System

Abstract

An object of a system according to an embodiment is to propose optimal fashion items on the basis of preferences and body types of customers.SOLUTION: A system includes a reception unit, an analysis unit, a search unit, and a proposal unit. The reception unit inputs information on a preference or a body type of the customer. The analysis unit analyzes the information input by the reception unit and proposes fashion items suitable for the customer. The search unit searches for a similar item from the plurality of shops based on the item proposed by the analysis unit. The proposal unit presents the item proposed by the search unit to the customer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to find the right fashion item from the vast amount of information available.

[0005] The system according to the embodiment aims to propose optimal fashion items based on the customer's preferences and body type. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a search unit, and a suggestion unit. The reception unit inputs information about a customer's preferences or body type. The analysis unit analyzes the information input by the reception unit and suggests fashion items that suit the customer. The search unit searches for similar items from multiple shops based on the items suggested by the analysis unit. The suggestion unit presents the items suggested by the search unit to the customer. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable fashion items based on the customer's preferences and body type. [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 suggestion system according to an embodiment of the present invention proposes optimal fashion items based on a customer's preferences and body type, and searches for and presents similar items from multiple shops. In the fashion suggestion system, customers input information about their preferences and body type, and AI analyzes that information to suggest fashion items that suit the customer. Furthermore, based on the suggested items, AI searches for similar items from multiple shops and presents them to the customer. The customer can then purchase from the suggested items. For example, the fashion suggestion system inputs information about a customer's favorite colors, styles, and body type. For example, the customer inputs information such as "I like casual styles," "I'm 170 cm tall, and I weigh 60 kg." This information is then input into the AI. The fashion suggestion system then uses the AI ​​to analyze the input information and suggest fashion items that suit the customer. The AI ​​selects optimal fashion items based on the customer's preferences and body type. For example, for a customer who likes casual styles, casual shirts and pants are suggested. Furthermore, items of appropriate sizes are selected based on the customer's height and weight. Furthermore, based on the suggested items, AI searches for similar items from multiple shops and presents them to the customer. The AI ​​searches multiple shops for items similar to the suggested item and presents them to the customer. For example, it searches multiple shops for items similar to the suggested casual shirt and suggests them to the customer. The customer can then purchase from the suggested items. This allows the fashion suggestion system to improve the convenience of online shopping, making it easier for customers to find fashion items that suit them. This allows the fashion suggestion system to suggest the most suitable fashion items based on the customer's preferences and body type, and to search for and present similar items from multiple shops. For example, a customer enters information about their preferences and body type, and the AI ​​analyzes that information to suggest fashion items that suit the customer. Furthermore, based on the suggested items, the AI ​​searches for similar items from multiple shops and presents them to the customer.This will allow customers to easily find fashion items that suit them, improving the convenience of online shopping.

[0029] A fashion suggestion system according to an embodiment includes a reception unit, an analysis unit, a search unit, and a suggestion unit. The reception unit inputs information related to a customer's preferences or body type. The information related to the customer's preferences or body type includes, but is not limited to, information about size, color preference, and style preference. The reception unit provides an interface for the customer to input information related to their favorite colors, styles, and body types. The reception unit can also store the information input by the customer in a database. The analysis unit analyzes the information input by the reception unit and suggests fashion items suitable for the customer. The analysis unit selects optimal fashion items based on the customer's preferences and body type, for example, using AI. For example, the analysis unit suggests casual shirts and pants to a customer who likes a casual style. The analysis unit can also select items of appropriate size based on the customer's height and weight. The search unit searches for similar items from multiple shops based on the items suggested by the analysis unit. The search unit searches for items similar to the suggested items from multiple shops, for example, using AI. For example, the search unit searches multiple shops for items similar to a suggested casual shirt and suggests them to the customer. The suggestion unit presents the items suggested by the search unit to the customer. The suggestion unit, for example, uses AI to provide an interface for presenting the suggested items to the customer. For example, the suggestion unit displays a list of suggested items so that the customer can select one. The suggestion unit can also provide a link to purchase the suggested item. As a result, the fashion suggestion system according to the embodiment can suggest optimal fashion items based on the customer's preferences and body type, and search for and present similar items from multiple shops. For example, a customer inputs information about their preferences and body type, and the AI ​​analyzes that information to suggest fashion items that suit the customer. Furthermore, based on the suggested items, the AI ​​searches for similar items from multiple shops and presents them to the customer. This allows customers to easily find fashion items that suit them, improving the convenience of online shopping.

[0030] The suggestion unit may provide the customer with a link for purchasing the suggested item. Examples of the link include, but are not limited to, a URL link, a QR code (registered trademark), and the like. The suggestion unit may provide, for example, a link for purchasing the suggested item. For example, the suggestion unit may provide a URL link to a detail page of the suggested item. The suggestion unit may also generate a QR code for purchasing the suggested item and present it to the customer. This allows the customer to easily purchase the suggested item. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may provide the link using an AI model that generates a URL link to a detail page of the suggested item.

[0031] The reception unit can analyze the customer's past purchase history and select the optimal information input method. For example, the reception unit automatically displays information on items frequently purchased by the customer in the past as input candidates. For example, the reception unit prioritizes suggesting input methods (voice, text, etc.) that the customer has used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the customer's past purchase history. For example, the reception unit automatically displays information on items frequently purchased by the customer in the past as input candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the customer has used in the past. This improves the efficiency of information input by providing the optimal information input method based on the customer's past purchase history. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the customer's past purchase history data into a generation AI and have the generation AI select the optimal information input method.

[0032] When inputting information, the reception unit can filter the information based on the customer's current fashion trends and areas of interest. The reception unit, for example, prioritizes displaying related information based on fashion items recently searched by the customer. For example, the reception unit reflects information about brands and influencers the customer follows on social media. The reception unit can also filter related items based on the style and color of items previously purchased by the customer. For example, the reception unit prioritizes displaying related information based on fashion items recently searched by the customer. The reception unit can also reflect information about brands and influencers the customer follows on social media. This allows for filtering information based on the customer's current fashion trends and areas of interest, thereby providing more relevant information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the customer's social media data into the generation AI and have the generation AI filter the related information.

[0033] When inputting information, the reception unit can select an appropriate input means depending on the customer's input method. For example, when a customer inputs information by voice, the reception unit converts the input content into text using voice recognition technology. For example, when a customer uploads an image, the reception unit extracts features of the fashion item using image recognition technology. In addition, when a customer inputs information by text, the reception unit can automatically complete related candidates based on the input content. For example, when a customer inputs information by voice, the reception unit converts the input content into text using voice recognition technology. In addition, when a customer uploads an image, the reception unit can extract features of the fashion item using image recognition technology. This improves the convenience of information input by providing the optimal input means depending on the customer's input method. Some or all of the above-mentioned 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 customer's voice data into a generation AI and have the generation AI convert the voice data into text data.

[0034] When inputting information, the reception unit can prioritize inputting highly relevant information taking into account the customer's geographical location information. The reception unit, for example, prioritizes displaying information about shops close to the customer's current location. For example, if the customer is in a specific area, the reception unit can prioritize suggesting fashion items popular in that area. Furthermore, if the customer is traveling, the reception unit can also input information based on the fashion trends of the customer's travel destination. For example, the reception unit prioritizes displaying information about shops close to the customer's current location. Furthermore, if the customer is in a specific area, the reception unit can prioritize suggesting fashion items popular in that area. This improves the accuracy of information input by providing highly relevant information based on the customer's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the customer's geographical location data to the generation AI and cause the generation AI to prioritize input of highly relevant information.

