system

Abstract

The system according to this embodiment aims to propose the most suitable product based on the user's requests and to simplify the purchase process. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, a comparison unit, a proposal unit, and a procedure unit. The reception unit receives requests from users. The collection unit collects product information based on the requests received by the reception unit. The comparison unit compares the information collected by the collection unit. The proposal unit proposes the most suitable product based on the comparison results from the comparison unit. The procedure unit performs the procedure for purchasing the product proposed by the proposal 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, which is 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 a character of the chatbot, 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, in shopping on an EC service, there is a problem that the process of finding an optimal product while comparing multiple services is complicated and the convenience for users is low.

[0005] The system according to the embodiment aims to propose an optimal product based on the user's desire and simplify the purchase procedure.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a collection unit, a comparison unit, a proposal unit, and a procedure unit. The reception unit receives requests from users. The collection unit collects product information based on the requests received by the reception unit. The comparison unit compares the information collected by the collection unit. The proposal unit proposes the most suitable product based on the comparison results from the comparison unit. The procedure unit performs the procedure for purchasing the product proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose the most suitable product based on the user's requests and simplify the purchase process. [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 labeled 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 shopping automation system according to an embodiment of the present invention is a system in which a generating AI handles everything from product consideration to purchase procedures simply by the user telling it what product they want. The shopping automation system works by having the user decide on an image of the product they want and inputting it into the generating AI. Next, the generating AI performs a web search to obtain candidate product information, images, and videos. The generating AI collects product images and videos from e-commerce services and social media, and compares product specifications and reviews. The generating AI then suggests the most suitable product to the user, and the user decides whether or not to purchase it. Finally, the generating AI automatically completes the purchase procedure. This allows the user to easily complete their shopping without having to perform complex operations. For example, the shopping automation system works by having the user decide on an image of the product they want and inputting it into the generating AI. For example, the generating AI performs a web search to obtain candidate product information, images, and videos. The generating AI collects product images and videos from e-commerce services and social media, and compares product specifications and reviews. The generating AI suggests the most suitable product to the user, and the user decides whether or not to purchase it. Finally, the generating AI automatically completes the purchase procedure. This allows the user to easily complete their shopping without having to perform complex operations. This system improves user convenience when shopping on e-commerce services, making it easier to compare multiple services and thus enabling users to shop more affordably. In this way, shopping automation systems can automate the user's shopping process and improve convenience.

[0029] The automated shopping system according to this embodiment comprises a reception unit, a collection unit, a comparison unit, a proposal unit, and a procedure unit. The reception unit receives user requests. User requests include, for example, product type, price range, and functions, but are not limited to such examples. The reception unit, for example, determines the image of the product the user wants and inputs it into the generation AI. The collection unit collects product information based on the requests received by the reception unit. The collection unit, for example, performs a web search and collects product information, images, and videos. The collection unit collects product images and videos from e-commerce services and social media, and compares product specifications and reviews. The comparison unit compares the information collected by the collection unit. The comparison unit, for example, compares product specifications and reviews. Based on the collected information, the comparison unit compares product performance, functions, size, weight, etc. The proposal unit proposes the most suitable product based on the results of the comparison by the comparison unit. The proposal unit, for example, proposes the most suitable product to the user. The proposal unit proposes the product that best matches the user's requests and the product with the highest cost performance. The Procedure Unit performs the procedure for purchasing the products proposed by the Proposal Unit. For example, the Procedure Unit adds products to the cart, enters the address and payment information, and confirms the purchase. The Procedure Unit automatically performs operations such as adding items to the cart and entering the address and payment information. As a result, the automated shopping system according to this embodiment improves user convenience when shopping on e-commerce services by receiving user requests, collecting product information, comparing, proposing, and performing the purchase procedure.

[0030] The reception department receives user requests. User requests include, but are not limited to, product type, price range, and features. For example, the reception department determines the image of the product the user wants and inputs it into the generating AI. Specifically, the user uses a smartphone or computer to access a dedicated application or website and inputs details of the product they want. For example, they might input requests such as "I want a high-performance laptop," "My budget is under 100,000 yen," and "It should be lightweight and easy to carry." The generating AI analyzes these requests and extracts appropriate keywords and conditions. The generating AI uses natural language processing technology to understand the user's requests and generates prompts to search for relevant product information. For example, it extracts keywords such as "high performance," "laptop," "under 100,000 yen," and "lightweight," and sends instructions to the collection department to collect product information based on these. This allows the reception department to accurately understand the user's requests and smoothly move on to the next step of collecting product information. Furthermore, the reception department saves user requests as a history and can refer to them when making future purchases. For example, based on information and requests for products purchased in the past, it can analyze the user's preferences and trends and provide more personalized suggestions. This allows the reception desk to efficiently receive user requests and improve the shopping experience.

