System

Abstract

An object of a system according to an embodiment is to enable a purchase applicant to purchase a product at a desired price and enable a seller to efficiently manage stock.SOLUTION: A system includes a desired price registration part, a notification part, a price information collection part, and a stock management part. A desired price registration part registers a commodity desired to be purchased by a purchase applicant at a desired price. The notification part performs notification when the desired price registered by the desired price registration part is reached. A price information collection part collects information on which commodity can be sold at which price by a seller. The inventory management unit manages inventory based on the information collected by the price information collection 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 technologies have had the problem that it is difficult for prospective buyers to purchase products at the prices they desire, and it is difficult for sellers to efficiently manage inventory.

[0005] The system according to the embodiment aims to enable potential buyers to purchase products at their desired prices and to enable sellers to efficiently manage inventory. [Means for solving the problem]

[0006] The system according to the embodiment includes a desired price registration unit, a notification unit, a price information collection unit, and an inventory management unit. The desired price registration unit registers the desired price of the product that a prospective purchaser wishes to purchase. The notification unit issues a notification when the desired price registered by the desired price registration unit is reached. The price information collection unit collects information on which products a seller can sell at what price. The inventory management unit manages inventory based on the information collected by the price information collection unit. [Effects of the Invention]

[0007] The system according to the embodiment allows prospective buyers to purchase products at their desired prices and allows sellers to efficiently manage inventory. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The price notification and sales support system according to an embodiment of the present invention is a system that allows potential buyers to register the price at which they wish to purchase a product and receive a notification when the product reaches the desired price. Furthermore, sellers can collect information on which products are available for sale at what price, thereby streamlining price negotiations and inventory management. This allows the price notification and sales support system to enable potential buyers to purchase products at their desired price, and for sellers to efficiently manage inventory and negotiate prices.

[0029] A price notification and sales support system according to an embodiment includes a desired price registration unit, a notification unit, a price information collection unit, and an inventory management unit. The desired price registration unit registers a desired price for a product that a prospective buyer wishes to purchase. For example, if a prospective buyer wishes to purchase a specific smartphone for 50,000 yen, the price is registered in the system. The notification unit notifies the user when the desired price registered by the desired price registration unit is reached. For example, if the desired price is reached due to a price reduction or campaign, the notification unit notifies the user via a pop-up notification. The price information collection unit collects information on which products a seller can sell at what price. For example, if a seller has a specific product in stock, the seller can confirm the desired price for that product. The inventory management unit manages inventory based on the information collected by the price information collection unit. For example, to reduce the risk of sellers holding inventory, the seller can sell products at a price close to the desired price. As a result, the price notification and sales support system according to an embodiment allows prospective buyers to purchase products at their desired price and allows sellers to efficiently manage inventory and negotiate prices.

[0030] The desired price registration unit uses the generation AI to analyze purchase history and market trends and propose the optimal desired price. For example, when a prospective buyer registers a desired price, the generation AI analyzes the buyer's past purchase history and proposes the optimal desired price based on the prices at which similar products have been purchased in the past. For example, it refers to the prices at which smartphones of the same brand have been purchased in the past. The desired price registration unit also analyzes market trends in real time using the generation AI to propose a desired price based on the current market price and sale information. For example, if the current market price is on a downward trend, it proposes a lower desired price. The desired price registration unit also combines the prospective buyer's past purchase history with market trends, allowing the generation AI to propose the optimal desired price. For example, it compares the price range of products purchased in the past with the current market price and calculates an appropriate desired price. This allows the prospective buyer to set the optimal desired price.

[0031] The notification unit can provide real-time notification via a voice assistant when the desired price is reached. For example, the notification unit adds a function to provide real-time notification via a voice assistant when the desired price is reached. For example, a smart speaker may notify the user that "the desired price has been reached." The notification unit also diversifies the notification method when the desired price is reached, notifying the user via the voice assistant in addition to push notifications on a smartphone or email notifications. For example, the user may receive a voice notification when they are at home. The notification unit also builds a system that uses a voice assistant to provide real-time notification when the desired price is reached. For example, when a user asks the voice assistant, "Are there any products that have reached the desired price?", the voice assistant will respond immediately. This allows the user to receive notifications in real time.