[0035] The reception unit can analyze the customer's social media activity and input related information when inputting information. The reception unit, for example, reflects information about brands and influencers the customer follows on social media. For example, the reception unit can input related information based on fashion items the customer shared on social media. The reception unit can also analyze the content of the customer's social media posts and suggest related fashion items. For example, the reception unit reflects information about brands and influencers the customer follows on social media. The reception unit can also input related information based on fashion items the customer shared on social media. This improves the accuracy of information input by providing related information based on the customer's social media activity. Some or all of the above-described processing by 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 customer's social media data into a generation AI and have the generation AI input related information.

[0036] The reception unit can customize the input method by reflecting the customer's past feedback when inputting information. The reception unit customizes the input interface, for example, based on feedback provided by the customer in the past. For example, the reception unit can prioritize suggesting input methods that the customer has previously preferred. The reception unit can also analyze the customer's past feedback and select the optimal input means. For example, the reception unit customizes the input interface based on feedback provided by the customer in the past. The reception unit can also prioritize suggesting input methods that the customer has previously preferred. This improves the convenience of information input by customizing the input method based on the customer's past feedback. Some or all of the above-described processing by 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 customer's past feedback data into a generation AI and have the generation AI customize the input method.

[0037] During analysis, the analysis unit can adjust the level of detail of the analysis taking into account changes in customer preferences. For example, if a customer's preferences change frequently, the analysis unit performs a detailed analysis and makes suggestions based on the latest preferences. For example, if a customer's preferences remain stable, the analysis unit performs a brief analysis and makes basic suggestions. Furthermore, if a customer's preferences change according to a particular season or event, the analysis unit can also perform the analysis taking into account such changes. For example, if a customer's preferences change frequently, the analysis unit performs a detailed analysis and makes suggestions based on the latest preferences. Furthermore, if a customer's preferences remain stable, the analysis unit can also perform a brief analysis and make basic suggestions. Thus, by adjusting the level of detail of the analysis according to changes in customer preferences, more appropriate suggestions can be made. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the customer's past purchase history data into the generation AI and cause the generation AI to adjust the level of detail of the analysis taking into account changes in preferences.

[0038] During analysis, the analysis unit can apply different analysis algorithms depending on the customer's fashion category. For example, the analysis unit applies an analysis algorithm specialized for casual items to a customer who prefers casual fashion. For example, the analysis unit applies an analysis algorithm specialized for formal items to a customer who prefers formal fashion. The analysis unit can also apply an analysis algorithm specialized for sports items to a customer who prefers sports fashion. For example, the analysis unit applies an analysis algorithm specialized for casual items to a customer who prefers casual fashion. The analysis unit can also apply an analysis algorithm specialized for formal items to a customer who prefers formal fashion. In this way, by applying an analysis algorithm depending on the customer's fashion category, the accuracy of the analysis is improved. 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 customer fashion category data into the generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0039] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on feedback provided by the customer in the past. For example, the analysis unit analyzes the customer's past analysis results and makes highly accurate suggestions. The analysis unit can also adjust the level of detail of the analysis by referring to the customer's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on feedback provided by the customer in the past. The analysis unit can also analyze the customer's past analysis results and make highly accurate suggestions. In this way, the accuracy of the analysis is improved by referring to the customer's past analysis results. 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 customer's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0040] During analysis, the analysis unit can determine analysis priorities based on the time of submission of the customer's fashion items. For example, the analysis unit prioritizes analysis of fashion items recently submitted by the customer. For example, the analysis unit prioritizes analysis of fashion items submitted by the customer for a particular event or season. The analysis unit can also prioritize analysis of fashion items frequently submitted by the customer. For example, the analysis unit prioritizes analysis of fashion items recently submitted by the customer. The analysis unit can also prioritize analysis of fashion items submitted by the customer for a particular event or season. This improves analysis efficiency by determining analysis priorities based on the time of submission of the customer's 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 data on the time of submission of the customer's fashion items to the generation AI and have the generation AI determine the analysis priorities.

[0041] During analysis, the analysis unit can adjust the order of analysis based on customer relevance. For example, the analysis unit prioritizes analyzing items that the customer has given high ratings to in the past. For example, the analysis unit prioritizes analyzing items that the customer frequently purchases. The analysis unit can also prioritize analyzing items that the customer associates with a particular brand or style. For example, the analysis unit prioritizes analyzing items that the customer has given high ratings to in the past. The analysis unit can also prioritize analyzing items that the customer frequently purchases. This improves the efficiency of analysis by adjusting the order of analysis based on customer relevance. 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 customer's past purchase history data into the generation AI and cause the generation AI to adjust the order of analysis based on relevance.

[0042] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the customer's level of expertise. For example, the analysis unit provides analysis results that use a lot of technical terminology to a customer who is knowledgeable about fashion. For example, the analysis unit can provide analysis results that are concise and easy to understand to a customer who is not knowledgeable about fashion. The analysis unit can also adjust the level of detail of the analysis results according to the customer's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terminology to a customer who is knowledgeable about fashion. The analysis unit can also provide analysis results that are concise and easy to understand to a customer who is not knowledgeable about fashion. In this way, adjusting the use of technical terminology in the analysis according to the customer's level of expertise deepens the understanding of the analysis results. 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 customer expertise level data into the generation AI and have the generation AI adjust the use of technical terminology.

[0043] The search unit can improve search accuracy by taking into account the interrelationships between items during a search. For example, the search unit may prioritize displaying items related to an item searched for by a customer. For example, the search unit may suggest items that go well with items previously purchased by the customer. The search unit may also display related items based on the category or style of the item searched for by the customer. For example, the search unit may prioritize displaying items related to an item searched for by a customer. The search unit may also suggest items that go well with items previously purchased by the customer. This improves search accuracy by taking into account the interrelationships between items. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input the customer's past purchase history data into the generation AI and cause the generation AI to improve search accuracy based on the interrelationships.

[0044] The search unit can perform a search while taking into account attribute information of item providers. For example, if a customer prefers a particular brand, the search unit can prioritize displaying items from that brand. For example, if a customer prefers a particular designer, the search unit can prioritize displaying items from that designer. Also, if a customer prefers a particular shop, the search unit can prioritize displaying items from that shop. For example, if a customer prefers a particular brand, the search unit can prioritize displaying items from that brand. Also, if a customer prefers a particular designer, the search unit can prioritize displaying items from that designer. In this way, by taking into account the attribute information of item providers, more relevant search results are provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input customer brand and designer preference data into the generation AI and cause the generation AI to perform a search based on the provider's attribute information.

[0045] During a search, the search unit can weight the search based on the frequency of item provision. For example, the search unit prioritizes displaying items that customers frequently search for. For example, the search unit prioritizes displaying items that customers have given high ratings to in the past. The search unit can also prioritize displaying items that customers frequently search for during a specific period. For example, the search unit prioritizes displaying items that customers frequently search for. The search unit can also prioritize displaying items that customers have given high ratings to in the past. In this way, weighting the search based on the frequency of item provision provides more relevant search results. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input customer search history data into the generation AI and cause the generation AI to weight the search based on the frequency of provision.

