System

Abstract

An object of a system according to an embodiment is to simplify a price assessment and a selling process of a used article and improve convenience of a user.SOLUTION: A system includes a price proposal part, a purchase option part, a loan option part, and an option proposal part. The price proposing part properly assesses the commodity and its price by using the AI. The buy-out option portion provides a buy-out option if the item assessed by the price suggestion portion did not sell within a certain period of time. The Loan Option section provides a loan option if money is suddenly needed. The option proposal unit automatically proposes the option after a lapse of a certain period after exhibition as a normal used article.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that the process of assessing the price and selling of used goods is complicated and inconvenient for users.

[0005] The system according to the embodiment aims to simplify the price assessment and selling process of used goods and improve user convenience. [Means for solving the problem]

[0006] The system according to the embodiment includes a price proposal unit, a purchase option unit, a loan option unit, and an option proposal unit. The price proposal unit uses AI to properly appraise products and their prices. The purchase option unit offers purchase options if the product appraised by the price proposal unit is not sold within a certain period of time. The loan option unit offers loan options if money is suddenly needed. The option proposal unit automatically suggests the above options after a certain period of time has passed since the product was listed as a regular second-hand item. [Effects of the Invention]

[0007] The system according to the embodiment can simplify the process of assessing the price and selling of used items, thereby improving user convenience. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The user acquisition system according to the embodiment of the present invention uses AI to perform price assessments and provides added value with labor-saving operation. This allows the user to sell their products at a fair price, and also allows for flexible response when sales are unsuccessful or when funds are suddenly needed.

[0029] A user acquisition system according to an embodiment includes a price proposal unit, a purchase option unit, a loan option unit, and an option proposal unit. The price proposal unit uses AI to appropriately appraise a product and its price. For example, the generation AI receives detailed information and photos of the product as input, analyzes past transaction data and market data, and proposes an appropriate price. The generation AI calculates the price using a text generation AI (e.g., LLM) or a multimodal generation AI. The purchase option unit provides purchase options if the product appraised by the price proposal unit is not sold within a certain period of time. For example, the generation AI reevaluates the value of the product and proposes an appropriate purchase price. The loan option unit provides loan options if money is suddenly needed. For example, the generation AI assesses the value of the product and proposes a loan amount based on that value. The option proposal unit automatically proposes the above options after a certain period of time has passed since the product was listed as a regular used item. For example, the generation AI automatically proposes purchase options and loan options after a certain period of time has passed since the product was listed. As a result, the user acquisition system according to the embodiment not only allows users to sell products at a fair price, but also flexibly responds to cases where products are not sold or where funds are suddenly needed. For example, users can sell products quickly and accurately, and can use a buyback option if they are not sold, or a loan option if they suddenly need funds.

[0030] The price proposal unit can analyze the detailed usage history and condition of a product and reflect this in the price proposal. For example, the generation AI collects data provided by the user and evaluates the frequency of product use and storage conditions. For example, it analyzes the usage time of a smartphone and the battery deterioration status and reflects this in the price. The price proposal unit also analyzes the presence or absence of damage to a product and its maintenance history and reflects this in the price proposal. For example, it evaluates scratches and repair history on furniture and reflects this in the price. Furthermore, the price proposal unit analyzes the number of times a product has been used and the environment in which it is used and reflects this in the price proposal. For example, it evaluates the number of times a home appliance has been used and the environment in which it is used and reflects this in the price. In this way, by analyzing the detailed usage history and condition of a product, a more appropriate price can be proposed.

[0031] The price proposal unit can propose optimal prices by taking into account seasonal demand fluctuations in the market. For example, the price proposal unit uses generation AI to collect seasonal demand data in the market and reflect it in product price proposals. For example, the price of heating appliances, which see high demand in winter, is adjusted according to the season. The price proposal unit also analyzes seasonal sales data and reflects it in price proposals. For example, the price of air conditioning appliances, which see high demand in summer, is adjusted according to the season. Furthermore, the price proposal unit uses a demand forecasting model to predict seasonal demand fluctuations and reflect them in price proposals. For example, the price of toys, which see high demand during the Christmas season, is predicted and reflected in price proposals. This allows for more appropriate prices to be proposed by taking into account seasonal demand fluctuations in the market.