[0032] The desired price registration unit can also be applied to other fields such as travel and event tickets, allowing users to make reservations at their desired prices. For example, the desired price registration unit applies the desired price registration and notification function to the travel field, allowing users to reserve airline tickets and hotels at their desired prices. For example, the desired price registration unit notifies the user when an airline ticket is sold at the desired price. The desired price registration unit also applies the desired price registration and notification function to the event ticket field, allowing users to purchase tickets for concerts and sporting events at their desired prices. For example, the desired price registration unit notifies the user when the desired price is reached. The desired price registration unit also applies the desired price registration and notification function to various fields, allowing users to reserve products and services at their desired prices. For example, the desired price registration unit can also be applied to restaurant reservations and rental car reservations. This allows users to reserve travel and event tickets at their desired prices.

[0033] The desired price registration unit can also simultaneously register customization options for a product desired by the user. For example, the desired price registration unit allows the user to simultaneously register the customization options (color, size, specifications, etc.) for the product desired by the user when registering the desired price. For example, a smartphone of a specific color and size is registered at the desired price. The desired price registration unit also builds a system that allows the user to register customization options for the product desired by the user, and notifies the user when the desired price is reached, taking those options into consideration. For example, a laptop computer of specific specifications is registered at the desired price. The desired price registration unit also allows the user to input detailed customization options for the product desired by the user when registering the desired price. For example, clothing of a specific color, size, and specifications is registered at the desired price. This allows the user to simultaneously register the customization options for the product desired by the user.

[0034] The price information collection unit uses the generation AI to predict market demand and propose the optimal selling price. For example, when a seller collects price information, the generation AI predicts market demand and proposes the optimal selling price. For example, it predicts demand based on past sales data and market trends. The price information collection unit also uses the generation AI to predict market demand when a seller collects price information and builds a system that proposes the optimal selling price. For example, it proposes to raise the price when demand is high. The price information collection unit also develops a system that uses the generation AI to predict market demand and proposes the optimal selling price to the seller. For example, it proposes to lower the price when demand is low. This allows the seller to set the optimal selling price based on the market demand forecast.

[0035] The price information collection unit monitors the price trends of competitors in real time, enabling quick price adjustments. For example, when a seller collects price information, the price information collection unit monitors the price trends of competitors in real time and builds a system that enables quick price adjustments. For example, it responds immediately if a competitor lowers their prices. The price information collection unit also monitors the price trends of competitors in real time, enabling the seller to make quick price adjustments. For example, it adjusts its own prices if a competitor raises their prices. The price information collection unit also monitors the price trends of competitors in real time when a seller collects price information, developing a system that enables quick price adjustments. For example, it responds immediately to price fluctuations by competitors. This allows the seller to respond quickly to price trends by competitors.

[0036] The price information collection department and inventory management department can be extended to the entire supply chain and work with suppliers and logistics companies to optimize inventory. For example, the price information collection department and inventory management department can extend the price information collection and inventory management functions to the entire supply chain and work with suppliers and logistics companies to build a system that optimizes inventory. For example, they can adjust inventory based on supply information from suppliers. The price information collection department and inventory management department can also share price information throughout the supply chain and develop a system that streamlines inventory management. For example, they can optimize inventory by taking into account the delivery schedules of logistics companies. The price information collection department and inventory management department can also work with suppliers and logistics companies to build a system that collects price information and manages inventory throughout the supply chain. For example, they can adjust inventory based on suppliers' production plans. This allows inventory to be optimized throughout the supply chain.

