system

Abstract

The system according to this embodiment aims to enable users to purchase products at the lowest possible price. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, an offer unit, a bidding unit, and a supply unit. The reception unit receives product requests from users. The collection unit searches for the lowest price of the product on any website on the internet based on the information received by the reception unit. The offer unit makes an offer to the store to see if it can offer a price lower than the lowest price identified by the collection unit. The bidding unit receives bids from the store after the offer has been made by the offer unit. The supply unit provides the user with the lowest price based on the bids received by the bidding unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that there were too many sites and it was impossible to know which one was the cheapest, and price negotiation was not possible.

[0005] The system according to the embodiment aims to enable a user to purchase a product at the lowest price.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a collection unit, an offer unit, a bidding unit, and a supply unit. The reception unit receives product requests from users. The collection unit searches for the lowest price of the product on any website on the internet based on the information received by the reception unit. The offer unit makes an offer to the store, based on the lowest price identified by the collection unit, to see if it can offer a price lower than that amount. After the offer has been made by the offer unit, the bidding unit receives bids from the store. The supply unit provides the user with the lowest price based on the bids received by the bidding unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to purchase products at the lowest price. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The lowest price purchase system according to an embodiment of the present invention is a system that improves the user's experience of purchasing the lowest price product when shopping online. In this lowest price purchase system, when a user requests a product they want, a generating AI searches for the lowest price of that product on all websites on the internet. Next, based on the lowest price found by the generating AI, it collaborates with e-commerce sites to make an offer to the store to see if they can offer a price lower than that amount. If the store makes a bid, the user can purchase the product at that price. This mechanism allows users to purchase products at the lowest price and also increases the market share of e-commerce sites. For example, a user requests a product they want. In this case, the user only needs to input information such as the product name and category. For example, the user might input "I want the latest smartphone." This information is input into the generating AI. Next, the generating AI analyzes the input information and searches for the lowest price of that product on all websites on the internet. The generating AI crawls multiple shopping sites and collects price information from each site. For example, it collects price information from e-commerce sites and auction sites. As a result, the generating AI can identify the lowest price of that product. Based on the lowest price identified by the generating AI, it collaborates with e-commerce sites to make an offer to the store to see if they can offer a price lower than that amount. For example, if the generating AI identifies the lowest price at 5,000 yen, it will send an offer to the e-commerce site's store asking, "Can you offer this product for less than 5,000 yen?" This offer is made automatically. If the store submits a bid, the user can purchase the product at that price. For example, if the store bids "We can offer it for 4,500 yen," the user can purchase the product at that price. This system allows users to purchase products at the lowest price. This system makes it easy for users to find and purchase the cheapest products when shopping online. It also improves the market share of e-commerce sites. For example, as users have more opportunities to purchase products on e-commerce sites, the number of users on e-commerce sites increases, and their market share improves. In this way, the lowest price purchase system can improve the user's online shopping experience and expand the market share of e-commerce sites.

[0029] The lowest price purchase system according to this embodiment comprises a reception unit, a collection unit, an offer unit, a bidding unit, and a supply unit. The reception unit receives product requests from users. Product requests from users include, but are not limited to, information such as product name and category. The reception unit accepts product requests when, for example, a user inputs "I want the latest smartphone." The reception unit can also support multiple input methods, such as voice input and image input. For example, a product request can be accepted when a user says "I want the latest smartphone" by voice. The collection unit uses a generation AI to search for the lowest price of a product on any website on the internet based on the information received by the reception unit. The collection unit crawls multiple shopping sites and collects price information from each site. For example, the collection unit collects price information from e-commerce sites and auction sites. The collection unit can also identify the lowest price of a product using a generation AI. For example, the collection unit receives a prompt from the generation AI saying "Please find the lowest price for this product" and searches for the lowest price of that product on any website on the internet. The Offer Unit, based on the lowest price identified by the Collection Unit, makes an offer to the store asking if they can offer a price lower than that. For example, if the lowest price identified by the Generating AI is 5000 yen, the Offer Unit will offer the store, "Can you offer this product for less than 5000 yen?" This offer is made automatically. The Offer Unit can also optimize the content of the offer using the Generating AI. For example, the Offer Unit receives the prompt "Can you offer this product for less than 5000 yen?" from the Generating AI and makes an offer to the store. After the Offer Unit makes an offer, the Bidding Unit accepts bids from the store. For example, if the store bids "We can offer it for 4500 yen," the Bidding Unit accepts that bid. The Bidding Unit can also optimize the content of the bid using the Generating AI. For example, the Bidding Unit receives the prompt "We can offer it for 4500 yen" from the Generating AI and accepts that bid. The Provider Unit provides the user with the lowest price based on the bids accepted by the Bidding Unit.The offering unit, for example, enables users to purchase products for 4,500 yen. The offering unit can also optimize the offering content using a generation AI. For example, the offering unit receives a prompt from the generation AI saying "We will offer the product for 4,500 yen" and provides the user with the lowest price. This allows the lowest price purchase system according to the embodiment to improve the user's online shopping experience and expand the e-commerce site's market share.

[0030] The reception desk receives product requests from users. These requests may include, but are not limited to, information such as product names and categories. For example, the reception desk can accept a request if a user types "I want the latest smartphone." The reception desk can also support multiple input methods, such as voice input and image input. For example, a user can say "I want the latest smartphone" and the request will be accepted. Furthermore, to enhance user convenience, the reception desk can use natural language processing technology to analyze user input and automatically extract appropriate product categories and keywords. For example, if a user types "I want a smartphone with a high-performance camera," the reception desk will extract the keywords "smartphone" and "high-performance camera" and perform a product search based on these. The reception desk can also refer to the user's past purchase and search history to suggest products tailored to their preferences and needs. For example, a user who has previously purchased a high-performance smartphone with a camera will be given priority in being offered smartphones equipped with the latest camera technology. This allows the reception desk to respond flexibly to user needs and improve the user experience.

[0031] The data collection unit uses generative AI to search for the lowest prices on any website on the internet based on information received by the reception unit. For example, the data collection unit crawls multiple shopping sites and collects price information from each site. For instance, it collects price information from e-commerce sites and auction sites. The data collection unit can also identify the lowest price for a product using generative AI. For example, the data collection unit can receive a prompt such as "Please find the lowest price for this product" and search for the lowest price for that product on any website on the internet. Specifically, the generative AI uses natural language processing technology to analyze the user's request and extract relevant product information. Then, it uses web scraping technology to collect price information from multiple shopping sites and stores it in a database. The collected price information is analyzed by the generative AI to identify the lowest price. Furthermore, the data collection unit can collect not only price information but also related information such as product availability, shipping conditions, and review ratings to perform a comprehensive evaluation. This allows users to choose the most advantageous place to buy, not just the lowest price, but the most overall value. The data collection unit can also update price information in real time, always providing the latest information. This allows the data collection unit to provide users with the most accurate and reliable pricing information, supporting an optimal purchasing experience.

[0032] The Offer Unit, based on the lowest price identified by the Collection Unit, makes an offer to the store asking if they can offer a price lower than that. For example, if the lowest price identified by the Generating AI is 5,000 yen, the Offer Unit will offer the store, "Can you offer this product for less than 5,000 yen?" This offer is made automatically. The Offer Unit can also optimize the content of the offer using the Generating AI. For example, upon receiving the prompt from the Generating AI, "Can you offer this product for less than 5,000 yen?", the Offer Unit will make an offer to the store. Specifically, the Generating AI learns from past offer history and store pricing patterns to generate the most effective offer. For example, if a particular store has accepted an offer at a certain discount rate in the past, it will generate a new offer based on that discount rate. The Offer Unit can also make offers to multiple stores simultaneously to extract the most favorable conditions. This allows users to choose the best place to buy from a wider range of options. Furthermore, the Offer Unit can collect feedback from stores and continuously improve the accuracy and effectiveness of the offer content. For example, if a store accepts an offer, the conditions are recorded in the database and reflected in the next offer. This allows the offer department to always generate the optimal offer and provide users with the most favorable purchase conditions.

