System

Abstract

An object of a system according to an embodiment is to improve the efficiency of service counter support in a retail chain.SOLUTION: A system according to an embodiment includes a generated AI model preparing unit, a query responding unit, and a platform providing unit. The generative AI model preparer prepares a generative AI model for each chain. The inquiry handling unit handles the inquiry by using the smartphone application. The platform providing unit provides a platform of each model.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not efficiently handle service counters in retail chains, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of service counter operations in retail chains. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI model preparation unit, an inquiry response unit, and a platform provision unit. The generation AI model preparation unit prepares a generation AI model for each chain. The inquiry response unit responds to inquiries using a smartphone app. The platform provision unit provides a platform for each model. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of service counter operations in retail chains. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A service counter support system according to an embodiment of the present invention is a system that uses a smartphone generative AI app to provide support at service counters in retail chains such as home improvement stores, supermarkets, and large-scale commercial facilities. This allows the service counter support system to prepare generative AI models customized for each chain or tenant and provide services for the service counter. The smartphone generative AI app automatically responds to inquiries from customers who visit the store. For example, in response to an inquiry such as "Where is XX?", the generative AI can generate a specific answer such as "XX is on the second floor, on the left side" based on store layout information.

[0029] A service counter response system according to an embodiment includes a generative AI model preparation unit, an inquiry response unit, and a platform provision unit. The generative AI model preparation unit prepares a generative AI model for each chain. For example, the generative AI model preparation unit prepares models customized for each chain, such as a model for home improvement stores, a model for supermarkets, and a model for large-scale commercial facilities. The generative AI learns the layout, product information, tenant information, etc. of each store and generates appropriate answers to user inquiries. The inquiry response unit responds to inquiries using a smartphone app. For example, when a customer makes an inquiry using the generative AI app on their smartphone, the generative AI analyzes the inquiry and provides an appropriate answer. For example, in response to an inquiry such as "Where is XX?", the generative AI generates a specific answer such as "XX is on the second floor, on the left" based on the store layout information. The platform provision unit provides a platform for each model. For example, the platform provision unit provides a platform that integrates models customized for each chain. This platform centrally manages information about each store and updates and maintains the generative AI model. For example, when new products arrive or the store layout is changed, the generative AI model is updated through the platform to reflect the latest information. As a result, the service counter response system according to the embodiment can prepare generative AI models for each chain, respond to inquiries using a smartphone app, and provide a platform for each model, thereby streamlining service counter responses.

[0030] The generative AI model preparation unit can learn from each chain's past sales data and propose optimization of product placement according to seasons and events. For example, the generative AI model preparation unit collects each chain's past sales data and has the generative AI learn it. For example, it analyzes best-selling product data during event periods such as Christmas and Valentine's Day and proposes optimal product placement. The generative AI model preparation unit also optimizes product placement according to the season based on seasonal sales data. For example, it proposes placing cooling products and outdoor equipment at the forefront in the summer. The generative AI model preparation unit also analyzes event and promotion data, and the generative AI proposes optimization of product placement based on that information. For example, it changes the placement of products that sold well during a specific event. In this way, sales can be increased by learning from past sales data and proposing optimization of product placement according to seasons and events.

[0031] The generative AI model preparation unit can analyze customer reviews and feedback from each store and propose layout changes to improve customer satisfaction. For example, the generative AI model preparation unit collects customer reviews from each store, and the generative AI analyzes that data. For example, if there is a lot of feedback that a particular product is difficult to find, the generative AI model preparation unit proposes changing the placement of that product. The generative AI model preparation unit also proposes store layout changes based on customer feedback. For example, it proposes reducing customer stress by expanding areas that tend to get crowded. The generative AI model preparation unit also proposes specific layout changes to improve customer satisfaction based on the analysis of customer reviews. For example, it could change the placement of popular products to a more prominent location. In this way, the customer experience can be improved by analyzing customer reviews and feedback and proposing layout changes to improve customer satisfaction.

[0032] The generative AI model preparation unit can add a voice recognition function to enable inquiries to be handled via voice input. For example, the generative AI model preparation unit adds a voice recognition function to the generative AI model for each chain to enable customers to make inquiries via voice. For example, when a customer asks a question by voice, "Where is XX?", the generative AI responds by voice. The generative AI model preparation unit also uses the voice recognition function to analyze the customer's voice input, and the generative AI provides an appropriate response. For example, the generative AI responds by voice to the voice input, "Do you sell XX?" The generative AI model preparation unit also adds a voice recognition function, allowing the generative AI to handle inquiries via voice input. For example, when a customer asks a question by voice, the generative AI responds by voice. This enables inquiries to be handled via voice input, improving customer convenience.

