System

Abstract

An object of a system according to an embodiment is to quickly and accurately calculate an appropriate price of an item by using generated AI.SOLUTION: A system includes a data analysis unit, a price calculation unit, and a notification unit. The data-analyzing unit analyzes the data-using the generated AI. The price calculation unit calculates an appropriate price based on the data analyzed by the data analysis unit. The notification unit notifies the appropriate price calculated by the price calculation 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] With conventional technology, it was difficult to quickly and accurately calculate the fair price of a product, making it difficult to maximize profits.

[0005] The system according to the embodiment aims to quickly and accurately calculate the fair price of a product using a generative AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a data analysis unit, a price calculation unit, and a notification unit. The data analysis unit analyzes data using a generation AI. The price calculation unit calculates a fair price based on the data analyzed by the data analysis unit. The notification unit notifies the fair price calculated by the price calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately calculate the fair price of a product using a generative AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A price calculation system according to an embodiment of the present invention is a system that uses a generative AI to calculate a fair price for a product and provide value to a retailer. As a result, the price calculation system calculates a fair price for a product and maximizes the profits of the retailer.

[0029] A price calculation system according to an embodiment includes a data analysis unit, a price calculation unit, and a notification unit. The data analysis unit analyzes data using a generation AI. For example, the data analysis unit analyzes past sales data. The data analysis unit can also analyze competitors' price data. The data analysis unit can also analyze factors such as seasons and events. For example, the data analysis unit predicts product sales based on past sales data. The data analysis unit sets a competitive price based on competitors' price data. The price is adjusted taking into account factors such as seasons and events. The price calculation unit calculates a fair price based on the data analyzed by the data analysis unit. For example, the price calculation unit calculates an optimal price using a generation AI. The price calculation unit can also calculate a price taking into account the balance between supply and demand. The price calculation unit can also calculate a cost-based price. For example, the price calculation unit calculates an optimal price for a product using a generation AI. The price is adjusted taking into account the balance between supply and demand. The price is set based on cost. The notification unit notifies the fair price calculated by the price calculation unit. For example, the notification unit sends an email notification. The notification unit can also send push notifications. The notification unit can also send SMS notifications. For example, the notification unit notifies of price changes by email. The price change is notified by push notification. The price change is notified by SMS. This allows the price calculation system according to the embodiment to calculate a fair price using generative AI and provide value to retailers. For example, the price calculation system maximizes profits for retailers by calculating a fair price. Sales are improved by suggesting the timing of price changes. Price changes are promptly notified via the notification unit.

[0030] The price calculation unit can propose optimal prices to consumers based on their purchasing history and preference data. For example, the price calculation unit analyzes the consumer's past purchasing history against the base price calculated by the generation AI to propose optimal prices to individual consumers. For example, it offers discounted prices to consumers who purchase frequently. The price calculation unit also uses the generation AI to propose personalized prices to individual consumers based on the consumer's preference data. For example, for consumers who prefer specific brands or product categories, it sets prices that match those preferences. The price calculation unit also integrates the consumer's purchasing history and preference data, and the generation AI proposes optimal prices to individual consumers in real time. For example, it proposes discounted prices for specific products based on the purchase history on an online shopping site. This makes it possible to propose optimal prices based on the consumer's individual data.

[0031] The price calculation unit can analyze a product's life cycle and market trends in real time and dynamically adjust the price. For example, the generation AI in the price calculation unit analyzes a product's life cycle and dynamically adjusts the price according to the introduction, growth, maturity, and decline stages. For example, a product is released to the market at a low price during the introduction stage and the price is raised during the growth stage. The price calculation unit also analyzes market trends in real time, and the generation AI dynamically adjusts the price. For example, prices can be adjusted to meet demand by changing them according to seasons and events. The price calculation unit also performs an integrated analysis of a product's life cycle and market trends, and the generation AI calculates the optimal price in real time. For example, prices can be set taking into account the timing of new product releases and the movements of competitors. This makes it possible to dynamically adjust prices based on a product's life cycle and market trends.

[0032] The price calculation unit can propose sales channels and promotion strategies in addition to the appropriate price for a product. For example, the generation AI in the price calculation unit calculates the appropriate price for a product and at the same time proposes the optimal sales channel. For example, it proposes sales strategies for online stores and physical stores. The price calculation unit also proposes the optimal promotion strategy along with the price calculation. For example, it provides a promotion plan that utilizes social media advertising and email marketing. The price calculation unit also builds a system in which the generation AI proposes the appropriate price, sales channels, and promotion strategies for a product in an integrated manner. For example, it proposes the optimal advertising campaign along with the pricing of a specific product. This makes it possible to propose sales channels and promotion strategies in addition to the appropriate price for a product.