[0046] The search unit can perform a search taking into account the geographical distribution of items. For example, the search unit can prioritize displaying items from shops close to the customer's current location. For example, if the customer is in a specific area, the search unit can prioritize displaying items that are popular in that area. Furthermore, if the customer is traveling, the search unit can search for items based on the fashion trends of the customer's travel destination. For example, the search unit can prioritize displaying items from shops close to the customer's current location. Furthermore, if the customer is in a specific area, the search unit can prioritize displaying items that are popular in that area. In this way, by taking the geographical distribution of items into consideration, more relevant search results are provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input customer geographical distribution data into the generation AI and cause the generation AI to perform a search based on the geographical distribution.

[0047] The search unit can improve search accuracy by referring to literature related to the item during a search. For example, the search unit can suggest related items by referring to fashion articles related to the item searched for by the customer. For example, the search unit can refer to reviews related to the item searched for by the customer and prioritize displaying highly rated items. The search unit can also suggest the latest items by referring to trend reports related to the item searched for by the customer. For example, the search unit can suggest related items by referring to fashion articles related to the item searched for by the customer. The search unit can also suggest highly rated items by referring to reviews related to the item searched for by the customer. This improves search accuracy by referring to literature related to the item. Some or all of the above-described processing in the search unit may be performed using, or without, AI. For example, the search unit can input customer search history data into the generation AI and cause the generation AI to improve search accuracy based on related literature.

[0048] The search unit may perform a search while taking into account the market value of the item. For example, the search unit may suggest items in an optimal price range based on the market value of the item searched for by the customer. For example, the search unit may prioritize displaying items with high cost performance based on the market value of the item searched for by the customer. The search unit may also provide discount and sale information based on the market value of the item searched for by the customer. For example, the search unit may suggest items in an optimal price range based on the market value of the item searched for by the customer. The search unit may also prioritize displaying items with high cost performance based on the market value of the item searched for by the customer. In this way, by taking the market value of the item into consideration, more relevant search results are provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input customer search history data into the generation AI and cause the generation AI to perform a search based on market value.

[0049] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the item when making a suggestion. For example, the suggestion unit displays detailed suggestion content for items that the customer considers important. For example, the suggestion unit displays concise suggestion content for items that the customer considers less important. The suggestion unit can also display detailed suggestion content for items that the customer considers important for a particular event or season. For example, the suggestion unit displays detailed suggestion content for items that the customer considers important. The suggestion unit can also display concise suggestion content for items that the customer considers less important. In this way, by adjusting the level of detail of the suggestion based on the importance of the item, more appropriate suggestions can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input importance data of the customer's items to the generation AI and cause the generation AI to adjust the level of detail of the suggestion based on the importance.

[0050] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the item. For example, the suggestion unit applies a suggestion algorithm specialized for casual items to a customer who prefers casual fashion. For example, the suggestion unit applies a suggestion algorithm specialized for formal items to a customer who prefers formal fashion. The suggestion unit can also apply a suggestion algorithm specialized for sports items to a customer who prefers sports fashion. For example, the suggestion unit applies a suggestion algorithm specialized for casual items to a customer who prefers casual fashion. The suggestion unit can also apply a suggestion algorithm specialized for formal items to a customer who prefers formal fashion. This improves the accuracy of suggestions by applying a suggestion algorithm according to the item category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input customer fashion category data into the generation AI and cause the generation AI to apply a suggestion algorithm according to the category.

[0051] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the customer's past proposal results. The proposal unit makes a highly accurate proposal, for example, based on proposal content that the customer has given a high rating in the past. For example, the proposal unit analyzes the customer's past proposal results and provides optimal proposal content. The proposal unit can also adjust the level of detail of the proposal by referring to the customer's past proposal results. For example, the proposal unit makes a highly accurate proposal based on proposal content that the customer has given a high rating in the past. The proposal unit can also analyze the customer's past proposal results and provide optimal proposal content. In this way, the accuracy of the proposal is improved by referring to the customer's past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the customer's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0052] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the time of submission of the items. For example, the suggestion unit prioritizes suggesting items recently submitted by the customer. For example, the suggestion unit prioritizes suggesting items submitted by the customer for a particular event or season. The suggestion unit can also prioritize suggesting items frequently submitted by the customer. For example, the suggestion unit prioritizes suggesting items recently submitted by the customer. The suggestion unit can also prioritize suggesting items submitted by the customer for a particular event or season. In this way, by determining the priority of suggestions based on the time of submission of the items, more appropriate suggestions can be provided. Some or all of the above-described 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 data on the time of submission of the customer's items into the generation AI and cause the generation AI to determine the priority of suggestions based on the time of submission.

[0053] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of the items. For example, the suggestion unit prioritizes suggesting items that the customer has given high ratings to in the past. For example, the suggestion unit prioritizes suggesting items that the customer frequently purchases. The suggestion unit can also prioritize suggesting items that the customer associates with a specific brand or style. For example, the suggestion unit prioritizes suggesting items that the customer has given high ratings to in the past. The suggestion unit can also prioritize suggesting items that the customer frequently purchases. In this way, by adjusting the order of suggestions based on the relevance of the items, more appropriate suggestions can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the customer's past purchase history data into the generation AI and cause the generation AI to adjust the order of suggestions based on the relevance.

[0054] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the customer's level of expertise. For example, the suggestion unit provides proposal content that uses a lot of technical terminology to a customer who is knowledgeable about fashion. For example, the suggestion unit provides proposal content that is concise and easy to understand to a customer who is not knowledgeable about fashion. The suggestion unit can also adjust the level of detail of the proposal content according to the customer's level of expertise. For example, the suggestion unit provides proposal content that uses a lot of technical terminology to a customer who is knowledgeable about fashion. The suggestion unit can also provide proposal content that is concise and easy to understand to a customer who is not knowledgeable about fashion. In this way, by adjusting the use of technical terminology in the proposal according to the customer's level of expertise, more appropriate proposals 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 customer expertise level data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] When entering information about a customer's preferences and body type, the reception unit can automatically refer to the customer's past purchase history and search history to present input candidates. For example, appropriate input candidates can be displayed based on the size and style of items the customer has previously purchased. Related input candidates can also be presented based on information about items the customer has previously searched for. This saves the customer the trouble of entering information, allowing them to enter information more quickly. Furthermore, the reception unit can also prioritize the display of information about items that the customer has previously given high ratings to. This makes it possible to enter information that suits the customer's preferences.

[0057] The suggestion unit can display reviews and ratings from other customers when a customer purchases a suggested item. For example, it can display a list of reviews from other customers for the suggested item, providing reference information for the customer when considering a purchase. It can also display a rating score for the suggested item, which the customer can use as an indicator to judge the quality and satisfaction of the item. This allows the customer to purchase the item with greater peace of mind. Furthermore, the suggestion unit can make additional suggestions to the customer based on the reviews and ratings. For example, it can suggest related items that have been highly rated by other customers who have purchased the same item.