[0033] The generative AI model preparation unit can be customized so that it can be applied to other retail formats. For example, the generative AI model preparation unit customizes a generative AI model for each chain for a drugstore, having it learn product information and layout information specific to that store. For example, the generative AI provides an appropriate answer to the query, "Where is the pharmacy?" The generative AI model preparation unit also customizes a generative AI model for a bookstore, having it learn product information and layout information specific to that bookstore. For example, the generative AI provides an appropriate answer to the query, "Where is the new releases section?" The generative AI model preparation unit also customizes the generative AI model for other retail formats, having it learn information specific to each format. For example, it can be made to support formats other than home improvement stores, supermarkets, and large-scale commercial facilities. This customization to enable application to other retail formats improves the versatility of the system.

[0034] The inquiry response unit can automatically provide promotional information and coupons for related products based on the content of the inquiry. For example, the inquiry response unit analyzes the content of the inquiry and automatically provides promotional information for related products. For example, in response to an inquiry such as "Where is XX?", promotional information for that product is displayed. The inquiry response unit also uses a generation AI to automatically provide related coupons based on the content of the inquiry. For example, in response to an inquiry such as "Do you sell XX?", a coupon for that product is displayed. The inquiry response unit also uses a generation AI to provide promotional information and coupons for related products based on the content of the inquiry. For example, in response to an inquiry such as "Where is the store that sells XX?", a coupon for that store is displayed. In this way, by automatically providing promotional information and coupons for related products based on the content of the inquiry, it is possible to increase customer purchasing motivation.

[0035] The inquiry response unit can analyze the inquiry history, predict the customer's purchasing tendencies, and make personalized product suggestions. The inquiry response unit, for example, analyzes the inquiry history and predicts the customer's purchasing tendencies. For example, it suggests products that the customer may be interested in based on the content of past inquiries. Furthermore, the inquiry response unit uses a generation AI to make personalized product suggestions based on the customer's inquiry history. For example, it suggests new products related to products that the customer has inquired about in the past. Furthermore, the inquiry response unit analyzes the inquiry history and uses a generation AI to predict the customer's purchasing tendencies and make personalized product suggestions. For example, it recommends products that the customer may be interested in. In this way, the customer's purchasing experience can be improved by analyzing the inquiry history, predicting the customer's purchasing tendencies, and making personalized product suggestions.

[0036] The inquiry response unit can add an AR function to the smartphone app to provide visual support for customers navigating within the store. For example, the inquiry response unit adds an AR function to the smartphone app to provide visual support for customers navigating within the store. For example, it displays in-store guidance through the smartphone camera. The inquiry response unit also uses the AR function to provide visual support for customers navigating within the store. For example, it displays a route to a destination using AR. The inquiry response unit also adds an AR function to the smartphone app to provide visual support for customers navigating within the store. For example, it displays the location of products using AR. In this way, adding an AR function to the smartphone app to provide visual support for in-store navigation can improve customer convenience.

[0037] The inquiry response unit can make the smartphone app available on other devices. For example, the inquiry response unit can make the smartphone app available on a smartwatch, allowing customers to make inquiries through the smartwatch. For example, the smartwatch's voice input function can be used. The inquiry response unit can also make the smartphone app available on smartglasses, allowing customers to make inquiries through the smartglasses. For example, the smartglasses display can display answers. The inquiry response unit can also make the smartphone app available on other devices, allowing customers to make inquiries through various devices. For example, the smartwatch or smartglasses can be used. This makes the smartphone app available on other devices, thereby improving customer convenience.

[0038] The platform providing unit can link with the inventory management systems of each store on the platform and update inventory information in real time. The platform providing unit, for example, links with the inventory management systems of each store on the platform and updates inventory information in real time. For example, when new products arrive, the inventory information is updated immediately. The platform providing unit also integrates the inventory management system with the platform, allowing the generation AI to obtain inventory information in real time. For example, it automatically notifies customers of products that are low in stock. The platform providing unit also links with the inventory management system on the platform and updates inventory information in real time, thereby providing customers with accurate inventory information. For example, it instantly displays products that are out of stock. This makes it possible to link with the inventory management systems of each store on the platform and update inventory information in real time, thereby providing accurate inventory information.