[0031] The collection unit collects product information based on requests received by the reception unit. For example, the collection unit performs web searches to collect product information, images, and videos. Specifically, the collection unit uses multiple search engines, e-commerce sites, and social media platforms to collect relevant product information from a wide range of sources. For example, it uses a search engine to search for keywords such as "high-performance laptop under 100,000 yen lightweight" and obtains information from the resulting product pages and review sites. On e-commerce sites, it collects specifications, prices, and user reviews from product detail pages, and on social media platforms, it collects product images and videos of users' experiences posting them. The collection unit automatically analyzes this information and extracts the necessary data. For example, it extracts product specifications such as CPU type, memory capacity, storage capacity, weight, and battery life, and collects user ratings and comments as review information. Furthermore, the collection unit centrally manages the collected information and stores it in a database. This allows the collection unit to efficiently collect product information from diverse sources and provide it to the comparison unit, which is the next step. The collection unit can also verify the reliability and timeliness of the collected information and update or collect additional information as needed. This allows the data collection unit to always provide the latest and most accurate product information, supporting users in selecting the optimal product to meet their needs.

[0032] The comparison unit compares the information collected by the collection unit. For example, the comparison unit compares product specifications and reviews. Specifically, it compares detailed specifications such as performance, functions, size, and weight of collected products to identify the product that best matches the user's needs. For example, it compares the CPU performance, memory capacity, storage capacity, and battery life of multiple laptops to select the product that best suits the user's needs. It also analyzes user reviews to compare product evaluations and user experience. The content of the reviews is analyzed using natural language processing technology to classify positive and negative evaluations and calculate an overall evaluation. Furthermore, the comparison unit also compares product price information to identify products with high cost performance. For example, even if laptops have the same performance, if the prices differ, it selects the optimal product by considering the balance between price and performance. Based on this information, the comparison unit lists the products that best match the user's needs and provides them to the recommendation unit. The comparison unit can also flexibly change the comparison criteria according to the user's needs. For example, it can perform a performance-oriented comparison for performance-oriented users and a price-oriented comparison for price-oriented users. This allows the comparison unit to respond to diverse user needs and support the selection of the most suitable product.

[0033] The Proposal Department proposes the most suitable product based on the comparison results from the Comparison Department. For example, the Proposal Department proposes the best product to the user. Specifically, based on the information provided by the Comparison Department, it selects and proposes the product that best matches the user's needs and offers the best cost performance. The Proposal Department can also present multiple options depending on the user's needs. For example, in response to requests such as "high-performance laptop," "budget under 100,000 yen," and "lightweight," it will list several candidate products and explain the features and benefits of each. The Proposal Department provides users with detailed product information, reviews, and pricing information to support their comparison and consideration. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it can collect feedback on user satisfaction and usage after purchasing a proposed product and incorporate it into future proposals. The Proposal Department can analyze user preferences and trends to provide more personalized proposals. This allows the Proposal Department to propose products that best match the user's needs and improve the shopping experience.

[0034] The Procedures Unit handles the process of purchasing products suggested by the Proposal Unit. For example, the Procedures Unit adds products to the cart, enters the address and payment information, and confirms the purchase. Specifically, based on the user's account information, the Procedures Unit automatically adds products to the cart, enters the shipping address, and enters payment information. For example, if a user decides to purchase a suggested product, the Procedures Unit automatically accesses the e-commerce site and adds the product to the cart. Next, it retrieves the shipping address from the user's account information and enters the necessary information. Furthermore, based on the user's payment information, it enters credit card information and other payment methods to complete the purchase process. The Procedures Unit performs these operations quickly and accurately, significantly reducing the user's effort. The Procedures Unit notifies the user of the progress of the purchase process in real time, allowing for confirmation and correction as needed. For example, if there are errors in the shipping address or payment information, it notifies the user and prompts them to correct it. The Procedures Unit also saves the purchase history, which can be used as a reference for future purchases. In this way, the Procedures Unit can efficiently support the user's shopping process and improve the shopping experience.