[0058] The reception unit can analyze a customer's past purchase history and search history to suggest the optimal information input method. For example, it can prioritize suggestions of input methods (voice, text, etc.) that the customer has frequently used in the past. It can also automatically display information about items the customer has previously purchased as input candidates. It can also predict and suggest the input method that will be used during a specific time period based on the customer's past purchase history. This improves the efficiency of information input by providing the optimal information input method based on the customer's past behavior.

[0059] When entering information, the reception unit can filter the information based on the customer's current fashion trends and areas of interest. For example, it can prioritize displaying relevant information based on fashion items that the customer has recently searched for. It can also reflect information about brands and influencers that the customer follows on social media. It can also filter related items based on the style and color of items that the customer has previously purchased. This allows the information to be filtered based on the customer's current fashion trends and areas of interest, providing more relevant information.

[0060] When entering information, the reception unit can select an appropriate input means depending on the customer's input method. For example, if the customer enters information by voice, the input content can be converted into text using voice recognition technology. Also, if the customer uploads an image, the characteristics of the fashion item can be extracted using image recognition technology. Furthermore, if the customer enters information by text, related candidates can be automatically completed based on the input content. This improves the convenience of information entry by providing the optimal input means depending on the customer's input method.

[0061] When inputting information, the reception unit can prioritize inputting highly relevant information taking into account the customer's geographical location information. For example, it can prioritize displaying information about shops close to the customer's current location. Also, if the customer is in a specific area, it can prioritize suggesting fashion items that are popular in that area. Furthermore, if the customer is traveling, it can also input information based on the fashion trends of the travel destination. This improves the accuracy of information input by providing highly relevant information based on the customer's geographical location information.

[0062] When entering information, the reception unit can analyze the customer's social media activity and enter relevant information. For example, this can reflect information about brands and influencers that the customer follows on social media. It can also enter relevant information based on fashion items that the customer has shared on social media. It can also analyze the content of the customer's social media posts and suggest related fashion items. This improves the accuracy of information entry by providing relevant information based on the customer's social media activity.

[0063] When inputting information, the reception unit can customize the input method by reflecting the customer's past feedback. For example, the input interface can be customized based on feedback provided by the customer in the past. The reception unit can also preferentially suggest input methods that the customer has previously preferred. Furthermore, the reception unit can analyze the customer's past feedback and select the optimal input means. In this way, customizing the input method based on the customer's past feedback improves the convenience of information input.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit inputs information about the customer's preferences or body type. The information about the customer's preferences or body type includes, for example, size, color preference, style preference, etc. The reception unit provides an interface for the customer to input information about their favorite color, style, or body type, and can also store the input information in a database. Step 2: The analysis unit analyzes the information entered by the reception unit and suggests fashion items that suit the customer. The analysis unit uses AI to select the most suitable fashion items based on the customer's preferences and body type. For example, it suggests casual shirts and pants to a customer who likes casual styles, and selects items of the appropriate size based on the customer's height and weight. Step 3: The search unit searches multiple shops for similar items based on the items suggested by the analysis unit. The search unit uses AI to search multiple shops for items similar to the suggested item. For example, it searches multiple shops for items similar to the suggested casual shirt and suggests them to the customer. Step 4: The suggestion unit presents the items suggested by the search unit to the customer. The suggestion unit provides an interface for presenting the suggested items to the customer using AI, lists the suggested items so that the customer can select them, and can also provide a link to purchase the suggested items.

[0066] (Example 2) A fashion suggestion system according to an embodiment of the present invention proposes optimal fashion items based on a customer's preferences and body type, and searches for and presents similar items from multiple shops. In the fashion suggestion system, customers input information about their preferences and body type, and AI analyzes that information to suggest fashion items that suit the customer. Furthermore, based on the suggested items, AI searches for similar items from multiple shops and presents them to the customer. The customer can then purchase from the suggested items. For example, the fashion suggestion system inputs information about a customer's favorite colors, styles, and body type. For example, the customer inputs information such as "I like casual styles," "I'm 170 cm tall, and I weigh 60 kg." This information is then input into the AI. The fashion suggestion system then uses the AI ​​to analyze the input information and suggest fashion items that suit the customer. The AI ​​selects optimal fashion items based on the customer's preferences and body type. For example, for a customer who likes casual styles, casual shirts and pants are suggested. Furthermore, items of appropriate sizes are selected based on the customer's height and weight. Furthermore, based on the suggested items, AI searches for similar items from multiple shops and presents them to the customer. The AI ​​searches multiple shops for items similar to the suggested item and presents them to the customer. For example, it searches multiple shops for items similar to the suggested casual shirt and suggests them to the customer. The customer can then purchase from the suggested items. This allows the fashion suggestion system to improve the convenience of online shopping, making it easier for customers to find fashion items that suit them. This allows the fashion suggestion system to suggest the most suitable fashion items based on the customer's preferences and body type, and to search for and present similar items from multiple shops. For example, a customer enters information about their preferences and body type, and the AI ​​analyzes that information to suggest fashion items that suit the customer. Furthermore, based on the suggested items, the AI ​​searches for similar items from multiple shops and presents them to the customer.This will allow customers to easily find fashion items that suit them, improving the convenience of online shopping.

[0067] A fashion suggestion system according to an embodiment includes a reception unit, an analysis unit, a search unit, and a suggestion unit. The reception unit inputs information related to a customer's preferences or body type. The information related to the customer's preferences or body type includes, but is not limited to, information about size, color preference, and style preference. The reception unit provides an interface for the customer to input information related to their favorite colors, styles, and body types. The reception unit can also store the information input by the customer in a database. The analysis unit analyzes the information input by the reception unit and suggests fashion items suitable for the customer. The analysis unit selects optimal fashion items based on the customer's preferences and body type, for example, using AI. For example, the analysis unit suggests casual shirts and pants to a customer who likes a casual style. The analysis unit can also select items of appropriate size based on the customer's height and weight. The search unit searches for similar items from multiple shops based on the items suggested by the analysis unit. The search unit searches for items similar to the suggested items from multiple shops, for example, using AI. For example, the search unit searches multiple shops for items similar to a suggested casual shirt and suggests them to the customer. The suggestion unit presents the items suggested by the search unit to the customer. The suggestion unit, for example, uses AI to provide an interface for presenting the suggested items to the customer. For example, the suggestion unit displays a list of suggested items so that the customer can select one. The suggestion unit can also provide a link to purchase the suggested item. As a result, the fashion suggestion system according to the embodiment can suggest optimal fashion items based on the customer's preferences and body type, and search for and present similar items from multiple shops. For example, a customer inputs information about their preferences and body type, and the AI ​​analyzes that information to suggest fashion items that suit the customer. Furthermore, based on the suggested items, the AI ​​searches for similar items from multiple shops and presents them to the customer. This allows customers to easily find fashion items that suit them, improving the convenience of online shopping.

[0068] The suggestion unit may provide the customer with a link for purchasing the suggested item. Examples of the link include, but are not limited to, a URL link, a QR code, and the like. The suggestion unit may provide, for example, a link for purchasing the suggested item. For example, the suggestion unit may provide a URL link to a detail page of the suggested item. The suggestion unit may also generate a QR code for purchasing the suggested item and present it to the customer. This allows the customer to easily purchase the suggested item. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may provide the link using an AI model that generates a URL link to a detail page of the suggested item.