[0039] The platform providing unit can analyze sales data from each store on the platform and propose strategies to increase sales. For example, the platform providing unit collects sales data from each store on the platform, and the generation AI analyzes the data. For example, the platform providing unit proposes a promotion strategy for a product with sluggish sales. The platform providing unit also has the generation AI propose a strategy to increase sales based on the sales data. For example, the platform providing unit proposes changing the placement of products that sell well during specific time periods. The platform providing unit also analyzes sales data on the platform, and the generation AI proposes a specific strategy to increase sales. For example, the platform providing unit proposes a promotion strategy based on seasonal sales data. In this way, sales can be increased by analyzing sales data from each store on the platform and proposing strategies to increase sales.

[0040] The platform providing unit can expand the platform so that it can be applied to other retail chains and business types. For example, the platform providing unit expands the platform so that it can be applied to other retail chains and has it learn information specific to each chain. For example, it customizes it for drugstores and bookstores. The platform providing unit also expands the platform so that it can be applied to other business types and has it learn information specific to each business type. For example, it customizes it for restaurants and the service industry. The platform providing unit also expands the platform so that it can be applied to other retail chains and business types. For example, it can be applied to business types other than home improvement stores, supermarkets, and large-scale commercial facilities. In this way, by expanding the platform so that it can be applied to other retail chains and business types, the versatility of the system can be improved.

[0041] The platform providing unit can analyze the energy consumption data of each store on the platform and make suggestions for improving energy efficiency. For example, the platform providing unit collects energy consumption data of each store on the platform, and the generation AI analyzes the data. For example, it identifies time periods with high energy consumption and makes suggestions for improving energy efficiency during those time periods. The platform providing unit also makes suggestions for improving energy efficiency based on the energy consumption data, with the generation AI making suggestions for improving energy efficiency. For example, it makes suggestions for optimizing the use of lighting and air conditioning. The platform providing unit also analyzes the energy consumption data on the platform, and the generation AI makes suggestions for improving energy efficiency. For example, it makes suggestions for introducing equipment that consumes less energy. In this way, by analyzing the energy consumption data of each store on the platform and making suggestions for improving energy efficiency, it is possible to reduce energy costs.

[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 generative AI model preparation unit can analyze customer reviews and feedback from each store and suggest layout changes to improve customer satisfaction. For example, if there is a lot of feedback that a particular product is difficult to find, it can suggest changing the placement of that product. It can also suggest expanding areas that tend to get crowded, reducing customer stress. Furthermore, it can improve customer satisfaction by changing the placement of popular products to more prominent locations. This makes it possible to improve the customer experience by analyzing customer reviews and feedback and suggesting layout changes to improve customer satisfaction.

[0044] The inquiry response unit can automatically provide promotional information and coupons for related products based on the content of the inquiry. For example, in response to an inquiry such as "Where can I find XX?", promotional information for that product is displayed. In response to an inquiry such as "Do you sell XX?", coupons for that product are displayed. Furthermore, in response to an inquiry such as "Where can I find XX?", coupons for that store are displayed. In this way, by automatically providing promotional information and coupons for related products based on the content of the inquiry, it is possible to increase customers' purchasing motivation.

[0045] The customer support department can add AR functionality to the smartphone app to provide visual support for in-store navigation. For example, it can display in-store guidance through the smartphone camera. It can also display the route to the destination using AR, allowing customers to reach the desired product or area without getting lost. It can also display the product's location using AR, making it easier for customers to find the product. By adding AR functionality to the smartphone app and providing visual support for in-store navigation, it is possible to improve customer convenience.

[0046] The inquiry response unit can make the smartphone app available on other devices. For example, the smartphone app can be made available on smartwatches so that customers can make inquiries through the smartwatches. It can also be made available on smartglasses so that customers can make inquiries through the smartglasses. Furthermore, by enabling inquiries by voice using a smart speaker, customers can make inquiries through various devices. By making the smartphone app available on other devices, customer convenience can be improved.

[0047] The platform provider can link with each store's inventory management system on the platform and update inventory information in real time. For example, when new products arrive, inventory information is updated immediately. In addition, stockouts can be prevented by automatically notifying customers of products that are running low on stock. Furthermore, by integrating the inventory management system with the platform and enabling the generation AI to obtain inventory information in real time, accurate inventory information can be provided to customers. This allows the platform to link with each store's inventory management system and update inventory information in real time, making it possible to provide accurate inventory information.

