System

Abstract

An object of a system according to an embodiment is to propose an optimum model based on a customer's request and display store stock information.SOLUTION: A system includes a demand acquisition unit, a proposal unit, and a stock display unit. The demand acquisition unit acquires a demand of a customer. The proposal unit proposes an optimal model based on the demand acquired by the demand acquisition unit. The stock display unit displays the store stock information of the model proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology is inefficient in the process of suggesting the most suitable model based on customer needs and displaying store inventory information, which can result in long waiting times for customers.

[0005] The system according to the embodiment aims to propose the most suitable model based on the customer's request and display store inventory information. [Means for solving the problem]

[0006] The system according to the embodiment includes a request acquisition unit, a proposal unit, and an inventory display unit. The request acquisition unit acquires customer requests. The proposal unit proposes an optimal model based on the requests acquired by the request acquisition unit. The inventory display unit displays store inventory information for the model proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose the most suitable model based on the customer's request and display store inventory information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The customer suggestion system according to an embodiment of the present invention uses a generation AI to listen to customer requests and display the optimal model and store inventory based on those requests. As a result, the customer suggestion system improves customer service efficiency and reduces waiting times, reduces the time spent thinking about purchasing online, and provides a pleasant purchasing experience.

[0029] A customer proposal system according to an embodiment includes a request acquisition unit, a proposal unit, and an inventory display unit. The request acquisition unit acquires customer requests. For example, the request acquisition unit can allow customers to input their requests in natural language using a generation AI. The request acquisition unit can also provide a multimodal interface, such as voice input or gesture input. The request acquisition unit can support request input in different languages ​​to accommodate international customers. The proposal unit proposes an optimal model based on the requests acquired by the request acquisition unit. For example, the proposal unit can select a model that best suits the customer's request using a generation AI. The proposal unit can also collect user reviews and ratings in real time to improve the accuracy of the proposal. The proposal unit can also develop an algorithm to propose the optimal model taking into account the customer's lifestyle and usage scenario. The inventory display unit displays store inventory information for the model proposed by the proposal unit. For example, the inventory display unit updates the store inventory information in real time and notifies the customer according to inventory changes. The inventory display unit can also provide navigation information to the nearest store based on the inventory information to support the purchase process. Furthermore, the inventory display unit integrates not only store inventory information but also online store inventory information to provide multiple purchasing options. As a result, the customer suggestion system according to the embodiment proposes the optimal model based on the customer's request and displays store inventory information, thereby realizing efficient customer service and reducing waiting times. For example, the generation AI listens to the customer's request and proposes the optimal model based on that request. Store inventory information for the proposed model is displayed, allowing the customer to find a store where they can immediately purchase it.

[0030] The request acquisition unit can analyze a customer's past purchase history and usage history to predict and suggest potential requests. For example, the request acquisition unit predicts potential requests by using a generation AI to analyze past purchase history and compare it with data from other customers with similar patterns. For example, for a customer who has made purchases in the past with an emphasis on camera performance, the unit can suggest a model with the latest camera functions. The request acquisition unit also analyzes which functions are frequently used based on usage history and predicts potential requests based on the results. For example, for a customer who frequently uses game apps, the unit can suggest a model with a high-performance processor. The request acquisition unit also integrates purchase history and usage history to make suggestions tailored to the customer's lifestyle. For example, for a customer who travels frequently, the unit can suggest a model with good battery life and high camera performance. In this way, by analyzing customers' past purchase history and usage history, it is possible to predict potential requests and make more appropriate suggestions.

[0031] The request acquisition unit allows the generation AI to ask follow-up questions in a dialogue format to elicit more specific requests. For example, if a customer inputs, "I want a smartphone with good camera performance," the request acquisition unit will ask the follow-up question, "In what situations will you use the camera?" to elicit more specific requests. The request acquisition unit also allows the generation AI to ask follow-up questions in a dialogue format to clarify the request. For example, it may ask questions such as, "What is your budget?" or "How long do you need the battery to last?" The request acquisition unit also allows the generation AI to ask follow-up questions in a dialogue format to elicit more specific requests. For example, it may ask questions such as, "Do you also care about design?" or "Are you looking for a specific brand?" By asking follow-up questions to the customer's request, it is possible to elicit more specific requests and make appropriate proposals.

[0032] The request acquisition unit can provide a multimodal interface, such as voice input or gesture input, when inputting customer requests. The request acquisition unit, for example, allows customers to use voice input when inputting requests. For example, by inputting "I want a smartphone with good camera performance" by voice, the customer can easily convey their request. The request acquisition unit also uses gesture input to simplify the operation when customers input their requests. For example, requests such as "I prioritize budget" or "I prioritize design" can be input using specific gestures. The request acquisition unit also provides a multimodal interface, allowing customers to input their requests by combining voice input and gesture input. For example, a customer can input "a smartphone with good camera performance" by voice and specify "my budget is within 50,000 yen" by gesture. This improves the convenience of input by providing a multimodal interface, such as voice input and gesture input, when inputting customer requests.