[0035] The data collection unit can perform web searches and collect product information, images, and videos. For example, the data collection unit can perform web searches and collect product information, images, and videos. The data collection unit can collect product information using searches on search engines or specific e-commerce site searches. The data collection unit collects product photos, promotional videos, user review videos, etc. This allows the data collection unit to efficiently collect product information, images, and videos by performing web searches. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input a web search prompt into a generative AI, and the generative AI can collect the search results.

[0036] The comparison unit can compare collected information and compare product specifications and reviews. For example, the comparison unit can compare collected information and compare product specifications and reviews. The comparison unit can compare product performance, functions, size, weight, etc. The comparison unit can compare product reviews based on user ratings, comments, star ratings, etc. In this way, the comparison unit can select the optimal product by comparing product specifications and reviews. Some or all of the above processing in the comparison unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the comparison unit can input collected information into a generation AI, and the generation AI can compare the information.

[0037] The proposal department can propose the most suitable product to the user. For example, the proposal department can propose the most suitable product to the user. The proposal department can propose the product that best matches the user's needs or the product with the best cost performance. As a result, the proposal department improves user satisfaction by proposing the most suitable product. Some or all of the above processing in the proposal department may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal department can input the user's needs into a generative AI, and the generative AI can propose the most suitable product.

[0038] The processing unit can add items to the cart, enter the address and payment information, and confirm the purchase. For example, the processing unit can add items to the cart, enter the address and payment information, and confirm the purchase. The processing unit can automate the operations of adding items to the cart and entering the address and payment information. This improves user convenience by automating the purchase process. Some or all of the above processes in the processing unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the processing unit can have a generative AI perform the operation of adding items to the cart, and have a generative AI enter the address and payment information.

[0039] The reception department can analyze a user's past purchase history and select the most suitable method for receiving requests. For example, the reception department can prioritize requests based on the product categories the user has previously purchased. It can also prioritize suggesting specific brands or stores based on the user's past purchase history. Furthermore, the reception department can adjust the timing of request acceptance according to the user's purchase frequency. This allows the reception department to select the most suitable method for receiving requests by analyzing the user's past purchase history. Some or all of the above processing in the reception department may be performed using or without a generative AI. For example, the reception department can input the user's past purchase history into a generative AI, which can then select the most suitable method for receiving requests.

[0040] The reception desk can filter requests based on the user's current areas of interest. For example, the reception desk can prioritize requests based on product categories the user has recently searched for. It can also filter requests based on brands and stores the user follows on social media. Furthermore, it can filter requests based on the content of articles and videos the user has recently viewed. This allows the reception desk to prioritize requests that are highly relevant by filtering them based on the user's current areas of interest. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's current areas of interest into a generative AI, which can then filter the requests.

[0041] The reception desk can prioritize requests based on their relevance, taking into account the user's geographical location. For example, the reception desk can prioritize suggesting products from stores near the user's current location. Furthermore, if the user is traveling, the reception desk can prioritize suggesting stores and services in their travel destination. Additionally, if the user frequently visits a particular region, the reception desk can prioritize suggesting stores and services in that region. This allows the reception desk to prioritize requests based on their relevance by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's geographical location into a generative AI, which can then prioritize requests based on their relevance.

[0042] The reception desk can analyze a user's social media activity when receiving a request and accept relevant requests. For example, the reception desk can accept requests based on products or services that the user has shared on social media. It can also accept requests based on brands or stores that the user follows on social media. Furthermore, it can accept requests based on groups or communities that the user participates in on social media. This allows the reception desk to prioritize requests by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's social media activity into a generative AI, which can then accept relevant requests.

[0043] The data collection unit can collect optimal information by referring to the user's past purchase history when collecting product information. For example, the data collection unit can collect relevant information based on the product categories the user has previously purchased. The data collection unit can also prioritize the collection of information on specific brands or stores from the user's past purchase history. Furthermore, the data collection unit can adjust the amount and type of information collected according to the user's purchase frequency. This allows the data collection unit to collect optimal information by referring to the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the user's past purchase history into a generation AI, which can then collect the optimal information.