[0048] The platform provider can analyze sales data from each store on the platform and propose strategies to increase sales. For example, it can propose promotion strategies for products with sluggish sales. It can also propose changing the placement of products that sell well during specific time periods. Furthermore, it can improve sales by proposing promotion strategies based on seasonal sales data. In this way, it can improve sales by analyzing sales data from each store on the platform and proposing strategies to increase sales.

[0049] The platform provider can expand the platform so that it can be applied to other retail chains and business types. For example, the platform can be expanded so that it can be applied to other retail chains, and the platform can learn information specific to each chain. The platform can also be expanded so that it can be applied to other business types, and the platform can learn information specific to each business type. Furthermore, the versatility of the system can be improved by making the platform compatible with business types other than home improvement centers, supermarkets, and large-scale commercial facilities. In this way, the versatility of the system can be improved by expanding the platform so that it can be applied to other retail chains and business types.

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

[0051] Step 1: The generative AI model preparation unit prepares a generative AI model for each chain. For example, a model customized for each chain is prepared, such as a model for home improvement stores, a model for supermarkets, or a model for large-scale commercial facilities. The generative AI learns the layout, product information, tenant information, etc. of each store, and generates appropriate answers to user inquiries. Step 2: The inquiry response department responds to inquiries using a smartphone app. For example, when a customer makes an inquiry using the smartphone's generation AI app, the generation AI analyzes the inquiry and provides an appropriate answer. For example, in response to an inquiry such as "Where is XX?", the generation AI generates a specific answer such as "XX is on the second floor, on the left side" based on the store's layout information. Step 3: The platform provider provides a platform for each model. For example, it provides a platform that integrates models customized for each chain. This platform centrally manages information from each store and updates and maintains the generative AI model. For example, when new products arrive or the store layout changes, the generative AI model is updated through the platform to reflect the latest information.

[0052] (Example 2) A service counter support system according to an embodiment of the present invention is a system that uses a smartphone generative AI app to provide support at service counters in retail chains such as home improvement stores, supermarkets, and large-scale commercial facilities. This allows the service counter support system to prepare generative AI models customized for each chain or tenant and provide services for the service counter. The smartphone generative AI app automatically responds to inquiries from customers who visit the store. For example, in response to an inquiry such as "Where is XX?", the generative AI can generate a specific answer such as "XX is on the second floor, on the left side" based on store layout information.

[0053] A service counter response system according to an embodiment includes a generative AI model preparation unit, an inquiry response unit, and a platform provision unit. The generative AI model preparation unit prepares a generative AI model for each chain. For example, the generative AI model preparation unit prepares models customized for each chain, such as a model for home improvement stores, a model for supermarkets, and a model for large-scale commercial facilities. The generative AI learns the layout, product information, tenant information, etc. of each store and generates appropriate answers to user inquiries. The inquiry response unit responds to inquiries using a smartphone app. For example, when a customer makes an inquiry using the generative AI app on their smartphone, the generative AI analyzes the inquiry and provides an appropriate answer. For example, in response to an inquiry such as "Where is XX?", the generative AI generates a specific answer such as "XX is on the second floor, on the left" based on the store layout information. The platform provision unit provides a platform for each model. For example, the platform provision unit provides a platform that integrates models customized for each chain. This platform centrally manages information about each store and updates and maintains the generative AI model. For example, when new products arrive or the store layout is changed, the generative AI model is updated through the platform to reflect the latest information. As a result, the service counter response system according to the embodiment can prepare generative AI models for each chain, respond to inquiries using a smartphone app, and provide a platform for each model, thereby streamlining service counter responses.

[0054] The generative AI model preparation unit can learn from each chain's past sales data and propose optimization of product placement according to seasons and events. For example, the generative AI model preparation unit collects each chain's past sales data and has the generative AI learn it. For example, it analyzes best-selling product data during event periods such as Christmas and Valentine's Day and proposes optimal product placement. The generative AI model preparation unit also optimizes product placement according to the season based on seasonal sales data. For example, it proposes placing cooling products and outdoor equipment at the forefront in the summer. The generative AI model preparation unit also analyzes event and promotion data, and the generative AI proposes optimization of product placement based on that information. For example, it changes the placement of products that sold well during a specific event. In this way, sales can be increased by learning from past sales data and proposing optimization of product placement according to seasons and events.

