System

Abstract

An object of a system according to an embodiment is to link try-on information in a real store with a WEB store.SOLUTION: A system includes a try-on information acquisition unit, an evaluation unit, and a cooperation unit. The try-on information acquisition unit acquires try-on information in a real store. The evaluation unit analyzes the try-on information acquired by the try-on information acquisition unit. The linkage unit links the information analyzed by the evaluation unit to the WEB store.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] Previous technology had the problem of insufficient coordination between physical stores and online stores, making it impossible to effectively utilize try-on information from physical stores.

[0005] The system according to the embodiment aims to link try-on information from physical stores with a web store. [Means for solving the problem]

[0006] The system according to the embodiment includes a fitting information acquisition unit, an evaluation unit, and a linking unit. The fitting information acquisition unit acquires fitting information from physical stores. The evaluation unit analyzes the fitting information acquired by the fitting information acquisition unit. The linking unit links the information analyzed by the evaluation unit to the web store. [Effects of the Invention]

[0007] The system according to the embodiment can link try-on information from physical stores to a web store. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The linked sales system according to an embodiment of the present invention links a physical store and an online store, maximizes the advantages of each, and connects them to an evaluation system for store staff. This allows the physical store and the online store to coexist, realizing an environment where customers can try on and try on products at the physical store without feeling guilty, and then purchase them at the online store.

[0029] The linked sales system according to the embodiment includes a try-on information acquisition unit, an evaluation unit, and a linking unit. The try-on information acquisition unit acquires try-on information from a physical store. For example, the try-on information acquisition unit collects information such as the type and size of products tried on by customers and the time spent trying them on. The try-on information acquisition unit can also collect feedback on the products tried on by customers. For example, customers provide feedback via a dedicated app after trying on products. The evaluation unit analyzes the try-on information acquired by the try-on information acquisition unit. For example, the evaluation unit predicts customer preferences and purchasing trends based on the number of times products are tried on and the feedback after trying them on. The evaluation unit can also provide point rewards or discount coupons based on the try-on information. For example, the evaluation unit provides point rewards to customers who try on products frequently. The linking unit links the information analyzed by the evaluation unit to a web store. For example, the linking unit transmits the try-on information to the web store so that customers can use the information when making purchases from the web store. The linking unit can also share inventory information between the physical store and the web store in real time. For example, when inventory at a physical store is low, the linking unit replenishes inventory from the web store. As a result, the linked sales system according to the embodiment can improve the customer's purchasing experience by linking try-on information from the physical store to the web store.

[0030] The fitting information acquisition unit can acquire fitting information using a QR code or a dedicated app. The fitting information acquisition unit acquires fitting information using, for example, a QR code. For example, the QR code of the product that the customer tried on is scanned and the information is sent to the fitting information acquisition unit. The fitting information acquisition unit can also acquire fitting information using a dedicated app. For example, the customer inputs information about the product that the customer tried on through the dedicated app and sends the information to the fitting information acquisition unit. This makes it easy to acquire fitting information using a QR code or a dedicated app.

[0031] The evaluation unit can provide point redemption or discount coupons based on the try-on information. The evaluation unit, for example, provides point redemption based on the try-on information. For example, the evaluation unit redeems points to customers who try on a large number of items. The evaluation unit can also provide discount coupons based on the try-on information. For example, the evaluation unit provides customers with discount coupons for items they have tried on. In this way, by providing point redemption or discount coupons, it is possible to increase customers' purchasing motivation.

[0032] The collaboration unit can share inventory information between physical stores and the web store in real time. For example, the collaboration unit shares inventory information between physical stores and the web store in real time. For example, if there is a shortage of inventory in a physical store, the collaboration unit replenishes the inventory from the web store. The collaboration unit can also optimally allocate inventory based on the inventory information. For example, the collaboration unit moves inventory from a store with a lot of inventory to a store with a little inventory. In this way, sharing inventory information in real time makes it possible to optimally allocate inventory.