[0032] The purchase options department can analyze the resale potential of a product and optimize the purchase price. For example, the purchase options department collects past transaction data and market trends so that the generation AI can analyze the resale potential of a product. For example, the purchase price is optimized based on the frequency and price at which similar products are resold. The purchase options department also forecasts product demand and evaluates the resale potential. For example, it predicts whether demand for a particular product will increase in the future and adjusts the purchase price. Furthermore, the purchase options department evaluates the market competitiveness of a product and analyzes its resale potential. For example, if there are many similar products on the market, it adjusts the purchase price. In this way, by analyzing the resale potential of a product, the optimal purchase price can be proposed.

[0033] The purchase option department can adjust the purchase price by taking into account the storage costs of the product. For example, the generation AI in the purchase option department analyzes the storage costs of the product and reflects them in the purchase price. For example, the purchase price is adjusted by taking into account the storage costs of large furniture and home appliances. The purchase option department also evaluates the storage period of the product and reflects this in the purchase price. For example, for products that require long-term storage, the purchase price is adjusted by taking into account the storage costs. Furthermore, the purchase option department evaluates the storage conditions of the product and reflects this in the purchase price. For example, for products that need to be stored at specific temperatures and humidity, the purchase price is adjusted by taking into account the storage costs. This makes it possible to propose the optimal purchase price by taking into account the storage costs of the product.

[0034] The loan option unit can predict future fluctuations in the value of a product and optimize the loan amount. For example, the loan option unit analyzes past market data and trends so that the generative AI can predict future fluctuations in the value of a product. For example, it evaluates the likelihood that the value of a watch will increase in the future and optimizes the loan amount. The loan option unit also predicts future demand for the product and reflects this in the loan amount. For example, it predicts whether demand for a specific product will increase in the future and adjusts the loan amount. Furthermore, the loan option unit evaluates the product's future market competitiveness and reflects this in the loan amount. For example, it predicts whether similar products will become more common on the market in the future and adjusts the loan amount. This makes it possible to propose the optimal loan amount by predicting future fluctuations in the value of the product.

[0035] The loan option unit can analyze the user's credit information and adjust the loan amount. For example, the generation AI analyzes the user's credit information and adjusts the loan amount based on the credit score. For example, it proposes a higher loan amount to a user with a high credit score. The loan option unit also evaluates the user's past borrowing history and reflects this in the loan amount. For example, it proposes a higher loan amount to a user who has no history of late repayments. Furthermore, the loan option unit evaluates the user's repayment ability and reflects this in the loan amount. For example, it proposes a higher loan amount to a user with a stable income. In this way, the optimal loan amount can be proposed by analyzing the user's credit information.

[0036] The option proposal unit monitors market trends for products in real time and can propose options at the optimal timing. For example, the generation AI in the option proposal unit monitors market trends for products in real time and analyzes fluctuations in demand and supply. For example, if demand for a particular product suddenly increases, the optimal option is proposed. The option proposal unit also collects market data and monitors market trends for products in real time. For example, it analyzes price fluctuations and inventory status of products and proposes the optimal option. Furthermore, the option proposal unit analyzes market trends and monitors market trends for products in real time. For example, it predicts demand for products related to specific seasons or events and proposes the optimal option. In this way, by monitoring market trends in real time, options can be proposed at the optimal timing.

[0037] The option suggestion unit can analyze a user's past transaction history and suggest optimal options. For example, the option suggestion unit uses a generation AI to analyze a user's past transaction history and learn transaction patterns and trends. For example, it suggests optimal options based on data on past successful transactions. The option suggestion unit also analyzes a user's purchase history and suggests optimal options. For example, for a user who frequently purchases products in a specific category, it suggests options related to that category. Furthermore, the option suggestion unit analyzes a user's transaction amount and transaction frequency and suggests optimal options. For example, it suggests special options to a user who frequently makes high-value transactions. In this way, it is possible to suggest optimal options by analyzing a user's past transaction history.

