferentially suggest items from that brand. Furthermore, if the user has a particular body type concern, it can suggest items designed to cover that concern. This allows for specific purchasing suggestions to be made based on the user's body type and size information.

[0080] The purchase suggestion system can further analyze the user's emotions and make purchase suggestions based on those emotions. For example, if the user is nervous about a particular event, it can suggest relaxing items suitable for that event. Also, if the user has special feelings about a particular season, it can suggest items suitable for that season. Furthermore, if the user has strong feelings about a particular color or design, it can suggest items with that color or design. This makes it possible to make specific purchase suggestions based on the user's emotions.

[0081] The purchase suggestion system can further consider the user's budget and price range to make optimal suggestions. For example, it can suggest items that can be purchased within the budget set by the user. Also, if the user prefers items in a specific price range, it can preferentially suggest items in that price range. Furthermore, if the user prefers items from a specific brand, it can suggest items from that brand that fit the user's budget. This makes it possible to make specific purchase suggestions that take the user's budget and price range into consideration.

[0082] The purchasing suggestion system can further analyze the user's emotions and customize purchasing suggestions based on those emotions. For example, if the user is very satisfied with a particular item, it can suggest items of the same brand or style as that item. Also, if the user is dissatisfied with a particular item, it can suggest that the user avoid that item. Furthermore, if the user has special feelings about a particular event or occasion, it can suggest items that are suitable for that event or occasion. This makes it possible to customize specific purchasing suggestions based on the user's emotions.

[0083] The purchase suggestion system can further analyze a user's purchasing history and preferences and make suggestions based on that information. For example, based on the brand and style of items the user has previously purchased, it can suggest items of the same brand or similar style. Also, based on items the user purchased in a particular season, it can suggest items suitable for the same season. Furthermore, based on items the user purchased for a specific event or occasion, it can suggest items suitable for a similar event or occasion. This allows for specific purchase suggestions to be made based on the user's purchasing history and preferences.

[0084] The purchase suggestion system can further analyze the user's emotions and provide recommended purchase suggestions to other users based on the emotions. For example, if a user is very satisfied with a particular item, the system can recommend that item to other users. Also, if a user is dissatisfied with a particular item, the system can advise other users to avoid that item. Furthermore, if a user has strong emotions toward a particular brand, the system can recommend other items from that brand to other users. This makes it possible to provide recommended purchase suggestions to other users based on the user's emotions.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The photography unit takes photos of the clothes the user owns. For example, the user takes photos of their jacket, shirt, pants, etc. and uploads them to the system, where the information is registered. Step 2: The information addition unit adds detailed information to the photograph of the clothes. For example, a user can add detailed information such as the brand name, the store where the clothes were purchased, and the date of purchase in text form to the photograph of the clothes they uploaded. Step 3: The analysis unit analyzes the added detailed information and interprets the user's preferences and purchasing habits. For example, the generation AI analyzes the uploaded photos of clothes and the added detailed information to interpret the user's "preferences," "purchasing habits," and "available options (= what is lacking)." The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI performs analysis based on that prompt. Step 4: The proposal generator generates specific purchasing suggestions based on the interpreted information. For example, by providing information such as "I want to buy more clothes that go well with the ones I already have," "I want clothes that are easy to move in in early summer," or "I was told business casual is a problem," the AI ​​generator will make specific purchasing suggestions based on general fashion knowledge and information from various apparel and e-commerce sites.

[0087] 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.

[0088] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0089] 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.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0100] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0101] 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.

[0102] 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.

[0103] 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 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.

[0104] 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.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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.

[0114] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] 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 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.

[0119] 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.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0153] 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]

[0154] 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 photography unit that takes a photo of clothes owned by the user; an information adding unit that adds detailed information to the photograph of the clothes taken by the photographing unit; an analysis unit that analyzes the detailed information added by the information addition unit and interprets the user's preferences and purchasing habits; a proposal generation unit that generates a specific purchase proposal based on the information interpreted by the analysis unit. A system characterized by:

2. The photography unit is A 360-degree camera is used to capture images from all directions, providing more detailed information to the system.

2. The system of claim 1.

3. The information addition unit Allow users to use voice input to add details more quickly and accurately 2. The system of claim 1.

4. The analysis unit When the AI ​​analyzes, it reflects clothing trends and fashions in real time, providing the latest fashion information.

2. The system of claim 1.

5. The proposal generation unit When the AI ​​makes a purchasing suggestion, it takes into account the user's budget and price range to make the most suitable suggestion.

2. The system of claim 1.

6. The photography unit is Analyzes the emotions felt by the user when taking a photo and provides shooting guidance based on those emotions 2. The system of claim 1.

7. The information addition unit Analyzes the user's feelings regarding the information they input and provides input assistance based on those feelings.

2. The system of claim 1.

8. The analysis unit Analyze user sentiment and customize purchasing suggestions based on that sentiment 2. The system of claim 1.

Images