system

Abstract

The system according to the embodiment aims to suggest optimal fashion items based on the individual style of the user. [Solution] A system according to an embodiment includes a receiving unit, an analysis unit, a suggestion unit, and a providing unit. The receiving unit receives information from a user. The analysis unit analyzes the information received by the receiving unit. The suggestion unit suggests items based on the results of the analysis by the analysis unit. The providing unit provides a purchase link for the item suggested by the suggestion unit.

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 not adequately proposed fashion items based on a user's individual style, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest optimal fashion items based on the individual style of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analysis unit, a suggestion unit, and a providing unit. The receiving unit receives information from a user. The analysis unit analyzes the information received by the receiving unit. The suggestion unit suggests items based on the results of the analysis by the analysis unit. The providing unit provides a purchase link for the item suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable fashion items based on the individual style of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A fashion advice and shopping support system according to an embodiment of the present invention provides fashion advice and shopping support based on a user's individual style. This system allows a user to interact with an AI assistant and provide information such as their preferences, body type, and budget. The system then recommends suitable fashion items, easing the hassle of online shopping. For example, if a user provides information such as "casual style, budget of less than ¥10,000, and height of 160 cm," the AI ​​assistant uses that information to select casual style items and suggest items within the user's budget. Links to purchase the suggested items are also provided, allowing the user to easily shop online. This service allows users to easily find fashion items that match their style and simplifies online shopping. This service is particularly useful for users who are not knowledgeable about fashion or who are too busy to find time to shop. This allows the fashion advice and shopping support system to provide fashion advice and shopping support based on the user's individual style.

[0029] A fashion advice and shopping support system according to an embodiment includes a reception unit, an analysis unit, a suggestion unit, and a provision unit. The reception unit receives information from a user. The information from the user includes, but is not limited to, preferences, body type, and budget, for example. The reception unit receives, for example, text information input by the user. The reception unit can also receive image information uploaded by the user. The reception unit can also receive numerical information provided by the user. For example, the reception unit analyzes the text information input by the user to identify the user's preferences. The reception unit can also analyze the image information uploaded by the user to identify the user's body type. The reception unit can also analyze the numerical information provided by the user to identify the user's budget. The analysis unit analyzes the information received by the reception unit. The analysis unit can analyze the user's preferences using, for example, data mining technology. The analysis unit can also analyze the user's body type using statistical analysis technology. The analysis unit can also analyze the user's budget using a machine learning algorithm. For example, the analysis unit may identify the user's preferences using data mining technology. The analysis unit may also identify the user's body type using statistical analysis technology. The analysis unit may also identify the user's budget using a machine learning algorithm. The suggestion unit suggests items based on the results of the analysis by the analysis unit. The suggestion unit may, for example, use a recommendation system to suggest fashion items that are optimal for the user. The suggestion unit may also use a filtering algorithm to suggest fashion items that are optimal for the user. The suggestion unit may also suggest items based on the user's preferences, body type, and budget. For example, the suggestion unit may use a recommendation system to suggest fashion items that are optimal for the user. The suggestion unit may also use a filtering algorithm to suggest fashion items that are optimal for the user. The suggestion unit may also suggest items based on the user's preferences, body type, and budget. The provision unit provides a purchase link for the item suggested by the suggestion unit. The provision unit may, for example, provide a URL link.The providing unit may also provide a two-dimensional code (e.g., a QR code (registered trademark)). Furthermore, the providing unit may provide a link in a format that is easily accessible to the user. For example, the providing unit may provide a URL link. The providing unit may also provide a two-dimensional code. Furthermore, the providing unit may provide a link in a format that is easily accessible to the user. In this way, the fashion advice and shopping support system according to the embodiment can provide fashion advice and shopping support based on the user's individual style.

[0030] The reception unit can accept information on the user's preferences, body type, and budget. The reception unit, for example, accepts text information entered by the user. For example, if the user enters "I like casual styles," the reception unit accepts the information. The reception unit can also accept image information uploaded by the user. For example, if the user uploads a photo showing their body type, the reception unit accepts the information. The reception unit can also accept numerical information provided by the user. For example, if the user enters "My budget is within 10,000 yen," the reception unit accepts the information. By accepting detailed information from the user, more appropriate fashion items can be suggested. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the text information entered by the user into a generation AI and have the generation AI perform an analysis to identify the user's preferences.

[0031] The analysis unit analyzes the information received by the reception unit and can identify fashion items suitable for the user. The analysis unit analyzes the user's preferences using, for example, data mining technology. For example, the analysis unit analyzes text information entered by the user and identifies the user's preferences. The analysis unit can also analyze the user's body type using statistical analysis technology. For example, the analysis unit analyzes image information uploaded by the user and identifies the user's body type. The analysis unit can also analyze the user's budget using a machine learning algorithm. For example, the analysis unit analyzes numerical information provided by the user and identifies the user's budget. In this way, the analysis of the user's information can identify the optimal fashion items. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information provided by the user into a generation AI and cause the generation AI to perform an analysis to identify fashion items suitable for the user.