[0033] The price calculation unit can be adapted to different regions and cultural spheres, providing the optimal price for each region. For example, the generation AI analyzes market data from different regions and calculates the optimal price for each region. For example, different prices are set for urban and rural areas. The price calculation unit also analyzes consumer behavior for each cultural sphere, and the generation AI proposes the optimal price. For example, it sets a fair price for products that are popular in a particular cultural sphere. The price calculation unit also builds a system in which the generation AI calculates the optimal price in real time based on data for each region and cultural sphere. For example, it sets prices that are appropriate for international markets. This allows the system to be adapted to different regions and cultural spheres, providing the optimal price for each region.

[0034] The price calculation unit can optimize inventory management and ordering plans to reduce inventory costs. For example, the price calculation unit optimizes the inventory management system based on the prices calculated by the generation AI. For example, it improves inventory turnover based on fair prices. The price calculation unit also optimizes ordering plans based on the prices calculated by the generation AI to reduce inventory costs. For example, it sets appropriate order quantities based on demand forecasts. The price calculation unit also integrates the prices calculated by the generation AI with inventory data to build a system that optimizes inventory management and ordering plans in real time. For example, it proposes ordering plans to prevent inventory surpluses and shortages. This makes it possible to optimize inventory management and ordering plans and reduce inventory costs.

[0035] The price calculation unit monitors the price trends of competitors in real time and is able to offer competitive prices. For example, the generation AI in the price calculation unit monitors the price trends of competitors in real time and calculates competitive prices. For example, it responds immediately if a competitor lowers their prices. The price calculation unit also analyzes competitors' price data using the generation AI and proposes optimal prices. For example, it sets prices to counter competitors' pricing strategies. The price calculation unit also builds a system in which the generation AI monitors the price trends of competitors in real time and offers competitive prices. For example, it sets prices to immediately respond to competitors' price changes. This allows the price calculation unit to monitor the price trends of competitors in real time and offer competitive prices.

[0036] The price calculation unit can propose subscription models and bundle sales. For example, the price calculation unit proposes a subscription model based on the price calculated by the generation AI. For example, it provides a regular purchase service to ensure stable revenue. The price calculation unit also proposes bundle sales based on the price calculated by the generation AI. For example, it sells multiple products as a set to provide comprehensive value. The price calculation unit also builds a system that proposes new sales formats based on the price calculated by the generation AI. For example, it provides a sales strategy that combines a subscription model and bundle sales. This makes it possible to propose new sales formats such as subscription models and bundle sales.

[0037] The price calculation unit can be applied to other industries to provide the optimal price for each industry. For example, the generation AI analyzes market data for the food and beverage industry and calculates the optimal price. For example, it optimizes menu prices for restaurants. The price calculation unit also analyzes market data for the service industry and proposes the optimal price. For example, it optimizes pricing for beauty salons and fitness gyms. The price calculation unit also builds a system in which the generation AI calculates the optimal price in real time based on data from different industries. For example, it sets prices that are appropriate for the food and beverage industry and the service industry. This makes it possible to apply the system to other industries and provide the optimal price for each industry.

[0038] The price calculation unit can work in conjunction with electronic payment services and communication apps to notify consumers in real time. For example, the price calculation unit can link the price calculated by the generation AI with an electronic payment service and notify consumers in real time. For example, it can notify consumers of price changes via PayPay. The price calculation unit can also work in conjunction with communication apps to notify consumers of the price calculated by the generation AI in real time. For example, it can send a message of price changes via LINE. The price calculation unit can also integrate the price calculated by the generation AI with electronic payment services and communication apps to build a system that notifies consumers in real time. For example, it can notify price changes on Yahoo Shopping. This allows it to work in conjunction with electronic payment services and communication apps to notify consumers in real time.

[0039] The price calculation unit can calculate prices taking into account other services within the corporate group. For example, when the generation AI calculates a price, the price calculation unit takes into account point programs within the corporate group and proposes the optimal price. For example, the price calculation unit sets the price based on the point redemption rate. The price calculation unit also has the generation AI analyze coupon usage status and reflect this in price calculations. For example, the price calculation unit adjusts the price according to the frequency of coupon use. The price calculation unit also takes into consideration other services within the corporate group (for example, point programs and coupons) in an integrated manner, building a system in which the generation AI calculates the optimal price. For example, it sets a price that combines point redemption and coupon discounts. This allows prices to be calculated taking into account other services within the corporate group.