[0033] The bidding unit accepts bids from stores after an offer has been made by the offer unit. For example, if a store bids "We can offer it for 4500 yen," the bidding unit will accept that bid. The bidding unit can also optimize the content of bids using generative AI. For example, if the generative AI receives a prompt saying "We can offer it for 4500 yen," the bidding unit will accept that bid. Specifically, the generative AI analyzes the content of the bids from stores and selects the most favorable conditions. For example, it comprehensively evaluates factors such as not only price but also delivery conditions, inventory status, and review ratings to select the optimal bid. The bidding unit can also compare bids from multiple stores and provide the user with the most favorable conditions. Furthermore, the bidding unit can update bidding information in real time, always providing the latest information. This allows users to purchase products under the most favorable conditions. The bidding unit can also collect feedback from stores and continuously improve the accuracy and effectiveness of the bids. For example, if a store accepts a bid, the conditions are recorded in the database and reflected in the next bid. This allows the bidding department to always select the best bid and offer users the most favorable purchase conditions.

[0034] The offering department provides users with the lowest price based on bids received by the bidding department. For example, the offering department might enable a user to purchase a product for 4500 yen. The offering department can also optimize the content of the offering using generative AI. For example, the offering department receives a prompt from the generative AI saying "We will offer the product for 4500 yen" and provides the user with the lowest price. Specifically, the generative AI analyzes the user's purchase history and preferences to select the optimal offering method. For example, if a user has previously preferred a particular shipping method, that shipping method will be suggested preferentially. The offering department can also provide users with multiple payment methods and shipping options to enhance user convenience. Furthermore, the offering department can update offering information in real time, always providing the latest information. This allows users to purchase products under the most favorable conditions. The offering department can also collect feedback from users and continuously improve the accuracy and effectiveness of the offering. For example, if a user is satisfied with the product or service offered, that feedback is recorded in the database and reflected in the next offering. This allows the service provider to always select the optimal delivery method and offer users the most favorable purchase conditions.

[0035] The data collection unit can crawl multiple shopping sites using a generative AI and collect price information. For example, the data collection unit uses a generative AI to crawl multiple shopping sites and collect price information from each site. For example, the data collection unit collects price information from e-commerce sites and auction sites. The data collection unit can also use a generative AI to identify the lowest price for a product. For example, the data collection unit receives a prompt from the generative AI saying, "Please find the lowest price for this product," and searches for the lowest price for that product on all sites on the internet. In this way, by using a generative AI, price information can be efficiently collected from multiple shopping sites. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can identify the lowest price based on the price information collected by the generative AI.

[0036] The offer unit can make an offer to the store based on the lowest price identified by the generating AI, asking if they can offer a price lower than that. For example, if the lowest price identified by the generating AI is 5,000 yen, the offer unit will ask the store, "Can you offer this product for less than 5,000 yen?" This offer is made automatically. The offer unit can also optimize the content of the offer using the generating AI. For example, the offer unit receives a prompt from the generating AI, "Can you offer this product for less than 5,000 yen?", and makes an offer to the store. This allows for efficient offers to be made to stores by using the generating AI. The generating AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the offer unit may be performed using AI, for example, or without AI. For example, the offer unit can make an offer to the store based on the lowest price identified by the generating AI.

[0037] The bidding section can accept bids from stores. For example, if a store bids "We can offer it for 4500 yen," the bidding section will accept that bid. The bidding section can also optimize the content of bids using a generation AI. For example, the bidding section receives a prompt from the generation AI saying "We can offer it for 4500 yen" and accepts that bid. This allows for efficient acceptance of bids from stores. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the bidding section may be performed using AI, for example, or not using AI. For example, the bidding section can offer the lowest price to the user based on the bids received by the generation AI.

[0038] The service provider can offer users the lowest price. For example, the service provider can enable users to purchase a product for 4500 yen. The service provider can also optimize the content of the offer using generative AI. For example, the service provider can receive a prompt from the generative AI saying "We will offer the product for 4500 yen" and offer the lowest price to the user. This improves the user's purchasing experience by offering the lowest price. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the processing described above in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can offer a product to a user based on the lowest price provided by the generative AI.

[0039] The reception desk can analyze a user's past purchase history and suggest the most suitable ordering method. For example, the reception desk can automatically display products that the user has frequently purchased in the past as suggestions. The reception desk can also prioritize suggesting ordering methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest products that the user will use at a specific time of day based on their past purchase history. This improves user convenience by suggesting the most suitable ordering method based on past purchase history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can suggest the most suitable ordering method based on the user's past purchase history analyzed by AI.

[0040] The reception desk can filter product requests based on the user's current purchasing intent and budget. For example, if a user shows high purchasing intent, the reception desk will prioritize displaying higher-priced products. If a user has set a low budget, the reception desk can also filter and display products that are within that budget. Furthermore, if a user has low purchasing intent, the reception desk can prioritize suggesting products with discounts or special offers. This allows for the suggestion of appropriate products by filtering according to the user's purchasing intent and budget. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can filter products based on the user's purchasing intent and budget estimated by AI.

[0041] The reception desk can prioritize recommending highly relevant products by considering the user's geographical location when a product request is made. For example, if the user is in a specific region, the reception desk can prioritize displaying popular products in that region. Furthermore, if the user is traveling, the reception desk can suggest products needed at their travel destination. Additionally, if the user is at home, the reception desk can prioritize displaying products available at nearby stores. This allows for the suggestion of highly relevant products by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can make product requests based on the user's geographical location information obtained by AI.

[0042] The reception desk can analyze a user's social media activity when a product request is made and suggest relevant products. For example, the reception desk can prioritize displaying products that the user is talking about on social media. It can also suggest products that the user's friends have purchased. Furthermore, the reception desk can display products recommended by brands and influencers that the user follows. In this way, by analyzing social media activity, it is possible to suggest products relevant to the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can suggest products based on the user's social media activity analyzed by AI.

[0043] The data collection unit can adjust the level of detail collected based on the popularity and ratings of the products during the collection process. For example, the data collection unit can collect detailed price information for popular products. It can also collect detailed price information for highly-rated products. Furthermore, for low-rated products, the data collection unit can collect only basic price information. This allows for the provision of appropriate price information by adjusting the level of detail collected based on the popularity and ratings of the products. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can adjust the level of detail collected based on the popularity and ratings of products analyzed by AI.

[0044] The data collection unit can apply different collection algorithms depending on the product category during collection. For example, in the case of electronic devices, the data collection unit can also collect technical specifications and warranty information. In the case of clothing, the data collection unit can also collect size and material information. Furthermore, in the case of food products, the data collection unit can also collect expiration date and ingredient information. This allows for the provision of appropriate price information by applying a collection algorithm appropriate to the product category. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to determine the product category and apply an appropriate collection algorithm.

[0045] The data collection unit can adjust the scope of collection based on the product's sales region. For example, if the user is in a specific region, the data collection unit will prioritize collecting price information for products sold in that region. Furthermore, if the user is traveling, the data collection unit can also collect price information for products sold at their travel destination. Additionally, if the user is at home, the data collection unit can collect price information for products sold at nearby stores. This allows for the provision of appropriate price information by adjusting the scope of collection based on the product's sales region. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can adjust the scope of collection based on sales region information obtained by AI.