[0032] The suggestion unit can suggest items identified by the analysis unit to the user. The suggestion unit, for example, uses a recommendation system to suggest fashion items that are optimal for the user. For example, the suggestion unit suggests items based on the user's preferences, body type, and budget. The suggestion unit can also suggest fashion items that are optimal for the user using a filtering algorithm. For example, the suggestion unit suggests items based on the user's preferences, body type, and budget. This allows the suggestion unit to suggest items that are optimal for the user based on the analysis results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input items identified by the analysis unit into a generation AI and have the generation AI identify items to suggest to the user.

[0033] The providing unit can provide a purchase link for the item suggested by the suggestion unit. The providing unit, for example, provides a URL link. For example, the providing unit provides the link in a format that is easily accessible to the user. The providing unit can also provide a two-dimensional code. For example, the providing unit provides the link in a format that is easily accessible to the user on a smartphone. By providing a purchase link for the suggested item, it is possible to reduce the effort required for online shopping. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the link for the item suggested by the suggestion unit into a generating AI and cause the generating AI to generate a link to provide to the user.

[0034] The reception unit can analyze the user's past fashion history and select an appropriate information reception method. The reception unit, for example, provides input options that match the user's preferences based on a history of items purchased in the past. For example, the reception unit automatically inputs frequently used information from the user's past purchase history. The reception unit can also analyze the user's past fashion history and suggest an optimal input method. For example, the reception unit provides input options that match the user's preferences based on a history of items purchased in the past. In this way, by analyzing the past fashion history, a more appropriate information reception method can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past fashion history into a generation AI and cause the generation AI to perform an analysis to select an optimal information reception method.

[0035] The reception unit can filter information based on the user's current living situation and areas of interest when receiving the information. For example, when the user inputs their current living situation, the reception unit suggests related fashion items based on the information. For example, the reception unit preferentially accepts related information based on the user's areas of interest. The reception unit can also provide an optimal information reception method taking into account the user's living situation and areas of interest. For example, when the user inputs their current living situation, the reception unit suggests related fashion items based on the information. This allows more relevant information to be received by filtering information based on the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's living situation and areas of interest to a generation AI and have the generation AI perform information filtering.

[0036] When accepting information, the reception unit can prioritize accepting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes accepting fashion items related to that area. For example, the reception unit filters relevant information based on the user's geographical location information. The reception unit can also provide an optimal information acceptance method based on the user's current location. For example, if the user is in a specific area, the reception unit prioritizes accepting fashion items related to that area. This makes it possible to accept more relevant information by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information into the generation AI and cause the generation AI to perform analysis to preferentially accept highly relevant information.

[0037] When receiving information, the reception unit can analyze the user's social media activity and receive related information. The reception unit, for example, analyzes the user's social media activity and prioritizes receiving related fashion items. For example, the reception unit receives optimal information based on the user's areas of interest on social media. The reception unit can also filter related information taking the user's social media activity into consideration. For example, the reception unit analyzes the user's social media activity and prioritizes receiving related fashion items. In this way, by analyzing the user's social media activity, more relevant information can be received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into the generation AI and cause the generation AI to perform analysis to receive related information.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the fashion item. For example, the analysis unit performs a detailed analysis of important fashion items. For example, the analysis unit performs a brief analysis of less important items. The analysis unit can also prioritize analysis of more important items based on the user's preferences. For example, the analysis unit performs a detailed analysis of important fashion items. This allows for adjusting the level of detail of the analysis based on the importance of the fashion item, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the fashion item to the generation AI and cause the generation AI to perform an analysis to adjust the level of detail of the analysis.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the fashion item. For example, the analysis unit applies a casual analysis algorithm to casual items. For example, the analysis unit applies a formal analysis algorithm to formal items. The analysis unit can also apply a sports analysis algorithm to sportswear. For example, the analysis unit applies a casual analysis algorithm to casual items. In this way, by applying different analysis algorithms depending on the category of the fashion item, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the fashion item into the generation AI and cause the generation AI to perform an analysis to apply a different analysis algorithm.

[0040] During analysis, the analysis unit can determine the analysis priority based on the submission date of the fashion items. For example, the analysis unit prioritizes analysis of recently submitted fashion items. For example, the analysis unit postpones analysis of items submitted earlier. The analysis unit can also adjust the analysis priority based on the submission date. For example, the analysis unit prioritizes analysis of recently submitted fashion items. This allows for determining the analysis priority based on the submission date of the fashion items, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the submission date of the fashion items into the generation AI and cause the generation AI to perform an analysis to determine the analysis priority.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of fashion items. For example, the analysis unit prioritizes analysis of highly related items. For example, the analysis unit postpones analysis of less related items. The analysis unit can also adjust the order of analysis based on the relevance of items. For example, the analysis unit prioritizes analysis of highly related items. By adjusting the order of analysis based on the relevance of fashion items, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of fashion items to the generation AI and cause the generation AI to perform an analysis to adjust the order of analysis.