[0033] The evaluation unit can make personalized product suggestions based on the try-on information. The evaluation unit makes personalized product suggestions based on, for example, the try-on information. For example, the evaluation unit suggests related products based on information about products tried on by the customer. The evaluation unit can also make personalized product suggestions based on the customer's preferences and purchasing trends. For example, the evaluation unit identifies products that the customer likes based on past purchase history and try-on history, and suggests those products. This makes it possible to improve customer satisfaction by making personalized product suggestions.

[0034] The evaluation unit can provide a function to link with social media based on the try-on information and share the products that have been tried on on SNS. The evaluation unit, for example, can link with social media based on the try-on information and provide a function to share the products that have been tried on on SNS. For example, the evaluation unit can provide a function to post photos of the products that the customer has tried on on SNS. The evaluation unit can also provide a function to post reviews and comments on the products that the customer has tried on on SNS. For example, the evaluation unit can provide a function to automatically post information about the products that the customer has tried on on SNS. This can increase the customer's desire to purchase by sharing the products that they have tried on on SNS.

[0035] Generative AI can be used to analyze evaluations of store staff to identify their strengths and areas for improvement and provide individual training plans. Generative AI can be used to analyze evaluations of store staff to identify their strengths and areas for improvement and provide individual training plans. For example, generative AI can be used to analyze evaluation data of store staff to identify their strengths and areas for improvement. For example, customer service skills and product knowledge can be evaluated. Generative AI can also be used to analyze evaluation data of store staff to provide individual training plans. For example, training programs can be proposed to improve specific skills. Generative AI can also be used to analyze evaluations of store staff to identify their strengths and areas for improvement and provide individual training plans. For example, a customized training plan can be created for each staff member based on the evaluation data. This allows for identifying staff strengths and areas for improvement and providing individual training plans, thereby improving staff skills.

[0036] Customer feedback can be collected in real time and reflected in evaluations of store staff. A system can be built to collect customer feedback in real time and reflect it in evaluations of store staff. For example, customers can evaluate staff responses through a dedicated app. It is also possible to build a system to reflect customer feedback collected in real time in staff evaluations. For example, staff compensation and promotions can be determined based on customer evaluation data. It is also possible to build a system to collect customer feedback in real time and reflect it in staff evaluations. For example, staff training plans can be adjusted based on customer evaluations. In this way, customer feedback can be collected in real time and reflected in staff evaluations, improving the accuracy of evaluations.

[0037] The evaluations of store staff can be reflected in other tasks such as inventory management or display installation to provide a comprehensive performance evaluation. The evaluations of store staff can be reflected in other tasks such as inventory management or display installation to provide a comprehensive performance evaluation. For example, the accuracy of inventory management or the beauty of display installation can be evaluated. It is also possible to create a system in which evaluations of other tasks are reflected in the overall performance evaluation of the staff. For example, the efficiency of inventory management or the creativity of display installation can be evaluated. It is also possible to create a system in which the evaluations of store staff can be reflected in other tasks to provide a comprehensive performance evaluation. For example, the evaluations of inventory management and display installation can be reflected in staff compensation and promotion. In this way, comprehensive performance evaluation makes it possible to evaluate staff from multiple perspectives.

[0038] Generative AI can be used to analyze try-on information in physical stores and provide customers with the optimal timing for purchase and discount information. Generative AI can be used to analyze try-on information in physical stores and suggest the optimal timing for purchase to customers. For example, it can predict the best time for a customer to make a purchase based on past purchase history and try-on history. Generative AI can also be used to analyze try-on information in physical stores and suggest discount information to customers. For example, it can suggest discount coupons for items tried on. Generative AI can also be used to analyze try-on information in physical stores and suggest the optimal timing for purchase and discount information to customers. For example, it can suggest information on discounts that apply if purchased during a specific time period. This can increase customers' desire to purchase by providing them with the optimal timing for purchase and discount information.