[0046] The data collection unit can improve the accuracy of its collection by referring to relevant literature and reviews of the product during the collection process. For example, the data collection unit can analyze product reviews to collect reliable price information. It can also refer to relevant literature to collect information on the quality and performance of the product. Furthermore, the data collection unit can collect the most reliable price information based on user ratings. This allows for improved collection accuracy by referring to relevant literature and reviews. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can improve the accuracy of its collection based on relevant literature and reviews analyzed by AI.

[0047] The offer unit can adjust the level of detail of an offer based on the product's inventory status and sales strategy. For example, if inventory is low, the offer unit will issue an offer quickly. The offer unit can also issue a detailed offer for a specific product based on the sales strategy. Furthermore, if inventory is abundant, the offer unit can issue a detailed offer. This allows for the provision of appropriate offers by adjusting the level of detail based on the product's inventory status and sales strategy. Some or all of the above processes in the offer unit may be performed using AI, for example, or not. For example, the offer unit can adjust the level of detail of an offer based on inventory status and sales strategy analyzed by AI.

[0048] The offer unit can optimize the timing of an offer based on the store's past response history. For example, the offer unit can quickly offer an offer to stores that have responded quickly in the past. It can also offer an offer earlier to stores that have been slow to respond in the past. Furthermore, the offer unit can analyze past response history and offer an offer at the optimal time. This allows for efficient offers by optimizing the timing of offers based on the store's past response history. Some or all of the above processing in the offer unit may be performed using AI, for example, or without AI. For example, the offer unit can optimize the timing of an offer based on the store's past response history analyzed by AI.

[0049] The offer unit can adjust the scope of an offer by considering the geographical location of the store when making an offer. For example, if the store is located in a specific region, the offer unit will make an offer to users in that region. Furthermore, if the store is located in multiple regions, the offer unit can make an offer to users in each region. In addition, if the stores are concentrated in a particular region, the offer unit can prioritize offering offers to users in that region. This allows for appropriate offers to be made by considering the geographical location of the store. Some or all of the above processing in the offer unit may be performed using AI, for example, or without AI. For example, the offer unit can adjust the scope of an offer based on the geographical location of the store obtained by AI.

[0050] The Offers Unit can analyze a store's social media activity and make relevant offers when making an offer. For example, the Offers Unit can make offers for products that a store is promoting on social media. It can also make offers to stores with a large number of followers. Furthermore, the Offers Unit can make offers for products that a store is trending on social media. This allows for the making of appropriate offers by analyzing social media activity. Some or all of the above processing in the Offers Unit may be performed using AI, for example, or not. For example, the Offers Unit can make offers based on the store's social media activity analyzed by AI.

[0051] The bidding department can analyze a store's past bidding history to select the optimal bidding method at the time of bidding. For example, the bidding department may prioritize suggesting bidding methods that have been successful in the past. The bidding department can also analyze past bidding history and suggest the optimal bidding timing. Furthermore, the bidding department can suggest the optimal bid amount based on past bidding history. In this way, the optimal bidding method can be selected by analyzing a store's past bidding history. Some or all of the above processes in the bidding department may be performed using AI, for example, or not using AI. For example, the bidding department can select the optimal bidding method based on the store's past bidding history analyzed by AI.

[0052] The bidding system can adjust the level of detail in bids based on the product category and sales strategy. For example, in the case of electronic devices, the bidding system can include technical specifications and warranty information in its bids. Similarly, in the case of clothing, it can include size and material information in its bids. Furthermore, in the case of food products, it can include expiration date and ingredient information in its bids. This allows for appropriate bids by adjusting the level of detail based on the product category and sales strategy. Some or all of the above processing in the bidding system may be performed using AI, for example, or not. For example, the bidding system can adjust the level of detail in bids based on product categories and sales strategies analyzed by AI.

[0053] The bidding unit can adjust the scope of its bids by considering the geographical location of the stores during the bidding process. For example, if a store is located in a specific region, the bidding unit will place bids to users in that region. If a store is located in multiple regions, the bidding unit can also place bids to users in each region. Furthermore, if stores are concentrated in a particular region, the bidding unit can prioritize bidding to users in that region. This allows for appropriate bidding by considering the geographical location of the stores. Some or all of the above processing in the bidding unit may be performed using AI, for example, or without AI. For example, the bidding unit can adjust the scope of its bids based on the geographical location of the stores obtained by AI.

[0054] The bidding system can improve the accuracy of its bids by referring to relevant literature and reviews of stores during the bidding process. For example, the bidding system can analyze product reviews to make more reliable bids. It can also refer to relevant literature and make bids based on information about product quality and performance. Furthermore, it can make the most reliable bids based on user ratings. In this way, the accuracy of bids can be improved by referring to relevant literature and reviews. Some or all of the above processes in the bidding system may be performed using AI, for example, or not. For example, the bidding system can improve the accuracy of its bids based on relevant literature and reviews analyzed by AI.

[0055] The delivery department can adjust the level of detail in its deliveries based on product inventory status and sales strategies. For example, if inventory is low, the delivery department will deliver quickly. The delivery department can also provide detailed deliveries for specific products based on sales strategies. Furthermore, if inventory is abundant, the delivery department can also provide detailed deliveries. This allows for appropriate deliveries by adjusting the level of detail based on product inventory status and sales strategies. Some or all of the above processes in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can adjust the level of detail in its deliveries based on inventory status and sales strategies analyzed by AI.

[0056] The service provider can optimize product suggestions based on the user's past purchase history at the time of delivery. For example, the service provider can suggest products related to products the user has previously purchased. Furthermore, the service provider can predict and suggest products that the user will purchase at a specific time based on their past purchase history. In addition, the service provider can analyze the user's past purchase history and suggest the most suitable products. This allows for improved user satisfaction by optimizing product suggestions based on past purchase history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can optimize product suggestions based on the user's past purchase history analyzed by AI.

[0057] The service provider can adjust the range of products offered, taking into account the user's geographical location information, at the time of delivery. For example, if the user is in a specific region, the service provider will prioritize offering products sold in that region. Furthermore, if the user is traveling, the service provider can offer products sold at their travel destination. Additionally, if the user is at home, the service provider can offer products sold at nearby stores. This allows the service provider to offer appropriate products by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can adjust the range of products offered based on the user's geographical location information obtained by AI.

[0058] The service provider can analyze the user's social media activity and suggest relevant products at the time of delivery. For example, the service provider can prioritize providing products that the user is talking about on social media. It can also suggest products that the user's friends have purchased. Furthermore, the service provider can provide products recommended by brands and influencers that the user follows. In this way, by analyzing social media activity, it is possible to suggest products relevant to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can suggest products based on the user's social media activity analyzed by AI.

[0059] The service provider can make suggestions based on the user's schedule by referring to the user's calendar information at the time of service delivery. For example, the service provider can refer to the schedule registered in the user's calendar and suggest related products. The service provider can also suggest products related to a specific event from the user's calendar information. Furthermore, the service provider can suggest the most suitable products based on the schedule based on the user's calendar information. In this way, by referring to the calendar information, it is possible to suggest appropriate products based on the user's schedule. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can make product suggestions based on the user's calendar information obtained by AI.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The lowest price purchase system can further analyze the user's purchase history and suggest the most suitable products based on past purchase patterns. For example, if a user has frequently purchased products from a particular brand in the past, it can prioritize suggesting new or related products from that brand. Similarly, if a user regularly purchases products from a specific category, it can suggest new or popular products from that category. Furthermore, it can suggest products related to specific seasons or events based on the user's past purchase history. This allows for improved user satisfaction by suggesting the most suitable products based on the user's purchase history.