[0042] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the fashion item when making a suggestion. For example, the suggestion unit makes detailed suggestions for important fashion items. For example, the suggestion unit makes brief suggestions for items with low importance. The suggestion unit can also prioritize suggesting items with high importance based on the user's preferences. For example, the suggestion unit makes detailed suggestions for important fashion items. This allows for adjusting the level of detail of the suggestion based on the importance of the fashion item, thereby providing more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the importance of the fashion item to the generation AI and cause the generation AI to perform an analysis to adjust the level of detail of the suggestion.

[0043] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the fashion item. For example, the suggestion unit applies a suggestion algorithm for casual to casual items. For example, the suggestion unit applies a suggestion algorithm for formal to formal items. The suggestion unit can also apply a suggestion algorithm for sports to sportswear. For example, the suggestion unit applies a suggestion algorithm for casual to casual items. In this way, by applying different suggestion algorithms depending on the category of the fashion item, more appropriate suggestions can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the category of the fashion item into the generation AI and cause the generation AI to perform analysis to apply different suggestion algorithms.

[0044] When making a suggestion, the suggestion unit can determine the priority of the suggestion based on the submission date of the fashion item. For example, the suggestion unit prioritizes suggesting fashion items that were submitted recently. For example, the suggestion unit postpones items that were submitted earlier. The suggestion unit can also adjust the priority of the suggestion based on the submission date. For example, the suggestion unit prioritizes suggesting fashion items that were submitted recently. This allows more appropriate suggestions to be provided by determining the priority of the suggestion based on the submission date of the fashion item. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the submission date of the fashion item into the generation AI and cause the generation AI to perform an analysis to determine the priority of the suggestion.

[0045] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of the fashion items. For example, the suggestion unit prioritizes suggesting highly relevant items. For example, the suggestion unit postpones suggesting less relevant items. The suggestion unit can also adjust the order of suggestions based on the relevance of the items. For example, the suggestion unit prioritizes suggesting highly relevant items. This allows for adjusting the order of suggestions based on the relevance of the fashion items, making it possible to provide more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit may input the relevance of the fashion items into the generation AI and cause the generation AI to perform an analysis to adjust the order of suggestions.

[0046] When providing the link, the providing unit can select the optimal link by analyzing the user's past purchase history. The providing unit provides related links based on, for example, a history of items previously purchased by the user. For example, the providing unit prioritizes providing frequently used links based on the user's past purchase history. The providing unit can also analyze the user's past purchase history and select the optimal link. For example, the providing unit provides related links based on a history of items previously purchased by the user. In this way, by analyzing the user's past purchase history, more appropriate links can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past purchase history into the generating AI and cause the generating AI to perform analysis to select the optimal link.

[0047] The providing unit can customize the link display method based on the user's current living situation when providing the link. For example, when the user inputs their current living situation, the providing unit provides related links based on the information. For example, the providing unit provides an optimal link display method based on the user's living situation. The providing unit can also customize the link display method taking the user's current living situation into consideration. For example, when the user inputs their current living situation, the providing unit provides related links based on the information. In this way, by customizing the link display method based on the user's current living situation, more appropriate links can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current living situation into the generating AI and cause the generating AI to perform analysis to customize the link display method.

[0048] The providing unit can select the optimal link by taking into account the user's geographical location information when providing the link. For example, if the user is in a specific area, the providing unit preferentially provides links related to that area. For example, the providing unit filters related links based on the user's geographical location information. The providing unit can also provide the optimal link based on the user's current location. For example, if the user is in a specific area, the providing unit preferentially provides links related to that area. This allows for more appropriate links to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into the generating AI and cause the generating AI to perform analysis to select the optimal link.

[0049] At the time of providing, the providing unit can analyze the user's social media activity and suggest a link display method. The providing unit, for example, analyzes the user's social media activity and provides relevant links preferentially. For example, the providing unit provides optimal links based on the user's areas of interest in social media. The providing unit can also suggest a link display method taking the user's social media activity into consideration. For example, the providing unit analyzes the user's social media activity and provides relevant links preferentially. In this way, more appropriate links can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity into the generation AI and cause the generation AI to perform an analysis to suggest a link display method.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The analysis unit can analyze a user's preferences and trends based on the user's past purchase history. For example, the analysis unit can analyze the colors and styles of items the user has purchased in the past to identify the user's preferences. The analysis unit can also identify brands and designers that the user frequently purchases. Furthermore, the analysis unit can analyze seasonal trends from the user's purchase history and suggest items that are most suitable for the user. This makes it possible to provide more personalized fashion advice by utilizing the user's past purchase history.

[0052] The providing unit can provide links to related fashion blogs and style guides based on the user's past purchase history and preferences. For example, if the user likes a particular brand, it can provide blog articles and style guides related to that brand. Also, if the user likes a particular style, it can provide fashion advice and coordination examples related to that style. Furthermore, if the user is interested in new trends, it can provide the latest trend information. This makes it possible to provide a more fulfilling shopping experience by providing information tailored to the user's interests and preferences.