[0040] The price calculation unit can link with other services within the corporate group to provide integrated services. For example, the price calculation unit links the prices calculated by the generation AI with advertising services within the corporate group to provide integrated marketing strategies. For example, it implements advertising campaigns in line with price changes. The price calculation unit also has the generation AI analyze marketing data within the corporate group to calculate optimal prices. For example, it sets prices based on the effectiveness of marketing campaigns. The price calculation unit also integrates the prices calculated by the generation AI with advertising and marketing services within the corporate group to build a system that provides consistent messages to consumers. For example, it delivers advertisements in conjunction with price changes. This allows it to link with other services within the corporate group to provide integrated services.

[0041] The price calculation unit can be applied to other industries within the corporate group to provide the optimal price for each industry. For example, the generation AI in the price calculation unit analyzes data from the financial industry to calculate the optimal price. For example, it optimizes the pricing of loans and insurance products. The generation AI in the price calculation unit also analyzes data from the insurance industry to propose the optimal price. For example, it optimizes the setting of insurance premiums. The price calculation unit also builds a system in which the generation AI calculates the optimal price in real time based on data from different industries. For example, it sets prices that are appropriate for the financial and insurance industries. This allows it to be applied to other industries within the corporate group to provide the optimal price for each industry.

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

[0043] The price calculation system can further suggest loyalty programs based on a consumer's purchasing history. For example, the price calculation unit can provide special discounts or points to consumers who make frequent purchases. The price calculation unit can also analyze a consumer's purchasing history and suggest loyalty programs for a specific product category. For example, an additional discount can be provided when a consumer purchases a certain amount or more of a specific brand of product. The price calculation unit can also integrate a consumer's purchasing history with preference data to suggest individual loyalty programs. For example, if a consumer tends to make purchases during a specific event or season, a special offer tailored to that time can be provided. This makes it possible to suggest loyalty programs based on a consumer's purchasing history and improve customer satisfaction.

[0044] The price calculation system can further provide a recommendation function based on a consumer's purchase history and preference data. For example, the price calculation unit analyzes a consumer's past purchase history and recommends related products. The price calculation unit can also make individual recommendations based on the consumer's preference data. For example, for a consumer who likes a particular brand or product category, the price calculation unit can recommend products that match those preferences. The price calculation unit can also integrate a consumer's purchase history and preference data to build a system that makes recommendations in real time. For example, it can recommend specific products based on a consumer's purchase history on an online shopping site. This makes it possible to provide a recommendation function based on the consumer's individual data and improve customer satisfaction.

[0045] The price calculation system can further propose a subscription model based on the consumer's purchasing history. For example, the price calculation unit analyzes the consumer's past purchasing history and proposes a regular purchase service. The price calculation unit can also propose an optimal subscription plan based on the consumer's purchasing patterns. For example, it can provide a regular delivery service for products that the consumer frequently purchases. The price calculation unit can also build a system that integrates the consumer's purchasing history and preference data to propose individual subscription plans. For example, if a consumer prefers a particular product category, it can offer a subscription plan specialized for that category. This makes it possible to propose a subscription model based on the consumer's purchasing history and improve customer satisfaction.

[0046] The price calculation system can also propose bundle sales based on a consumer's purchasing history. For example, the price calculation unit analyzes a consumer's past purchasing history and sells related products as a set. The price calculation unit can also propose an optimal bundle plan based on the consumer's purchasing patterns. For example, it can sell bundles by combining products that the consumer frequently purchases. The price calculation unit can also integrate a consumer's purchasing history and preference data to build a system that proposes individual bundle plans. For example, if a consumer has a preference for a particular product category, it can provide a bundle plan specialized for that category. This makes it possible to propose bundle sales based on the consumer's purchasing history and improve customer satisfaction.

[0047] The price calculation system can further optimize inventory management based on consumer purchasing history. For example, the price calculation unit analyzes consumers' past purchasing history to improve inventory turnover. The price calculation unit can also propose an optimal inventory management plan based on consumers' purchasing patterns. For example, it can set an appropriate order quantity based on demand forecasts. The price calculation unit can also integrate consumers' purchasing history with inventory data to build a system that optimizes inventory management in real time. For example, it can propose an order plan to prevent inventory surpluses and shortages. This allows inventory management to be optimized based on consumers' purchasing history, reducing inventory costs.