[0033] The request acquisition unit supports request input in different languages, making it possible to build a system that can accommodate international customers. For example, the generation AI supports multiple languages, making it possible to input requests in different languages. For example, it supports input in major languages ​​such as English, French, and Chinese. The request acquisition unit also has an automatic translation function when inputting requests to accommodate international customers. For example, a request input in Japanese is translated into English and analyzed by the generation AI. The request acquisition unit also supports request input in different languages, making it possible to build a system that can accommodate international customers. For example, it provides a language selection menu so that customers can input requests in their own language. This makes it possible to build a system that can accommodate international customers by supporting request input in different languages.

[0034] The suggestion unit can collect user reviews or ratings in real time for models suggested by the generation AI, thereby improving the accuracy of the suggestions. For example, the suggestion unit collects user reviews and ratings in real time for models suggested by the generation AI, and improves the accuracy of the suggestions based on that data. For example, it analyzes the latest reviews and prioritizes proposing models with high ratings. The suggestion unit also collects user reviews and ratings in real time and reflects them in the selection of models suggested by the generation AI. For example, it proposes models with high ratings for specific functions. The suggestion unit also collects user reviews and ratings in real time for models suggested by the generation AI, and improves the accuracy of the suggestions based on that data. For example, it excludes models with many negative reviews. In this way, by collecting user reviews and ratings in real time, the accuracy of the suggestions can be improved.

[0035] The proposal unit can develop an algorithm for proposing the most suitable model by taking into consideration the customer's lifestyle and daily usage scenarios. The proposal unit develops an algorithm for proposing the most suitable model by taking into consideration the customer's lifestyle and daily usage scenarios, for example. For example, a model with high waterproof performance is proposed for a customer who likes the outdoors. The proposal unit also develops an algorithm for analyzing the customer's usage scenarios and proposing the most suitable model based on the results. For example, a model with good battery life and compatible with business apps is proposed for a customer who uses the phone frequently for business purposes. The proposal unit also develops an algorithm for proposing the most suitable model by taking into consideration the customer's lifestyle and daily usage scenarios. For example, a model equipped with a high-performance camera is proposed for a customer whose hobby is photography. In this way, more suitable models can be proposed by taking into consideration the customer's lifestyle and daily usage scenarios.

[0036] The suggestion department can provide a total purchasing experience by simultaneously suggesting accessories and related products for the proposed model. For example, the suggestion department can simultaneously suggest accessories and related products for the model suggested by the generation AI. For example, it can suggest cases and screen protectors along with the smartphone. The suggestion department can also simultaneously suggest accessories and products related to the proposed model based on the customer's requests. For example, it can suggest camera lenses and tripods for customers who prioritize camera performance. The suggestion department can also simultaneously suggest accessories and related products for the proposed model, providing a total purchasing experience. For example, it can suggest earphones and chargers along with the smartphone. This allows it to provide a total purchasing experience by simultaneously suggesting accessories and related products.

[0037] The proposal unit can provide an interactive comparison tool for comparing multiple models based on customer requests. The proposal unit, for example, provides an interactive comparison tool for comparing multiple models based on customer requests. For example, it provides a tool that allows customers to compare specs such as camera performance and battery life. The proposal unit also provides an interactive tool for comparing multiple models suggested by the generation AI, allowing customers to select the model that is best for them. For example, it provides a function that allows customers to compare prices and designs. The proposal unit also provides an interactive comparison tool for comparing multiple models based on customer requests. For example, it provides a function that allows customers to compare models based on user reviews and ratings. In this way, providing an interactive comparison tool for comparing multiple models makes it easier for customers to select the model that is best for them.

[0038] The inventory display unit can add a function to update store inventory information in real time and notify customers according to inventory changes. For example, the inventory display unit can add a function to update store inventory information in real time and notify customers according to inventory changes. For example, a notification can be sent when inventory is low. The inventory display unit can also add a function to update inventory information in real time and notify customers according to inventory changes. For example, a notification can be sent when new stock arrives. The inventory display unit can also add a function to update store inventory information in real time and notify customers according to inventory changes. For example, an alternative model can be suggested when stock runs out. In this way, by updating store inventory information in real time and notifying customers according to inventory changes, inventory status can be quickly grasped.