[0039] It is possible to make personalized product suggestions in a web store based on a customer's purchase history. A system can be built that makes personalized product suggestions in a web store based on a customer's purchase history. For example, it can suggest products related to products purchased in the past. It is also possible to build a system that analyzes purchase history and makes personalized product suggestions in a web store. For example, it can suggest products that match the customer's preferences. It is also possible to build a system that makes personalized product suggestions in a web store based on a customer's purchase history. For example, it can suggest products that the customer might be interested in based on past purchase data. In this way, it is possible to make personalized product suggestions and improve customer satisfaction.

[0040] By linking the information about trying on clothes at a physical store with related services, it is possible to make comprehensive fashion suggestions. By linking the information about trying on clothes at a physical store with a fashion consulting service, it is possible to make comprehensive fashion suggestions. For example, it is possible to make styling suggestions for the items that have been tried on. It is also possible to link the information about trying on clothes at a physical store with a stylist service, it is possible to make comprehensive fashion suggestions. For example, it is possible to make coordination suggestions for the items that have been tried on. It is also possible to link the information about trying on clothes at a physical store with other related services, it is possible to make comprehensive fashion suggestions. For example, it is possible to provide fashion consulting or stylist advice based on the items that have been tried on. In this way, it is possible to make comprehensive fashion suggestions and improve customer satisfaction.

[0041] It is possible to provide a function that links information about fittings at a physical store with a customer's smart device and receives feedback on the products they have tried on in real time. It is possible to provide a function that links information about fittings at a physical store with a customer's smart device and receives feedback on the products they have tried on in real time. For example, it is possible to link information about fittings at a physical store with a customer's smart device and receive feedback on the products they have tried on in real time. For example, ratings and comments on the products they have tried on can be received via a smartphone. It is also possible to provide a function that links information about fittings at a physical store with a customer's smart device and receives feedback on the products they have tried on in real time. For example, feedback on the products they have tried on can be received via a tablet or smartwatch. It is also possible to provide a function that links information about fittings at a physical store with a customer's smart device and receives feedback on the products they have tried on in real time. For example, feedback on the products they have tried on can be received via a notification function on the smart device. This allows for feedback to be received in real time, thereby improving the customer's purchasing experience.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The linked sales system can further include a proposal unit that makes personalized product proposals based on the customer's purchase history. The proposal unit, for example, proposes products related to products previously purchased. The proposal unit can also propose products that match the customer's preferences. For example, the proposal unit analyzes the customer's purchase history, identifies products that the customer might be interested in, and proposes those products. This makes it possible to improve customer satisfaction by making personalized product proposals.

[0044] The linked sales system can further include a feedback collection unit that collects customer feedback in real time and reflects it in evaluations of store staff. The feedback collection unit allows customers to evaluate staff responses, for example, through a dedicated app. The feedback collection unit can also reflect the customer feedback collected in real time in staff evaluations. For example, the feedback collection unit determines staff compensation and promotions based on customer evaluation data. In this way, by collecting customer feedback in real time and reflecting it in staff evaluations, the accuracy of evaluations can be improved.

[0045] The linked sales system can also include a suggestion unit that uses generation AI to analyze try-on information from physical stores and provides customers with optimal purchase timing and discount information. The suggestion unit predicts the best time for a customer to make a purchase, for example, based on past purchase history and try-on history. The suggestion unit can also provide discount coupons for items tried on. For example, it can provide information on discounts that apply if purchased during a specific time period. This can increase customers' purchasing motivation by providing them with optimal purchase timing and discount information.

[0046] The linked sales system may further include a proposal unit that links the fitting information from the physical store with related services and makes comprehensive fashion proposals. The proposal unit, for example, makes styling proposals for the items that have been tried on. The proposal unit may also make coordination proposals for the items that have been tried on. For example, the proposal unit may provide fashion consulting or stylist advice based on the items that have been tried on. This makes it possible to improve customer satisfaction by making comprehensive fashion proposals.