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

[0039] The user acquisition system can also analyze a user's purchasing history and make individually customized price suggestions. For example, a higher price can be suggested to a user who has frequently purchased expensive items in the past. Also, for a user who prefers products from a specific brand or category, products related to that brand or category can be preferentially suggested. Furthermore, based on the user's purchasing history, products related to a specific season or event can also be suggested. In this way, analyzing a user's purchasing history makes it possible to make more personalized price suggestions.

[0040] The user acquisition system can also analyze a user's social media activity and reflect it in its price suggestions. For example, if a user mentions a particular product on social media, the price of that product can be adjusted. Price suggestions can also be made taking into account the influence of the brands and influencers the user follows. Furthermore, price suggestions can be customized based on the frequency of a user's social media activity and engagement. This allows for more appropriate price suggestions by analyzing a user's social media activity.

[0041] The user acquisition system can further analyze the user's geographic location information and propose prices that take into account market trends in each region. For example, for products that are in high demand in a specific region, the market price in that region can be reflected. Prices can also be adjusted to take into account seasonal demand fluctuations in each region. Furthermore, the competitive situation in each region can be analyzed and competitive prices can be proposed. This makes it possible to propose prices that reflect market trends in each region by analyzing the user's geographic location information.

[0042] The user acquisition system can also predict future purchases based on the user's purchase history and reflect this in price proposals. For example, it can analyze past purchase data to identify products that the user is likely to purchase in the future and adjust the prices of those products. It can also learn the user's purchasing patterns and make price proposals at specific times. Furthermore, it can adjust the prices of products related to specific seasons or events based on the user's purchase history. This makes it possible to propose prices that reflect future purchase predictions by analyzing the user's purchase history.

[0043] The user acquisition system can also suggest options related to specific brands or categories based on the user's purchase history. For example, for a user who has frequently purchased products from a specific brand in the past, options related to that brand can be preferentially suggested. Also, for a user who prefers products from a specific category, options related to that category can be suggested. Furthermore, based on the user's purchase history, options related to specific seasons or events can be suggested. This makes it possible to suggest more personalized options by analyzing the user's purchase history.

[0044] The user acquisition system can also suggest options related to specific seasons or events based on the user's purchase history. For example, for products that are in high demand during the Christmas season, options tailored to the season can be suggested. Also, for products related to specific events or campaigns, options can be suggested at the appropriate time. Furthermore, based on the user's purchase history, the price of products related to specific seasons or events can be adjusted. In this way, by analyzing the user's purchase history, it becomes possible to suggest options related to specific seasons or events.

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

[0046] Step 1: The price proposal unit uses AI to properly assess the product and its price. For example, the generation AI receives detailed product information and photos as input, analyzes past transaction data and market data, and proposes a fair price. The generation AI calculates the price using text generation AI (e.g., LLM) or multimodal generation AI. Step 2: The purchase option section provides purchase options if the product appraised by the price proposal section is not sold within a certain period of time. For example, the generation AI reevaluates the value of the product and proposes a fair purchase price. Step 3: The loan options section provides loan options for when money is needed urgently. For example, the generation AI can assess the value of a product and suggest a loan amount based on that value. Step 4: The option suggestion unit automatically suggests the above options after a certain period of time has passed since the item was listed as a regular second-hand item. For example, the generation AI automatically suggests purchase options or loan options after a certain period of time has passed since the item was listed.

[0047] (Example 2) The user acquisition system according to the embodiment of the present invention uses AI to perform price assessments and provides added value with labor-saving operation. This allows the user to sell their products at a fair price, and also allows for flexible response when sales are unsuccessful or when funds are suddenly needed.