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

[0049] Step 1: The data analysis department uses generative AI to analyze data, for example, by analyzing past sales data, competitor pricing data, and factors such as seasons and events to predict product sales, set competitive prices, and adjust prices. Step 2: The price calculation unit calculates the appropriate price based on the data analyzed by the data analysis unit. For example, it calculates the optimal price using a generation AI, adjusts the price taking into account the balance between supply and demand, and sets the price based on cost. Step 3: The notification unit notifies the user of the appropriate price calculated by the price calculation unit. For example, the notification unit sends an email notification, a push notification, or an SMS notification to promptly notify the user of the price change.

[0050] (Example 2) A price calculation system according to an embodiment of the present invention is a system that uses a generative AI to calculate a fair price for a product and provide value to a retailer. As a result, the price calculation system calculates a fair price for a product and maximizes the profits of the retailer.

[0051] A price calculation system according to an embodiment includes a data analysis unit, a price calculation unit, and a notification unit. The data analysis unit analyzes data using a generation AI. For example, the data analysis unit analyzes past sales data. The data analysis unit can also analyze competitors' price data. The data analysis unit can also analyze factors such as seasons and events. For example, the data analysis unit predicts product sales based on past sales data. The data analysis unit sets a competitive price based on competitors' price data. The price is adjusted taking into account factors such as seasons and events. The price calculation unit calculates a fair price based on the data analyzed by the data analysis unit. For example, the price calculation unit calculates an optimal price using a generation AI. The price calculation unit can also calculate a price taking into account the balance between supply and demand. The price calculation unit can also calculate a cost-based price. For example, the price calculation unit calculates an optimal price for a product using a generation AI. The price is adjusted taking into account the balance between supply and demand. The price is set based on cost. The notification unit notifies the fair price calculated by the price calculation unit. For example, the notification unit sends an email notification. The notification unit can also send push notifications. The notification unit can also send SMS notifications. For example, the notification unit notifies of price changes by email. The price change is notified by push notification. The price change is notified by SMS. This allows the price calculation system according to the embodiment to calculate a fair price using generative AI and provide value to retailers. For example, the price calculation system maximizes profits for retailers by calculating a fair price. Sales are improved by suggesting the timing of price changes. Price changes are promptly notified via the notification unit.

[0052] The price calculation unit can propose optimal prices to consumers based on their purchasing history and preference data. For example, the price calculation unit analyzes the consumer's past purchasing history against the base price calculated by the generation AI to propose optimal prices to individual consumers. For example, it offers discounted prices to consumers who purchase frequently. The price calculation unit also uses the generation AI to propose personalized prices to individual consumers based on the consumer's preference data. For example, for consumers who prefer specific brands or product categories, it sets prices that match those preferences. The price calculation unit also integrates the consumer's purchasing history and preference data, and the generation AI proposes optimal prices to individual consumers in real time. For example, it proposes discounted prices for specific products based on the purchase history on an online shopping site. This makes it possible to propose optimal prices based on the consumer's individual data.

[0053] The price calculation unit can analyze a product's life cycle and market trends in real time and dynamically adjust the price. For example, the generation AI in the price calculation unit analyzes a product's life cycle and dynamically adjusts the price according to the introduction, growth, maturity, and decline stages. For example, a product is released to the market at a low price during the introduction stage and the price is raised during the growth stage. The price calculation unit also analyzes market trends in real time, and the generation AI dynamically adjusts the price. For example, prices can be adjusted to meet demand by changing them according to seasons and events. The price calculation unit also performs an integrated analysis of a product's life cycle and market trends, and the generation AI calculates the optimal price in real time. For example, prices can be set taking into account the timing of new product releases and the movements of competitors. This makes it possible to dynamically adjust prices based on a product's life cycle and market trends.

[0054] The price calculation unit can use the emotion estimation function to analyze the emotional state of the consumer and set a price based on the emotional state. For example, the price calculation unit uses the emotion estimation function to raise the price when the consumer has positive emotions. For example, the price is raised when the consumer is feeling happy or excited. The price calculation unit also analyzes the emotional state of the consumer in real time and sets a price based on the emotion. For example, a discount price is offered when the consumer is feeling stressed. The price calculation unit also uses the emotion estimation function to build a system that sets a price according to the consumer's emotions. For example, the price is raised when the consumer has positive emotions and lowered when the consumer has negative emotions. This makes it possible to set a price based on the consumer's emotional state.