[0047] The linked sales system can further include a feedback collection unit that links fitting information from the physical store with the customer's smart device and provides a function for receiving feedback on the items tried on in real time. For example, the feedback collection unit receives ratings and comments on the items tried on via a smartphone. The feedback collection unit can also receive feedback on the items tried on via a tablet or smartwatch. For example, the feedback is received via the notification function of the smart device. This allows for real-time feedback to be received, improving the customer's purchasing experience.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The fitting information acquisition unit acquires fitting information from physical stores. For example, it collects information such as the type and size of the product that the customer tried on, and the time spent trying it on. Customers can also provide feedback through a dedicated app after trying on the products. Step 2: The evaluation unit analyzes the fitting information acquired by the fitting information acquisition unit. For example, it predicts the customer's preferences and purchasing trends based on the number of fittings and feedback after trying on. It can also provide point rewards and discount coupons based on the fitting information. Step 3: The linking unit links the information analyzed by the evaluation unit to the web store. For example, it can send try-on information to the web store so that customers can use that information when making a purchase from the web store. It can also share inventory information between the physical store and the web store in real time.

[0050] (Example 2) The linked sales system according to an embodiment of the present invention links a physical store and an online store, maximizes the advantages of each, and connects them to an evaluation system for store staff. This allows the physical store and the online store to coexist, realizing an environment where customers can try on and try on products at the physical store without feeling guilty, and then purchase them at the online store.

[0051] The linked sales system according to the embodiment includes a try-on information acquisition unit, an evaluation unit, and a linking unit. The try-on information acquisition unit acquires try-on information from a physical store. For example, the try-on information acquisition unit collects information such as the type and size of products tried on by customers and the time spent trying them on. The try-on information acquisition unit can also collect feedback on the products tried on by customers. For example, customers provide feedback via a dedicated app after trying on products. The evaluation unit analyzes the try-on information acquired by the try-on information acquisition unit. For example, the evaluation unit predicts customer preferences and purchasing trends based on the number of times products are tried on and the feedback after trying them on. The evaluation unit can also provide point rewards or discount coupons based on the try-on information. For example, the evaluation unit provides point rewards to customers who try on products frequently. The linking unit links the information analyzed by the evaluation unit to a web store. For example, the linking unit transmits the try-on information to the web store so that customers can use the information when making purchases from the web store. The linking unit can also share inventory information between the physical store and the web store in real time. For example, when inventory at a physical store is low, the linking unit replenishes inventory from the web store. As a result, the linked sales system according to the embodiment can improve the customer's purchasing experience by linking try-on information from the physical store to the web store.

[0052] The fitting information acquisition unit can acquire fitting information using a QR code or a dedicated app. The fitting information acquisition unit acquires fitting information using, for example, a QR code. For example, the QR code of the product that the customer tried on is scanned and the information is sent to the fitting information acquisition unit. The fitting information acquisition unit can also acquire fitting information using a dedicated app. For example, the customer inputs information about the product that the customer tried on through the dedicated app and sends the information to the fitting information acquisition unit. This makes it easy to acquire fitting information using a QR code or a dedicated app.

[0053] The evaluation unit can provide point redemption or discount coupons based on the try-on information. The evaluation unit, for example, provides point redemption based on the try-on information. For example, the evaluation unit redeems points to customers who try on a large number of items. The evaluation unit can also provide discount coupons based on the try-on information. For example, the evaluation unit provides customers with discount coupons for items they have tried on. In this way, by providing point redemption or discount coupons, it is possible to increase customers' purchasing motivation.

[0054] The collaboration unit can share inventory information between physical stores and the web store in real time. For example, the collaboration unit shares inventory information between physical stores and the web store in real time. For example, if there is a shortage of inventory in a physical store, the collaboration unit replenishes the inventory from the web store. The collaboration unit can also optimally allocate inventory based on the inventory information. For example, the collaboration unit moves inventory from a store with a lot of inventory to a store with a little inventory. In this way, sharing inventory information in real time makes it possible to optimally allocate inventory.

[0055] The evaluation unit can make personalized product suggestions based on the try-on information. The evaluation unit makes personalized product suggestions based on, for example, the try-on information. For example, the evaluation unit suggests related products based on information about products tried on by the customer. The evaluation unit can also make personalized product suggestions based on the customer's preferences and purchasing trends. For example, the evaluation unit identifies products that the customer likes based on past purchase history and try-on history, and suggests those products. This makes it possible to improve customer satisfaction by making personalized product suggestions.