[0069] The reception unit can estimate the customer's emotions and adjust the timing of information input based on the estimated customer emotions. For example, if the customer is feeling stressed, the reception unit can simplify the input procedure and prompt the customer to input the minimum amount of information. For example, if the customer is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the customer is in a hurry, the reception unit can prioritize voice input to enable quick information input. For example, if the customer is feeling stressed, the reception unit can simplify the input procedure and prompt the customer to input the minimum amount of information. Furthermore, if the customer is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. This provides a more comfortable information input experience by adjusting the timing of information input according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input facial expression data of a customer into the generation AI and have the generation AI estimate the customer's emotions.

[0070] The reception unit can analyze the customer's past purchase history and select the optimal information input method. For example, the reception unit automatically displays information on items frequently purchased by the customer in the past as input candidates. For example, the reception unit prioritizes suggesting input methods (voice, text, etc.) that the customer has used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the customer's past purchase history. For example, the reception unit automatically displays information on items frequently purchased by the customer in the past as input candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the customer has used in the past. This improves the efficiency of information input by providing the optimal information input method based on the customer's past purchase history. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the customer's past purchase history data into a generation AI and have the generation AI select the optimal information input method.

[0071] When inputting information, the reception unit can filter the information based on the customer's current fashion trends and areas of interest. The reception unit, for example, prioritizes displaying related information based on fashion items recently searched by the customer. For example, the reception unit reflects information about brands and influencers the customer follows on social media. The reception unit can also filter related items based on the style and color of items previously purchased by the customer. For example, the reception unit prioritizes displaying related information based on fashion items recently searched by the customer. The reception unit can also reflect information about brands and influencers the customer follows on social media. This allows for filtering information based on the customer's current fashion trends and areas of interest, thereby providing more relevant information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the customer's social media data into the generation AI and have the generation AI filter the related information.

[0072] When inputting information, the reception unit can select an appropriate input means depending on the customer's input method. For example, when a customer inputs information by voice, the reception unit converts the input content into text using voice recognition technology. For example, when a customer uploads an image, the reception unit extracts features of the fashion item using image recognition technology. In addition, when a customer inputs information by text, the reception unit can automatically complete related candidates based on the input content. For example, when a customer inputs information by voice, the reception unit converts the input content into text using voice recognition technology. In addition, when a customer uploads an image, the reception unit can extract features of the fashion item using image recognition technology. This improves the convenience of information input by providing the optimal input means depending on the customer's input method. Some or all of the above-mentioned 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 customer's voice data into a generation AI and have the generation AI convert the voice data into text data.

[0073] The reception unit can estimate the customer's emotions and prioritize the information to be entered based on the estimated customer emotions. For example, if the customer is feeling stressed, the reception unit can prioritize input of important information and postpone input of detailed information. For example, if the customer is relaxed, the reception unit can prioritize input of detailed information and provide customizable options. Furthermore, if the customer is in a hurry, the reception unit can also promptly input the most important information first to expedite processing. For example, if the customer is feeling stressed, the reception unit can prioritize input of important information and postpone input of detailed information. Furthermore, if the customer is relaxed, the reception unit can prioritize input of detailed information and provide customizable options. This improves the efficiency of information entry by prioritizing the information to be entered based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input facial expression data of a customer into the generation AI and have the generation AI estimate the customer's emotions.

[0074] When inputting information, the reception unit can prioritize inputting highly relevant information taking into account the customer's geographical location information. The reception unit, for example, prioritizes displaying information about shops close to the customer's current location. For example, if the customer is in a specific area, the reception unit can prioritize suggesting fashion items popular in that area. Furthermore, if the customer is traveling, the reception unit can also input information based on the fashion trends of the customer's travel destination. For example, the reception unit prioritizes displaying information about shops close to the customer's current location. Furthermore, if the customer is in a specific area, the reception unit can prioritize suggesting fashion items popular in that area. This improves the accuracy of information input by providing highly relevant information based on the customer's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the customer's geographical location data to the generation AI and cause the generation AI to prioritize input of highly relevant information.

[0075] The reception unit can analyze the customer's social media activity and input related information when inputting information. The reception unit, for example, reflects information about brands and influencers the customer follows on social media. For example, the reception unit can input related information based on fashion items the customer shared on social media. The reception unit can also analyze the content of the customer's social media posts and suggest related fashion items. For example, the reception unit reflects information about brands and influencers the customer follows on social media. The reception unit can also input related information based on fashion items the customer shared on social media. This improves the accuracy of information input by providing related information based on the customer's social media activity. Some or all of the above-described processing by 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 customer's social media data into a generation AI and have the generation AI input related information.

[0076] The reception unit can customize the input method by reflecting the customer's past feedback when inputting information. The reception unit customizes the input interface, for example, based on feedback provided by the customer in the past. For example, the reception unit can prioritize suggesting input methods that the customer has previously preferred. The reception unit can also analyze the customer's past feedback and select the optimal input means. For example, the reception unit customizes the input interface based on feedback provided by the customer in the past. The reception unit can also prioritize suggesting input methods that the customer has previously preferred. This improves the convenience of information input by customizing the input method based on the customer's past feedback. Some or all of the above-described processing by 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 customer's past feedback data into a generation AI and have the generation AI customize the input method.

[0077] The analysis unit can estimate the customer's emotions and adjust the presentation of the analysis based on the estimated customer emotions. For example, if the customer is relaxed, the analysis unit provides detailed analysis results and suggests customizable options. For example, if the customer is in a hurry, the analysis unit provides concise and to-the-point analysis results. The analysis unit can also provide visually appealing analysis results if the customer is excited. For example, if the customer is relaxed, the analysis unit provides detailed analysis results and suggests customizable options. For example, if the customer is in a hurry, the analysis unit can also provide concise and to-the-point analysis results. This allows for a deeper understanding of the analysis results by adjusting the presentation of the analysis according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input customer facial expression data into the generation AI and have the generation AI estimate emotions.