[0055] The generative AI model preparation unit can analyze customer reviews and feedback from each store and propose layout changes to improve customer satisfaction. For example, the generative AI model preparation unit collects customer reviews from each store, and the generative AI analyzes that data. For example, if there is a lot of feedback that a particular product is difficult to find, the generative AI model preparation unit proposes changing the placement of that product. The generative AI model preparation unit also proposes store layout changes based on customer feedback. For example, it proposes reducing customer stress by expanding areas that tend to get crowded. The generative AI model preparation unit also proposes specific layout changes to improve customer satisfaction based on the analysis of customer reviews. For example, it could change the placement of popular products to a more prominent location. In this way, the customer experience can be improved by analyzing customer reviews and feedback and proposing layout changes to improve customer satisfaction.

[0056] The generative AI model preparation unit can use the emotion estimation function to optimize product placement and layout based on customer emotions. For example, the generative AI model preparation unit collects customer emotion data, and the generative AI analyzes that data. For example, if a customer is feeling stressed in a particular area, the generative AI model preparation unit proposes changing the layout of that area. The generative AI model preparation unit also uses the emotion estimation function to optimize product placement based on customer emotions. For example, it proposes a placement that allows the customer to relax. The generative AI model preparation unit also optimizes the layout based on customer emotion data. For example, it proposes a product placement that excites the customer. In this way, customer satisfaction can be improved by optimizing product placement and layout based on customer emotions.

[0057] The generative AI model preparation unit can add a voice recognition function to enable inquiries to be handled via voice input. For example, the generative AI model preparation unit adds a voice recognition function to the generative AI model for each chain to enable customers to make inquiries via voice. For example, when a customer asks a question by voice, "Where is XX?", the generative AI responds by voice. The generative AI model preparation unit also uses the voice recognition function to analyze the customer's voice input, and the generative AI provides an appropriate response. For example, the generative AI responds by voice to the voice input, "Do you sell XX?" The generative AI model preparation unit also adds a voice recognition function, allowing the generative AI to handle inquiries via voice input. For example, when a customer asks a question by voice, the generative AI responds by voice. This enables inquiries to be handled via voice input, improving customer convenience.

[0058] The generative AI model preparation unit can be customized so that it can be applied to other retail formats. For example, the generative AI model preparation unit customizes a generative AI model for each chain for a drugstore, having it learn product information and layout information specific to that store. For example, the generative AI provides an appropriate answer to the query, "Where is the pharmacy?" The generative AI model preparation unit also customizes a generative AI model for a bookstore, having it learn product information and layout information specific to that bookstore. For example, the generative AI provides an appropriate answer to the query, "Where is the new releases section?" The generative AI model preparation unit also customizes the generative AI model for other retail formats, having it learn information specific to each format. For example, it can be made to support formats other than home improvement stores, supermarkets, and large-scale commercial facilities. This customization to enable application to other retail formats improves the versatility of the system.

[0059] The generative AI model preparation unit uses the emotion estimation function to analyze a customer's first impression when they enter a store and make suggestions for improving the store's atmosphere and design. The generative AI model preparation unit, for example, uses the emotion estimation function to analyze a customer's first impression when they enter a store. For example, it analyzes whether the customer is relaxed and makes suggestions to improve the store's atmosphere. The generative AI model preparation unit also uses the emotion estimation function to make suggestions for improving the store's design based on the customer's first impression data. For example, it proposes a design that will excite the customer. The generative AI model preparation unit also uses the emotion estimation function to analyze a customer's first impression and make suggestions for improving the store's atmosphere and design. For example, it proposes an atmosphere that will make the customer feel relaxed. In this way, by analyzing a customer's first impression when they enter a store and making suggestions for improving the store's atmosphere and design, it is possible to improve customer satisfaction.

[0060] The inquiry response unit can automatically provide promotional information and coupons for related products based on the content of the inquiry. For example, the inquiry response unit analyzes the content of the inquiry and automatically provides promotional information for related products. For example, in response to an inquiry such as "Where is XX?", promotional information for that product is displayed. The inquiry response unit also uses a generation AI to automatically provide related coupons based on the content of the inquiry. For example, in response to an inquiry such as "Do you sell XX?", a coupon for that product is displayed. The inquiry response unit also uses a generation AI to provide promotional information and coupons for related products based on the content of the inquiry. For example, in response to an inquiry such as "Where is the store that sells XX?", a coupon for that store is displayed. In this way, by automatically providing promotional information and coupons for related products based on the content of the inquiry, it is possible to increase customer purchasing motivation.