[0053] The analysis unit can analyze a user's preferences and trends based on the user's past purchase history. For example, the analysis unit can analyze the colors and styles of items the user has purchased in the past to identify the user's preferences. The analysis unit can also identify brands and designers that the user frequently purchases. Furthermore, the analysis unit can analyze seasonal trends from the user's purchase history and suggest items that are most suitable for the user. This makes it possible to provide more personalized fashion advice by utilizing the user's past purchase history.

[0054] The providing unit can provide links to related fashion blogs and style guides based on the user's past purchase history and preferences. For example, if the user likes a particular brand, it can provide blog articles and style guides related to that brand. Also, if the user likes a particular style, it can provide fashion advice and coordination examples related to that style. Furthermore, if the user is interested in new trends, it can provide the latest trend information. This makes it possible to provide a more fulfilling shopping experience by providing information tailored to the user's interests and preferences.

[0055] The analysis unit can analyze a user's preferences and trends based on the user's past purchase history. For example, the analysis unit can analyze the colors and styles of items the user has purchased in the past to identify the user's preferences. The analysis unit can also identify brands and designers that the user frequently purchases. Furthermore, the analysis unit can analyze seasonal trends from the user's purchase history and suggest items that are most suitable for the user. This makes it possible to provide more personalized fashion advice by utilizing the user's past purchase history.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit receives information from the user. The information from the user includes, for example, preferences, body type, budget, etc. The reception unit can receive text information entered by the user, image information uploaded by the user, and numerical information provided by the user. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the user's preferences, body type, and budget using data mining technology, statistical analysis technology, and machine learning algorithms. Step 3: The suggestion unit suggests items based on the results of the analysis by the analysis unit. The suggestion unit uses a recommendation system and filtering algorithms to suggest the most suitable fashion items based on the user's preferences, body type, and budget. Step 4: The provider provides a link to purchase the item suggested by the suggester. The provider provides a URL link or a two-dimensional code, and provides the link in a format that is easily accessible to the user.

[0058] (Example 2) A fashion advice and shopping support system according to an embodiment of the present invention provides fashion advice and shopping support based on a user's individual style. This system allows a user to interact with an AI assistant and provide information such as their preferences, body type, and budget. The system then recommends suitable fashion items, easing the hassle of online shopping. For example, if a user provides information such as "casual style, budget of less than ¥10,000, and height of 160 cm," the AI ​​assistant uses that information to select casual style items and suggest items within the user's budget. Links to purchase the suggested items are also provided, allowing the user to easily shop online. This service allows users to easily find fashion items that match their style and simplifies online shopping. This service is particularly useful for users who are not knowledgeable about fashion or who are too busy to find time to shop. This allows the fashion advice and shopping support system to provide fashion advice and shopping support based on the user's individual style.

[0059] A fashion advice and shopping support system according to an embodiment includes a reception unit, an analysis unit, a suggestion unit, and a provision unit. The reception unit receives information from a user. The information from the user includes, but is not limited to, preferences, body type, and budget, for example. The reception unit receives, for example, text information input by the user. The reception unit can also receive image information uploaded by the user. The reception unit can also receive numerical information provided by the user. For example, the reception unit analyzes the text information input by the user to identify the user's preferences. The reception unit can also analyze the image information uploaded by the user to identify the user's body type. The reception unit can also analyze the numerical information provided by the user to identify the user's budget. The analysis unit analyzes the information received by the reception unit. The analysis unit can analyze the user's preferences using, for example, data mining technology. The analysis unit can also analyze the user's body type using statistical analysis technology. The analysis unit can also analyze the user's budget using a machine learning algorithm. For example, the analysis unit may identify the user's preferences using data mining technology. The analysis unit may also identify the user's body type using statistical analysis technology. The analysis unit may also identify the user's budget using a machine learning algorithm. The suggestion unit suggests items based on the results of the analysis by the analysis unit. The suggestion unit may, for example, use a recommendation system to suggest fashion items that are optimal for the user. The suggestion unit may also use a filtering algorithm to suggest fashion items that are optimal for the user. The suggestion unit may also suggest items based on the user's preferences, body type, and budget. For example, the suggestion unit may use a recommendation system to suggest fashion items that are optimal for the user. The suggestion unit may also use a filtering algorithm to suggest fashion items that are optimal for the user. The suggestion unit may also suggest items based on the user's preferences, body type, and budget. The provision unit provides a purchase link for the item suggested by the suggestion unit. The provision unit may, for example, provide a URL link.The providing unit may also provide a two-dimensional code (e.g., a QR code). Furthermore, the providing unit may provide a link in a format that is easily accessible to the user. For example, the providing unit may provide a URL link. The providing unit may also provide a two-dimensional code. Furthermore, the providing unit may provide a link in a format that is easily accessible to the user. In this way, the fashion advice and shopping support system according to the embodiment can provide fashion advice and shopping support based on the user's individual style.