[0055] The price calculation unit can propose sales channels and promotion strategies in addition to the appropriate price for a product. For example, the generation AI in the price calculation unit calculates the appropriate price for a product and at the same time proposes the optimal sales channel. For example, it proposes sales strategies for online stores and physical stores. The price calculation unit also proposes the optimal promotion strategy along with the price calculation. For example, it provides a promotion plan that utilizes social media advertising and email marketing. The price calculation unit also builds a system in which the generation AI proposes the appropriate price, sales channels, and promotion strategies for a product in an integrated manner. For example, it proposes the optimal advertising campaign along with the pricing of a specific product. This makes it possible to propose sales channels and promotion strategies in addition to the appropriate price for a product.

[0056] The price calculation unit can be adapted to different regions and cultural spheres, providing the optimal price for each region. For example, the generation AI analyzes market data from different regions and calculates the optimal price for each region. For example, different prices are set for urban and rural areas. The price calculation unit also analyzes consumer behavior for each cultural sphere, and the generation AI proposes the optimal price. For example, it sets a fair price for products that are popular in a particular cultural sphere. The price calculation unit also builds a system in which the generation AI calculates the optimal price in real time based on data for each region and cultural sphere. For example, it sets prices that are appropriate for international markets. This allows the system to be adapted to different regions and cultural spheres, providing the optimal price for each region.

[0057] The price calculation unit uses the emotion estimation function to notify consumers of price changes based on their emotions, thereby increasing their willingness to purchase. The price calculation unit, for example, uses the emotion estimation function to notify consumers of price changes when they have positive emotions. For example, notifying them of a discounted price when they are feeling happy. The price calculation unit also analyzes the emotional state of the consumer in real time and notifies them of price changes based on their emotions. For example, offering a special price when the consumer is excited. The price calculation unit also uses the emotion estimation function to build a system that notifies consumers of price changes based on their emotions. For example, notifying them of a price change when they are feeling positive emotions increases their willingness to purchase. In this way, notifying them of price changes based on their emotions increases their willingness to purchase.

[0058] The price calculation unit can optimize inventory management and ordering plans to reduce inventory costs. For example, the price calculation unit optimizes the inventory management system based on the prices calculated by the generation AI. For example, it improves inventory turnover based on fair prices. The price calculation unit also optimizes ordering plans based on the prices calculated by the generation AI to reduce inventory costs. For example, it sets appropriate order quantities based on demand forecasts. The price calculation unit also integrates the prices calculated by the generation AI with inventory data to build a system that optimizes inventory management and ordering plans in real time. For example, it proposes ordering plans to prevent inventory surpluses and shortages. This makes it possible to optimize inventory management and ordering plans and reduce inventory costs.

[0059] The price calculation unit monitors the price trends of competitors in real time and is able to offer competitive prices. For example, the generation AI in the price calculation unit monitors the price trends of competitors in real time and calculates competitive prices. For example, it responds immediately if a competitor lowers their prices. The price calculation unit also analyzes competitors' price data using the generation AI and proposes optimal prices. For example, it sets prices to counter competitors' pricing strategies. The price calculation unit also builds a system in which the generation AI monitors the price trends of competitors in real time and offers competitive prices. For example, it sets prices to immediately respond to competitors' price changes. This allows the price calculation unit to monitor the price trends of competitors in real time and offer competitive prices.

[0060] The price calculation unit can use the emotion estimation function to suggest promotions and campaigns based on the consumer's emotions, thereby maximizing sales. For example, the price calculation unit uses the emotion estimation function to suggest promotions and campaigns when the consumer is feeling positive emotions. For example, it provides a special discount when the consumer is feeling happy. The price calculation unit also analyzes the consumer's emotional state in real time to suggest promotions and campaigns based on the emotions. For example, it runs a limited sale when the consumer is excited. The price calculation unit also uses the emotion estimation function to build a system that suggests promotions and campaigns according to the consumer's emotions. For example, it provides a special offer when the consumer is feeling positive emotions. In this way, it is possible to suggest promotions and campaigns based on the consumer's emotions, thereby maximizing sales.

[0061] The price calculation unit can propose subscription models and bundle sales. For example, the price calculation unit proposes a subscription model based on the price calculated by the generation AI. For example, it provides a regular purchase service to ensure stable revenue. The price calculation unit also proposes bundle sales based on the price calculated by the generation AI. For example, it sells multiple products as a set to provide comprehensive value. The price calculation unit also builds a system that proposes new sales formats based on the price calculated by the generation AI. For example, it provides a sales strategy that combines a subscription model and bundle sales. This makes it possible to propose new sales formats such as subscription models and bundle sales.