[0048] A user acquisition system according to an embodiment includes a price proposal unit, a purchase option unit, a loan option unit, and an option proposal unit. The price proposal unit uses AI to appropriately appraise a product and its price. For example, the generation AI receives detailed information and photos of the product as input, analyzes past transaction data and market data, and proposes an appropriate price. The generation AI calculates the price using a text generation AI (e.g., LLM) or a multimodal generation AI. The purchase option unit provides purchase options if the product appraised by the price proposal unit is not sold within a certain period of time. For example, the generation AI reevaluates the value of the product and proposes an appropriate purchase price. The loan option unit provides loan options if money is suddenly needed. For example, the generation AI assesses the value of the product and proposes a loan amount based on that value. The option proposal unit automatically proposes the above options after a certain period of time has passed since the product was listed as a regular used item. For example, the generation AI automatically proposes purchase options and loan options after a certain period of time has passed since the product was listed. As a result, the user acquisition system according to the embodiment not only allows users to sell products at a fair price, but also flexibly responds to cases where products are not sold or where funds are suddenly needed. For example, users can sell products quickly and accurately, and can use a buyback option if they are not sold, or a loan option if they suddenly need funds.

[0049] The price proposal unit can analyze the detailed usage history and condition of a product and reflect this in the price proposal. For example, the generation AI collects data provided by the user and evaluates the frequency of product use and storage conditions. For example, it analyzes the usage time of a smartphone and the battery deterioration status and reflects this in the price. The price proposal unit also analyzes the presence or absence of damage to a product and its maintenance history and reflects this in the price proposal. For example, it evaluates scratches and repair history on furniture and reflects this in the price. Furthermore, the price proposal unit analyzes the number of times a product has been used and the environment in which it is used and reflects this in the price proposal. For example, it evaluates the number of times a home appliance has been used and the environment in which it is used and reflects this in the price. In this way, by analyzing the detailed usage history and condition of a product, a more appropriate price can be proposed.

[0050] The price proposal unit can propose optimal prices by taking into account seasonal demand fluctuations in the market. For example, the price proposal unit uses generation AI to collect seasonal demand data in the market and reflect it in product price proposals. For example, the price of heating appliances, which see high demand in winter, is adjusted according to the season. The price proposal unit also analyzes seasonal sales data and reflects it in price proposals. For example, the price of air conditioning appliances, which see high demand in summer, is adjusted according to the season. Furthermore, the price proposal unit uses a demand forecasting model to predict seasonal demand fluctuations and reflect them in price proposals. For example, the price of toys, which see high demand during the Christmas season, is predicted and reflected in price proposals. This allows for more appropriate prices to be proposed by taking into account seasonal demand fluctuations in the market.

[0051] The price proposal unit uses the emotion estimation function to analyze the user's emotions and propose a price that is easy for the user to accept. The price proposal unit, for example, uses the emotion estimation function to analyze the user's emotional reaction when receiving a price proposal. For example, it analyzes the user's facial expressions and voice and evaluates the degree of acceptance. The price proposal unit also uses the emotion estimation function to collect user emotion data and reflect it in the price proposal. For example, it adjusts the price based on the user's emotion score. Furthermore, the price proposal unit uses the emotion estimation function to monitor the user's emotions in real time and reflect them in the price proposal. For example, it analyzes emotional changes when the user receives a price proposal and adjusts the price. In this way, by analyzing the user's emotions, it is possible to propose a price that is easy for the user to accept.

[0052] The purchase options department can analyze the resale potential of a product and optimize the purchase price. For example, the purchase options department collects past transaction data and market trends so that the generation AI can analyze the resale potential of a product. For example, the purchase price is optimized based on the frequency and price at which similar products are resold. The purchase options department also forecasts product demand and evaluates the resale potential. For example, it predicts whether demand for a particular product will increase in the future and adjusts the purchase price. Furthermore, the purchase options department evaluates the market competitiveness of a product and analyzes its resale potential. For example, if there are many similar products on the market, it adjusts the purchase price. In this way, by analyzing the resale potential of a product, the optimal purchase price can be proposed.