[0044] The data collection unit can filter information based on the user's current areas of interest when collecting product information. For example, the data collection unit can collect relevant information based on the product categories the user has recently searched for. It can also prioritize collecting information on brands and stores the user follows on social media. Furthermore, the data collection unit can filter information based on the content of articles and videos the user has recently viewed. This allows the data collection unit to prioritize the collection of highly relevant information by filtering it based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's current areas of interest into a generative AI, which can then filter the information.

[0045] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting product information. For example, the data collection unit can prioritize the collection of product information from stores near the user's current location. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of information from stores and services in their travel destination. Additionally, if the user frequently visits a particular region, the data collection unit can prioritize the collection of information from stores and services in that region. In this way, the data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant information.

[0046] The data collection unit can analyze the user's social media activity and collect relevant information when collecting product information. For example, the data collection unit can collect information based on products and services that the user has shared on social media. The data collection unit can also prioritize collecting information on brands and stores that the user follows on social media. Furthermore, the data collection unit can collect information based on groups and communities that the user participates in on social media. In this way, the data collection unit can prioritize collecting relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's social media activity into a generative AI, and the generative AI can collect relevant information.

[0047] The comparison unit can select the optimal comparison criteria by referring to the user's past purchase history when comparing product information. For example, the comparison unit can provide relevant comparison criteria based on the product categories the user has previously purchased. The comparison unit can also prioritize providing comparison criteria for specific brands or stores based on the user's past purchase history. Furthermore, the comparison unit can adjust the level of detail of the comparison criteria according to the user's purchase frequency. This allows the comparison unit to select the optimal comparison criteria by referring to the user's past purchase history. Some or all of the above processing in the comparison unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the comparison unit can input the user's past purchase history into a generating AI, which can then select the optimal comparison criteria.

[0048] The comparison unit can filter comparison criteria based on the user's current areas of interest when comparing product information. For example, the comparison unit can provide relevant comparison criteria based on product categories the user has recently searched for. It can also prioritize providing comparison criteria for brands and stores the user follows on social media. Furthermore, the comparison unit can filter comparison criteria based on the content of articles and videos the user has recently viewed. This allows the comparison unit to perform highly relevant comparisons by filtering comparison criteria based on the user's current areas of interest. Some or all of the above processing in the comparison unit may be performed using or without a generating AI. For example, the comparison unit can input the user's current areas of interest into a generating AI, which can then filter the comparison criteria.

[0049] The comparison unit can prioritize comparing highly relevant information by considering the user's geographical location when comparing product information. For example, the comparison unit can prioritize comparing product information from stores near the user's current location. Furthermore, if the user is traveling, the comparison unit can prioritize comparing information from stores and services in their travel destination. Additionally, if the user frequently visits a particular region, the comparison unit can prioritize comparing information from stores and services in that region. In this way, the comparison unit can prioritize comparing highly relevant information by considering the user's geographical location. Some or all of the above processing in the comparison unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the comparison unit can input the user's geographical location information into a generating AI, which can then prioritize comparing highly relevant information.

[0050] The comparison unit can analyze a user's social media activity and compare relevant information when comparing product information. For example, the comparison unit can compare information based on products and services shared by the user on social media. It can also prioritize the comparison of information on brands and stores that the user follows on social media. Furthermore, the comparison unit can compare information based on groups and communities that the user participates in on social media. In this way, the comparison unit can prioritize the comparison of relevant information by analyzing the user's social media activity. Some or all of the above processing in the comparison unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the comparison unit can input the user's social media activity into a generative AI, which can then compare relevant information.

[0051] The suggestion unit can select the optimal suggestion method by referring to the user's past purchase history when suggesting products. For example, the suggestion unit can suggest related products based on the product categories the user has previously purchased. Furthermore, the suggestion unit can prioritize suggesting products from specific brands or stores based on the user's past purchase history. In addition, the suggestion unit can adjust the quantity and type of products suggested according to the user's purchase frequency. This allows the suggestion unit to select the optimal suggestion method by referring to the user's past purchase history. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or without one. For example, the suggestion unit can input the user's past purchase history into a generation AI, which can then select the optimal suggestion method.