[0062] The data collection unit can further analyze product ratings and reviews to collect reliable price information. For example, the unit analyzes product reviews and prioritizes collecting price information for highly-rated products. The unit can also choose not to collect price information for products with low ratings. Furthermore, the unit can analyze the content of product reviews to provide reliable price information. In this way, by analyzing product ratings and reviews, it can provide highly reliable price information.

[0063] The bidding system can further adjust its bids based on the store's inventory status. For example, if a store has low inventory, it can bid quickly to provide the product to the user sooner. Conversely, if a store has ample inventory, it can bid in detail to offer the user multiple options. Furthermore, it can adjust the priority of bids according to the store's inventory status. This allows for appropriate bidding by considering the store's inventory situation.

[0064] The service provider can further adjust the product delivery method by considering the user's geographical location. For example, if a user is in a specific region, it can prioritize providing products sold in that region. If a user is traveling, it can also provide products sold at their travel destination. Furthermore, if a user is at home, it can provide products sold at nearby stores. This allows for the provision of appropriate products by considering the user's geographical location.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives product requests from users. These requests include information such as product name and category. The reception desk accepts a request when the user enters something like, "I want the latest smartphone." The system can also support multiple input methods, such as voice input and image input. Step 2: The collection unit searches for the lowest price of the product on all websites on the internet based on the information received by the reception unit. The collection unit crawls multiple shopping sites and collects price information from each site. For example, it collects price information from e-commerce sites and auction sites, and uses a generating AI to identify the lowest price of the product. Step 3: The Offer Unit, based on the lowest price identified by the Collection Unit, makes an offer to the store asking if they can offer a price lower than that. For example, if the lowest price identified by the Generating AI is 5,000 yen, the Offer Unit will ask the store, "Can you offer this product for less than 5,000 yen?" This offer is made automatically, and the Generating AI can be used to optimize the content of the offer. Step 4: The bidding section accepts bids from stores after the offer section has made an offer. For example, if a store bids "We can offer it for 4500 yen," the bidding section accepts that bid. The content of the bids can also be optimized using generation AI. Step 5: The offering department provides the user with the lowest price based on the bids received by the bidding department. For example, it might allow the user to purchase the product for 4500 yen. The content of the offering can also be optimized using generational AI.

[0067] (Example of form 2) The lowest price purchase system according to an embodiment of the present invention is a system that improves the user's experience of purchasing the lowest price product when shopping online. In this lowest price purchase system, when a user requests a product they want, a generating AI searches for the lowest price of that product on all websites on the internet. Next, based on the lowest price found by the generating AI, it collaborates with e-commerce sites to make an offer to the store to see if they can offer a price lower than that amount. If the store makes a bid, the user can purchase the product at that price. This mechanism allows users to purchase products at the lowest price and also increases the market share of e-commerce sites. For example, a user requests a product they want. In this case, the user only needs to input information such as the product name and category. For example, the user might input "I want the latest smartphone." This information is input into the generating AI. Next, the generating AI analyzes the input information and searches for the lowest price of that product on all websites on the internet. The generating AI crawls multiple shopping sites and collects price information from each site. For example, it collects price information from e-commerce sites and auction sites. As a result, the generating AI can identify the lowest price of that product. Based on the lowest price identified by the generating AI, it collaborates with e-commerce sites to make an offer to the store to see if they can offer a price lower than that amount. For example, if the generating AI identifies the lowest price at 5,000 yen, it will send an offer to the e-commerce site's store asking, "Can you offer this product for less than 5,000 yen?" This offer is made automatically. If the store submits a bid, the user can purchase the product at that price. For example, if the store bids "We can offer it for 4,500 yen," the user can purchase the product at that price. This system allows users to purchase products at the lowest price. This system makes it easy for users to find and purchase the cheapest products when shopping online. It also improves the market share of e-commerce sites. For example, as users have more opportunities to purchase products on e-commerce sites, the number of users on e-commerce sites increases, and their market share improves. In this way, the lowest price purchase system can improve the user's online shopping experience and expand the market share of e-commerce sites.

[0068] The lowest price purchase system according to this embodiment comprises a reception unit, a collection unit, an offer unit, a bidding unit, and a supply unit. The reception unit receives product requests from users. Product requests from users include, but are not limited to, information such as product name and category. The reception unit accepts product requests when, for example, a user inputs "I want the latest smartphone." The reception unit can also support multiple input methods, such as voice input and image input. For example, a product request can be accepted when a user says "I want the latest smartphone" by voice. The collection unit uses a generation AI to search for the lowest price of a product on any website on the internet based on the information received by the reception unit. The collection unit crawls multiple shopping sites and collects price information from each site. For example, the collection unit collects price information from e-commerce sites and auction sites. The collection unit can also identify the lowest price of a product using a generation AI. For example, the collection unit receives a prompt from the generation AI saying "Please find the lowest price for this product" and searches for the lowest price of that product on any website on the internet. The Offer Unit, based on the lowest price identified by the Collection Unit, makes an offer to the store asking if they can offer a price lower than that. For example, if the lowest price identified by the Generating AI is 5000 yen, the Offer Unit will offer the store, "Can you offer this product for less than 5000 yen?" This offer is made automatically. The Offer Unit can also optimize the content of the offer using the Generating AI. For example, the Offer Unit receives the prompt "Can you offer this product for less than 5000 yen?" from the Generating AI and makes an offer to the store. After the Offer Unit makes an offer, the Bidding Unit accepts bids from the store. For example, if the store bids "We can offer it for 4500 yen," the Bidding Unit accepts that bid. The Bidding Unit can also optimize the content of the bid using the Generating AI. For example, the Bidding Unit receives the prompt "We can offer it for 4500 yen" from the Generating AI and accepts that bid. The Provider Unit provides the user with the lowest price based on the bids accepted by the Bidding Unit.The offering unit, for example, enables users to purchase products for 4,500 yen. The offering unit can also optimize the offering content using a generation AI. For example, the offering unit receives a prompt from the generation AI saying "We will offer the product for 4,500 yen" and provides the user with the lowest price. This allows the lowest price purchase system according to the embodiment to improve the user's online shopping experience and expand the e-commerce site's market share.

[0069] The reception desk receives product requests from users. These requests may include, but are not limited to, information such as product names and categories. For example, the reception desk can accept a request if a user types "I want the latest smartphone." The reception desk can also support multiple input methods, such as voice input and image input. For example, a user can say "I want the latest smartphone" and the request will be accepted. Furthermore, to enhance user convenience, the reception desk can use natural language processing technology to analyze user input and automatically extract appropriate product categories and keywords. For example, if a user types "I want a smartphone with a high-performance camera," the reception desk will extract the keywords "smartphone" and "high-performance camera" and perform a product search based on these. The reception desk can also refer to the user's past purchase and search history to suggest products tailored to their preferences and needs. For example, a user who has previously purchased a high-performance smartphone with a camera will be given priority in being offered smartphones equipped with the latest camera technology. This allows the reception desk to respond flexibly to user needs and improve the user experience.

[0070] The data collection unit uses generative AI to search for the lowest prices on any website on the internet based on information received by the reception unit. For example, the data collection unit crawls multiple shopping sites and collects price information from each site. For instance, it collects price information from e-commerce sites and auction sites. The data collection unit can also use generative AI to identify the lowest price for a product. For example, the data collection unit can receive a prompt such as "Please find the lowest price for this product" and search for the lowest price for that product on any website on the internet. Specifically, the generative AI uses natural language processing technology to analyze the user's request and extract relevant product information. Then, it uses web scraping technology to collect price information from multiple shopping sites and stores it in a database. The collected price information is analyzed by the generative AI to identify the lowest price. Furthermore, the data collection unit can collect not only price information but also related information such as product availability, shipping conditions, and review ratings to perform a comprehensive evaluation. This allows users to choose the most advantageous place to buy, not just the lowest price, but the most overall value. The data collection unit can also update price information in real time, always providing the most up-to-date information. This allows the data collection unit to provide users with the most accurate and reliable pricing information, supporting an optimal purchasing experience.