[0039] The inventory display unit can provide navigation information to the nearest store based on the inventory information, thereby supporting the process leading up to the purchase. The inventory display unit, for example, provides navigation information to the nearest store based on the inventory information. For example, it works in conjunction with a map app to display a route to the store. The inventory display unit also provides navigation information to the nearest store based on the inventory information, thereby supporting the process leading up to the purchase. For example, it displays transportation means and the required time. The inventory display unit also provides navigation information to the nearest store based on the inventory information, thereby supporting the process leading up to the purchase. For example, it displays parking information and store opening hours. In this way, the process leading up to the purchase can be supported by providing navigation information to the nearest store based on the inventory information.

[0040] The inventory display unit can integrate not only store inventory information but also inventory information from online shops to provide multiple purchasing options. For example, the inventory display unit can integrate store inventory information with inventory information from online shops to provide multiple purchasing options. For example, it can make it possible to compare in-store purchases with online purchases. The inventory display unit can also integrate inventory information from online shops to provide multiple purchasing options. For example, it can display information about available stock at the online shop. The inventory display unit can also integrate store inventory information with inventory information from online shops to provide multiple purchasing options. For example, it can provide a link to available online purchases when available. In this way, by integrating store inventory information with inventory information from online shops, multiple purchasing options can be provided.

[0041] The inventory display unit can provide in-store reservation and reservation services based on inventory information, thereby increasing the certainty of a purchase. The inventory display unit, for example, provides in-store reservation and reservation services based on inventory information. For example, it allows a model to be reserved when it is in stock. The inventory display unit can also provide in-store reservation and reservation services based on inventory information, thereby increasing the certainty of a purchase. For example, it allows a model to be purchased at a store at a later date by using the reservation service. The inventory display unit can also provide in-store reservation and reservation services based on inventory information, thereby increasing the certainty of a purchase. For example, it sends a notification when the reserved model arrives at the store. In this way, it is possible to increase the certainty of a purchase by providing in-store reservation and reservation services based on inventory information.

[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 customer proposal system can also be equipped with a recommendation function based on a customer's purchasing history. For example, it can analyze data on past purchases of products and services to suggest new or related products that the customer might be interested in. The recommendation function can also learn a customer's purchasing patterns and make suggestions based on the season or trends. For example, in the summer, it can suggest home appliances with excellent cooling functions. The recommendation function can also make customized suggestions based on a customer's lifestyle and hobbies. For example, it can suggest camping equipment or highly waterproof electronic devices to a customer who likes the outdoors. This makes it possible to make more personalized suggestions by utilizing a customer's purchasing history.

[0044] The customer recommendation system can also be equipped with the function of analyzing customers' social media activity. For example, it can analyze the posts and "likes" that customers have shared on social media to understand their interests. It can also collect social media trends in real time and suggest the latest trending products to customers. For example, it can suggest popular gadgets and fashion items. It can also analyze the activities of customers' followers and friends to suggest products that share their interests. For example, suggesting products that their friends have purchased can make it easier to attract customers' interest. This makes it possible to make more targeted recommendations by utilizing social media data.

[0045] The customer recommendation system can also be equipped with the function of analyzing customers' health data. For example, it can analyze data obtained from smartwatches and fitness trackers to suggest products based on their health status. It can also suggest fitness equipment and supplements that are suitable for customers based on their health data. For example, it can suggest home training equipment to customers who are not getting enough exercise. It can also analyze health data and make suggestions based on the season and physical condition. For example, it can suggest an air purifier during hay fever season. This makes it possible to utilize customers' health data to make suggestions that take health into consideration.

[0046] Customer suggestion systems can also be equipped with gamification features to further increase customers' motivation to purchase. For example, points can be awarded for purchases and review submissions, and customers can receive special benefits and discounts by accumulating points. Limited-edition products and special services can also be offered if customers complete specific missions. For example, limited-edition goods can be given as gifts when customers purchase over a certain amount. A ranking function can also be introduced in which customers compete with each other, and special benefits can be offered to customers who rank highly. For example, if a company ranks high in the monthly purchase amount rankings, a special discount coupon can be offered. In this way, gamification can be used to increase customers' motivation to purchase.

[0047] Customer suggestion systems can also be equipped with the ability to predict customer purchasing behavior. For example, they can analyze past purchase data and website browsing history to predict the next product a customer is likely to purchase. They can also learn a customer's purchasing cycle and predict the timing of repeat purchases. For example, they can suggest repeat purchases of consumable items that are purchased regularly. They can also predict customer purchasing behavior and make suggestions tailored to specific events or seasons. For example, they can suggest gift items during the Christmas season. This makes it possible to predict customer purchasing behavior and make timely suggestions.