[0062] The price calculation unit can be applied to other industries to provide the optimal price for each industry. For example, the generation AI analyzes market data for the food and beverage industry and calculates the optimal price. For example, it optimizes menu prices for restaurants. The price calculation unit also analyzes market data for the service industry and proposes the optimal price. For example, it optimizes pricing for beauty salons and fitness gyms. The price calculation unit also builds a system in which the generation AI calculates the optimal price in real time based on data from different industries. For example, it sets prices that are appropriate for the food and beverage industry and the service industry. This makes it possible to apply the system to other industries and provide the optimal price for each industry.

[0063] The price calculation unit can use the emotion estimation function to suggest the timing of price changes based on consumer emotions and maximize sales. The price calculation unit, for example, uses the emotion estimation function to suggest price changes when consumers have positive emotions. For example, it raises the price when consumers are feeling happy. The price calculation unit also analyzes the consumer's emotional state in real time and suggests the timing of price changes based on emotions. For example, it changes the price when consumers are excited. The price calculation unit also uses the emotion estimation function to build a system that suggests the timing of price changes according to consumer emotions. For example, it suggests price changes when consumers have positive emotions and maximizes sales. In this way, it is possible to suggest the timing of price changes based on consumer emotions and maximize sales.

[0064] The price calculation unit can work in conjunction with electronic payment services and communication apps to notify consumers in real time. For example, the price calculation unit can link the price calculated by the generation AI with an electronic payment service and notify consumers in real time. For example, it can notify consumers of price changes via PayPay. The price calculation unit can also work in conjunction with communication apps to notify consumers of the price calculated by the generation AI in real time. For example, it can send a message of price changes via LINE. The price calculation unit can also integrate the price calculated by the generation AI with electronic payment services and communication apps to build a system that notifies consumers in real time. For example, it can notify price changes on Yahoo Shopping. This allows it to work in conjunction with electronic payment services and communication apps to notify consumers in real time.

[0065] The price calculation unit can calculate prices taking into account other services within the corporate group. For example, when the generation AI calculates a price, the price calculation unit takes into account point programs within the corporate group and proposes the optimal price. For example, the price calculation unit sets the price based on the point redemption rate. The price calculation unit also has the generation AI analyze coupon usage status and reflect this in price calculations. For example, the price calculation unit adjusts the price according to the frequency of coupon use. The price calculation unit also takes into consideration other services within the corporate group (for example, point programs and coupons) in an integrated manner, building a system in which the generation AI calculates the optimal price. For example, it sets a price that combines point redemption and coupon discounts. This allows prices to be calculated taking into account other services within the corporate group.

[0066] The price calculation unit uses the emotion estimation function to notify consumers of price changes based on their emotions, thereby maximizing synergy across the entire corporate group. The price calculation unit, for example, uses the emotion estimation function to notify consumers of price changes when they are feeling positive emotions, thereby maximizing synergy across the entire corporate group. For example, the price calculation unit notifies consumers of price changes when they are feeling happy. The price calculation unit also analyzes consumers' emotional states in real time and notifies consumers of price changes based on their emotions. For example, the price calculation unit notifies consumers of price changes when they are excited. The price calculation unit also uses the emotion estimation function to build a system that notifies consumers of price changes based on their emotions, thereby maximizing synergy across the entire corporate group. For example, the price calculation unit notifies consumers of price changes when they are feeling positive emotions, thereby increasing their desire to purchase. In this way, the price calculation unit notifies consumers of price changes based on their emotions, thereby maximizing synergy across the entire corporate group.

[0067] The price calculation unit can link with other services within the corporate group to provide integrated services. For example, the price calculation unit links the prices calculated by the generation AI with advertising services within the corporate group to provide integrated marketing strategies. For example, it implements advertising campaigns in line with price changes. The price calculation unit also has the generation AI analyze marketing data within the corporate group to calculate optimal prices. For example, it sets prices based on the effectiveness of marketing campaigns. The price calculation unit also integrates the prices calculated by the generation AI with advertising and marketing services within the corporate group to build a system that provides consistent messages to consumers. For example, it delivers advertisements in conjunction with price changes. This allows it to link with other services within the corporate group to provide integrated services.