[0071] The Offer Unit, based on the lowest price identified by the Collection Unit, makes an offer to the store asking if they can offer a price lower than that. For example, if the lowest price identified by the Generating AI is 5,000 yen, the Offer Unit will offer the store, "Can you offer this product for less than 5,000 yen?" This offer is made automatically. The Offer Unit can also optimize the content of the offer using the Generating AI. For example, upon receiving the prompt from the Generating AI, "Can you offer this product for less than 5,000 yen?", the Offer Unit will make an offer to the store. Specifically, the Generating AI learns from past offer history and store pricing patterns to generate the most effective offer. For example, if a particular store has accepted an offer at a certain discount rate in the past, it will generate a new offer based on that discount rate. The Offer Unit can also make offers to multiple stores simultaneously to extract the most favorable conditions. This allows users to choose the best place to buy from a wider range of options. Furthermore, the Offer Unit can collect feedback from stores and continuously improve the accuracy and effectiveness of the offer content. For example, if a store accepts an offer, the conditions are recorded in the database and reflected in the next offer. This allows the offer department to always generate the optimal offer and provide users with the most favorable purchase conditions.

[0072] The bidding unit accepts bids from stores after an offer has been made by the offer unit. For example, if a store bids "We can offer it for 4500 yen," the bidding unit will accept that bid. The bidding unit can also optimize the content of bids using generative AI. For example, if the generative AI receives a prompt saying "We can offer it for 4500 yen," the bidding unit will accept that bid. Specifically, the generative AI analyzes the content of the bids from stores and selects the most favorable conditions. For example, it comprehensively evaluates factors such as not only price but also delivery conditions, inventory status, and review ratings to select the optimal bid. The bidding unit can also compare bids from multiple stores and provide the user with the most favorable conditions. Furthermore, the bidding unit can update bidding information in real time, always providing the latest information. This allows users to purchase products under the most favorable conditions. The bidding unit can also collect feedback from stores and continuously improve the accuracy and effectiveness of the bids. For example, if a store accepts a bid, the conditions are recorded in the database and reflected in the next bid. This allows the bidding department to always select the best bid and offer users the most favorable purchase conditions.

[0073] The offering department provides users with the lowest price based on bids received by the bidding department. For example, the offering department might enable a user to purchase a product for 4500 yen. The offering department can also optimize the content of the offering using generative AI. For example, the offering department receives a prompt from the generative AI saying "We will offer the product for 4500 yen" and provides the user with the lowest price. Specifically, the generative AI analyzes the user's purchase history and preferences to select the optimal offering method. For example, if a user has previously preferred a particular shipping method, that shipping method will be suggested preferentially. The offering department can also provide users with multiple payment methods and shipping options to enhance user convenience. Furthermore, the offering department can update offering information in real time, always providing the latest information. This allows users to purchase products under the most favorable conditions. The offering department can also collect feedback from users and continuously improve the accuracy and effectiveness of the offering. For example, if a user is satisfied with the product or service offered, that feedback is recorded in the database and reflected in the next offering. This allows the service provider to always select the optimal delivery method and offer users the most favorable purchase conditions.

[0074] The data collection unit can crawl multiple shopping sites using a generative AI and collect price information. For example, the data collection unit uses a generative AI to crawl multiple shopping sites and collect price information from each site. For example, the data collection unit collects price information from e-commerce sites and auction sites. The data collection unit can also use a generative AI to identify the lowest price for a product. For example, the data collection unit receives a prompt from the generative AI saying, "Please find the lowest price for this product," and searches for the lowest price for that product on all sites on the internet. In this way, by using a generative AI, price information can be efficiently collected from multiple shopping sites. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can identify the lowest price based on the price information collected by the generative AI.

[0075] The offer unit can make an offer to the store based on the lowest price identified by the generating AI, asking if they can offer a price lower than that. For example, if the lowest price identified by the generating AI is 5,000 yen, the offer unit will ask the store, "Can you offer this product for less than 5,000 yen?" This offer is made automatically. The offer unit can also optimize the content of the offer using the generating AI. For example, the offer unit receives a prompt from the generating AI, "Can you offer this product for less than 5,000 yen?", and makes an offer to the store. This allows for efficient offers to be made to stores by using the generating AI. The generating AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the offer unit may be performed using AI, for example, or without AI. For example, the offer unit can make an offer to the store based on the lowest price identified by the generating AI.

[0076] The bidding section can accept bids from stores. For example, if a store bids "We can offer it for 4500 yen," the bidding section will accept that bid. The bidding section can also optimize the content of bids using a generation AI. For example, the bidding section receives a prompt from the generation AI saying "We can offer it for 4500 yen" and accepts that bid. This allows for efficient acceptance of bids from stores. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the bidding section may be performed using AI, for example, or not using AI. For example, the bidding section can offer the lowest price to the user based on the bids received by the generation AI.

[0077] The service provider can offer users the lowest price. For example, the service provider can enable users to purchase a product for 4500 yen. The service provider can also optimize the content of the offer using generative AI. For example, the service provider can receive a prompt from the generative AI saying "We will offer the product for 4500 yen" and offer the lowest price to the user. This improves the user's purchasing experience by offering the lowest price. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the processing described above in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can offer a product to a user based on the lowest price provided by the generative AI.

[0078] The reception desk can estimate the user's emotions and customize the product request based on those emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick product requests. This improves user satisfaction by customizing the request according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can customize the request based on the user's emotions estimated by the generative AI.

[0079] The reception desk can analyze a user's past purchase history and suggest the most suitable ordering method. For example, the reception desk can automatically display products that the user has frequently purchased in the past as suggestions. The reception desk can also prioritize suggesting ordering methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest products that the user will use at a specific time of day based on their past purchase history. This improves user convenience by suggesting the most suitable ordering method based on past purchase history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can suggest the most suitable ordering method based on the user's past purchase history analyzed by AI.

[0080] The reception desk can filter product requests based on the user's current purchasing intent and budget. For example, if a user shows high purchasing intent, the reception desk will prioritize displaying higher-priced products. If a user has set a low budget, the reception desk can also filter and display products that are within that budget. Furthermore, if a user has low purchasing intent, the reception desk can prioritize suggesting products with discounts or special offers. This allows for the suggestion of appropriate products by filtering according to the user's purchasing intent and budget. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can filter products based on the user's purchasing intent and budget estimated by AI.

[0081] The reception desk can estimate the user's emotions and determine the priority of requests based on those emotions. For example, if the user is in a hurry, the reception desk will process the request with the highest priority. If the user is relaxed, the reception desk can process the request with the normal priority. Furthermore, if the user is stressed, the reception desk can process the request quickly to reduce the user's burden. This improves user satisfaction by determining the priority of requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can determine the priority of requests based on the user's emotions estimated by the generative AI.

[0082] The reception desk can prioritize recommending highly relevant products by considering the user's geographical location when a product request is made. For example, if the user is in a specific region, the reception desk can prioritize displaying popular products in that region. Furthermore, if the user is traveling, the reception desk can suggest products needed at their travel destination. Additionally, if the user is at home, the reception desk can prioritize displaying products available at nearby stores. This allows for the suggestion of highly relevant products by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can make product requests based on the user's geographical location information obtained by AI.