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

[0049] Step 1: The request acquisition unit acquires the customer's request. For example, the request acquisition unit can use generation AI to have the customer input their request in natural language. The request acquisition unit can also provide a multimodal interface, such as voice input or gesture input. Furthermore, the request acquisition unit can support request input in different languages, making it possible to accommodate international customers. Step 2: The proposal unit proposes the optimal model based on the requests acquired by the request acquisition unit. For example, the proposal unit uses a generative AI to select the model that best suits the customer's requests. The proposal unit can also collect user reviews and ratings in real time to improve the accuracy of its proposals. Furthermore, the proposal unit develops an algorithm to propose the optimal model, taking into account the customer's lifestyle and usage scenario. Step 3: The inventory display unit displays store inventory information for the model suggested by the suggestion unit. For example, the inventory display unit updates store inventory information in real time and notifies customers as inventory changes. The inventory display unit can also provide navigation information to the nearest store based on the inventory information, supporting the process leading up to the purchase. Furthermore, the inventory display unit integrates not only store inventory information but also inventory information from online shops to provide multiple purchasing options.

[0050] (Example 2) The customer suggestion system according to an embodiment of the present invention uses a generation AI to listen to customer requests and display the optimal model and store inventory based on those requests. As a result, the customer suggestion system improves customer service efficiency and reduces waiting times, reduces the time spent thinking about purchasing online, and provides a pleasant purchasing experience.

[0051] A customer proposal system according to an embodiment includes a request acquisition unit, a proposal unit, and an inventory display unit. The request acquisition unit acquires customer requests. For example, the request acquisition unit can allow customers to input their requests in natural language using a generation AI. The request acquisition unit can also provide a multimodal interface, such as voice input or gesture input. The request acquisition unit can support request input in different languages ​​to accommodate international customers. The proposal unit proposes an optimal model based on the requests acquired by the request acquisition unit. For example, the proposal unit can select a model that best suits the customer's request using a generation AI. The proposal unit can also collect user reviews and ratings in real time to improve the accuracy of the proposal. The proposal unit can also develop an algorithm to propose the optimal model taking into account the customer's lifestyle and usage scenario. The inventory display unit displays store inventory information for the model proposed by the proposal unit. For example, the inventory display unit updates the store inventory information in real time and notifies the customer according to inventory changes. The inventory display unit can also provide navigation information to the nearest store based on the inventory information to support the purchase process. Furthermore, the inventory display unit integrates not only store inventory information but also online store inventory information to provide multiple purchasing options. As a result, the customer suggestion system according to the embodiment proposes the optimal model based on the customer's request and displays store inventory information, thereby realizing efficient customer service and reducing waiting times. For example, the generation AI listens to the customer's request and proposes the optimal model based on that request. Store inventory information for the proposed model is displayed, allowing the customer to find a store where they can immediately purchase it.

[0052] The request acquisition unit can analyze a customer's past purchase history and usage history to predict and suggest potential requests. For example, the request acquisition unit predicts potential requests by using a generation AI to analyze past purchase history and compare it with data from other customers with similar patterns. For example, for a customer who has made purchases in the past with an emphasis on camera performance, the unit can suggest a model with the latest camera functions. The request acquisition unit also analyzes which functions are frequently used based on usage history and predicts potential requests based on the results. For example, for a customer who frequently uses game apps, the unit can suggest a model with a high-performance processor. The request acquisition unit also integrates purchase history and usage history to make suggestions tailored to the customer's lifestyle. For example, for a customer who travels frequently, the unit can suggest a model with good battery life and high camera performance. In this way, by analyzing customers' past purchase history and usage history, it is possible to predict potential requests and make more appropriate suggestions.

[0053] The request acquisition unit can analyze the tone of a customer's voice and facial expression and extract requests based on their emotions using the emotion estimation function. For example, when a customer inputs a request, the request acquisition unit analyzes the tone of voice and facial expression using a camera or microphone and identifies the emotion using the emotion estimation function. For example, if an excited tone of voice or a smile is detected, the request acquisition unit makes a proposal based on that emotion. The request acquisition unit also uses the emotion estimation function to analyze the emotion a customer has when inputting a request in real time and makes a proposal to elicit positive emotions. For example, a model with a high level of design is suggested to a customer who smiles a lot. The request acquisition unit also analyzes the tone of a customer's voice and facial expression and extracts requests based on their emotions using the emotion estimation function. For example, a model that emphasizes ease of use is suggested to a customer who is feeling stressed. In this way, by analyzing the tone of a customer's voice and facial expression, requests based on their emotions can be extracted, enabling more appropriate proposals.

[0054] The request acquisition unit allows the generation AI to ask follow-up questions in a dialogue format to elicit more specific requests. For example, if a customer inputs, "I want a smartphone with good camera performance," the request acquisition unit will ask the follow-up question, "In what situations will you use the camera?" to elicit more specific requests. The request acquisition unit also allows the generation AI to ask follow-up questions in a dialogue format to clarify the request. For example, it may ask questions such as, "What is your budget?" or "How long do you need the battery to last?" The request acquisition unit also allows the generation AI to ask follow-up questions in a dialogue format to elicit more specific requests. For example, it may ask questions such as, "Do you also care about design?" or "Are you looking for a specific brand?" By asking follow-up questions to the customer's request, it is possible to elicit more specific requests and make appropriate proposals.