[0061] The inquiry response unit can analyze the inquiry history, predict the customer's purchasing tendencies, and make personalized product suggestions. The inquiry response unit, for example, analyzes the inquiry history and predicts the customer's purchasing tendencies. For example, it suggests products that the customer may be interested in based on the content of past inquiries. Furthermore, the inquiry response unit uses a generation AI to make personalized product suggestions based on the customer's inquiry history. For example, it suggests new products related to products that the customer has inquired about in the past. Furthermore, the inquiry response unit analyzes the inquiry history and uses a generation AI to predict the customer's purchasing tendencies and make personalized product suggestions. For example, it recommends products that the customer may be interested in. In this way, the customer's purchasing experience can be improved by analyzing the inquiry history, predicting the customer's purchasing tendencies, and making personalized product suggestions.

[0062] The inquiry response unit can add an AR function to the smartphone app to provide visual support for customers navigating within the store. For example, the inquiry response unit adds an AR function to the smartphone app to provide visual support for customers navigating within the store. For example, it displays in-store guidance through the smartphone camera. The inquiry response unit also uses the AR function to provide visual support for customers navigating within the store. For example, it displays a route to a destination using AR. The inquiry response unit also adds an AR function to the smartphone app to provide visual support for customers navigating within the store. For example, it displays the location of products using AR. In this way, adding an AR function to the smartphone app to provide visual support for in-store navigation can improve customer convenience.

[0063] The inquiry response unit can make the smartphone app available on other devices. For example, the inquiry response unit can make the smartphone app available on a smartwatch, allowing customers to make inquiries through the smartwatch. For example, the smartwatch's voice input function can be used. The inquiry response unit can also make the smartphone app available on smartglasses, allowing customers to make inquiries through the smartglasses. For example, the smartglasses display can display answers. The inquiry response unit can also make the smartphone app available on other devices, allowing customers to make inquiries through various devices. For example, the smartwatch or smartglasses can be used. This makes the smartphone app available on other devices, thereby improving customer convenience.

[0064] The inquiry response unit can use the emotion estimation function to analyze the stress level of a customer when making an inquiry and improve the interface to reduce stress. The inquiry response unit, for example, uses the emotion estimation function to analyze the stress level of a customer when making an inquiry. For example, if the customer is feeling stressed, the interface is improved to reduce that stress. The inquiry response unit also uses a generation AI to improve the interface based on the customer's stress level data. For example, a simple design to reduce stress is proposed. The inquiry response unit also uses the emotion estimation function to analyze the stress level of a customer when making an inquiry and improves the interface based on that data. For example, a user-friendly interface to reduce stress is provided. In this way, customer satisfaction can be improved by analyzing the stress level of a customer when making an inquiry and improving the interface to reduce stress.

[0065] The platform providing unit can link with the inventory management systems of each store on the platform and update inventory information in real time. The platform providing unit, for example, links with the inventory management systems of each store on the platform and updates inventory information in real time. For example, when new products arrive, the inventory information is updated immediately. The platform providing unit also integrates the inventory management system with the platform, allowing the generation AI to obtain inventory information in real time. For example, it automatically notifies customers of products that are low in stock. The platform providing unit also links with the inventory management system on the platform and updates inventory information in real time, thereby providing customers with accurate inventory information. For example, it instantly displays products that are out of stock. This makes it possible to link with the inventory management systems of each store on the platform and update inventory information in real time, thereby providing accurate inventory information.

[0066] The platform providing unit can analyze sales data from each store on the platform and propose strategies to increase sales. For example, the platform providing unit collects sales data from each store on the platform, and the generation AI analyzes the data. For example, the platform providing unit proposes a promotion strategy for a product with sluggish sales. The platform providing unit also has the generation AI propose a strategy to increase sales based on the sales data. For example, the platform providing unit proposes changing the placement of products that sell well during specific time periods. The platform providing unit also analyzes sales data on the platform, and the generation AI proposes a specific strategy to increase sales. For example, the platform providing unit proposes a promotion strategy based on seasonal sales data. In this way, sales can be increased by analyzing sales data from each store on the platform and proposing strategies to increase sales.