[0060] The reception unit can accept information on the user's preferences, body type, and budget. The reception unit, for example, accepts text information entered by the user. For example, if the user enters "I like casual styles," the reception unit accepts the information. The reception unit can also accept image information uploaded by the user. For example, if the user uploads a photo showing their body type, the reception unit accepts the information. The reception unit can also accept numerical information provided by the user. For example, if the user enters "My budget is within 10,000 yen," the reception unit accepts the information. By accepting detailed information from the user, more appropriate fashion items can be suggested. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the text information entered by the user into a generation AI and have the generation AI perform an analysis to identify the user's preferences.

[0061] The analysis unit analyzes the information received by the reception unit and can identify fashion items suitable for the user. The analysis unit analyzes the user's preferences using, for example, data mining technology. For example, the analysis unit analyzes text information entered by the user and identifies the user's preferences. The analysis unit can also analyze the user's body type using statistical analysis technology. For example, the analysis unit analyzes image information uploaded by the user and identifies the user's body type. The analysis unit can also analyze the user's budget using a machine learning algorithm. For example, the analysis unit analyzes numerical information provided by the user and identifies the user's budget. In this way, the analysis of the user's information can identify the optimal fashion items. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information provided by the user into a generation AI and cause the generation AI to perform an analysis to identify fashion items suitable for the user.

[0062] The suggestion unit can suggest items identified by the analysis unit to the user. The suggestion unit, for example, uses a recommendation system to suggest fashion items that are optimal for the user. For example, the suggestion unit suggests items based on the user's preferences, body type, and budget. The suggestion unit can also suggest fashion items that are optimal for the user using a filtering algorithm. For example, the suggestion unit suggests items based on the user's preferences, body type, and budget. This allows the suggestion unit to suggest items that are optimal for the user based on the analysis results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input items identified by the analysis unit into a generation AI and have the generation AI identify items to suggest to the user.

[0063] The providing unit can provide a purchase link for the item suggested by the suggestion unit. The providing unit, for example, provides a URL link. For example, the providing unit provides the link in a format that is easily accessible to the user. The providing unit can also provide a two-dimensional code. For example, the providing unit provides the link in a format that is easily accessible to the user on a smartphone. By providing a purchase link for the suggested item, it is possible to reduce the effort required for online shopping. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the link for the item suggested by the suggestion unit into a generating AI and cause the generating AI to generate a link to provide to the user.

[0064] The reception unit can estimate the user's emotions and adjust the information reception method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. For example, if the user is relaxed, the reception unit provides detailed input options and suggests a customizable input method. Furthermore, if the user is in a hurry, the reception unit prioritizes voice input to enable quick information input. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. This allows the information reception method to be adjusted according to the user's emotions, thereby allowing more appropriate information to be received. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit may input data for estimating the user's emotions into the generation AI and cause the generation AI to perform emotion estimation.

[0065] The reception unit can analyze the user's past fashion history and select an appropriate information reception method. The reception unit, for example, provides input options that match the user's preferences based on a history of items purchased in the past. For example, the reception unit automatically inputs frequently used information from the user's past purchase history. The reception unit can also analyze the user's past fashion history and suggest an optimal input method. For example, the reception unit provides input options that match the user's preferences based on a history of items purchased in the past. In this way, by analyzing the past fashion history, a more appropriate information reception method can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past fashion history into a generation AI and cause the generation AI to perform an analysis to select an optimal information reception method.

[0066] The reception unit can filter information based on the user's current living situation and areas of interest when receiving the information. For example, when the user inputs their current living situation, the reception unit suggests related fashion items based on the information. For example, the reception unit preferentially accepts related information based on the user's areas of interest. The reception unit can also provide an optimal information reception method taking into account the user's living situation and areas of interest. For example, when the user inputs their current living situation, the reception unit suggests related fashion items based on the information. This allows more relevant information to be received by filtering information based on the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's living situation and areas of interest to a generation AI and have the generation AI perform information filtering.

[0067] The reception unit can estimate the user's emotions and determine the priority of information to be received based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit prioritizes receiving important information. For example, when the user is relaxed, the reception unit prioritizes receiving detailed information. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving information that can be processed quickly. For example, when the user is feeling stressed, the reception unit prioritizes receiving important information. This allows more appropriate information to be received by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input data for estimating the user's emotions into the generation AI and cause the generation AI to perform emotion estimation.

[0068] When accepting information, the reception unit can prioritize accepting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes accepting fashion items related to that area. For example, the reception unit filters relevant information based on the user's geographical location information. The reception unit can also provide an optimal information acceptance method based on the user's current location. For example, if the user is in a specific area, the reception unit prioritizes accepting fashion items related to that area. This makes it possible to accept more relevant information by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information into the generation AI and cause the generation AI to perform analysis to preferentially accept highly relevant information.

[0069] When receiving information, the reception unit can analyze the user's social media activity and receive related information. The reception unit, for example, analyzes the user's social media activity and prioritizes receiving related fashion items. For example, the reception unit receives optimal information based on the user's areas of interest on social media. The reception unit can also filter related information taking the user's social media activity into consideration. For example, the reception unit analyzes the user's social media activity and prioritizes receiving related fashion items. In this way, by analyzing the user's social media activity, more relevant information can be received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into the generation AI and cause the generation AI to perform analysis to receive related information.