[0037] The price information collection unit integrates data from the entire ecosystem, enabling more accurate pricing. For example, when a seller collects price information, the price information collection unit integrates data from the entire ecosystem, such as weather information and economic indicators, to build a system that enables more accurate pricing. For example, it makes a proposal to lower the price when the weather is bad. The price information collection unit also integrates data from the entire ecosystem, enabling more accurate pricing when a seller collects price information. For example, it adjusts prices when economic indicators are worsening. The price information collection unit also integrates data such as weather information and economic indicators, to develop a system that enables more accurate pricing when a seller collects price information. For example, it makes a proposal to raise the price when the weather is good. This makes it possible to integrate data from the entire ecosystem and enable more accurate pricing.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The desired price registration unit can also simultaneously register customization options for a product desired by the user. For example, when registering a desired price, the user can also simultaneously register the customization options (color, size, specifications, etc.) for the product desired by the user. For example, a smartphone of a specific color and size can be registered at the desired price. The desired price registration unit can also build a system that allows the user to register customization options for a product desired by the user, and when the desired price is reached, the system takes these options into consideration and notifies the user. For example, a laptop computer of specific specifications can be registered at the desired price. The desired price registration unit can also allow the user to input detailed customization options for the product desired by the user when registering the desired price. For example, clothing of a specific color, size, and specifications can be registered at the desired price. This allows the user to simultaneously register the customization options for the product desired by the user.

[0040] The notification unit can provide real-time notifications via a voice assistant when the desired price is reached. For example, a function is added to provide real-time notifications via a voice assistant when the desired price is reached. For example, a smart speaker could notify the user, saying, "The desired price has been reached." The notification unit also diversifies the notification methods when the desired price is reached, notifying the user via the voice assistant in addition to push notifications on a smartphone or email notifications. For example, the user could receive voice notifications while at home. The notification unit also builds a system that uses a voice assistant to provide real-time notifications when the desired price is reached. For example, if a user asks the voice assistant, "Are there any products that have reached the desired price?", the voice assistant will respond immediately. This allows the user to receive notifications in real time.

[0041] The price information collection department monitors the price trends of competitors in real time, enabling quick price adjustments. For example, when a seller collects price information, it monitors the price trends of competitors in real time and builds a system that enables quick price adjustments. For example, it responds immediately if a competitor lowers their prices. The price information collection department also monitors the price trends of competitors in real time, enabling sellers to make quick price adjustments. For example, it adjusts its own prices if a competitor raises their prices. The price information collection department also monitors the price trends of competitors in real time when a seller collects price information, developing a system that enables quick price adjustments. For example, it responds immediately to price fluctuations by competitors. This allows sellers to respond quickly to price trends by competitors.

[0042] The price information collection department uses the generation AI to predict market demand and propose the optimal selling price. For example, when a seller collects price information, the generation AI predicts market demand and proposes the optimal selling price. For example, it predicts demand based on past sales data and market trends. The price information collection department also uses the generation AI to predict market demand when a seller collects price information and builds a system that proposes the optimal selling price. For example, it proposes to raise the price when demand is high. The price information collection department also develops a system that uses the generation AI to predict market demand and proposes the optimal selling price to the seller. For example, it proposes to lower the price when demand is low. This allows the seller to set the optimal selling price based on the market demand forecast.

[0043] The price information collection unit integrates data from the entire ecosystem, enabling more accurate pricing. For example, when a seller collects price information, it integrates data from the entire ecosystem, such as weather information and economic indicators, to build a system that enables more accurate pricing. For example, it makes a suggestion to lower the price when the weather is bad. The price information collection unit also integrates data from the entire ecosystem, enabling more accurate pricing when a seller collects price information. For example, it adjusts prices when economic indicators are worsening. The price information collection unit also integrates data such as weather information and economic indicators, to develop a system that enables more accurate pricing when a seller collects price information. For example, it makes a suggestion to raise the price when the weather is good. This makes it possible to integrate data from the entire ecosystem and enable more accurate pricing.