[0067] The platform providing unit can use the emotion estimation function to analyze customer feedback and improve the platform's user interface. The platform providing unit, for example, uses the emotion estimation function to analyze customer feedback and improve the platform's user interface. For example, it identifies areas where customers feel stressed and improves those areas. The platform providing unit also uses the generative AI to propose improvements to the platform's user interface based on customer emotion data. For example, it proposes a design that helps customers relax. The platform providing unit also uses the emotion estimation function to analyze customer feedback and improve the platform's user interface. For example, it simplifies the interface so that it is easier for customers to use. In this way, customer satisfaction can be improved by analyzing customer feedback and improving the platform's user interface.

[0068] The platform providing unit can expand the platform so that it can be applied to other retail chains and business types. For example, the platform providing unit expands the platform so that it can be applied to other retail chains and has it learn information specific to each chain. For example, it customizes it for drugstores and bookstores. The platform providing unit also expands the platform so that it can be applied to other business types and has it learn information specific to each business type. For example, it customizes it for restaurants and the service industry. The platform providing unit also expands the platform so that it can be applied to other retail chains and business types. For example, it can be applied to business types other than home improvement stores, supermarkets, and large-scale commercial facilities. In this way, by expanding the platform so that it can be applied to other retail chains and business types, the versatility of the system can be improved.

[0069] The platform providing unit can analyze the energy consumption data of each store on the platform and make suggestions for improving energy efficiency. For example, the platform providing unit collects energy consumption data of each store on the platform, and the generation AI analyzes the data. For example, it identifies time periods with high energy consumption and makes suggestions for improving energy efficiency during those time periods. The platform providing unit also makes suggestions for improving energy efficiency based on the energy consumption data, with the generation AI making suggestions for improving energy efficiency. For example, it makes suggestions for optimizing the use of lighting and air conditioning. The platform providing unit also analyzes the energy consumption data on the platform, and the generation AI makes suggestions for improving energy efficiency. For example, it makes suggestions for introducing equipment that consumes less energy. In this way, by analyzing the energy consumption data of each store on the platform and making suggestions for improving energy efficiency, it is possible to reduce energy costs.

[0070] The platform providing unit can use the emotion estimation function to analyze the satisfaction of platform users and add functions according to user needs. The platform providing unit, for example, uses the emotion estimation function to analyze the satisfaction of platform users. For example, it identifies functions that users are satisfied with and enhances those functions. Furthermore, the platform providing unit uses the generation AI to propose additional functions for the platform based on the user's emotion data. For example, it proposes new functions that users are requesting. Furthermore, the platform providing unit uses the emotion estimation function to analyze the satisfaction of platform users and add functions according to user needs. For example, it improves the interface to make it easier for users to use. In this way, the convenience of the platform can be improved by analyzing the satisfaction of platform users and adding functions according to user needs.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The inquiry handling unit can estimate the customer's emotions and respond appropriately to the inquiry based on the estimated emotions. For example, if the customer is feeling stressed, the inquiry handling unit can respond more politely and kindly. If the customer is excited, the inquiry handling unit can provide a quick and concise response. Furthermore, if the customer is relaxed, the inquiry handling unit can provide detailed information to improve customer satisfaction. This makes it possible to improve the customer experience by responding based on the customer's emotions.

[0073] The generative AI model preparation unit can analyze customer reviews and feedback from each store and suggest layout changes to improve customer satisfaction. For example, if there is a lot of feedback that a particular product is difficult to find, it can suggest changing the placement of that product. It can also suggest expanding areas that tend to get crowded, reducing customer stress. Furthermore, it can improve customer satisfaction by changing the placement of popular products to more prominent locations. This makes it possible to improve the customer experience by analyzing customer reviews and feedback and suggesting layout changes to improve customer satisfaction.

[0074] The inquiry response unit can automatically provide promotional information and coupons for related products based on the content of the inquiry. For example, in response to an inquiry such as "Where can I find XX?", promotional information for that product is displayed. In response to an inquiry such as "Do you sell XX?", coupons for that product are displayed. Furthermore, in response to an inquiry such as "Where can I find XX?", coupons for that store are displayed. In this way, by automatically providing promotional information and coupons for related products based on the content of the inquiry, it is possible to increase customers' purchasing motivation.

[0075] The customer support department can add AR functionality to the smartphone app to provide visual support for in-store navigation. For example, it can display in-store guidance through the smartphone camera. It can also display the route to the destination using AR, allowing customers to reach the desired product or area without getting lost. It can also display the product's location using AR, making it easier for customers to find the product. By adding AR functionality to the smartphone app and providing visual support for in-store navigation, it is possible to improve customer convenience.