[0070] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, the analysis unit provides detailed analysis results when the user is relaxed. For example, the analysis unit provides concise analysis results when the user is in a hurry. The analysis unit can also provide visually stimulating analysis results when the user is excited. For example, the analysis unit provides detailed analysis results when the user is relaxed. This allows for adjusting the way the analysis is presented according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input data for estimating the user's emotions into the generation AI and have the generation AI perform emotion estimation.

[0071] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the fashion item. For example, the analysis unit performs a detailed analysis of important fashion items. For example, the analysis unit performs a brief analysis of less important items. The analysis unit can also prioritize analysis of more important items based on the user's preferences. For example, the analysis unit performs a detailed analysis of important fashion items. This allows for adjusting the level of detail of the analysis based on the importance of the fashion item, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the fashion item to the generation AI and cause the generation AI to perform an analysis to adjust the level of detail of the analysis.

[0072] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the fashion item. For example, the analysis unit applies a casual analysis algorithm to casual items. For example, the analysis unit applies a formal analysis algorithm to formal items. The analysis unit can also apply a sports analysis algorithm to sportswear. For example, the analysis unit applies a casual analysis algorithm to casual items. In this way, by applying different analysis algorithms depending on the category of the fashion item, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the fashion item into the generation AI and cause the generation AI to perform an analysis to apply a different analysis algorithm.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit performs a short and to-the-point analysis. For example, if the user is relaxed, the analysis unit performs a detailed analysis. The analysis unit can also perform a visually stimulating analysis if the user is excited. For example, if the user is in a hurry, the analysis unit performs a short and to-the-point analysis. This allows for adjusting the length of the analysis according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using an AI, for example, or without an AI. For example, the analysis unit can input data for estimating the user's emotions into the generation AI and cause the generation AI to estimate the emotions.

[0074] During analysis, the analysis unit can determine the analysis priority based on the submission date of the fashion items. For example, the analysis unit prioritizes analysis of recently submitted fashion items. For example, the analysis unit postpones analysis of items submitted earlier. The analysis unit can also adjust the analysis priority based on the submission date. For example, the analysis unit prioritizes analysis of recently submitted fashion items. This allows for determining the analysis priority based on the submission date of the fashion items, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the submission date of the fashion items into the generation AI and cause the generation AI to perform an analysis to determine the analysis priority.

[0075] During analysis, the analysis unit can adjust the order of analysis based on the relevance of fashion items. For example, the analysis unit prioritizes analysis of highly related items. For example, the analysis unit postpones analysis of less related items. The analysis unit can also adjust the order of analysis based on the relevance of items. For example, the analysis unit prioritizes analysis of highly related items. By adjusting the order of analysis based on the relevance of fashion items, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of fashion items to the generation AI and cause the generation AI to perform an analysis to adjust the order of analysis.

[0076] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. For example, the suggestion unit can provide concise suggestions when the user is in a hurry. The suggestion unit can also provide visually stimulating suggestions when the user is excited. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. This allows the suggestion unit to adjust the way the suggestions are expressed according to the user's emotions, thereby providing more appropriate suggestions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using an AI, for example, or without an AI. For example, the suggestion unit can input data for estimating the user's emotions into the generation AI and cause the generation AI to estimate the emotions.

[0077] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the fashion item when making a suggestion. For example, the suggestion unit makes detailed suggestions for important fashion items. For example, the suggestion unit makes brief suggestions for items with low importance. The suggestion unit can also prioritize suggesting items with high importance based on the user's preferences. For example, the suggestion unit makes detailed suggestions for important fashion items. This allows for adjusting the level of detail of the suggestion based on the importance of the fashion item, thereby providing more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the importance of the fashion item to the generation AI and cause the generation AI to perform an analysis to adjust the level of detail of the suggestion.

[0078] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the fashion item. For example, the suggestion unit applies a suggestion algorithm for casual to casual items. For example, the suggestion unit applies a suggestion algorithm for formal to formal items. The suggestion unit can also apply a suggestion algorithm for sports to sportswear. For example, the suggestion unit applies a suggestion algorithm for casual to casual items. In this way, by applying different suggestion algorithms depending on the category of the fashion item, more appropriate suggestions can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the category of the fashion item into the generation AI and cause the generation AI to perform analysis to apply different suggestion algorithms.

[0079] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating suggestions. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. This allows for adjusting the length of the suggestions according to the user's emotions, thereby providing more appropriate suggestions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input data for estimating the user's emotions into the generation AI and cause the generation AI to estimate the emotions.

[0080] When making a suggestion, the suggestion unit can determine the priority of the suggestion based on the submission date of the fashion item. For example, the suggestion unit prioritizes suggesting fashion items that were submitted recently. For example, the suggestion unit postpones items that were submitted earlier. The suggestion unit can also adjust the priority of the suggestion based on the submission date. For example, the suggestion unit prioritizes suggesting fashion items that were submitted recently. This allows more appropriate suggestions to be provided by determining the priority of the suggestion based on the submission date of the fashion item. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the submission date of the fashion item into the generation AI and cause the generation AI to perform an analysis to determine the priority of the suggestion.