[0053] The purchase option department can adjust the purchase price by taking into account the storage costs of the product. For example, the generation AI in the purchase option department analyzes the storage costs of the product and reflects them in the purchase price. For example, the purchase price is adjusted by taking into account the storage costs of large furniture and home appliances. The purchase option department also evaluates the storage period of the product and reflects this in the purchase price. For example, for products that require long-term storage, the purchase price is adjusted by taking into account the storage costs. Furthermore, the purchase option department evaluates the storage conditions of the product and reflects this in the purchase price. For example, for products that need to be stored at specific temperatures and humidity, the purchase price is adjusted by taking into account the storage costs. This makes it possible to propose the optimal purchase price by taking into account the storage costs of the product.

[0054] The purchase option unit uses the emotion estimation function to analyze the user's emotions regarding the purchase price and adjust the purchase price accordingly. The purchase option unit, for example, uses the emotion estimation function to analyze the user's emotions when receiving the purchase price. For example, it analyzes the user's facial expressions and voice and evaluates the degree of satisfaction. The purchase option unit also uses the emotion estimation function to collect user emotion data and reflect it in the purchase price. For example, it adjusts the purchase price based on the user's emotion score. Furthermore, the purchase option unit uses the emotion estimation function to monitor the user's emotions in real time and reflect them in the purchase price. For example, it analyzes emotional changes when the user receives the purchase price and adjusts the purchase price accordingly. In this way, by analyzing the user's emotions, it is possible to propose a purchase price that is easy for the user to accept.

[0055] The loan option unit can predict future fluctuations in the value of a product and optimize the loan amount. For example, the loan option unit analyzes past market data and trends so that the generative AI can predict future fluctuations in the value of a product. For example, it evaluates the likelihood that the value of a watch will increase in the future and optimizes the loan amount. The loan option unit also predicts future demand for the product and reflects this in the loan amount. For example, it predicts whether demand for a specific product will increase in the future and adjusts the loan amount. Furthermore, the loan option unit evaluates the product's future market competitiveness and reflects this in the loan amount. For example, it predicts whether similar products will become more common on the market in the future and adjusts the loan amount. This makes it possible to propose the optimal loan amount by predicting future fluctuations in the value of the product.

[0056] The loan option unit can analyze the user's credit information and adjust the loan amount. For example, the generation AI analyzes the user's credit information and adjusts the loan amount based on the credit score. For example, it proposes a higher loan amount to a user with a high credit score. The loan option unit also evaluates the user's past borrowing history and reflects this in the loan amount. For example, it proposes a higher loan amount to a user who has no history of late repayments. Furthermore, the loan option unit evaluates the user's repayment ability and reflects this in the loan amount. For example, it proposes a higher loan amount to a user with a stable income. In this way, the optimal loan amount can be proposed by analyzing the user's credit information.

[0057] The loan option unit uses the emotion estimation function to analyze the user's emotions regarding the loan amount and adjust the loan amount accordingly. The loan option unit, for example, uses the emotion estimation function to analyze the user's emotions when receiving the loan amount. For example, it analyzes the user's facial expressions and voice and evaluates the degree of satisfaction. The loan option unit also uses the emotion estimation function to collect the user's emotion data and reflect it in the loan amount. For example, it adjusts the loan amount based on the user's emotion score. Furthermore, the loan option unit uses the emotion estimation function to monitor the user's emotions in real time and reflect them in the loan amount. For example, it analyzes the user's emotional changes when receiving the loan amount and adjusts the loan amount accordingly. In this way, by analyzing the user's emotions, it is possible to propose a loan amount that is easy for the user to accept.

[0058] The option proposal unit monitors market trends for products in real time and can propose options at the optimal timing. For example, the generation AI in the option proposal unit monitors market trends for products in real time and analyzes fluctuations in demand and supply. For example, if demand for a particular product suddenly increases, the optimal option is proposed. The option proposal unit also collects market data and monitors market trends for products in real time. For example, it analyzes price fluctuations and inventory status of products and proposes the optimal option. Furthermore, the option proposal unit analyzes market trends and monitors market trends for products in real time. For example, it predicts demand for products related to specific seasons or events and proposes the optimal option. In this way, by monitoring market trends in real time, options can be proposed at the optimal timing.