[0055] The request acquisition unit can provide a multimodal interface, such as voice input or gesture input, when inputting customer requests. The request acquisition unit, for example, allows customers to use voice input when inputting requests. For example, by inputting "I want a smartphone with good camera performance" by voice, the customer can easily convey their request. The request acquisition unit also uses gesture input to simplify the operation when customers input their requests. For example, requests such as "I prioritize budget" or "I prioritize design" can be input using specific gestures. The request acquisition unit also provides a multimodal interface, allowing customers to input their requests by combining voice input and gesture input. For example, a customer can input "a smartphone with good camera performance" by voice and specify "my budget is within 50,000 yen" by gesture. This improves the convenience of input by providing a multimodal interface, such as voice input and gesture input, when inputting customer requests.

[0056] The request acquisition unit supports request input in different languages, making it possible to build a system that can accommodate international customers. For example, the generation AI supports multiple languages, making it possible to input requests in different languages. For example, it supports input in major languages ​​such as English, French, and Chinese. The request acquisition unit also has an automatic translation function when inputting requests to accommodate international customers. For example, a request input in Japanese is translated into English and analyzed by the generation AI. The request acquisition unit also supports request input in different languages, making it possible to build a system that can accommodate international customers. For example, it provides a language selection menu so that customers can input requests in their own language. This makes it possible to build a system that can accommodate international customers by supporting request input in different languages.

[0057] The request acquisition unit can use the emotion estimation function to analyze the emotions of customers when they input their requests in real time and make suggestions to elicit positive emotions. For example, the request acquisition unit can use the emotion estimation function to analyze the emotions of customers when they input their requests in real time and make suggestions to elicit positive emotions. For example, a model with a well-designed design can be suggested for a customer who smiles a lot. The request acquisition unit can also analyze the emotions of customers in real time and provide an interface to elicit positive emotions. For example, it can present encouraging messages and success stories. The request acquisition unit can also use the emotion estimation function to analyze the emotions of customers when they input their requests in real time and make suggestions to elicit positive emotions. For example, it can display appropriate words of encouragement or praise according to the input content. In this way, a better purchasing experience can be provided by analyzing the emotions of customers in real time and making suggestions to elicit positive emotions.

[0058] The suggestion unit can collect user reviews or ratings in real time for models suggested by the generation AI, thereby improving the accuracy of the suggestions. For example, the suggestion unit collects user reviews and ratings in real time for models suggested by the generation AI, and improves the accuracy of the suggestions based on that data. For example, it analyzes the latest reviews and prioritizes proposing models with high ratings. The suggestion unit also collects user reviews and ratings in real time and reflects them in the selection of models suggested by the generation AI. For example, it proposes models with high ratings for specific functions. The suggestion unit also collects user reviews and ratings in real time for models suggested by the generation AI, and improves the accuracy of the suggestions based on that data. For example, it excludes models with many negative reviews. In this way, by collecting user reviews and ratings in real time, the accuracy of the suggestions can be improved.

[0059] The proposal unit can develop an algorithm for proposing the most suitable model by taking into consideration the customer's lifestyle and daily usage scenarios. The proposal unit develops an algorithm for proposing the most suitable model by taking into consideration the customer's lifestyle and daily usage scenarios, for example. For example, a model with high waterproof performance is proposed for a customer who likes the outdoors. The proposal unit also develops an algorithm for analyzing the customer's usage scenarios and proposing the most suitable model based on the results. For example, a model with good battery life and compatible with business apps is proposed for a customer who uses the phone frequently for business purposes. The proposal unit also develops an algorithm for proposing the most suitable model by taking into consideration the customer's lifestyle and daily usage scenarios. For example, a model equipped with a high-performance camera is proposed for a customer whose hobby is photography. In this way, more suitable models can be proposed by taking into consideration the customer's lifestyle and daily usage scenarios.

[0060] The suggestion unit can use the emotion estimation function to analyze the emotion a customer has toward a proposed model and prioritize suggest models that provide high emotional satisfaction. The suggestion unit, for example, uses the emotion estimation function to analyze the emotion a customer has toward a proposed model and prioritize suggest models that provide high emotional satisfaction. For example, a model with a well-designed design is suggested for a customer who smiles a lot. The suggestion unit also analyzes the customer's emotion in real time and prioritizes suggesting models that provide high emotional satisfaction. For example, if an excited tone of voice is detected, a suggestion is made based on that emotion. The suggestion unit also uses the emotion estimation function to analyze the emotion a customer has toward a proposed model and prioritize suggest models that provide high emotional satisfaction. For example, models that evoke strong positive emotions are prioritized. In this way, by analyzing the customer's emotions, it is possible to prioritize suggest models that provide high emotional satisfaction.