[0081] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of the fashion items. For example, the suggestion unit prioritizes suggesting highly relevant items. For example, the suggestion unit postpones suggesting less relevant items. The suggestion unit can also adjust the order of suggestions based on the relevance of the items. For example, the suggestion unit prioritizes suggesting highly relevant items. This allows for adjusting the order of suggestions based on the relevance of the fashion items, making it possible to provide more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit may input the relevance of the fashion items into the generation AI and cause the generation AI to perform an analysis to adjust the order of suggestions.

[0082] The providing unit can estimate the user's emotions and adjust the display method of the provided links based on the estimated user emotions. For example, when the user is relaxed, the providing unit provides detailed link information. For example, when the user is in a hurry, the providing unit provides concise link information. Furthermore, when the user is excited, the providing unit can provide visually stimulating link information. For example, when the user is relaxed, the providing unit provides detailed link information. This allows for adjusting the link display method according to the user's emotions, thereby providing more appropriate links. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input data for estimating the user's emotions into the generation AI and cause the generation AI to estimate the emotions.

[0083] When providing the link, the providing unit can select the optimal link by analyzing the user's past purchase history. The providing unit provides related links based on, for example, a history of items previously purchased by the user. For example, the providing unit prioritizes providing frequently used links based on the user's past purchase history. The providing unit can also analyze the user's past purchase history and select the optimal link. For example, the providing unit provides related links based on a history of items previously purchased by the user. In this way, by analyzing the user's past purchase history, more appropriate links can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past purchase history into the generating AI and cause the generating AI to perform analysis to select the optimal link.

[0084] The providing unit can customize the link display method based on the user's current living situation when providing the link. For example, when the user inputs their current living situation, the providing unit provides related links based on the information. For example, the providing unit provides an optimal link display method based on the user's living situation. The providing unit can also customize the link display method taking the user's current living situation into consideration. For example, when the user inputs their current living situation, the providing unit provides related links based on the information. In this way, by customizing the link display method based on the user's current living situation, more appropriate links can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current living situation into the generating AI and cause the generating AI to perform analysis to customize the link display method.

[0085] The providing unit can estimate the user's emotions and determine the priority of links to provide based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can prioritize providing detailed link information. For example, when the user is in a hurry, the providing unit can prioritize providing concise link information. Furthermore, when the user is excited, the providing unit can prioritize providing visually stimulating link information. For example, when the user is relaxed, the providing unit can prioritize providing detailed link information. This allows for determining the priority of links according to the user's emotions, thereby providing more appropriate links. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input data for estimating the user's emotions into the generation AI and cause the generation AI to estimate the emotions.

[0086] The providing unit can select the optimal link by taking into account the user's geographical location information when providing the link. For example, if the user is in a specific area, the providing unit preferentially provides links related to that area. For example, the providing unit filters related links based on the user's geographical location information. The providing unit can also provide the optimal link based on the user's current location. For example, if the user is in a specific area, the providing unit preferentially provides links related to that area. This allows for more appropriate links to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into the generating AI and cause the generating AI to perform analysis to select the optimal link.

[0087] At the time of providing, the providing unit can analyze the user's social media activity and suggest a link display method. The providing unit, for example, analyzes the user's social media activity and provides relevant links preferentially. For example, the providing unit provides optimal links based on the user's areas of interest in social media. The providing unit can also suggest a link display method taking the user's social media activity into consideration. For example, the providing unit analyzes the user's social media activity and provides relevant links preferentially. In this way, more appropriate links can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity into the generation AI and cause the generation AI to perform an analysis to suggest a link display method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives information from the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests items based on the analysis results. The provision unit is realized by the control unit 46A of the smart device 14 and provides a purchase link for the suggested item. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives information from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests items based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides a purchase link for the suggested item. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives information from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests items based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides a purchase link for the suggested item. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives information from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests items based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides a purchase link for the suggested item.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The reception unit can analyze the user's tone of voice and speaking style to estimate the user's emotions. For example, if the user is excited, the reception unit can detect that emotion and provide a more energetic interface. If the user is calm, the reception unit can provide a calm interface. Furthermore, if the user is feeling anxious, the reception unit can provide an interface that gives a sense of security. This makes it possible to provide an interface that corresponds to the user's emotions, thereby achieving a more comfortable user experience.

[0090] The analysis unit can analyze a user's preferences and trends based on the user's past purchase history. For example, the analysis unit can analyze the colors and styles of items the user has purchased in the past to identify the user's preferences. The analysis unit can also identify brands and designers that the user frequently purchases. Furthermore, the analysis unit can analyze seasonal trends from the user's purchase history and suggest items that are most suitable for the user. This makes it possible to provide more personalized fashion advice by utilizing the user's past purchase history.