[0059] The option suggestion unit can analyze a user's past transaction history and suggest optimal options. For example, the option suggestion unit uses a generation AI to analyze a user's past transaction history and learn transaction patterns and trends. For example, it suggests optimal options based on data on past successful transactions. The option suggestion unit also analyzes a user's purchase history and suggests optimal options. For example, for a user who frequently purchases products in a specific category, it suggests options related to that category. Furthermore, the option suggestion unit analyzes a user's transaction amount and transaction frequency and suggests optimal options. For example, it suggests special options to a user who frequently makes high-value transactions. In this way, it is possible to suggest optimal options by analyzing a user's past transaction history.

[0060] The option suggestion unit can use the emotion estimation function to analyze the emotions the user feels toward the option proposal and adjust the proposal accordingly. The option suggestion unit, for example, uses the emotion estimation function to analyze the emotions the user feels when receiving the option proposal. For example, it analyzes the user's facial expressions and voice and evaluates the degree of satisfaction. The option suggestion unit also uses the emotion estimation function to collect user emotion data and reflect it in the option proposal. For example, it adjusts options based on the user's emotion score. Furthermore, the option suggestion unit uses the emotion estimation function to monitor the user's emotions in real time and reflect them in the option proposal. For example, it analyzes emotional changes when the user receives the option proposal and adjusts the options. In this way, by analyzing the user's emotions, it is possible to suggest options that are easy to accept.

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

[0062] The user acquisition system can also analyze a user's purchasing history and make individually customized price suggestions. For example, a higher price can be suggested to a user who has frequently purchased expensive items in the past. Also, for a user who prefers products from a specific brand or category, products related to that brand or category can be preferentially suggested. Furthermore, based on the user's purchasing history, products related to a specific season or event can also be suggested. In this way, analyzing a user's purchasing history makes it possible to make more personalized price suggestions.

[0063] The user acquisition system can also analyze a user's social media activity and reflect it in its price suggestions. For example, if a user mentions a particular product on social media, the price of that product can be adjusted. Price suggestions can also be made taking into account the influence of the brands and influencers the user follows. Furthermore, price suggestions can be customized based on the frequency of a user's social media activity and engagement. This allows for more appropriate price suggestions by analyzing a user's social media activity.

[0064] The user acquisition system can further analyze the user's geographic location information and propose prices that take into account market trends in each region. For example, for products that are in high demand in a specific region, the market price in that region can be reflected. Prices can also be adjusted to take into account seasonal demand fluctuations in each region. Furthermore, the competitive situation in each region can be analyzed and competitive prices can be proposed. This makes it possible to propose prices that reflect market trends in each region by analyzing the user's geographic location information.

[0065] The user acquisition system can also estimate a user's purchasing intent and reflect this in price proposals. For example, it can analyze when a user has purchased a product in the past and make price proposals when the user's purchasing intent is at its peak. It can also analyze the user's level of interest in a particular product and adjust the price for products that interest the user highly. Furthermore, it can monitor a user's purchasing intent in real time and reflect this in price proposals. This allows for more effective price proposals by estimating the user's purchasing intent.

[0066] The user acquisition system can also estimate the user's emotions and make price proposals based on their emotions. For example, it can analyze the user's emotional response when receiving a price proposal and adjust the price if the user is not satisfied. It can also collect user emotional data and customize price proposals based on past emotional responses. Furthermore, it can monitor user emotions in real time and adjust prices according to changes in emotions. This makes it possible to propose prices that are easy for users to accept by analyzing their emotions.

[0067] The user acquisition system can also predict future purchases based on the user's purchase history and reflect this in price proposals. For example, it can analyze past purchase data to identify products that the user is likely to purchase in the future and adjust the prices of those products. It can also learn the user's purchasing patterns and make price proposals at specific times. Furthermore, it can adjust the prices of products related to specific seasons or events based on the user's purchase history. This makes it possible to propose prices that reflect future purchase predictions by analyzing the user's purchase history.