[0061] The suggestion department can provide a total purchasing experience by simultaneously suggesting accessories and related products for the proposed model. For example, the suggestion department can simultaneously suggest accessories and related products for the model suggested by the generation AI. For example, it can suggest cases and screen protectors along with the smartphone. The suggestion department can also simultaneously suggest accessories and products related to the proposed model based on the customer's requests. For example, it can suggest camera lenses and tripods for customers who prioritize camera performance. The suggestion department can also simultaneously suggest accessories and related products for the proposed model, providing a total purchasing experience. For example, it can suggest earphones and chargers along with the smartphone. This allows it to provide a total purchasing experience by simultaneously suggesting accessories and related products.

[0062] The proposal unit can provide an interactive comparison tool for comparing multiple models based on customer requests. The proposal unit, for example, provides an interactive comparison tool for comparing multiple models based on customer requests. For example, it provides a tool that allows customers to compare specs such as camera performance and battery life. The proposal unit also provides an interactive tool for comparing multiple models suggested by the generation AI, allowing customers to select the model that is best for them. For example, it provides a function that allows customers to compare prices and designs. The proposal unit also provides an interactive comparison tool for comparing multiple models based on customer requests. For example, it provides a function that allows customers to compare models based on user reviews and ratings. In this way, providing an interactive comparison tool for comparing multiple models makes it easier for customers to select the model that is best for them.

[0063] The proposal unit uses the emotion estimation function to monitor the emotional reactions of customers to proposed models in real time and continuously make optimal proposals. The proposal unit, for example, uses the emotion estimation function to monitor the emotional reactions of customers to proposed models in real time and continuously make optimal proposals. For example, it prioritizes proposing models with a high number of positive emotional reactions. The proposal unit also monitors the emotional reactions of customers in real time and continuously makes optimal proposals based on the results. For example, it excludes models with a high number of negative emotional reactions. The proposal unit also uses the emotion estimation function to monitor the emotional reactions of customers to proposed models in real time and continuously make optimal proposals. For example, it prioritizes proposing models with a high emotional score. In this way, by monitoring the emotional reactions of customers in real time, it is possible to continuously make optimal proposals.

[0064] The inventory display unit can add a function to update store inventory information in real time and notify customers according to inventory changes. For example, the inventory display unit can add a function to update store inventory information in real time and notify customers according to inventory changes. For example, a notification can be sent when inventory is low. The inventory display unit can also add a function to update inventory information in real time and notify customers according to inventory changes. For example, a notification can be sent when new stock arrives. The inventory display unit can also add a function to update store inventory information in real time and notify customers according to inventory changes. For example, an alternative model can be suggested when stock runs out. In this way, by updating store inventory information in real time and notifying customers according to inventory changes, inventory status can be quickly grasped.

[0065] The inventory display unit can provide navigation information to the nearest store based on the inventory information, thereby supporting the process leading up to the purchase. The inventory display unit, for example, provides navigation information to the nearest store based on the inventory information. For example, it works in conjunction with a map app to display a route to the store. The inventory display unit also provides navigation information to the nearest store based on the inventory information, thereby supporting the process leading up to the purchase. For example, it displays transportation means and the required time. The inventory display unit also provides navigation information to the nearest store based on the inventory information, thereby supporting the process leading up to the purchase. For example, it displays parking information and store opening hours. In this way, the process leading up to the purchase can be supported by providing navigation information to the nearest store based on the inventory information.

[0066] The inventory display unit uses the emotion estimation function to analyze the emotion of the customer when checking inventory information and make an alternative suggestion if the item is out of stock. The inventory display unit, for example, uses the emotion estimation function to analyze the emotion of the customer when checking inventory information and make an alternative suggestion if the item is out of stock. For example, it may suggest a similar model if the item is out of stock. The inventory display unit also analyzes the emotion of the customer in real time and make an alternative suggestion if the item is out of stock. For example, it may suggest purchasing from an online shop if the item is out of stock. The inventory display unit also uses the emotion estimation function to analyze the emotion of the customer when checking inventory information and make an alternative suggestion if the item is out of stock. For example, it may provide inventory information from other stores if the item is out of stock. In this way, satisfaction can be improved by analyzing the emotion of the customer and making an alternative suggestion if the item is out of stock.