[0083] The reception desk can analyze a user's social media activity when a product request is made and suggest relevant products. For example, the reception desk can prioritize displaying products that the user is talking about on social media. It can also suggest products that the user's friends have purchased. Furthermore, the reception desk can display products recommended by brands and influencers that the user follows. In this way, by analyzing social media activity, it is possible to suggest products relevant to the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can suggest products based on the user's social media activity analyzed by AI.

[0084] The data collection unit can estimate the user's emotions and adjust the method of collecting price information based on the estimated emotions. For example, if the user is relaxed, the data collection unit can collect detailed price information. If the user is in a hurry, the data collection unit can also collect only the most important price information. Furthermore, if the user is stressed, the data collection unit can collect concise and easy-to-understand price information. This allows for the provision of appropriate price information by adjusting the method of collecting price information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can adjust the method of collecting price information based on the user's emotions estimated by the generative AI.

[0085] The data collection unit can adjust the level of detail collected based on the popularity and ratings of the products during the collection process. For example, the data collection unit can collect detailed price information for popular products. It can also collect detailed price information for highly-rated products. Furthermore, for low-rated products, the data collection unit can collect only basic price information. This allows for the provision of appropriate price information by adjusting the level of detail collected based on the popularity and ratings of the products. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can adjust the level of detail collected based on the popularity and ratings of products analyzed by AI.

[0086] The data collection unit can apply different collection algorithms depending on the product category during collection. For example, in the case of electronic devices, the data collection unit can also collect technical specifications and warranty information. In the case of clothing, the data collection unit can also collect size and material information. Furthermore, in the case of food products, the data collection unit can also collect expiration date and ingredient information. This allows for the provision of appropriate price information by applying a collection algorithm appropriate to the product category. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to determine the product category and apply an appropriate collection algorithm.

[0087] The data collection unit can estimate the user's emotions and determine the priority of price information to collect based on the estimated emotions. For example, if the user is in a hurry, the data collection unit will prioritize collecting the cheapest price information. If the user is relaxed, the data collection unit can also collect detailed price information. Furthermore, if the user is stressed, the data collection unit can prioritize collecting concise and easy-to-understand price information. This allows for the provision of appropriate price information by prioritizing price information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can determine the priority of price information based on the user's emotions estimated by the generative AI.

[0088] The data collection unit can adjust the scope of collection based on the product's sales region. For example, if the user is in a specific region, the data collection unit will prioritize collecting price information for products sold in that region. Furthermore, if the user is traveling, the data collection unit can also collect price information for products sold at their travel destination. Additionally, if the user is at home, the data collection unit can collect price information for products sold at nearby stores. This allows for the provision of appropriate price information by adjusting the scope of collection based on the product's sales region. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can adjust the scope of collection based on sales region information obtained by AI.

[0089] The data collection unit can improve the accuracy of its collection by referring to relevant literature and reviews of the product during the collection process. For example, the data collection unit can analyze product reviews to collect reliable price information. It can also refer to relevant literature to collect information on the quality and performance of the product. Furthermore, the data collection unit can collect the most reliable price information based on user ratings. This allows for improved collection accuracy by referring to relevant literature and reviews. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can improve the accuracy of its collection based on relevant literature and reviews analyzed by AI.

[0090] The offer unit can estimate the user's emotions and adjust the way the offer is presented based on those emotions. For example, if the user is relaxed, the offer unit can offer a polite and detailed offer. If the user is in a hurry, the offer unit can offer a concise and quick offer. Furthermore, if the user is stressed, the offer unit can offer a clear and concise offer. By adjusting the way the offer is presented according to the user's emotions, user satisfaction can be improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the offer unit may be performed using AI, for example, or without AI. For example, the offer unit can adjust the way the offer is presented based on the user's emotions estimated by the generative AI.

[0091] The offer unit can adjust the level of detail of an offer based on the product's inventory status and sales strategy. For example, if inventory is low, the offer unit will issue an offer quickly. The offer unit can also issue a detailed offer for a specific product based on the sales strategy. Furthermore, if inventory is abundant, the offer unit can issue a detailed offer. This allows for the provision of appropriate offers by adjusting the level of detail based on the product's inventory status and sales strategy. Some or all of the above processes in the offer unit may be performed using AI, for example, or not. For example, the offer unit can adjust the level of detail of an offer based on inventory status and sales strategy analyzed by AI.

[0092] The offer unit can optimize the timing of an offer based on the store's past response history. For example, the offer unit can quickly offer an offer to stores that have responded quickly in the past. It can also offer an offer earlier to stores that have been slow to respond in the past. Furthermore, the offer unit can analyze past response history and offer an offer at the optimal time. This allows for efficient offers by optimizing the timing of offers based on the store's past response history. Some or all of the above processing in the offer unit may be performed using AI, for example, or without AI. For example, the offer unit can optimize the timing of an offer based on the store's past response history analyzed by AI.

[0093] The offer unit can estimate the user's emotions and determine the priority of offers based on those emotions. For example, if the user is in a hurry, the offer unit will process the offer with the highest priority. If the user is relaxed, the offer unit can process the offer with the normal priority. Furthermore, if the user is stressed, the offer unit can process the offer quickly to reduce the user's burden. This improves user satisfaction by determining the priority of offers according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples. Some or all of the processing described above in the offer unit may be performed using AI, for example, or without AI. For example, the offer unit can determine the priority of offers based on the user's emotions estimated by the generative AI.

[0094] The offer unit can adjust the scope of an offer by considering the geographical location of the store when making an offer. For example, if the store is located in a specific region, the offer unit will make an offer to users in that region. Furthermore, if the store is located in multiple regions, the offer unit can make an offer to users in each region. In addition, if the stores are concentrated in a particular region, the offer unit can prioritize offering offers to users in that region. This allows for appropriate offers to be made by considering the geographical location of the store. Some or all of the above processing in the offer unit may be performed using AI, for example, or without AI. For example, the offer unit can adjust the scope of an offer based on the geographical location of the store obtained by AI.

[0095] The Offers Unit can analyze a store's social media activity and make relevant offers when making an offer. For example, the Offers Unit can make offers for products that a store is promoting on social media. It can also make offers to stores with a large number of followers. Furthermore, the Offers Unit can make offers for products that a store is trending on social media. This allows for the making of appropriate offers by analyzing social media activity. Some or all of the above processing in the Offers Unit may be performed using AI, for example, or not. For example, the Offers Unit can make offers based on the store's social media activity analyzed by AI.

[0096] The bidding system can estimate the user's emotions and adjust the bid acceptance method based on the estimated emotions. For example, if the user is relaxed, the bidding system can provide detailed bidding options. If the user is in a hurry, it can also provide concise and quick bidding options. Furthermore, if the user is stressed, it can provide clear and concise bidding options. This allows for improved user satisfaction by adjusting the bid acceptance method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the bidding system may be performed using AI or not. For example, the bidding system can adjust the bid acceptance method based on the user's emotions estimated by the generative AI.

[0097] The bidding department can analyze a store's past bidding history to select the optimal bidding method at the time of bidding. For example, the bidding department may prioritize suggesting bidding methods that have been successful in the past. The bidding department can also analyze past bidding history and suggest the optimal bidding timing. Furthermore, the bidding department can suggest the optimal bid amount based on past bidding history. In this way, the optimal bidding method can be selected by analyzing a store's past bidding history. Some or all of the above processes in the bidding department may be performed using AI, for example, or not using AI. For example, the bidding department can select the optimal bidding method based on the store's past bidding history analyzed by AI.