[0044] The price information collection department and inventory management department can be extended to the entire supply chain and work with suppliers and logistics companies to optimize inventory. For example, the price information collection and inventory management functions can be extended to the entire supply chain, and a system can be built to optimize inventory in collaboration with suppliers and logistics companies. For example, inventory can be adjusted based on supply information from suppliers. The price information collection department and inventory management department can also share price information throughout the supply chain and develop a system to streamline inventory management. For example, inventory can be optimized by taking into account the delivery schedules of logistics companies. The price information collection department and inventory management department can also work with suppliers and logistics companies to build a system that collects price information and manages inventory throughout the supply chain. For example, inventory can be adjusted based on supplier production plans. This makes it possible to optimize inventory throughout the supply chain.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The desired price registration unit registers the product that the prospective buyer wants to purchase at the desired price. For example, if the prospective buyer wants to purchase a specific smartphone for 50,000 yen, they register that price in the system. Step 2: The notification unit notifies the user when the desired price registered by the desired price registration unit is reached. For example, when the desired price is reached due to a price reduction or campaign, the notification unit notifies the user through a pop-up notification. Step 3: The price information collection unit collects information on which products a seller can sell at what prices. For example, if a seller has a specific product in stock, it can check the price that potential buyers are willing to pay for that product. Step 4: The inventory management unit manages inventory based on the information collected by the price information collection unit. For example, the seller can sell the product at a price close to the desired price to reduce the risk of having inventory.

[0047] (Example 2) The price notification and sales support system according to an embodiment of the present invention is a system that allows potential buyers to register the price at which they wish to purchase a product and receive a notification when the product reaches the desired price. Furthermore, sellers can collect information on which products are available for sale at what price, thereby streamlining price negotiations and inventory management. This allows the price notification and sales support system to enable potential buyers to purchase products at their desired price, and for sellers to efficiently manage inventory and negotiate prices.

[0048] A price notification and sales support system according to an embodiment includes a desired price registration unit, a notification unit, a price information collection unit, and an inventory management unit. The desired price registration unit registers a desired price for a product that a prospective buyer wishes to purchase. For example, if a prospective buyer wishes to purchase a specific smartphone for 50,000 yen, the price is registered in the system. The notification unit notifies the user when the desired price registered by the desired price registration unit is reached. For example, if the desired price is reached due to a price reduction or campaign, the notification unit notifies the user via a pop-up notification. The price information collection unit collects information on which products a seller can sell at what price. For example, if a seller has a specific product in stock, the seller can confirm the desired price for that product. The inventory management unit manages inventory based on the information collected by the price information collection unit. For example, to reduce the risk of sellers holding inventory, the seller can sell products at a price close to the desired price. As a result, the price notification and sales support system according to an embodiment allows prospective buyers to purchase products at their desired price and allows sellers to efficiently manage inventory and negotiate prices.

[0049] The desired price registration unit uses the generation AI to analyze purchase history and market trends and propose the optimal desired price. For example, when a prospective buyer registers a desired price, the generation AI analyzes the buyer's past purchase history and proposes the optimal desired price based on the prices at which similar products have been purchased in the past. For example, it refers to the prices at which smartphones of the same brand have been purchased in the past. The desired price registration unit also analyzes market trends in real time using the generation AI to propose a desired price based on the current market price and sale information. For example, if the current market price is on a downward trend, it proposes a lower desired price. The desired price registration unit also combines the prospective buyer's past purchase history with market trends, allowing the generation AI to propose the optimal desired price. For example, it compares the price range of products purchased in the past with the current market price and calculates an appropriate desired price. This allows the prospective buyer to set the optimal desired price.

[0050] The desired price registration unit can use the emotion estimation function to evaluate the user's willingness to purchase and provide price setting advice based on the emotion. For example, when registering a desired price, the desired price registration unit analyzes the user's facial expressions and voice and evaluates the user's willingness to purchase using the emotion estimation function. For example, if the user is excited, it suggests a slightly higher desired price. The desired price registration unit also uses the emotion estimation function to evaluate the user's willingness to purchase in real time and provides price setting advice based on the emotion. For example, if the user is calm, it suggests a realistic price. The desired price registration unit also builds a system that gives advice on setting a desired price based on the user's emotion data. For example, if the user has positive emotions, it suggests a slightly higher desired price. This makes it possible to set a price based on the user's emotion.