[0052] The suggestion unit can filter product suggestions based on the user's current areas of interest. For example, it can suggest relevant products based on product categories the user has recently searched for. It can also prioritize suggesting products from brands and stores the user follows on social media. Furthermore, it can filter suggestions based on the content of articles and videos the user has recently viewed. This allows the suggestion unit to provide highly relevant suggestions by filtering suggestions based on the user's current areas of interest. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input the user's current areas of interest into a generative AI, which can then filter the suggestions.

[0053] The suggestion unit can prioritize suggesting highly relevant products by considering the user's geographical location when suggesting products. For example, the suggestion unit can prioritize suggesting products from stores near the user's current location. Furthermore, if the user is traveling, the suggestion unit can prioritize suggesting products from stores and services in their travel destination. Additionally, if the user frequently visits a particular region, the suggestion unit can prioritize suggesting products from stores and services in that region. In this way, the suggestion unit can prioritize suggesting highly relevant products by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI, which can then prioritize suggesting highly relevant products.

[0054] The suggestion unit can analyze a user's social media activity and suggest relevant products when making product recommendations. For example, the suggestion unit can suggest products based on products and services that the user has shared on social media. It can also prioritize suggesting products from brands and stores that the user follows on social media. Furthermore, the suggestion unit can suggest products based on groups and communities that the user participates in on social media. In this way, the suggestion unit can prioritize suggesting relevant products by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using generative AI, or it may be performed without generative AI. For example, the suggestion unit can input the user's social media activity into a generative AI, which can then suggest relevant products.

[0055] The procedure unit can select the optimal procedure method by referring to the user's past purchase history during the purchase process. For example, the procedure unit may prioritize suggesting payment methods the user has used in the past. It can also prioritize suggesting specific shipping methods based on the user's past purchase history. Furthermore, the procedure unit can adjust the level of detail in the procedure according to the user's purchase frequency. This allows the procedure unit to select the optimal procedure method by referring to the user's past purchase history. Some or all of the above processing in the procedure unit may be performed using a generating AI, or not. For example, the procedure unit can input the user's past purchase history into a generating AI, which can then select the optimal procedure method.

[0056] The procedure unit can filter the procedure content based on the user's current areas of interest during the purchase process. For example, the procedure unit can suggest relevant procedures based on the product categories the user has recently searched for. It can also prioritize suggesting procedures from brands and stores the user follows on social media. Furthermore, the procedure unit can filter the procedure content based on the content of articles and videos the user has recently viewed. In this way, the procedure unit can prioritize highly relevant procedures by filtering the procedure content based on the user's current areas of interest. Some or all of the above processing in the procedure unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the procedure unit can input the user's current areas of interest into a generative AI, which can then filter the procedure content.

[0057] The procedure unit can prioritize highly relevant procedures during the purchase process, taking into account the user's geographical location. For example, the procedure unit can prioritize purchasing products from stores near the user's current location. Furthermore, if the user is traveling, the procedure unit can prioritize procedures for stores and services in the user's travel destination. Additionally, if the user frequently visits a particular region, the procedure unit can prioritize procedures for stores and services in that region. In this way, the procedure unit can prioritize highly relevant procedures by considering the user's geographical location. Some or all of the above processing in the procedure unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the procedure unit can input the user's geographical location information into a generative AI, which can then prioritize highly relevant procedures.

[0058] The processing unit can analyze the user's social media activity during the purchase process and perform relevant procedures. For example, the processing unit can perform procedures based on products and services shared by the user on social media. It can also prioritize procedures for brands and stores that the user follows on social media. Furthermore, the processing unit can perform procedures based on groups and communities that the user participates in on social media. In this way, the processing unit can prioritize relevant procedures by analyzing the user's social media activity. Some or all of the above processing in the processing unit may be performed using generative AI, or not. For example, the processing unit can input the user's social media activity into generative AI, which can then perform the relevant procedures.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] Shopping automation systems can analyze a user's purchase history and suggest related products based on items and services the user has previously purchased. For example, they can suggest new or related products in the same category based on the product categories the user has previously purchased. Furthermore, if a user frequently purchases from a particular brand or store, the system can prioritize suggesting new products and sales information from that brand or store. It can also analyze the user's purchase frequency and seasonal purchasing patterns to suggest products at the appropriate time. In this way, shopping automation systems can leverage a user's purchase history to provide more personalized product suggestions.