[0098] The bidding system can adjust the level of detail in bids based on the product category and sales strategy. For example, in the case of electronic devices, the bidding system can include technical specifications and warranty information in its bids. Similarly, in the case of clothing, it can include size and material information in its bids. Furthermore, in the case of food products, it can include expiration date and ingredient information in its bids. This allows for appropriate bids by adjusting the level of detail based on the product category and sales strategy. Some or all of the above processing in the bidding system may be performed using AI, for example, or not. For example, the bidding system can adjust the level of detail in bids based on product categories and sales strategies analyzed by AI.

[0099] The bidding unit can estimate the user's emotions and determine the priority of bids based on the estimated emotions. For example, if the user is in a hurry, the bidding unit will process the bid with the highest priority. If the user is relaxed, the bidding unit can process the bid with the normal priority. Furthermore, if the user is stressed, the bidding unit can process the bid quickly to reduce the user's burden. This improves user satisfaction by determining the priority of bids according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the bidding unit may be performed using AI, for example, or not using AI. For example, the bidding unit can determine the priority of bids based on the user's emotions estimated by generative AI.

[0100] The bidding unit can adjust the scope of its bids by considering the geographical location of the stores during the bidding process. For example, if a store is located in a specific region, the bidding unit will place bids to users in that region. If a store is located in multiple regions, the bidding unit can also place bids to users in each region. Furthermore, if stores are concentrated in a particular region, the bidding unit can prioritize bidding to users in that region. This allows for appropriate bidding by considering the geographical location of the stores. Some or all of the above processing in the bidding unit may be performed using AI, for example, or without AI. For example, the bidding unit can adjust the scope of its bids based on the geographical location of the stores obtained by AI.

[0101] The bidding system can improve the accuracy of its bids by referring to relevant literature and reviews of stores during the bidding process. For example, the bidding system can analyze product reviews to make more reliable bids. It can also refer to relevant literature and make bids based on information about product quality and performance. Furthermore, it can make the most reliable bids based on user ratings. In this way, the accuracy of bids can be improved by referring to relevant literature and reviews. Some or all of the above processes in the bidding system may be performed using AI, for example, or not. For example, the bidding system can improve the accuracy of its bids based on relevant literature and reviews analyzed by AI.

[0102] The service provider can estimate the user's emotions and adjust how the products are displayed based on those estimated emotions. For example, if the user is relaxed, the service provider can display detailed product information. If the user is in a hurry, the service provider can also display concise and easy-to-understand product information. Furthermore, if the user is stressed, the service provider can display visually easy-to-understand product information. By adjusting how products are displayed according to the user's emotions, user satisfaction can be improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can adjust how products are displayed based on the user's emotions estimated by the generative AI.

[0103] The delivery department can adjust the level of detail in its deliveries based on product inventory status and sales strategies. For example, if inventory is low, the delivery department will deliver quickly. The delivery department can also provide detailed deliveries for specific products based on sales strategies. Furthermore, if inventory is abundant, the delivery department can also provide detailed deliveries. This allows for appropriate deliveries by adjusting the level of detail based on product inventory status and sales strategies. Some or all of the above processes in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can adjust the level of detail in its deliveries based on inventory status and sales strategies analyzed by AI.

[0104] The service provider can optimize product suggestions based on the user's past purchase history at the time of delivery. For example, the service provider can suggest products related to products the user has previously purchased. Furthermore, the service provider can predict and suggest products that the user will purchase at a specific time based on their past purchase history. In addition, the service provider can analyze the user's past purchase history and suggest the most suitable products. This allows for improved user satisfaction by optimizing product suggestions based on past purchase history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can optimize product suggestions based on the user's past purchase history analyzed by AI.

[0105] The service delivery unit can estimate the user's emotions and determine the priority of the products to offer based on the estimated emotions. For example, if the user is in a hurry, the service delivery unit will process the order with the highest priority. If the user is relaxed, the service delivery unit can process the order with the normal priority. Furthermore, if the user is stressed, the service delivery unit can process the order quickly to reduce the user's burden. This improves user satisfaction by determining the priority of products according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples. Some or all of the processing described above in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can determine the priority of products based on the user's emotions estimated by the generative AI.

[0106] The service provider can adjust the range of products offered, taking into account the user's geographical location information, at the time of delivery. For example, if the user is in a specific region, the service provider will prioritize offering products sold in that region. Furthermore, if the user is traveling, the service provider can offer products sold at their travel destination. Additionally, if the user is at home, the service provider can offer products sold at nearby stores. This allows the service provider to offer appropriate products by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can adjust the range of products offered based on the user's geographical location information obtained by AI.

[0107] The service provider can analyze the user's social media activity and suggest relevant products at the time of delivery. For example, the service provider can prioritize providing products that the user is talking about on social media. It can also suggest products that the user's friends have purchased. Furthermore, the service provider can provide products recommended by brands and influencers that the user follows. In this way, by analyzing social media activity, it is possible to suggest products relevant to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can suggest products based on the user's social media activity analyzed by AI.

[0108] The service provider can make suggestions based on the user's schedule by referring to the user's calendar information at the time of service delivery. For example, the service provider can refer to the schedule registered in the user's calendar and suggest related products. The service provider can also suggest products related to a specific event from the user's calendar information. Furthermore, the service provider can suggest the most suitable products based on the schedule based on the user's calendar information. In this way, by referring to the calendar information, it is possible to suggest appropriate products based on the user's schedule. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can make product suggestions based on the user's calendar information obtained by AI.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The lowest price purchase system can further analyze the user's purchase history and suggest the most suitable products based on past purchase patterns. For example, if a user has frequently purchased products from a particular brand in the past, it can prioritize suggesting new or related products from that brand. Similarly, if a user regularly purchases products from a specific category, it can suggest new or popular products from that category. Furthermore, it can suggest products related to specific seasons or events based on the user's past purchase history. This allows for improved user satisfaction by suggesting the most suitable products based on the user's purchase history.

[0111] The data collection unit can further analyze product ratings and reviews to collect reliable price information. For example, the unit analyzes product reviews and prioritizes collecting price information for highly-rated products. The unit can also choose not to collect price information for products with low ratings. Furthermore, the unit can analyze the content of product reviews to provide reliable price information. In this way, by analyzing product ratings and reviews, it can provide highly reliable price information.

[0112] The offer section can further estimate the user's purchase intent and adjust the offer content based on that estimation. For example, if a user shows high purchase intent, a detailed offer can be made, providing the user with multiple options. If a user shows low purchase intent, a concise and easy-to-understand offer can be made to reduce the user's burden. Furthermore, if a user's purchase intent is moderate, an offer with an appropriate level of detail can be made. In this way, by adjusting the offer content according to the user's purchase intent, user satisfaction can be improved.

[0113] The bidding system can further adjust its bids based on the store's inventory status. For example, if a store has low inventory, it can bid quickly to provide the product to the user sooner. Conversely, if a store has ample inventory, it can bid in detail to offer the user multiple options. Furthermore, it can adjust the priority of bids according to the store's inventory status. This allows for appropriate bidding by considering the store's inventory situation.

[0114] The service provider can further adjust the product delivery method by considering the user's geographical location. For example, if a user is in a specific region, it can prioritize providing products sold in that region. If a user is traveling, it can also provide products sold at their travel destination. Furthermore, if a user is at home, it can provide products sold at nearby stores. This allows for the provision of appropriate products by considering the user's geographical location.

[0115] The reception desk can further estimate the user's emotions and customize the product request based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick product requests. By customizing requests according to the user's emotions, this system can improve user satisfaction.

[0116] The data collection unit can further estimate the user's emotions and adjust the method of collecting price information based on those emotions. For example, if the user is relaxed, it can collect detailed price information. If the user is in a hurry, it can collect only the most important price information. Furthermore, if the user is stressed, it can collect concise and easy-to-understand price information. In this way, by adjusting the method of collecting price information according to the user's emotions, it can provide appropriate price information.