[0078] During analysis, the analysis unit can adjust the level of detail of the analysis taking into account changes in customer preferences. For example, if a customer's preferences change frequently, the analysis unit performs a detailed analysis and makes suggestions based on the latest preferences. For example, if a customer's preferences remain stable, the analysis unit performs a brief analysis and makes basic suggestions. Furthermore, if a customer's preferences change according to a particular season or event, the analysis unit can also perform the analysis taking into account such changes. For example, if a customer's preferences change frequently, the analysis unit performs a detailed analysis and makes suggestions based on the latest preferences. Furthermore, if a customer's preferences remain stable, the analysis unit can also perform a brief analysis and make basic suggestions. Thus, by adjusting the level of detail of the analysis according to changes in customer preferences, more appropriate suggestions can be made. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the customer's past purchase history data into the generation AI and cause the generation AI to adjust the level of detail of the analysis taking into account changes in preferences.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the customer's fashion category. For example, the analysis unit applies an analysis algorithm specialized for casual items to a customer who prefers casual fashion. For example, the analysis unit applies an analysis algorithm specialized for formal items to a customer who prefers formal fashion. The analysis unit can also apply an analysis algorithm specialized for sports items to a customer who prefers sports fashion. For example, the analysis unit applies an analysis algorithm specialized for casual items to a customer who prefers casual fashion. The analysis unit can also apply an analysis algorithm specialized for formal items to a customer who prefers formal fashion. In this way, by applying an analysis algorithm depending on the customer's fashion category, the accuracy of the analysis is improved. 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 customer fashion category data into the generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on feedback provided by the customer in the past. For example, the analysis unit analyzes the customer's past analysis results and makes highly accurate suggestions. The analysis unit can also adjust the level of detail of the analysis by referring to the customer's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on feedback provided by the customer in the past. The analysis unit can also analyze the customer's past analysis results and make highly accurate suggestions. In this way, the accuracy of the analysis is improved by referring to the customer's past analysis results. 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 customer's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0081] The analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated customer emotions. For example, if the customer is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the customer is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a visually appealing analysis result if the customer is excited. For example, if the customer is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the customer is relaxed, the analysis unit can also provide a detailed analysis result. This allows the length of the analysis to be adjusted according to the customer's emotions, thereby deepening understanding of the 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, for example, or without AI. For example, the analysis unit can input customer facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0082] During analysis, the analysis unit can determine analysis priorities based on the time of submission of the customer's fashion items. For example, the analysis unit prioritizes analysis of fashion items recently submitted by the customer. For example, the analysis unit prioritizes analysis of fashion items submitted by the customer for a particular event or season. The analysis unit can also prioritize analysis of fashion items frequently submitted by the customer. For example, the analysis unit prioritizes analysis of fashion items recently submitted by the customer. The analysis unit can also prioritize analysis of fashion items submitted by the customer for a particular event or season. This improves analysis efficiency by determining analysis priorities based on the time of submission of the customer's 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 data on the time of submission of the customer's fashion items to the generation AI and have the generation AI determine the analysis priorities.

[0083] During analysis, the analysis unit can adjust the order of analysis based on customer relevance. For example, the analysis unit prioritizes analyzing items that the customer has given high ratings to in the past. For example, the analysis unit prioritizes analyzing items that the customer frequently purchases. The analysis unit can also prioritize analyzing items that the customer associates with a particular brand or style. For example, the analysis unit prioritizes analyzing items that the customer has given high ratings to in the past. The analysis unit can also prioritize analyzing items that the customer frequently purchases. This improves the efficiency of analysis by adjusting the order of analysis based on customer relevance. 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 customer's past purchase history data into the generation AI and cause the generation AI to adjust the order of analysis based on relevance.

[0084] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the customer's level of expertise. For example, the analysis unit provides analysis results that use a lot of technical terminology to a customer who is knowledgeable about fashion. For example, the analysis unit can provide analysis results that are concise and easy to understand to a customer who is not knowledgeable about fashion. The analysis unit can also adjust the level of detail of the analysis results according to the customer's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terminology to a customer who is knowledgeable about fashion. The analysis unit can also provide analysis results that are concise and easy to understand to a customer who is not knowledgeable about fashion. In this way, adjusting the use of technical terminology in the analysis according to the customer's level of expertise deepens the understanding of the analysis results. 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 customer expertise level data into the generation AI and have the generation AI adjust the use of technical terminology.

[0085] The search unit can estimate a customer's emotions and adjust search criteria based on the estimated customer emotions. For example, if the customer is relaxed, the search unit can provide a wide range of search results. For example, if the customer is in a hurry, the search unit can prioritize providing the most relevant search results. The search unit can also provide visually appealing search results if the customer is excited. For example, if the customer is relaxed, the search unit can provide a wide range of search results. For example, if the customer is in a hurry, the search unit can prioritize providing the most relevant search results. This allows for adjusting search criteria according to the customer's emotions to provide more appropriate search results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit can be performed using AI, for example, or without AI. For example, the search unit can input customer facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0086] The search unit can improve search accuracy by taking into account the interrelationships between items during a search. For example, the search unit may prioritize displaying items related to an item searched for by a customer. For example, the search unit may suggest items that go well with items previously purchased by the customer. The search unit may also display related items based on the category or style of the item searched for by the customer. For example, the search unit may prioritize displaying items related to an item searched for by a customer. The search unit may also suggest items that go well with items previously purchased by the customer. This improves search accuracy by taking into account the interrelationships between items. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input the customer's past purchase history data into the generation AI and cause the generation AI to improve search accuracy based on the interrelationships.

[0087] The search unit can perform a search while taking into account attribute information of item providers. For example, if a customer prefers a particular brand, the search unit can prioritize displaying items from that brand. For example, if a customer prefers a particular designer, the search unit can prioritize displaying items from that designer. Also, if a customer prefers a particular shop, the search unit can prioritize displaying items from that shop. For example, if a customer prefers a particular brand, the search unit can prioritize displaying items from that brand. Also, if a customer prefers a particular designer, the search unit can prioritize displaying items from that designer. In this way, by taking into account the attribute information of item providers, more relevant search results are provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input customer brand and designer preference data into the generation AI and cause the generation AI to perform a search based on the provider's attribute information.

[0088] During a search, the search unit can weight the search based on the frequency of item provision. For example, the search unit prioritizes displaying items that customers frequently search for. For example, the search unit prioritizes displaying items that customers have given high ratings to in the past. The search unit can also prioritize displaying items that customers frequently search for during a specific period. For example, the search unit prioritizes displaying items that customers frequently search for. The search unit can also prioritize displaying items that customers have given high ratings to in the past. In this way, weighting the search based on the frequency of item provision provides more relevant search results. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input customer search history data into the generation AI and cause the generation AI to weight the search based on the frequency of provision.

[0089] The search unit can estimate the customer's emotions and adjust the order in which search results are displayed based on the estimated customer emotions. For example, if the customer is relaxed, the search unit displays a wide range of search results. For example, if the customer is in a hurry, the search unit can prioritize displaying the most relevant search results. The search unit can also display visually appealing search results if the customer is excited. For example, if the customer is relaxed, the search unit can display a wide range of search results. For example, if the customer is in a hurry, the search unit can prioritize displaying the most relevant search results. This adjusts the display order of search results according to the customer's emotions, providing more appropriate search results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit can be performed using AI, for example, or without AI. For example, the search unit can input customer facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0090] The search unit can perform a search taking into account the geographical distribution of items. For example, the search unit can prioritize displaying items from shops close to the customer's current location. For example, if the customer is in a specific area, the search unit can prioritize displaying items that are popular in that area. Furthermore, if the customer is traveling, the search unit can search for items based on the fashion trends of the customer's travel destination. For example, the search unit can prioritize displaying items from shops close to the customer's current location. Furthermore, if the customer is in a specific area, the search unit can prioritize displaying items that are popular in that area. In this way, by taking the geographical distribution of items into consideration, more relevant search results are provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input customer geographical distribution data into the generation AI and cause the generation AI to perform a search based on the geographical distribution.

[0091] The search unit can improve search accuracy by referring to literature related to the item during a search. For example, the search unit can suggest related items by referring to fashion articles related to the item searched for by the customer. For example, the search unit can refer to reviews related to the item searched for by the customer and prioritize displaying highly rated items. The search unit can also suggest the latest items by referring to trend reports related to the item searched for by the customer. For example, the search unit can suggest related items by referring to fashion articles related to the item searched for by the customer. The search unit can also suggest highly rated items by referring to reviews related to the item searched for by the customer. This improves search accuracy by referring to literature related to the item. Some or all of the above-described processing in the search unit may be performed using, or without, AI. For example, the search unit can input customer search history data into the generation AI and cause the generation AI to improve search accuracy based on related literature.