[0051] The notification unit can provide real-time notification via a voice assistant when the desired price is reached. For example, the notification unit adds a function to provide real-time notification via a voice assistant when the desired price is reached. For example, a smart speaker may notify the user that "the desired price has been reached." The notification unit also diversifies the notification method when the desired price is reached, notifying the user via the voice assistant in addition to push notifications on a smartphone or email notifications. For example, the user may receive a voice notification when they are at home. The notification unit also builds a system that uses a voice assistant to provide real-time notification when the desired price is reached. For example, when a user asks the voice assistant, "Are there any products that have reached the desired price?", the voice assistant will respond immediately. This allows the user to receive notifications in real time.

[0052] The desired price registration unit can also be applied to other fields such as travel and event tickets, allowing users to make reservations at their desired prices. For example, the desired price registration unit applies the desired price registration and notification function to the travel field, allowing users to reserve airline tickets and hotels at their desired prices. For example, the desired price registration unit notifies the user when an airline ticket is sold at the desired price. The desired price registration unit also applies the desired price registration and notification function to the event ticket field, allowing users to purchase tickets for concerts and sporting events at their desired prices. For example, the desired price registration unit notifies the user when the desired price is reached. The desired price registration unit also applies the desired price registration and notification function to various fields, allowing users to reserve products and services at their desired prices. For example, the desired price registration unit can also be applied to restaurant reservations and rental car reservations. This allows users to reserve travel and event tickets at their desired prices.

[0053] The desired price registration unit can also simultaneously register customization options for a product desired by the user. For example, the desired price registration unit allows the user to simultaneously register the customization options (color, size, specifications, etc.) for the product desired by the user when registering the desired price. For example, a smartphone of a specific color and size is registered at the desired price. The desired price registration unit also builds a system that allows the user to register customization options for the product desired by the user, and notifies the user when the desired price is reached, taking those options into consideration. For example, a laptop computer of specific specifications is registered at the desired price. The desired price registration unit also allows the user to input detailed customization options for the product desired by the user when registering the desired price. For example, clothing of a specific color, size, and specifications is registered at the desired price. This allows the user to simultaneously register the customization options for the product desired by the user.

[0054] The desired price registration unit can use the emotion estimation function to analyze the emotion of the user when entering the desired price in real time, and provide an interface for eliciting positive emotions. The desired price registration unit, for example, uses the emotion estimation function to analyze the emotion of the user when entering the desired price in real time, and provide an interface for eliciting positive emotions. For example, the desired price registration unit encourages the user to enter with a smile. The desired price registration unit also builds a system that analyzes the user's emotion in real time, and provides an interface for eliciting positive emotions. For example, the desired price registration unit allows the user to enter the desired price in a relaxed state. The desired price registration unit also uses the emotion estimation function to analyze the emotion of the user when entering the desired price, and provides advice for eliciting positive emotions. For example, the desired price registration unit allows the user to enter the desired price with a positive feeling. This allows the user to enter the desired price with a positive feeling.

[0055] The price information collection unit uses the generation AI to predict market demand and propose the optimal selling price. For example, when a seller collects price information, the generation AI predicts market demand and proposes the optimal selling price. For example, it predicts demand based on past sales data and market trends. The price information collection unit also uses the generation AI to predict market demand when a seller collects price information and builds a system that proposes the optimal selling price. For example, it proposes to raise the price when demand is high. The price information collection unit also develops a system that uses the generation AI to predict market demand and proposes the optimal selling price to the seller. For example, it proposes to lower the price when demand is low. This allows the seller to set the optimal selling price based on the market demand forecast.