[0068] The user acquisition system can also estimate the user's emotions and suggest options based on those emotions. For example, it can analyze the user's emotional response when receiving option suggestions and adjust the suggestion content if the user is not convinced. It can also collect user emotional data and customize option suggestions based on past emotional responses. Furthermore, it can monitor the user's emotions in real time and adjust option suggestions according to emotional changes. This makes it possible to suggest options that are easy for the user to accept by analyzing their emotions.

[0069] The user acquisition system can also suggest options related to specific brands or categories based on the user's purchase history. For example, for a user who has frequently purchased products from a specific brand in the past, options related to that brand can be preferentially suggested. Also, for a user who prefers products from a specific category, options related to that category can be suggested. Furthermore, based on the user's purchase history, options related to specific seasons or events can be suggested. This makes it possible to suggest more personalized options by analyzing the user's purchase history.

[0070] The user acquisition system can also estimate the user's emotions and propose loan options based on their emotions. For example, it can analyze the user's emotional response when receiving loan options and adjust the proposed options if the user is not convinced. It can also collect user emotional data and customize loan options based on the user's past emotional responses. Furthermore, it can monitor the user's emotions in real time and adjust loan options according to emotional changes. This makes it possible to propose loan options that are easy for the user to accept by analyzing the user's emotions.

[0071] The user acquisition system can also suggest options related to specific seasons or events based on the user's purchase history. For example, for products that are in high demand during the Christmas season, options tailored to the season can be suggested. Also, for products related to specific events or campaigns, options can be suggested at the appropriate time. Furthermore, based on the user's purchase history, the price of products related to specific seasons or events can be adjusted. In this way, by analyzing the user's purchase history, it becomes possible to suggest options related to specific seasons or events.

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

[0073] Step 1: The price proposal unit uses AI to properly assess the product and its price. For example, the generation AI receives detailed product information and photos as input, analyzes past transaction data and market data, and proposes a fair price. The generation AI calculates the price using text generation AI (e.g., LLM) or multimodal generation AI. Step 2: The purchase option section provides purchase options if the product appraised by the price proposal section is not sold within a certain period of time. For example, the generation AI reevaluates the value of the product and proposes a fair purchase price. Step 3: The loan options section provides loan options for when money is needed urgently. For example, the generation AI can assess the value of a product and suggest a loan amount based on that value. Step 4: The option suggestion unit automatically suggests the above options after a certain period of time has passed since the item was listed as a regular second-hand item. For example, the generation AI automatically suggests purchase options or loan options after a certain period of time has passed since the item was listed.

[0074] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0075] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0076] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0079] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0080] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0081] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0082] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0083] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0084] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0085] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0086] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

[0088] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0089] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0090] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0093] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0094] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0100] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

[0103] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0108] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0115] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0124] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0125] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0126] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0127] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0128] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0129] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0130] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0131] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0132] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0133] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0134] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0135] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0136] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0137] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0138] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0139] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0140] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0141] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A price proposal department that uses AI to properly assess products and their prices, a purchase option unit that provides a purchase option when the product appraised by the price proposal unit is not sold within a certain period of time; The Loan Options Department provides loan options for those who need money suddenly, and an option suggestion unit that automatically suggests the above options after a certain period of time has passed since the item was listed as a normal used item. A system characterized by:

2. The price proposal unit Analyze the detailed usage history and condition of the product and reflect this in the price proposal 2. The system of claim 1.

3. The purchase option section Analyzing the resale potential of the product and optimizing the purchase price 2. The system of claim 1.

4. The loan option section Predict future fluctuations in the value of the product and optimize the loan amount 2. The system of claim 1.

5. The option suggestion unit Monitor market trends for the product in real time and propose the option at the optimal time 2. The system of claim 1.

6. The price proposal unit Using emotion estimation function, the system analyzes the user's emotions and proposes a price that the user can easily accept.

2. The system of claim 1.

7. The purchase option section Using an emotion estimation function, the emotion that the user feels about the purchase price is analyzed, and the purchase price is adjusted.

2. The system of claim 1.

8. The loan option section Using an emotion estimation function, the user's emotions regarding the loan amount are analyzed, and the loan amount is adjusted accordingly.

2. The system of claim 1.

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