[0068] The price calculation unit can be applied to other industries within the corporate group to provide the optimal price for each industry. For example, the generation AI in the price calculation unit analyzes data from the financial industry to calculate the optimal price. For example, it optimizes the pricing of loans and insurance products. The generation AI in the price calculation unit also analyzes data from the insurance industry to propose the optimal price. For example, it optimizes the setting of insurance premiums. The price calculation unit also builds a system in which the generation AI calculates the optimal price in real time based on data from different industries. For example, it sets prices that are appropriate for the financial and insurance industries. This allows it to be applied to other industries within the corporate group to provide the optimal price for each industry.

[0069] The price calculation unit uses the emotion estimation function to notify consumers of price changes based on their emotions, thereby maximizing synergy across the entire corporate group. The price calculation unit, for example, uses the emotion estimation function to notify consumers of price changes when they are feeling positive emotions, thereby maximizing synergy across the entire corporate group. For example, the price calculation unit notifies consumers of price changes when they are feeling happy. The price calculation unit also analyzes consumers' emotional states in real time and notifies consumers of price changes based on their emotions. For example, the price calculation unit notifies consumers of price changes when they are excited. The price calculation unit also uses the emotion estimation function to build a system that notifies consumers of price changes based on their emotions, thereby maximizing synergy across the entire corporate group. For example, the price calculation unit notifies consumers of price changes when they are feeling positive emotions, thereby increasing their desire to purchase. In this way, the price calculation unit notifies consumers of price changes based on their emotions, thereby maximizing synergy across the entire corporate group.

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

[0071] The price calculation system can further suggest loyalty programs based on a consumer's purchasing history. For example, the price calculation unit can provide special discounts or points to consumers who make frequent purchases. The price calculation unit can also analyze a consumer's purchasing history and suggest loyalty programs for a specific product category. For example, an additional discount can be provided when a consumer purchases a certain amount or more of a specific brand of product. The price calculation unit can also integrate a consumer's purchasing history with preference data to suggest individual loyalty programs. For example, if a consumer tends to make purchases during a specific event or season, a special offer tailored to that time can be provided. This makes it possible to suggest loyalty programs based on a consumer's purchasing history and improve customer satisfaction.

[0072] The price calculation system can further provide personalized advertisements based on the emotional state of the consumer. For example, the price calculation unit displays advertisements for specific products or services when the consumer is feeling positive emotions. The price calculation unit can also analyze the emotional state of the consumer in real time and provide advertisements based on the emotions. For example, when the consumer is feeling stressed, advertisements for products with a relaxing effect are displayed. The price calculation unit can also use the emotion estimation function to build a system that provides advertisements according to the consumer's emotions. For example, when the consumer is feeling happy, advertisements for luxury products are displayed to increase the consumer's desire to purchase. This makes it possible to provide personalized advertisements based on the consumer's emotional state and maximize the effectiveness of the advertisements.

[0073] The price calculation system can further provide a recommendation function based on a consumer's purchase history and preference data. For example, the price calculation unit analyzes a consumer's past purchase history and recommends related products. The price calculation unit can also make individual recommendations based on the consumer's preference data. For example, for a consumer who likes a particular brand or product category, the price calculation unit can recommend products that match those preferences. The price calculation unit can also integrate a consumer's purchase history and preference data to build a system that makes recommendations in real time. For example, it can recommend specific products based on a consumer's purchase history on an online shopping site. This makes it possible to provide a recommendation function based on the consumer's individual data and improve customer satisfaction.

[0074] The price calculation system can further optimize customer support based on the emotional state of the consumer. For example, the price calculation unit strengthens customer support when the consumer has negative emotions. The price calculation unit can also analyze the emotional state of the consumer in real time and provide customer support based on the emotions. For example, it can respond quickly when the consumer is dissatisfied. The price calculation unit can also use the emotion estimation function to build a system that provides customer support according to the consumer's emotions. For example, it can provide additional services when the consumer has positive emotions, thereby improving customer satisfaction. In this way, it is possible to provide customer support based on the consumer's emotional state and improve customer satisfaction.

[0075] The price calculation system can further propose a subscription model based on the consumer's purchasing history. For example, the price calculation unit analyzes the consumer's past purchasing history and proposes a regular purchase service. The price calculation unit can also propose an optimal subscription plan based on the consumer's purchasing patterns. For example, it can provide a regular delivery service for products that the consumer frequently purchases. The price calculation unit can also build a system that integrates the consumer's purchasing history and preference data to propose individual subscription plans. For example, if a consumer prefers a particular product category, it can offer a subscription plan specialized for that category. This makes it possible to propose a subscription model based on the consumer's purchasing history and improve customer satisfaction.