[0056] The evaluation unit can provide a function to link with social media based on the try-on information and share the products that have been tried on on SNS. The evaluation unit, for example, can link with social media based on the try-on information and provide a function to share the products that have been tried on on SNS. For example, the evaluation unit can provide a function to post photos of the products that the customer has tried on on SNS. The evaluation unit can also provide a function to post reviews and comments on the products that the customer has tried on on SNS. For example, the evaluation unit can provide a function to automatically post information about the products that the customer has tried on on SNS. This can increase the customer's desire to purchase by sharing the products that they have tried on on SNS.

[0057] The evaluation unit can use the emotion estimation function to analyze the customer's emotions based on the try-on information and customize the purchasing experience at the web store based on the emotions. The evaluation unit can, for example, use the emotion estimation function to analyze the customer's emotions at the physical store. For example, the evaluation unit can analyze the customer's facial expressions and voice and calculate an emotion score. The evaluation unit can also customize the purchasing experience at the web store based on the customer's emotions. For example, the evaluation unit can provide special offers to customers with positive emotions. The evaluation unit can also make special suggestions to encourage purchases to customers with negative emotions. In this way, customer satisfaction can be improved by customizing the purchasing experience based on the customer's emotions.

[0058] Generative AI can be used to analyze evaluations of store staff to identify their strengths and areas for improvement and provide individual training plans. Generative AI can be used to analyze evaluations of store staff to identify their strengths and areas for improvement and provide individual training plans. For example, generative AI can be used to analyze evaluation data of store staff to identify their strengths and areas for improvement. For example, customer service skills and product knowledge can be evaluated. Generative AI can also be used to analyze evaluation data of store staff to provide individual training plans. For example, training programs can be proposed to improve specific skills. Generative AI can also be used to analyze evaluations of store staff to identify their strengths and areas for improvement and provide individual training plans. For example, a customized training plan can be created for each staff member based on the evaluation data. This allows for identifying staff strengths and areas for improvement and providing individual training plans, thereby improving staff skills.

[0059] Customer feedback can be collected in real time and reflected in evaluations of store staff. A system can be built to collect customer feedback in real time and reflect it in evaluations of store staff. For example, customers can evaluate staff responses through a dedicated app. It is also possible to build a system to reflect customer feedback collected in real time in staff evaluations. For example, staff compensation and promotions can be determined based on customer evaluation data. It is also possible to build a system to collect customer feedback in real time and reflect it in staff evaluations. For example, staff training plans can be adjusted based on customer evaluations. In this way, customer feedback can be collected in real time and reflected in staff evaluations, improving the accuracy of evaluations.

[0060] The emotion estimation function can be used to analyze the emotions that customers have toward store staff, and to evaluate the store staff based on those emotions. The emotion estimation function can be used to analyze the emotions that customers have toward store staff, and to evaluate the store staff based on those emotions. For example, the emotion estimation function can be used to analyze the emotions that customers have toward staff. For example, the customer's facial expressions and voice can be analyzed to calculate an emotion score. It is also possible to build a system that analyzes customer emotions and evaluates staff based on those emotions. For example, a customer with positive emotions can be given a higher rating. The emotion estimation function can also be used to analyze the emotions that customers have toward staff, and to evaluate the staff based on those emotions. For example, a customer with negative emotions can be given a lower rating. In this way, by evaluating staff based on customer emotions, it is possible to improve the fairness of the evaluations.

[0061] The evaluations of store staff can be reflected in other tasks such as inventory management or display installation to provide a comprehensive performance evaluation. The evaluations of store staff can be reflected in other tasks such as inventory management or display installation to provide a comprehensive performance evaluation. For example, the accuracy of inventory management or the beauty of display installation can be evaluated. It is also possible to create a system in which evaluations of other tasks are reflected in the overall performance evaluation of the staff. For example, the efficiency of inventory management or the creativity of display installation can be evaluated. It is also possible to create a system in which the evaluations of store staff can be reflected in other tasks to provide a comprehensive performance evaluation. For example, the evaluations of inventory management and display installation can be reflected in staff compensation and promotion. In this way, comprehensive performance evaluation makes it possible to evaluate staff from multiple perspectives.