[0061] An automated shopping system can leverage a user's geographical location to prioritize suggesting nearby stores and services. For example, it can suggest products from stores close to the user's current location, providing items that can be purchased or picked up immediately. If the user is traveling, it can suggest stores and services in their destination, supporting their shopping during their trip. Furthermore, if the user frequently visits a particular region, the system can prioritize suggesting information about stores and services in that area. In this way, an automated shopping system can provide more convenient product suggestions by utilizing the user's geographical location.

[0062] Shopping automation systems can analyze a user's social media activity and suggest relevant products and services. For example, they can suggest similar products based on what the user has shared on social media. They can also prioritize suggesting new products and sales information from brands and stores the user follows. Furthermore, they can suggest relevant products and services based on the activities of groups and communities the user participates in. In this way, shopping automation systems can leverage a user's social media activity to provide more personalized product suggestions.

[0063] An automated shopping system can analyze a user's past purchase history and suggest repeat purchases based on purchase frequency and patterns. For example, it can suggest consumables and food items that the user regularly buys as repeat purchases. It can also suggest products that the user purchases in accordance with specific seasons or events. Furthermore, it can suggest repeat purchases at the appropriate time based on the user's purchase frequency. In this way, an automated shopping system can support repeat purchases by utilizing the user's past purchase history.

[0064] Shopping automation systems can analyze a user's current areas of interest and suggest relevant products and services. For example, they can suggest similar products based on product categories the user has recently searched for. They can also prioritize suggesting products from brands and stores the user follows on social media. Furthermore, they can suggest relevant products and services based on the content of articles and videos the user has recently viewed. In this way, shopping automation systems can leverage a user's current areas of interest to provide more personalized product suggestions.

[0065] The shopping automation system can analyze a user's social media activity and prioritize displaying reviews and ratings of relevant products. For example, it can prioritize displaying reviews of products and services that the user has shared on social media. It can also prioritize product reviews from brands and stores that the user follows. Furthermore, it can prioritize displaying reviews posted by members of groups and communities that the user belongs to. In this way, the shopping automation system can leverage the user's social media activity to provide more relevant reviews and ratings.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives user requests. User requests include product type, price range, and features. For example, the user decides on an image of the product they want and inputs it into the generation AI. Step 2: The collection department collects product information based on requests received by the reception department. For example, they perform web searches to collect product information, images, and videos. The collection department collects product images and videos from e-commerce services and social media, and compares product specifications and reviews. Step 3: The comparison unit compares the information collected by the collection unit. For example, it compares product specifications and reviews, and compares product performance, functions, size, weight, etc. Step 4: The proposal department proposes the optimal product based on the comparison results from the comparison department. For example, it proposes the best product for the user, suggesting the product that best meets the user's needs or offers the best cost performance. Step 5: The Procedure Department carries out the process of purchasing the products proposed by the Proposal Department. For example, it adds products to the cart, enters the address and payment information, and confirms the purchase. The Procedure Department automatically performs the operations of adding items to the cart and entering the address and payment information.

[0068] (Example of form 2) The shopping automation system according to an embodiment of the present invention is a system in which a generating AI handles everything from product consideration to purchase procedures simply by the user telling it what product they want. The shopping automation system works by having the user decide on an image of the product they want and inputting it into the generating AI. Next, the generating AI performs a web search to obtain candidate product information, images, and videos. The generating AI collects product images and videos from e-commerce services and social media, and compares product specifications and reviews. The generating AI then suggests the most suitable product to the user, and the user decides whether or not to purchase it. Finally, the generating AI automatically completes the purchase procedure. This allows the user to easily complete their shopping without having to perform complex operations. For example, the shopping automation system works by having the user decide on an image of the product they want and inputting it into the generating AI. For example, the generating AI performs a web search to obtain candidate product information, images, and videos. The generating AI collects product images and videos from e-commerce services and social media, and compares product specifications and reviews. The generating AI suggests the most suitable product to the user, and the user decides whether or not to purchase it. Finally, the generating AI automatically completes the purchase procedure. This allows the user to easily complete their shopping without having to perform complex operations. This system improves user convenience when shopping on e-commerce services, making it easier to compare multiple services and thus enabling users to shop more affordably. In this way, shopping automation systems can automate the user's shopping process and improve convenience.