[0092] The search unit may perform a search while taking into account the market value of the item. For example, the search unit may suggest items in an optimal price range based on the market value of the item searched for by the customer. For example, the search unit may prioritize displaying items with high cost performance based on the market value of the item searched for by the customer. The search unit may also provide discount and sale information based on the market value of the item searched for by the customer. For example, the search unit may suggest items in an optimal price range based on the market value of the item searched for by the customer. The search unit may also prioritize displaying items with high cost performance based on the market value of the item searched for by the customer. In this way, by taking the market value of the item into consideration, more relevant search results are provided. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input customer search history data into the generation AI and cause the generation AI to perform a search based on market value.

[0093] The suggestion unit can estimate the customer's emotions and adjust the display method of the suggestions based on the estimated customer emotions. For example, if the customer is relaxed, the suggestion unit displays detailed suggestions. For example, if the customer is in a hurry, the suggestion unit displays concise and to-the-point suggestions. Furthermore, if the customer is excited, the suggestion unit can also display visually appealing suggestions. For example, if the customer is relaxed, the suggestion unit displays detailed suggestions. Furthermore, if the customer is in a hurry, the suggestion unit can also display concise and to-the-point suggestions. This allows for adjusting the display method of the suggestions according to the customer's emotions to provide 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input customer facial expression data into the generation AI and cause the generation AI to estimate the emotions.

[0094] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the item when making a suggestion. For example, the suggestion unit displays detailed suggestion content for items that the customer considers important. For example, the suggestion unit displays concise suggestion content for items that the customer considers less important. The suggestion unit can also display detailed suggestion content for items that the customer considers important for a particular event or season. For example, the suggestion unit displays detailed suggestion content for items that the customer considers important. The suggestion unit can also display concise suggestion content for items that the customer considers less important. In this way, by adjusting the level of detail of the suggestion based on the importance of the item, more appropriate suggestions can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input importance data of the customer's items to the generation AI and cause the generation AI to adjust the level of detail of the suggestion based on the importance.

[0095] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the item. For example, the suggestion unit applies a suggestion algorithm specialized for casual items to a customer who prefers casual fashion. For example, the suggestion unit applies a suggestion algorithm specialized for formal items to a customer who prefers formal fashion. The suggestion unit can also apply a suggestion algorithm specialized for sports items to a customer who prefers sports fashion. For example, the suggestion unit applies a suggestion algorithm specialized for casual items to a customer who prefers casual fashion. The suggestion unit can also apply a suggestion algorithm specialized for formal items to a customer who prefers formal fashion. This improves the accuracy of suggestions by applying a suggestion algorithm according to the item category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input customer fashion category data into the generation AI and cause the generation AI to apply a suggestion algorithm according to the category.

[0096] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the customer's past proposal results. The proposal unit makes a highly accurate proposal, for example, based on proposal content that the customer has given a high rating in the past. For example, the proposal unit analyzes the customer's past proposal results and provides optimal proposal content. The proposal unit can also adjust the level of detail of the proposal by referring to the customer's past proposal results. For example, the proposal unit makes a highly accurate proposal based on proposal content that the customer has given a high rating in the past. The proposal unit can also analyze the customer's past proposal results and provide optimal proposal content. In this way, the accuracy of the proposal is improved by referring to the customer's past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the customer's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0097] The suggestion unit can estimate the customer's emotions and adjust the length of the suggestion based on the estimated customer emotions. For example, if the customer is in a hurry, the suggestion unit can provide a short and to-the-point suggestion. For example, if the customer is relaxed, the suggestion unit can provide a detailed suggestion. Furthermore, if the customer is excited, the suggestion unit can provide a visually appealing suggestion. For example, if the customer is in a hurry, the suggestion unit can provide a short and to-the-point suggestion. Furthermore, if the customer is relaxed, the suggestion unit can provide a detailed suggestion. This allows the length of the suggestion to be adjusted according to the customer's emotions, thereby providing a more appropriate suggestion. 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-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input customer facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0098] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the time of submission of the items. For example, the suggestion unit prioritizes suggesting items recently submitted by the customer. For example, the suggestion unit prioritizes suggesting items submitted by the customer for a particular event or season. The suggestion unit can also prioritize suggesting items frequently submitted by the customer. For example, the suggestion unit prioritizes suggesting items recently submitted by the customer. The suggestion unit can also prioritize suggesting items submitted by the customer for a particular event or season. In this way, by determining the priority of suggestions based on the time of submission of the items, more appropriate suggestions can be provided. Some or all of the above-described 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 data on the time of submission of the customer's items into the generation AI and cause the generation AI to determine the priority of suggestions based on the time of submission.

[0099] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of the items. For example, the suggestion unit prioritizes suggesting items that the customer has given high ratings to in the past. For example, the suggestion unit prioritizes suggesting items that the customer frequently purchases. The suggestion unit can also prioritize suggesting items that the customer associates with a specific brand or style. For example, the suggestion unit prioritizes suggesting items that the customer has given high ratings to in the past. The suggestion unit can also prioritize suggesting items that the customer frequently purchases. In this way, by adjusting the order of suggestions based on the relevance of the items, more appropriate suggestions can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the customer's past purchase history data into the generation AI and cause the generation AI to adjust the order of suggestions based on the relevance.

[0100] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the customer's level of expertise. For example, the suggestion unit provides proposal content that uses a lot of technical terminology to a customer who is knowledgeable about fashion. For example, the suggestion unit provides proposal content that is concise and easy to understand to a customer who is not knowledgeable about fashion. The suggestion unit can also adjust the level of detail of the proposal content according to the customer's level of expertise. For example, the suggestion unit provides proposal content that uses a lot of technical terminology to a customer who is knowledgeable about fashion. The suggestion unit can also provide proposal content that is concise and easy to understand to a customer who is not knowledgeable about fashion. In this way, by adjusting the use of technical terminology in the proposal according to the customer's level of expertise, more appropriate proposals 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 customer expertise level data into the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, search unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit provides an interface for inputting information about a customer's preferences and body type using the reception device 38 of the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and selects optimal fashion items based on the customer's preferences and body type using AI. For example, the search unit is realized by the specific processing unit 290 of the data processing device 12 and searches multiple shops for items similar to the suggested items. For example, the suggestion unit presents the suggested items to the customer using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, search unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit provides an interface for inputting information about a customer's preferences and body type using the microphone 238 of the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and selects optimal fashion items based on the customer's preferences and body type using AI. For example, the search unit is realized by the specific processing unit 290 of the data processing device 12 and searches multiple shops for items similar to the suggested items. For example, the suggestion unit presents the suggested items to the customer using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, analysis unit, search unit, and suggestion unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit provides an interface for inputting information about a customer's preferences and body type using the microphone 238 of the headset terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and selects optimal fashion items based on the customer's preferences and body type using AI. For example, the search unit is realized by the specific processing unit 290 of the data processing device 12 and searches multiple shops for items similar to the suggested items. For example, the suggestion unit presents the suggested items to the customer using the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, search unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit provides an interface for inputting information about a customer's preferences and body type using the microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and selects optimal fashion items based on the customer's preferences and body type using AI. For example, the search unit is realized by the specific processing unit 290 of the data processing device 12 and searches multiple shops for items similar to the suggested items. For example, the suggestion unit presents the suggested items to the customer using the speaker 240 of the robot 414.