[0062] The emotion estimation function can be used to analyze the emotions customers have toward store staff, and to determine the store staff's compensation and promotion based on those emotions. A system can be constructed using the emotion estimation function to analyze the emotions customers have toward store staff and to determine the store staff's compensation and promotion based on those emotions. For example, the emotion estimation function can be used to analyze the emotions customers have toward staff. For example, the customer's facial expressions and voice can be analyzed to calculate an emotion score. It is also possible to construct a system that analyzes customer emotions and determines the staff's compensation and promotion based on those emotions. For example, a customer with positive emotions can be given a higher rating. The emotion estimation function can also be used to analyze the emotions customers have toward staff, and to determine the staff's compensation and promotion based on those emotions. For example, a customer with negative emotions can be given a lower rating. In this way, by determining compensation and promotion based on customer emotions, it is possible to improve staff motivation.

[0063] Generative AI can be used to analyze try-on information in physical stores and provide customers with the optimal timing for purchase and discount information. Generative AI can be used to analyze try-on information in physical stores and suggest the optimal timing for purchase to customers. For example, it can predict the best time for a customer to make a purchase based on past purchase history and try-on history. Generative AI can also be used to analyze try-on information in physical stores and suggest discount information to customers. For example, it can suggest discount coupons for items tried on. Generative AI can also be used to analyze try-on information in physical stores and suggest the optimal timing for purchase and discount information to customers. For example, it can suggest information on discounts that apply if purchased during a specific time period. This can increase customers' desire to purchase by providing them with the optimal timing for purchase and discount information.

[0064] It is possible to make personalized product suggestions in a web store based on a customer's purchase history. A system can be built that makes personalized product suggestions in a web store based on a customer's purchase history. For example, it can suggest products related to products purchased in the past. It is also possible to build a system that analyzes purchase history and makes personalized product suggestions in a web store. For example, it can suggest products that match the customer's preferences. It is also possible to build a system that makes personalized product suggestions in a web store based on a customer's purchase history. For example, it can suggest products that the customer might be interested in based on past purchase data. In this way, it is possible to make personalized product suggestions and improve customer satisfaction.

[0065] The emotion estimation function can be used to analyze the emotions customers feel when trying on clothes, and customize the purchasing experience at a web store based on those emotions. The emotion estimation function can be used to analyze the emotions customers feel when trying on clothes, and customize the purchasing experience at a web store based on those emotions. For example, the emotion estimation function can be used to analyze the emotions customers feel when trying on clothes. For example, the emotion estimation function can be used to analyze the emotions customers feel when trying on clothes. For example, the emotion estimation function can be used to analyze the emotions customers feel when trying on clothes. For example, the emotion estimation function can be used to calculate an emotion score. It is also possible to build a system that analyzes customer emotions and customize the purchasing experience at a web store based on those emotions. For example, special offers can be provided to customers with positive emotions. It is also possible to analyze customer emotions and customize the purchasing experience at a web store based on those emotions. For example, special suggestions can be made to customers with negative emotions to encourage purchases. The emotion estimation function can also be used to analyze the emotions customers feel when trying on clothes, and customize the purchasing experience at a web store based on those emotions. For example, product suggestions and coupons can be provided according to the customer's emotions. In this way, customer satisfaction can be improved by customizing the purchasing experience based on the customer's emotions.

[0066] By linking the information about trying on clothes at a physical store with related services, it is possible to make comprehensive fashion suggestions. By linking the information about trying on clothes at a physical store with a fashion consulting service, it is possible to make comprehensive fashion suggestions. For example, it is possible to make styling suggestions for the items that have been tried on. It is also possible to link the information about trying on clothes at a physical store with a stylist service, it is possible to make comprehensive fashion suggestions. For example, it is possible to make coordination suggestions for the items that have been tried on. It is also possible to link the information about trying on clothes at a physical store with other related services, it is possible to make comprehensive fashion suggestions. For example, it is possible to provide fashion consulting or stylist advice based on the items that have been tried on. In this way, it is possible to make comprehensive fashion suggestions and improve customer satisfaction.