[0067] The inventory display unit can integrate not only store inventory information but also inventory information from online shops to provide multiple purchasing options. For example, the inventory display unit can integrate store inventory information with inventory information from online shops to provide multiple purchasing options. For example, it can make it possible to compare in-store purchases with online purchases. The inventory display unit can also integrate inventory information from online shops to provide multiple purchasing options. For example, it can display information about available stock at the online shop. The inventory display unit can also integrate store inventory information with inventory information from online shops to provide multiple purchasing options. For example, it can provide a link to available online purchases when available. In this way, by integrating store inventory information with inventory information from online shops, multiple purchasing options can be provided.

[0068] The inventory display unit can provide in-store reservation and reservation services based on inventory information, thereby increasing the certainty of a purchase. The inventory display unit, for example, provides in-store reservation and reservation services based on inventory information. For example, it allows a model to be reserved when it is in stock. The inventory display unit can also provide in-store reservation and reservation services based on inventory information, thereby increasing the certainty of a purchase. For example, it allows a model to be purchased at a store at a later date by using the reservation service. The inventory display unit can also provide in-store reservation and reservation services based on inventory information, thereby increasing the certainty of a purchase. For example, it sends a notification when the reserved model arrives at the store. In this way, it is possible to increase the certainty of a purchase by providing in-store reservation and reservation services based on inventory information.

[0069] The inventory display unit can use the emotion estimation function to analyze the emotions of customers when checking inventory information in real time and make suggestions to elicit positive emotions. The inventory display unit, for example, uses the emotion estimation function to analyze the emotions of customers when checking inventory information in real time and make suggestions to elicit positive emotions. For example, providing special offer information when there is stock. The inventory display unit can also analyze the emotions of customers in real time and make suggestions to elicit positive emotions. For example, providing a discount coupon when there is stock. The inventory display unit can also use the emotion estimation function to analyze the emotions of customers when checking inventory information in real time and make suggestions to elicit positive emotions. For example, providing a purchase special offer when there is stock. In this way, by analyzing the emotions of customers in real time and making suggestions to elicit positive emotions, a better purchasing experience can be provided.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The customer proposal system can also be equipped with a recommendation function based on a customer's purchasing history. For example, it can analyze data on past purchases of products and services to suggest new or related products that the customer might be interested in. The recommendation function can also learn a customer's purchasing patterns and make suggestions based on the season or trends. For example, in the summer, it can suggest home appliances with excellent cooling functions. The recommendation function can also make customized suggestions based on a customer's lifestyle and hobbies. For example, it can suggest camping equipment or highly waterproof electronic devices to a customer who likes the outdoors. This makes it possible to make more personalized suggestions by utilizing a customer's purchasing history.

[0072] The customer recommendation system can also be equipped with the function of analyzing customers' social media activity. For example, it can analyze the posts and "likes" that customers have shared on social media to understand their interests. It can also collect social media trends in real time and suggest the latest trending products to customers. For example, it can suggest popular gadgets and fashion items. It can also analyze the activities of customers' followers and friends to suggest products that share their interests. For example, suggesting products that their friends have purchased can make it easier to attract customers' interest. This makes it possible to make more targeted recommendations by utilizing social media data.

[0073] The customer recommendation system can also be equipped with the function of analyzing customers' health data. For example, it can analyze data obtained from smartwatches and fitness trackers to suggest products based on their health status. It can also suggest fitness equipment and supplements that are suitable for customers based on their health data. For example, it can suggest home training equipment to customers who are not getting enough exercise. It can also analyze health data and make suggestions based on the season and physical condition. For example, it can suggest an air purifier during hay fever season. This makes it possible to utilize customers' health data to make suggestions that take health into consideration.

[0074] Customer suggestion systems can also be equipped with gamification features to further increase customers' motivation to purchase. For example, points can be awarded for purchases and review submissions, and customers can receive special benefits and discounts by accumulating points. Limited-edition products and special services can also be offered if customers complete specific missions. For example, limited-edition goods can be given as gifts when customers purchase over a certain amount. A ranking function can also be introduced in which customers compete with each other, and special benefits can be offered to customers who rank highly. For example, if a company ranks high in the monthly purchase amount rankings, a special discount coupon can be offered. In this way, gamification can be used to increase customers' motivation to purchase.

[0075] Customer suggestion systems can also be equipped with the ability to predict customer purchasing behavior. For example, they can analyze past purchase data and website browsing history to predict the next product a customer is likely to purchase. They can also learn a customer's purchasing cycle and predict the timing of repeat purchases. For example, they can suggest repeat purchases of consumable items that are purchased regularly. They can also predict customer purchasing behavior and make suggestions tailored to specific events or seasons. For example, they can suggest gift items during the Christmas season. This makes it possible to predict customer purchasing behavior and make timely suggestions.