[0069] The automated shopping system according to this embodiment comprises a reception unit, a collection unit, a comparison unit, a proposal unit, and a procedure unit. The reception unit receives user requests. User requests include, for example, product type, price range, and functions, but are not limited to such examples. The reception unit, for example, determines the image of the product the user wants and inputs it into the generation AI. The collection unit collects product information based on the requests received by the reception unit. The collection unit, for example, performs a web search and collects product information, images, and videos. The collection unit collects product images and videos from e-commerce services and social media, and compares product specifications and reviews. The comparison unit compares the information collected by the collection unit. The comparison unit, for example, compares product specifications and reviews. Based on the collected information, the comparison unit compares product performance, functions, size, weight, etc. The proposal unit proposes the most suitable product based on the results of the comparison by the comparison unit. The proposal unit, for example, proposes the most suitable product to the user. The proposal unit proposes the product that best matches the user's requests and the product with the highest cost performance. The Procedure Unit performs the procedure for purchasing the products proposed by the Proposal Unit. For example, the Procedure Unit adds products to the cart, enters the address and payment information, and confirms the purchase. The Procedure Unit automatically performs operations such as adding items to the cart and entering the address and payment information. As a result, the automated shopping system according to this embodiment improves user convenience when shopping on e-commerce services by receiving user requests, collecting product information, comparing, proposing, and performing the purchase procedure.

[0070] The reception department receives user requests. User requests include, but are not limited to, product type, price range, and features. For example, the reception department determines the image of the product the user wants and inputs it into the generating AI. Specifically, the user uses a smartphone or computer to access a dedicated application or website and inputs details of the product they want. For example, they might input requests such as "I want a high-performance laptop," "My budget is under 100,000 yen," and "It should be lightweight and easy to carry." The generating AI analyzes these requests and extracts appropriate keywords and conditions. The generating AI uses natural language processing technology to understand the user's requests and generates prompts to search for relevant product information. For example, it extracts keywords such as "high performance," "laptop," "under 100,000 yen," and "lightweight," and sends instructions to the collection department to collect product information based on these. This allows the reception department to accurately understand the user's requests and smoothly move on to the next step of collecting product information. Furthermore, the reception department saves user requests as a history and can refer to them when making future purchases. For example, based on information and requests for products purchased in the past, it can analyze the user's preferences and trends and provide more personalized suggestions. This allows the reception desk to efficiently receive user requests and improve the shopping experience.

[0071] The collection unit collects product information based on requests received by the reception unit. For example, the collection unit performs web searches to collect product information, images, and videos. Specifically, the collection unit uses multiple search engines, e-commerce sites, and social media platforms to collect relevant product information from a wide range of sources. For example, it uses a search engine to search for keywords such as "high-performance laptop under 100,000 yen lightweight" and obtains information from the resulting product pages and review sites. On e-commerce sites, it collects specifications, prices, and user reviews from product detail pages, and on social media platforms, it collects product images and videos of users' experiences posting them. The collection unit automatically analyzes this information and extracts the necessary data. For example, it extracts product specifications such as CPU type, memory capacity, storage capacity, weight, and battery life, and collects user ratings and comments as review information. Furthermore, the collection unit centrally manages the collected information and stores it in a database. This allows the collection unit to efficiently collect product information from diverse sources and provide it to the comparison unit, which is the next step. The collection unit can also verify the reliability and timeliness of the collected information and update or collect additional information as needed. This allows the data collection unit to always provide the latest and most accurate product information, supporting users in selecting the optimal product to meet their needs.

[0072] The comparison unit compares the information collected by the collection unit. For example, the comparison unit compares product specifications and reviews. Specifically, it compares detailed specifications such as performance, functions, size, and weight of collected products to identify the product that best matches the user's needs. For example, it compares the CPU performance, memory capacity, storage capacity, and battery life of multiple laptops to select the product that best suits the user's needs. It also analyzes user reviews to compare product evaluations and user experience. The content of the reviews is analyzed using natural language processing technology to classify positive and negative evaluations and calculate an overall evaluation. Furthermore, the comparison unit also compares product price information to identify products with high cost performance. For example, even if laptops have the same performance, if the prices differ, it selects the optimal product by considering the balance between price and performance. Based on this information, the comparison unit lists the products that best match the user's needs and provides them to the recommendation unit. The comparison unit can also flexibly change the comparison criteria according to the user's needs. For example, it can perform a performance-oriented comparison for performance-oriented users and a price-oriented comparison for price-oriented users. This allows the comparison unit to respond to diverse user needs and support the selection of the most suitable product.