[0056] The inventory management unit can use the emotion estimation function to evaluate the stress level of a seller and provide advice to improve the efficiency of inventory management. For example, the inventory management unit incorporates the emotion estimation function into an inventory management system to evaluate the stress level of a seller and provide advice to improve the efficiency of inventory management. For example, a suggestion to reduce inventory is made when stress is high. The inventory management unit also uses the emotion estimation function to build a system that evaluates the stress level of a seller in real time and provides advice to improve the efficiency of inventory management. For example, a suggestion to increase inventory when stress is low. The inventory management unit also evaluates the stress level of a seller using the emotion estimation function and provides advice to improve the efficiency of inventory management based on the results. For example, a suggestion to reduce inventory is made when stress is high. This makes it possible to improve the efficiency of inventory management based on the stress level of a seller.

[0057] The price information collection unit monitors the price trends of competitors in real time, enabling quick price adjustments. For example, when a seller collects price information, the price information collection unit monitors the price trends of competitors in real time and builds a system that enables quick price adjustments. For example, it responds immediately if a competitor lowers their prices. The price information collection unit also monitors the price trends of competitors in real time, enabling the seller to make quick price adjustments. For example, it adjusts its own prices if a competitor raises their prices. The price information collection unit also monitors the price trends of competitors in real time when a seller collects price information, developing a system that enables quick price adjustments. For example, it responds immediately to price fluctuations by competitors. This allows the seller to respond quickly to price trends by competitors.

[0058] The price information collection department and inventory management department can be extended to the entire supply chain and work with suppliers and logistics companies to optimize inventory. For example, the price information collection department and inventory management department can extend the price information collection and inventory management functions to the entire supply chain and work with suppliers and logistics companies to build a system that optimizes inventory. For example, they can adjust inventory based on supply information from suppliers. The price information collection department and inventory management department can also share price information throughout the supply chain and develop a system that streamlines inventory management. For example, they can optimize inventory by taking into account the delivery schedules of logistics companies. The price information collection department and inventory management department can also work with suppliers and logistics companies to build a system that collects price information and manages inventory throughout the supply chain. For example, they can adjust inventory based on suppliers' production plans. This allows inventory to be optimized throughout the supply chain.

[0059] The price information collection unit integrates data from the entire ecosystem, enabling more accurate pricing. For example, when a seller collects price information, the price information collection unit integrates data from the entire ecosystem, such as weather information and economic indicators, to build a system that enables more accurate pricing. For example, it makes a proposal to lower the price when the weather is bad. The price information collection unit also integrates data from the entire ecosystem, enabling more accurate pricing when a seller collects price information. For example, it adjusts prices when economic indicators are worsening. The price information collection unit also integrates data such as weather information and economic indicators, to develop a system that enables more accurate pricing when a seller collects price information. For example, it makes a proposal to raise the price when the weather is good. This makes it possible to integrate data from the entire ecosystem and enable more accurate pricing.

[0060] The inventory management unit can use the emotion estimation function to analyze the emotions felt by sellers when managing inventory and provide an interface for eliciting positive emotions. The inventory management unit, for example, uses the emotion estimation function to analyze the emotions felt by sellers when managing inventory and provide an interface for eliciting positive emotions. For example, it enables sellers to manage inventory in a relaxed state. The inventory management unit also builds a system that analyzes the emotions felt by sellers in real time and provides an interface for eliciting positive emotions. For example, it encourages sellers to manage inventory when stress levels are low. The inventory management unit also uses the emotion estimation function to analyze the emotions felt by sellers when managing inventory and provides advice for eliciting positive emotions. For example, it enables sellers to manage inventory with a positive attitude. This enables sellers to manage inventory with positive emotions.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The desired price registration unit can also simultaneously register customization options for a product desired by the user. For example, when registering a desired price, the user can also simultaneously register the customization options (color, size, specifications, etc.) for the product desired by the user. For example, a smartphone of a specific color and size can be registered at the desired price. The desired price registration unit can also build a system that allows the user to register customization options for a product desired by the user, and when the desired price is reached, the system takes these options into consideration and notifies the user. For example, a laptop computer of specific specifications can be registered at the desired price. The desired price registration unit can also allow the user to input detailed customization options for the product desired by the user when registering the desired price. For example, clothing of a specific color, size, and specifications can be registered at the desired price. This allows the user to simultaneously register the customization options for the product desired by the user.