[0076] The customer suggestion system can further analyze customer emotions and provide personalized messages based on emotions. For example, if a customer expresses positive emotions toward a proposed product, a message that reinforces those emotions can be displayed. Also, if a customer expresses negative emotions, a message to alleviate those emotions can be displayed. For example, if a customer expresses dissatisfaction with a proposed product, a message suggesting an alternative product can be displayed. In addition, the customer's emotions can be analyzed and rewards or discounts can be provided based on emotions. For example, a special discount coupon can be provided to customers who express positive emotions. In this way, the system can analyze customer emotions and provide more personalized messages.

[0077] The customer suggestion system can further analyze the customer's emotions and customize the interface based on the emotions. For example, if the customer is feeling stressed, a simple and intuitive interface can be provided. Also, if the customer is excited, a colorful and fun interface can be provided. For example, a brightly colored interface can be provided for a customer who shows positive emotions. The system can also analyze the customer's emotions and provide navigation based on the emotions. For example, a simple and easy-to-understand navigation can be provided for a customer who shows negative emotions. In this way, a more comfortable interface can be provided by analyzing the customer's emotions.

[0078] The customer suggestion system can further analyze customer emotions and filter product reviews based on emotions. For example, reviews that show positive emotions can be displayed preferentially. It can also filter reviews that show negative emotions to provide customers with appropriate information. For example, it can highlight positive reviews for a specific product. It can also analyze customer emotions and evaluate reviews based on emotions. For example, it can display reviews with a high emotion score preferentially. This makes it possible to analyze customer emotions and provide more appropriate product reviews.

[0079] The customer suggestion system can further analyze customer emotions and provide customer support based on those emotions. For example, if a customer expresses dissatisfaction, support can be provided to quickly respond. Also, if a customer is satisfied, support can be provided to maintain that emotion. For example, additional benefits or services can be provided to customers who express positive emotions. In addition, the customer's emotions can be analyzed and support priorities can be set based on emotions. For example, support can be provided preferentially to customers who express negative emotions. In this way, more appropriate customer support can be provided by analyzing customer emotions.

[0080] The customer suggestion system can further analyze customer emotions and display advertisements based on those emotions. For example, if a customer expresses positive emotions, an advertisement that reinforces those emotions can be displayed. Also, if a customer expresses negative emotions, an advertisement to alleviate those emotions can be displayed. For example, if a customer expresses dissatisfaction with a suggested product, an advertisement suggesting an alternative product can be displayed. Furthermore, the customer's emotions can be analyzed and an advertisement that offers special benefits or discounts based on the emotions can be displayed. For example, an advertisement that offers a special discount coupon can be displayed for a customer who expresses positive emotions. In this way, it is possible to analyze customer emotions and provide more personalized advertisements.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The request acquisition unit acquires the customer's request. For example, the request acquisition unit can use generation AI to have the customer input their request in natural language. The request acquisition unit can also provide a multimodal interface, such as voice input or gesture input. Furthermore, the request acquisition unit can support request input in different languages, making it possible to accommodate international customers. Step 2: The proposal unit proposes the optimal model based on the requests acquired by the request acquisition unit. For example, the proposal unit uses a generative AI to select the model that best suits the customer's requests. The proposal unit can also collect user reviews and ratings in real time to improve the accuracy of its proposals. Furthermore, the proposal unit develops an algorithm to propose the optimal model, taking into account the customer's lifestyle and usage scenario. Step 3: The inventory display unit displays store inventory information for the model suggested by the suggestion unit. For example, the inventory display unit updates store inventory information in real time and notifies customers as inventory changes. The inventory display unit can also provide navigation information to the nearest store based on the inventory information, supporting the process leading up to the purchase. Furthermore, the inventory display unit integrates not only store inventory information but also inventory information from online shops to provide multiple purchasing options.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 request acquisition unit that acquires customer requests; a suggestion unit that suggests an optimal model based on the request acquired by the request acquisition unit; an inventory display unit that displays store inventory information for the model proposed by the proposal unit; A system characterized by:

2. The request acquisition unit Analyze the customer's tone of voice and facial expressions to extract emotional requests.

2. The system of claim 1.

3. The request acquisition unit Analyzing emotions in real time when the customer inputs the request, and making the proposal to elicit positive emotions 2. The system of claim 1.

4. The proposal unit User reviews or ratings are collected in real time for the models suggested by the generation AI to improve the accuracy of the suggestions.

2. The system of claim 1.

5. The proposal unit Analyzing the feelings of the customer regarding the proposed model, and preferentially proposing the model that provides the highest emotional satisfaction.

2. The system of claim 1.

6. The inventory display unit Add a function to update the store inventory information in real time and notify the customer according to inventory fluctuations.

2. The system of claim 1.

7. The inventory display unit Analyze the customer's emotions when checking inventory information and suggest alternatives if the item is out of stock 2. The system of claim 1.

8. The inventory display unit Analyzing the emotions of the customer when checking inventory information in real time and making the proposal to elicit the positive emotions 2. The system of claim 1.

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