[0067] It is possible to provide a function that links information about fittings at a physical store with a customer's smart device and receives feedback on the products they have tried on in real time. It is possible to provide a function that links information about fittings at a physical store with a customer's smart device and receives feedback on the products they have tried on in real time. For example, it is possible to link information about fittings at a physical store with a customer's smart device and receive feedback on the products they have tried on in real time. For example, ratings and comments on the products they have tried on can be received via a smartphone. It is also possible to provide a function that links information about fittings at a physical store with a customer's smart device and receives feedback on the products they have tried on in real time. For example, feedback on the products they have tried on can be received via a tablet or smartwatch. It is also possible to provide a function that links information about fittings at a physical store with a customer's smart device and receives feedback on the products they have tried on in real time. For example, feedback on the products they have tried on can be received via a notification function on the smart device. This allows for feedback to be received in real time, thereby improving the customer's purchasing experience.

[0068] The emotion estimation function can be used to analyze the emotions customers feel when trying on clothes, and to provide special offers and coupons in the web store based on those emotions. The emotion estimation function can be used to analyze the emotions customers feel when trying on clothes, and to provide special offers and coupons in the web store based on those emotions. For example, the emotion estimation function can be used to analyze the emotions customers feel when trying on clothes. For example, the customer's facial expressions and voice can be analyzed to calculate an emotion score. It is also possible to build a system that analyzes customer emotions and provides special offers and coupons in the web store based on those emotions. For example, a discount coupon can be provided to customers with positive emotions. It is also possible to analyze customer emotions and provide special offers and coupons in the web store based on those emotions. For example, a special offer can be provided to customers with negative emotions. It is also possible to analyze the emotions customers feel when trying on clothes, and to provide special offers and coupons in the web store based on those emotions. For example, a special proposal can be made according to the customer's emotions. In this way, by providing special offers and coupons based on the customer's emotions, it is possible to increase customer purchasing motivation.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The linked sales system can further include a proposal unit that makes personalized product proposals based on the customer's purchase history. The proposal unit, for example, proposes products related to products previously purchased. The proposal unit can also propose products that match the customer's preferences. For example, the proposal unit analyzes the customer's purchase history, identifies products that the customer might be interested in, and proposes those products. This makes it possible to improve customer satisfaction by making personalized product proposals.

[0071] The linked sales system can further include a feedback collection unit that collects customer feedback in real time and reflects it in evaluations of store staff. The feedback collection unit allows customers to evaluate staff responses, for example, through a dedicated app. The feedback collection unit can also reflect the customer feedback collected in real time in staff evaluations. For example, the feedback collection unit determines staff compensation and promotions based on customer evaluation data. In this way, by collecting customer feedback in real time and reflecting it in staff evaluations, the accuracy of evaluations can be improved.

[0072] The linked sales system can also include a suggestion unit that uses generation AI to analyze try-on information from physical stores and provides customers with optimal purchase timing and discount information. The suggestion unit predicts the best time for a customer to make a purchase, for example, based on past purchase history and try-on history. The suggestion unit can also provide discount coupons for items tried on. For example, it can provide information on discounts that apply if purchased during a specific time period. This can increase customers' purchasing motivation by providing them with optimal purchase timing and discount information.

[0073] The linked sales system may further include a proposal unit that links the fitting information from the physical store with related services and makes comprehensive fashion proposals. The proposal unit, for example, makes styling proposals for the items that have been tried on. The proposal unit may also make coordination proposals for the items that have been tried on. For example, the proposal unit may provide fashion consulting or stylist advice based on the items that have been tried on. This makes it possible to improve customer satisfaction by making comprehensive fashion proposals.