[0117] The offer section can further estimate the user's emotions and adjust the way the offer is presented based on those estimates. For example, if the user is relaxed, a polite and detailed offer can be presented. If the user is in a hurry, a concise and quick offer can be presented. Furthermore, if the user is stressed, a clear and concise offer can be presented. By adjusting the way the offer is presented according to the user's emotions, user satisfaction can be improved.

[0118] The bidding system can further estimate the user's emotions and adjust the bid acceptance method based on those estimates. For example, if the user is relaxed, it can offer detailed bidding options. If the user is in a hurry, it can offer concise and quick bidding options. Furthermore, if the user is stressed, it can offer clear and concise bidding options. By adjusting the bid acceptance method according to the user's emotions, user satisfaction can be improved.

[0119] The service provider can further estimate the user's emotions and adjust how the products are displayed based on those estimates. For example, if the user is relaxed, detailed product information can be displayed. If the user is in a hurry, concise and easy-to-understand product information can be displayed. Furthermore, if the user is stressed, visually easy-to-understand product information can be displayed. By adjusting the product display method according to the user's emotions, user satisfaction can be improved.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The reception desk receives product requests from users. These requests include information such as product name and category. The reception desk accepts a request when the user enters something like, "I want the latest smartphone." The system can also support multiple input methods, such as voice input and image input. Step 2: The collection unit searches for the lowest price of the product on all websites on the internet based on the information received by the reception unit. The collection unit crawls multiple shopping sites and collects price information from each site. For example, it collects price information from e-commerce sites and auction sites, and uses a generating AI to identify the lowest price of the product. Step 3: The Offer Unit, based on the lowest price identified by the Collection Unit, makes an offer to the store asking if they can offer a price lower than that. For example, if the lowest price identified by the Generating AI is 5,000 yen, the Offer Unit will ask the store, "Can you offer this product for less than 5,000 yen?" This offer is made automatically, and the Generating AI can be used to optimize the content of the offer. Step 4: The bidding section accepts bids from stores after the offer section has made an offer. For example, if a store bids "We can offer it for 4500 yen," the bidding section accepts that bid. The content of the bids can also be optimized using generation AI. Step 5: The offering department provides the user with the lowest price based on the bids received by the bidding department. For example, it might allow the user to purchase the product for 4500 yen. The content of the offering can also be optimized using generational AI.

[0122] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0125] Each of the multiple elements described above, including the reception unit, collection unit, offer unit, bidding unit, and supply unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives product requests from users. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for the lowest price of a product on any website on the internet. The offer unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes offers to stores. The bidding unit is implemented by, for example, the control unit 46A of the smart device 14 and receives bids from stores. The supply unit is implemented by, for example, the control unit 46A of the smart device 14 and provides the user with the lowest price. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] As shown in Figure 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.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the reception unit, collection unit, offer unit, bidding unit, and supply unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives product requests from users. The collection unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and searches for the lowest price of a product on any website on the internet. The offer unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and makes offers to stores. The bidding unit is implemented by, for example, the control unit 46A of the smart glasses 214 and receives bids from stores. The supply unit is implemented by, for example, the control unit 46A of the smart glasses 214 and provides the user with the lowest price. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the reception unit, collection unit, offer unit, bidding unit, and supply unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives product requests from users. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for the lowest price of a product on any website on the internet. The offer unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes offers to stores. The bidding unit is implemented by, for example, the control unit 46A of the headset terminal 314 and receives bids from stores. The supply unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides the user with the lowest price. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] As shown in Figure 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.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the reception unit, collection unit, offer unit, bidding unit, and supply unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives product requests from users. The collection unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and searches for the lowest price of a product on any website on the internet. The offer unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and makes offers to stores. The bidding unit is implemented by, for example, the control unit 46A of the robot 414 and receives bids from stores. The supply unit is implemented by, for example, the control unit 46A of the robot 414 and provides the user with the lowest price. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0175] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] 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.

[0185] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) A reception desk that receives product requests from users, Based on the information received by the aforementioned reception department, the collection department searches for the lowest price of the product on all websites on the internet, Based on the lowest price identified by the aforementioned collection unit, the offer unit makes an offer to the store to see if they can offer a price lower than that amount. After the offer is made by the aforementioned offer department, the bidding department accepts bids from the stores, The system comprises a provisioning unit that provides the lowest price to the user based on bids received by the aforementioned bidding unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The generated AI crawls multiple shopping sites and collects price information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned offer section is, Based on the lowest price identified by the generating AI, the system will offer the store a price lower than that amount. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned bidding department, We accept bids from stores. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We offer our users the lowest prices. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and customizes the product request based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze the user's past purchase history and propose the most suitable request method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When a product is requested, filtering is performed based on the user's current purchasing intent and budget. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When a user requests a product, the system prioritizes recommending highly relevant products by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When a product is requested, we analyze the user's social media activity and suggest relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We estimate user sentiment and adjust the method of collecting price information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting items, adjust the level of detail based on the popularity and ratings of the products. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting data, different collection algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is It estimates user sentiment and prioritizes the price information to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is During collection, the scope of collection will be adjusted based on the product's sales region. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is During data collection, we refer to relevant literature and reviews of the products to improve the accuracy of the collection. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned offer section is, It estimates the user's emotions and adjusts how the offer is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned offer section is, When making an offer, adjust the level of detail based on product availability and sales strategy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned offer section is, When making an offer, optimize the timing of the offer based on the store's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned offer section is, It estimates the user's emotions and determines the priority of offers based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned offer section is, When making an offer, we adjust the scope of the offer considering the store's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned offer section is, When making an offer, we analyze the store's social media activity and make relevant offers. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned bidding department, We estimate user sentiment and adjust the bid acceptance method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned bidding department, During the bidding process, the store's past bidding history is analyzed to select the most suitable bidding method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned bidding department, When bidding, adjust the level of detail in your bid based on the product category and sales strategy. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned bidding department, The system estimates user sentiment and determines bid priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned bidding department, When bidding, adjust the bid range considering the store's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned bidding department, When bidding, refer to relevant literature and reviews for the store to improve the accuracy of your bid. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, We estimate the user's emotions and adjust how products are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the product, we will adjust the level of detail based on product availability and sales strategy. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing products, we optimize the product suggestions based on the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of products to offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing products, the range of products offered will be adjusted to take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and suggest relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing the service, it will refer to the user's calendar information to make suggestions based on their schedule. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives product requests from users, Based on the information received by the aforementioned reception department, the collection department searches for the lowest price of the product on all websites on the internet, Based on the lowest price identified by the aforementioned collection unit, the offer unit makes an offer to the store to see if they can offer a price lower than that amount. After the offer is made by the aforementioned offer department, the bidding department accepts bids from the stores, The system comprises a provisioning unit that provides the lowest price to the user based on bids received by the aforementioned bidding unit. A system characterized by the following features.

2. The aforementioned collection unit is The generated AI crawls multiple shopping sites and collects price information. The system according to feature 1.

3. The aforementioned offer section is, Based on the lowest price identified by the generating AI, the system will make an offer to the store to see if they can offer a price lower than that. The system according to feature 1.

4. The aforementioned bidding department, We accept bids from stores. The system according to feature 1.

5. The aforementioned supply unit is, We offer our users the lowest prices. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and customizes the product request based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is We analyze the user's past purchase history and propose the most suitable request method. The system according to feature 1.

8. The aforementioned reception unit is When a product is requested, filtering is performed based on the user's current purchasing intent and budget. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When a user requests a product, the system prioritizes recommending highly relevant products by considering the user's geographical location. The system according to feature 1.

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