[0076] The price calculation system can further adjust prices in real time based on the emotional state of the consumer. For example, the price calculation unit raises the price when the consumer has positive emotions. The price calculation unit can also analyze the emotional state of the consumer in real time and adjust the price based on the emotion. For example, it can offer a discounted price when the consumer is feeling stressed. The price calculation unit can also use the emotion estimation function to build a system that adjusts prices according to the consumer's emotions. For example, it can raise the price when the consumer has positive emotions and lower the price when the consumer has negative emotions. This makes it possible to adjust prices in real time based on the consumer's emotional state.

[0077] The price calculation system can also propose bundle sales based on a consumer's purchasing history. For example, the price calculation unit analyzes a consumer's past purchasing history and sells related products as a set. The price calculation unit can also propose an optimal bundle plan based on the consumer's purchasing patterns. For example, it can sell bundles by combining products that the consumer frequently purchases. The price calculation unit can also integrate a consumer's purchasing history and preference data to build a system that proposes individual bundle plans. For example, if a consumer has a preference for a particular product category, it can provide a bundle plan specialized for that category. This makes it possible to propose bundle sales based on the consumer's purchasing history and improve customer satisfaction.

[0078] The price calculation system can further provide special offers based on the emotional state of the consumer. For example, the price calculation unit provides special offers when the consumer is feeling positive emotions. The price calculation unit can also analyze the emotional state of the consumer in real time and provide special offers based on the emotions. For example, a limited sale can be held when the consumer is feeling happy. The price calculation unit can also use the emotion estimation function to build a system that provides special offers based on the consumer's emotions. For example, a special discount can be offered when the consumer is feeling positive emotions, increasing their desire to purchase. In this way, it is possible to provide special offers based on the consumer's emotional state and maximize sales.

[0079] The price calculation system can further optimize inventory management based on consumer purchasing history. For example, the price calculation unit analyzes consumers' past purchasing history to improve inventory turnover. The price calculation unit can also propose an optimal inventory management plan based on consumers' purchasing patterns. For example, it can set an appropriate order quantity based on demand forecasts. The price calculation unit can also integrate consumers' purchasing history with inventory data to build a system that optimizes inventory management in real time. For example, it can propose an order plan to prevent inventory surpluses and shortages. This allows inventory management to be optimized based on consumers' purchasing history, reducing inventory costs.

[0080] The price calculation system can further collect feedback based on the emotional state of the consumer. For example, the price calculation unit requests feedback when the consumer has positive emotions. The price calculation unit can also analyze the emotional state of the consumer in real time and collect feedback based on emotions. For example, when the consumer is satisfied, it requests feedback on a product or service. The price calculation unit can also use the emotion estimation function to build a system that collects feedback according to the consumer's emotions. For example, it requests feedback when the consumer has positive emotions, and collects areas for improvement when the consumer has negative emotions. This allows feedback to be collected based on the consumer's emotional state and can be used to improve products and services.

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

[0082] Step 1: The data analysis department uses generative AI to analyze data, for example, by analyzing past sales data, competitor pricing data, and factors such as seasons and events to predict product sales, set competitive prices, and adjust prices. Step 2: The price calculation unit calculates the appropriate price based on the data analyzed by the data analysis unit. For example, it calculates the optimal price using a generation AI, adjusts the price taking into account the balance between supply and demand, and sets the price based on cost. Step 3: The notification unit notifies the user of the appropriate price calculated by the price calculation unit. For example, the notification unit sends an email notification, a push notification, or an SMS notification to promptly notify the user of the price change.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0111] 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.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0127] 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 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data analysis unit that analyzes data using generative AI; a price calculation unit that calculates a fair price based on the data analyzed by the data analysis unit; a notification unit that notifies the fair price calculated by the price calculation unit. A system characterized by:

2. The price calculation unit Proposing optimal prices to consumers based on their purchasing history and preference data 2. The system of claim 1.

3. The price calculation unit Analyze product lifecycles and market trends in real time to dynamically adjust prices 2. The system of claim 1.

4. The price calculation unit Analyzing the emotional state of consumers and setting prices based on said emotional state 2. The system of claim 1.

5. The price calculation unit Propose appropriate prices for products, as well as sales channels and promotion strategies 2. The system of claim 1.

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