[0076] The inquiry response unit can make the smartphone app available on other devices. For example, the smartphone app can be made available on smartwatches so that customers can make inquiries through the smartwatches. It can also be made available on smartglasses so that customers can make inquiries through the smartglasses. Furthermore, by enabling inquiries by voice using a smart speaker, customers can make inquiries through various devices. By making the smartphone app available on other devices, customer convenience can be improved.

[0077] The inquiry response unit can use the emotion estimation function to analyze the stress level of customers when they make inquiries and improve the interface to reduce stress. For example, if a customer is feeling stressed, a simple design can be suggested to reduce that stress. It can also provide a user-friendly interface that allows the customer to relax. Furthermore, customer satisfaction can be improved by having the generation AI improve the interface based on the customer's stress level data. This allows for the stress level of customers when they make inquiries to be analyzed and for the interface to be improved to reduce stress, thereby improving customer satisfaction.

[0078] The platform provider can link with each store's inventory management system on the platform and update inventory information in real time. For example, when new products arrive, inventory information is updated immediately. In addition, stockouts can be prevented by automatically notifying customers of products that are running low on stock. Furthermore, by integrating the inventory management system with the platform and enabling the generation AI to obtain inventory information in real time, accurate inventory information can be provided to customers. This allows the platform to link with each store's inventory management system and update inventory information in real time, making it possible to provide accurate inventory information.

[0079] The platform provider can analyze sales data from each store on the platform and propose strategies to increase sales. For example, it can propose promotion strategies for products with sluggish sales. It can also propose changing the placement of products that sell well during specific time periods. Furthermore, it can improve sales by proposing promotion strategies based on seasonal sales data. In this way, it can improve sales by analyzing sales data from each store on the platform and proposing strategies to increase sales.

[0080] The platform provider can use the emotion estimation function to analyze customer feedback and improve the platform's user interface. For example, it can identify areas where customers feel stressed and improve those areas. It can also propose designs that help customers relax. Furthermore, based on customer emotion data, the generative AI can propose improvements to the platform's user interface, thereby improving customer satisfaction. In this way, customer feedback can be analyzed and the platform's user interface can be improved, thereby improving customer satisfaction.

[0081] The platform provider can expand the platform so that it can be applied to other retail chains and business types. For example, the platform can be expanded so that it can be applied to other retail chains, and the platform can learn information specific to each chain. The platform can also be expanded so that it can be applied to other business types, and the platform can learn information specific to each business type. Furthermore, the versatility of the system can be improved by making the platform compatible with business types other than home improvement centers, supermarkets, and large-scale commercial facilities. In this way, the versatility of the system can be improved by expanding the platform so that it can be applied to other retail chains and business types.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The generative AI model preparation unit prepares a generative AI model for each chain. For example, a model customized for each chain is prepared, such as a model for home improvement stores, a model for supermarkets, or a model for large-scale commercial facilities. The generative AI learns the layout, product information, tenant information, etc. of each store, and generates appropriate answers to user inquiries. Step 2: The inquiry response department responds to inquiries using a smartphone app. For example, when a customer makes an inquiry using the smartphone's generation AI app, the generation AI analyzes the inquiry and provides an appropriate answer. For example, in response to an inquiry such as "Where is XX?", the generation AI generates a specific answer such as "XX is on the second floor, on the left side" based on the store's layout information. Step 3: The platform provider provides a platform for each model. For example, it provides a platform that integrates models customized for each chain. This platform centrally manages information from each store and updates and maintains the generative AI model. For example, when new products arrive or the store layout changes, the generative AI model is updated through the platform to reflect the latest information.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

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

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

[0128] In the 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.

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

[0130] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 generative AI model preparation unit that prepares a generative AI model for each chain; An inquiry department that responds to inquiries using a smartphone app; A platform providing unit that provides a platform for each model. A system characterized by:

2. The generation AI model preparation unit Learns from each chain's past sales data and proposes optimal product placement according to seasons and events 2. The system of claim 1.

3. The generation AI model preparation unit Analyze customer reviews and feedback for each store and suggest layout changes to improve customer satisfaction 2. The system of claim 1.

4. The generation AI model preparation unit Optimize product placement and layout based on customer sentiment 2. The system of claim 1.

5. The generation AI model preparation unit Adding a voice recognition function to enable inquiries to be handled by voice input 2. The system of claim 1.

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