[0091] The suggestion unit can adjust the style and color of the suggested items based on the user's current mood and emotions. For example, if the user is relaxed, the suggestion unit can suggest items with a casual and relaxed style. If the user is feeling energetic, the suggestion unit can suggest items with bright colors and active styles. Furthermore, if the user is feeling down, the suggestion unit can suggest items with bright colors and designs that will lift the user's spirits. This makes it possible to improve user satisfaction by suggesting fashion items that correspond to the user's emotions.

[0092] The providing unit can provide links to related fashion blogs and style guides based on the user's past purchase history and preferences. For example, if the user likes a particular brand, it can provide blog articles and style guides related to that brand. Also, if the user likes a particular style, it can provide fashion advice and coordination examples related to that style. Furthermore, if the user is interested in new trends, it can provide the latest trend information. This makes it possible to provide a more fulfilling shopping experience by providing information tailored to the user's interests and preferences.

[0093] The reception unit can analyze the user's tone of voice and speaking style to estimate the user's emotions. For example, if the user is excited, the reception unit can detect that emotion and provide a more energetic interface. If the user is calm, the reception unit can provide a calm interface. Furthermore, if the user is feeling anxious, the reception unit can provide an interface that gives a sense of security. This makes it possible to provide an interface that corresponds to the user's emotions, thereby achieving a more comfortable user experience.

[0094] The analysis unit can analyze a user's preferences and trends based on the user's past purchase history. For example, the analysis unit can analyze the colors and styles of items the user has purchased in the past to identify the user's preferences. The analysis unit can also identify brands and designers that the user frequently purchases. Furthermore, the analysis unit can analyze seasonal trends from the user's purchase history and suggest items that are most suitable for the user. This makes it possible to provide more personalized fashion advice by utilizing the user's past purchase history.

[0095] The suggestion unit can adjust the style and color of the suggested items based on the user's current mood and emotions. For example, if the user is relaxed, the suggestion unit can suggest items with a casual and relaxed style. If the user is feeling energetic, the suggestion unit can suggest items with bright colors and active styles. Furthermore, if the user is feeling down, the suggestion unit can suggest items with bright colors and designs that will lift the user's spirits. This makes it possible to improve user satisfaction by suggesting fashion items that correspond to the user's emotions.

[0096] The providing unit can provide links to related fashion blogs and style guides based on the user's past purchase history and preferences. For example, if the user likes a particular brand, it can provide blog articles and style guides related to that brand. Also, if the user likes a particular style, it can provide fashion advice and coordination examples related to that style. Furthermore, if the user is interested in new trends, it can provide the latest trend information. This makes it possible to provide a more fulfilling shopping experience by providing information tailored to the user's interests and preferences.

[0097] The reception unit can analyze the user's tone of voice and speaking style to estimate the user's emotions. For example, if the user is excited, the reception unit can detect that emotion and provide a more energetic interface. If the user is calm, the reception unit can provide a calm interface. Furthermore, if the user is feeling anxious, the reception unit can provide an interface that gives a sense of security. This makes it possible to provide an interface that corresponds to the user's emotions, thereby achieving a more comfortable user experience.

[0098] The analysis unit can analyze a user's preferences and trends based on the user's past purchase history. For example, the analysis unit can analyze the colors and styles of items the user has purchased in the past to identify the user's preferences. The analysis unit can also identify brands and designers that the user frequently purchases. Furthermore, the analysis unit can analyze seasonal trends from the user's purchase history and suggest items that are most suitable for the user. This makes it possible to provide more personalized fashion advice by utilizing the user's past purchase history.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The reception unit receives information from the user. The information from the user includes, for example, preferences, body type, budget, etc. The reception unit can receive text information entered by the user, image information uploaded by the user, and numerical information provided by the user. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the user's preferences, body type, and budget using data mining technology, statistical analysis technology, and machine learning algorithms. Step 3: The suggestion unit suggests items based on the results of the analysis by the analysis unit. The suggestion unit uses a recommendation system and filtering algorithms to suggest the most suitable fashion items based on the user's preferences, body type, and budget. Step 4: The provider provides a link to purchase the item suggested by the suggester. The provider provides a URL link or a two-dimensional code, and provides the link in a format that is easily accessible to the user.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[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 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.

[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. 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.

[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 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.

[0115] 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.

[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 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.

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

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[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 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.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[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 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.

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

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 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 reception unit that receives information from a user; an analysis unit that analyzes the information received by the reception unit; a suggestion unit that suggests items based on the results of the analysis by the analysis unit; a providing unit that provides a purchase link for the item suggested by the suggesting unit. A system characterized by:

2. The reception unit Accepts user preferences, body type, and budget information 2. The system of claim 1.

3. The analysis unit The information received by the receiving unit is analyzed, and a fashion item suitable for the user is identified.

2. The system of claim 1.

4. The proposal unit Suggesting the items identified by the analysis unit to the user.

2. The system of claim 1.

5. The providing unit providing a purchase link for the item suggested by the suggestion unit; 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the way information is received based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past fashion history and select the most appropriate method of receiving information 2. The system of claim 1.

8. The reception unit When receiving information, it filters it based on the user's current life situation and areas of interest.

2. The system of claim 1.

Images