[0063] The notification unit can provide real-time notifications via a voice assistant when the desired price is reached. For example, a function is added to provide real-time notifications via a voice assistant when the desired price is reached. For example, a smart speaker could notify the user, saying, "The desired price has been reached." The notification unit also diversifies the notification methods when the desired price is reached, notifying the user via the voice assistant in addition to push notifications on a smartphone or email notifications. For example, the user could receive voice notifications while at home. The notification unit also builds a system that uses a voice assistant to provide real-time notifications when the desired price is reached. For example, if a user asks the voice assistant, "Are there any products that have reached the desired price?", the voice assistant will respond immediately. This allows the user to receive notifications in real time.

[0064] The desired price registration unit can use the emotion estimation function to evaluate the user's willingness to purchase and provide price setting advice based on the emotion. For example, when registering a desired price, the user's facial expressions and voice are analyzed and the willingness to purchase is evaluated using the emotion estimation function. For example, if the user is excited, a slightly higher desired price is suggested. The desired price registration unit also uses the emotion estimation function to evaluate the user's willingness to purchase in real time and provide price setting advice based on the emotion. For example, if the user is calm, a realistic price is suggested. The desired price registration unit also builds a system that gives advice on setting a desired price based on the user's emotion data. For example, if the user has positive emotions, a slightly higher desired price is suggested. This makes it possible to set a price based on the user's emotions.

[0065] The price information collection department monitors the price trends of competitors in real time, enabling quick price adjustments. For example, when a seller collects price information, it monitors the price trends of competitors in real time and builds a system that enables quick price adjustments. For example, it responds immediately if a competitor lowers their prices. The price information collection department also monitors the price trends of competitors in real time, enabling sellers to make quick price adjustments. For example, it adjusts its own prices if a competitor raises their prices. The price information collection department also monitors the price trends of competitors in real time when a seller collects price information, developing a system that enables quick price adjustments. For example, it responds immediately to price fluctuations by competitors. This allows sellers to respond quickly to price trends by competitors.

[0066] The inventory management unit can use the emotion estimation function to evaluate the stress level of a seller and provide advice to improve the efficiency of inventory management. For example, the emotion estimation function can be incorporated into an inventory management system to evaluate the stress level of a seller and provide advice to improve the efficiency of inventory management. For example, a suggestion to reduce inventory is made when stress is high. The inventory management unit can also use the emotion estimation function to build a system that evaluates the stress level of a seller in real time and provides advice to improve the efficiency of inventory management. For example, a suggestion to increase inventory is made when stress is low. The inventory management unit can also evaluate the stress level of a seller using the emotion estimation function and provide advice to improve the efficiency of inventory management based on the results. For example, a suggestion to reduce inventory is made when stress is high. This makes it possible to improve the efficiency of inventory management based on the stress level of a seller.

[0067] The price information collection department uses the generation AI to predict market demand and propose the optimal selling price. For example, when a seller collects price information, the generation AI predicts market demand and proposes the optimal selling price. For example, it predicts demand based on past sales data and market trends. The price information collection department also uses the generation AI to predict market demand when a seller collects price information and builds a system that proposes the optimal selling price. For example, it proposes to raise the price when demand is high. The price information collection department also develops a system that uses the generation AI to predict market demand and proposes the optimal selling price to the seller. For example, it proposes to lower the price when demand is low. This allows the seller to set the optimal selling price based on the market demand forecast.

[0068] The desired price registration unit can use the emotion estimation function to analyze the emotion of the user when entering the desired price in real time, and provide an interface for eliciting positive emotions. For example, the emotion estimation function can be used to analyze the emotion of the user when entering the desired price in real time, and provide an interface for eliciting positive emotions. For example, the user can be encouraged to enter with a smile. Furthermore, the desired price registration unit can construct a system that analyzes the emotion of the user in real time, and provides an interface for eliciting positive emotions. For example, the system can enable the user to enter the desired price in a relaxed state. Furthermore, the desired price registration unit can use the emotion estimation function to analyze the emotion of the user when entering the desired price, and provide advice for eliciting positive emotions. For example, the system can enable the user to enter the desired price with a positive feeling. This allows the user to enter the desired price with a positive feeling.