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

[0102] When entering information about a customer's preferences and body type, the reception unit can automatically refer to the customer's past purchase history and search history to present input candidates. For example, appropriate input candidates can be displayed based on the size and style of items the customer has previously purchased. Related input candidates can also be presented based on information about items the customer has previously searched for. This saves the customer the trouble of entering information, allowing them to enter information more quickly. Furthermore, the reception unit can also prioritize the display of information about items that the customer has previously given high ratings to. This makes it possible to enter information that suits the customer's preferences.

[0103] The suggestion unit can display reviews and ratings from other customers when a customer purchases a suggested item. For example, it can display a list of reviews from other customers for the suggested item, providing reference information for the customer when considering a purchase. It can also display a rating score for the suggested item, which the customer can use as an indicator to judge the quality and satisfaction of the item. This allows the customer to purchase the item with greater peace of mind. Furthermore, the suggestion unit can make additional suggestions to the customer based on the reviews and ratings. For example, it can suggest related items that have been highly rated by other customers who have purchased the same item.

[0104] The reception unit can estimate the customer's emotions and customize the information input interface based on the estimated customer emotions. For example, if the customer is feeling stressed, a simple and intuitive interface can be provided to reduce the burden of information input. Alternatively, if the customer is relaxed, detailed input options can be provided and a customizable interface can be suggested. Furthermore, if the customer is excited, a visually appealing interface can be provided to make information input a fun experience. In this way, by providing an optimal information input interface according to the customer's emotions, the efficiency and satisfaction of information input can be improved.

[0105] The reception unit can analyze a customer's past purchase history and search history to suggest the optimal information input method. For example, it can prioritize suggestions of input methods (voice, text, etc.) that the customer has frequently used in the past. It can also automatically display information about items the customer has previously purchased as input candidates. It can also predict and suggest the input method that will be used during a specific time period based on the customer's past purchase history. This improves the efficiency of information input by providing the optimal information input method based on the customer's past behavior.

[0106] When entering information, the reception unit can filter the information based on the customer's current fashion trends and areas of interest. For example, it can prioritize displaying relevant information based on fashion items that the customer has recently searched for. It can also reflect information about brands and influencers that the customer follows on social media. It can also filter related items based on the style and color of items that the customer has previously purchased. This allows the information to be filtered based on the customer's current fashion trends and areas of interest, providing more relevant information.

[0107] When entering information, the reception unit can select an appropriate input means depending on the customer's input method. For example, if the customer enters information by voice, the input content can be converted into text using voice recognition technology. Also, if the customer uploads an image, the characteristics of the fashion item can be extracted using image recognition technology. Furthermore, if the customer enters information by text, related candidates can be automatically completed based on the input content. This improves the convenience of information entry by providing the optimal input means depending on the customer's input method.

[0108] The reception unit can estimate the customer's emotions and determine the priority of information to be entered based on the estimated customer emotions. For example, if the customer is feeling stressed, important information can be entered first, and detailed information can be left for later. Alternatively, if the customer is relaxed, detailed information can be entered first, and customizable options can be provided. Furthermore, if the customer is in a hurry, the most important information can be entered first, allowing for quick processing. In this way, the efficiency of information entry can be improved by determining the priority of information to be entered based on the customer's emotions.

[0109] When inputting information, the reception unit can prioritize inputting highly relevant information taking into account the customer's geographical location information. For example, it can prioritize displaying information about shops close to the customer's current location. Also, if the customer is in a specific area, it can prioritize suggesting fashion items that are popular in that area. Furthermore, if the customer is traveling, it can also input information based on the fashion trends of the travel destination. This improves the accuracy of information input by providing highly relevant information based on the customer's geographical location information.

[0110] When entering information, the reception unit can analyze the customer's social media activity and enter relevant information. For example, this can reflect information about brands and influencers that the customer follows on social media. It can also enter relevant information based on fashion items that the customer has shared on social media. It can also analyze the content of the customer's social media posts and suggest related fashion items. This improves the accuracy of information entry by providing relevant information based on the customer's social media activity.

[0111] When inputting information, the reception unit can customize the input method by reflecting the customer's past feedback. For example, the input interface can be customized based on feedback provided by the customer in the past. The reception unit can also preferentially suggest input methods that the customer has previously preferred. Furthermore, the reception unit can analyze the customer's past feedback and select the optimal input means. In this way, customizing the input method based on the customer's past feedback improves the convenience of information input.

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

[0113] Step 1: The reception unit inputs information about the customer's preferences or body type. The information about the customer's preferences or body type includes, for example, size, color preference, style preference, etc. The reception unit provides an interface for the customer to input information about their favorite color, style, or body type, and can also store the input information in a database. Step 2: The analysis unit analyzes the information entered by the reception unit and suggests fashion items that suit the customer. The analysis unit uses AI to select the most suitable fashion items based on the customer's preferences and body type. For example, it suggests casual shirts and pants to a customer who likes casual styles, and selects items of the appropriate size based on the customer's height and weight. Step 3: The search unit searches multiple shops for similar items based on the items suggested by the analysis unit. The search unit uses AI to search multiple shops for items similar to the suggested item. For example, it searches multiple shops for items similar to the suggested casual shirt and suggests them to the customer. Step 4: The suggestion unit presents the items suggested by the search unit to the customer. The suggestion unit provides an interface for presenting the suggested items to the customer using AI, lists the suggested items so that the customer can select them, and can also provide a link to purchase the suggested items.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

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

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

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

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

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

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

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

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

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

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

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

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

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

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

[0172] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

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

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

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

[0176] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0177] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

[0179] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

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

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

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

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

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

[0185] [Explanation of symbols]

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

Claims

1. a reception unit for inputting information about a customer's preferences or body type; an analysis unit that analyzes the information input by the reception unit and suggests fashion items suitable for the customer; a search unit that searches for similar items from a plurality of shops based on the items suggested by the analysis unit; a suggestion unit that presents the items suggested by the search unit to the customer. A system characterized by:

2. The proposal unit Provide customers with a link to purchase the suggested item 2. The system of claim 1.

3. The reception unit Estimate customer sentiment and adjust the timing of information input based on the estimated sentiment 2. The system of claim 1.

4. The reception unit Analyze the customer's past purchase history and select the appropriate method for entering information 2. The system of claim 1.

5. The reception unit Filter information as it is entered based on the customer's current fashion trends and interests 2. The system of claim 1.

6. The reception unit When entering information, select the appropriate input method depending on the customer's input method.

2. The system of claim 1.

7. The reception unit Estimate customer sentiment and prioritize input information based on the estimated sentiment 2. The system of claim 1.

8. The reception unit When entering information, consider the customer's geographic location to prioritize the most relevant information 2. The system of claim 1.

9. The reception unit Analyze customer social media activity and enter relevant information when entering information 2. The system of claim 1.

10. The reception unit Customize the way you enter information based on past customer feedback 2. The system of claim 1.

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