[0074] The linked sales system can further include a feedback collection unit that links fitting information from the physical store with the customer's smart device and provides a function for receiving feedback on the items tried on in real time. For example, the feedback collection unit receives ratings and comments on the items tried on via a smartphone. The feedback collection unit can also receive feedback on the items tried on via a tablet or smartwatch. For example, the feedback is received via the notification function of the smart device. This allows for real-time feedback to be received, improving the customer's purchasing experience.

[0075] The evaluation unit can use an emotion estimation function based on the try-on information to analyze the customer's emotions and customize the purchasing experience at the web store based on those emotions. For example, the evaluation unit can analyze the customer's facial expressions and voice to calculate an emotion score. The evaluation unit can also provide special offers to customers with positive emotions. For example, it can make special suggestions to encourage purchases to customers with negative emotions. This makes it possible to improve customer satisfaction by customizing the purchasing experience based on the customer's emotions.

[0076] The evaluation unit can use the emotion estimation function to analyze the emotions customers have toward store staff and evaluate the store staff based on those emotions. For example, the evaluation unit can analyze the customer's facial expressions and voice and calculate an emotion score. The evaluation unit can also give a higher rating to customers who have positive emotions. For example, it can give a lower rating to customers who have negative emotions. In this way, by evaluating staff based on the customer's emotions, the fairness of the evaluation can be improved.

[0077] The evaluation unit uses the emotion estimation function to analyze the emotions customers feel when trying on clothes, and can provide special offers and coupons in the web store based on those emotions. For example, the evaluation unit analyzes the customer's facial expressions and voice to calculate an emotion score. The evaluation unit can also provide discount coupons to customers who have positive emotions. For example, it can provide special offers to customers who have negative emotions. In this way, by providing special offers and coupons based on the customer's emotions, it is possible to increase the customer's desire to purchase.

[0078] The evaluation unit can use the emotion estimation function to analyze the emotions customers have toward store staff and determine the store staff's compensation and promotion based on those emotions. For example, the evaluation unit can analyze the customer's facial expressions and voice and calculate an emotion score. The evaluation unit can also give a higher rating to customers who have positive emotions. For example, it can give a lower rating to customers who have negative emotions. In this way, by determining compensation and promotion based on customer emotions, it is possible to improve staff motivation.

[0079] The evaluation unit uses the emotion estimation function to analyze the emotions customers feel when trying on clothes and customize the purchasing experience on the web store based on those emotions. For example, the evaluation unit analyzes the customer's facial expressions and voice to calculate an emotion score. The evaluation unit can also provide special offers to customers with positive emotions. For example, it can make special suggestions to encourage purchases to customers with negative emotions. This makes it possible to improve customer satisfaction by customizing the purchasing experience based on the customer's emotions.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The fitting information acquisition unit acquires fitting information from physical stores. For example, it collects information such as the type and size of the product that the customer tried on, and the time spent trying it on. Customers can also provide feedback through a dedicated app after trying on the products. Step 2: The evaluation unit analyzes the fitting information acquired by the fitting information acquisition unit. For example, it predicts the customer's preferences and purchasing trends based on the number of fittings and feedback after trying on. It can also provide point rewards and discount coupons based on the fitting information. Step 3: The linking unit links the information analyzed by the evaluation unit to the web store. For example, it can send try-on information to the web store so that customers can use that information when making a purchase from the web store. It can also share inventory information between the physical store and the web store in real time.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 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 fitting information acquisition unit that acquires fitting information at a physical store; an evaluation unit that analyzes the fitting information acquired by the fitting information acquisition unit; A linking unit that links the information analyzed by the evaluation unit to a web store. A system characterized by:

2. The fitting information acquisition unit Obtain fitting information using a QR code or a dedicated app 2. The system of claim 1.

3. The evaluation unit Based on the fitting information, points can be redeemed or discount coupons can be provided.

2. The system of claim 1.

4. The linking unit is Share inventory information between the physical store and the web store in real time 2. The system of claim 1.

5. The evaluation unit Make personalized product suggestions based on the fitting information 2. The system of claim 1.

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