[0069] The price information collection unit integrates data from the entire ecosystem, enabling more accurate pricing. For example, when a seller collects price information, it integrates data from the entire ecosystem, such as weather information and economic indicators, to build a system that enables more accurate pricing. For example, it makes a suggestion to lower the price when the weather is bad. The price information collection unit also integrates data from the entire ecosystem, enabling more accurate pricing when a seller collects price information. For example, it adjusts prices when economic indicators are worsening. The price information collection unit also integrates data such as weather information and economic indicators, to develop a system that enables more accurate pricing when a seller collects price information. For example, it makes a suggestion to raise the price when the weather is good. This makes it possible to integrate data from the entire ecosystem and enable more accurate pricing.

[0070] The inventory management unit can use the emotion estimation function to analyze the emotions felt by sellers when managing inventory and provide an interface for eliciting positive emotions. For example, the emotion estimation function can be used to analyze the emotions felt by sellers when managing inventory and provide an interface for eliciting positive emotions. For example, the system can enable sellers to manage inventory in a relaxed state. The inventory management unit also builds a system that analyzes the emotions felt by sellers in real time and provides an interface for eliciting positive emotions. For example, the system can encourage sellers to manage inventory when stress levels are low. The inventory management unit also uses the emotion estimation function to analyze the emotions felt by sellers when managing inventory and provide advice for eliciting positive emotions. For example, the system can enable sellers to manage inventory with a positive attitude. This allows sellers to manage inventory with positive emotions.

[0071] The price information collection department and inventory management department can be extended to the entire supply chain and work with suppliers and logistics companies to optimize inventory. For example, the price information collection and inventory management functions can be extended to the entire supply chain, and a system can be built to optimize inventory in collaboration with suppliers and logistics companies. For example, inventory can be adjusted based on supply information from suppliers. The price information collection department and inventory management department can also share price information throughout the supply chain and develop a system to streamline inventory management. For example, inventory can be optimized by taking into account the delivery schedules of logistics companies. The price information collection department and inventory management department can also work with suppliers and logistics companies to build a system that collects price information and manages inventory throughout the supply chain. For example, inventory can be adjusted based on supplier production plans. This makes it possible to optimize inventory throughout the supply chain.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The desired price registration unit registers the product that the prospective buyer wants to purchase at the desired price. For example, if the prospective buyer wants to purchase a specific smartphone for 50,000 yen, they register that price in the system. Step 2: The notification unit notifies the user when the desired price registered by the desired price registration unit is reached. For example, when the desired price is reached due to a price reduction or campaign, the notification unit notifies the user through a pop-up notification. Step 3: The price information collection unit collects information on which products a seller can sell at what prices. For example, if a seller has a specific product in stock, it can check the price that potential buyers are willing to pay for that product. Step 4: The inventory management unit manages inventory based on the information collected by the price information collection unit. For example, the seller can sell the product at a price close to the desired price to reduce the risk of having inventory.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0087] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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]

[0141] 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 desired price registration unit for registering the product that a prospective purchaser wishes to purchase at the desired price; a notification unit that issues a notification when the desired price registered by the desired price registration unit is reached; a price information collection unit that collects information on which products a seller can sell at what prices; an inventory management unit that manages inventory based on the information collected by the price information collection unit. A system characterized by:

2. The desired price registration unit Generative AI is used to analyze purchase history and market trends, proposing the optimal asking price 2. The system of claim 1.

3. The desired price registration unit Evaluate users' purchasing intent and provide emotion-based pricing advice 2. The system of claim 1.

4. The notification unit Get real-time notifications via your voice assistant when your desired price is reached 2. The system of claim 1.

5. The desired price registration unit It can also be applied to other areas such as travel and event tickets, allowing users to make reservations at the price they desire.

2. The system of claim 1.

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