[0073] The Proposal Department proposes the most suitable product based on the comparison results from the Comparison Department. For example, the Proposal Department proposes the best product to the user. Specifically, based on the information provided by the Comparison Department, it selects and proposes the product that best matches the user's needs and offers the best cost performance. The Proposal Department can also present multiple options depending on the user's needs. For example, in response to requests such as "high-performance laptop," "budget under 100,000 yen," and "lightweight," it will list several candidate products and explain the features and benefits of each. The Proposal Department provides users with detailed product information, reviews, and pricing information to support their comparison and consideration. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it can collect feedback on user satisfaction and usage after purchasing a proposed product and incorporate it into future proposals. The Proposal Department can analyze user preferences and trends to provide more personalized proposals. This allows the Proposal Department to propose products that best match the user's needs and improve the shopping experience.

[0074] The Procedures Unit handles the process of purchasing products suggested by the Proposal Unit. For example, the Procedures Unit adds products to the cart, enters the address and payment information, and confirms the purchase. Specifically, based on the user's account information, the Procedures Unit automatically adds products to the cart, enters the shipping address, and enters payment information. For example, if a user decides to purchase a suggested product, the Procedures Unit automatically accesses the e-commerce site and adds the product to the cart. Next, it retrieves the shipping address from the user's account information and enters the necessary information. Furthermore, based on the user's payment information, it enters credit card information and other payment methods to complete the purchase process. The Procedures Unit performs these operations quickly and accurately, significantly reducing the user's effort. The Procedures Unit notifies the user of the progress of the purchase process in real time, allowing for confirmation and correction as needed. For example, if there are errors in the shipping address or payment information, it notifies the user and prompts them to correct it. The Procedures Unit also saves the purchase history, which can be used as a reference for future purchases. In this way, the Procedures Unit can efficiently support the user's shopping process and improve the shopping experience.

[0075] The data collection unit can perform web searches and collect product information, images, and videos. For example, the data collection unit can perform web searches and collect product information, images, and videos. The data collection unit can collect product information using searches on search engines or specific e-commerce site searches. The data collection unit collects product photos, promotional videos, user review videos, etc. This allows the data collection unit to efficiently collect product information, images, and videos by performing web searches. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input a web search prompt into a generative AI, and the generative AI can collect the search results.

[0076] The comparison unit can compare collected information and compare product specifications and reviews. For example, the comparison unit can compare collected information and compare product specifications and reviews. The comparison unit can compare product performance, functions, size, weight, etc. The comparison unit can compare product reviews based on user ratings, comments, star ratings, etc. In this way, the comparison unit can select the optimal product by comparing product specifications and reviews. Some or all of the above processing in the comparison unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the comparison unit can input collected information into a generation AI, and the generation AI can compare the information.

[0077] The proposal department can propose the most suitable product to the user. For example, the proposal department can propose the most suitable product to the user. The proposal department can propose the product that best matches the user's needs or the product with the best cost performance. As a result, the proposal department improves user satisfaction by proposing the most suitable product. Some or all of the above processing in the proposal department may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal department can input the user's needs into a generative AI, and the generative AI can propose the most suitable product.

[0078] The processing unit can add items to the cart, enter the address and payment information, and confirm the purchase. For example, the processing unit can add items to the cart, enter the address and payment information, and confirm the purchase. The processing unit can automate the operations of adding items to the cart and entering the address and payment information. This improves user convenience by automating the purchase process. Some or all of the above processes in the processing unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the processing unit can have a generative AI perform the operation of adding items to the cart, and have a generative AI enter the address and payment information.

[0079] The reception desk can estimate the user's emotions and adjust the request processing method based on the estimated 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 provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input and process the request quickly. This allows the reception desk to process requests more appropriately by adjusting the request processing 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 reception desk may be performed using generative AI or not. For example, the reception desk can have generative AI perform facial recognition or voice analysis to estimate the user's emotions.

[0080] The reception department can analyze a user's past purchase history and select the most suitable method for receiving requests. For example, the reception department can prioritize requests based on the product categories the user has previously purchased. It can also prioritize suggesting specific brands or stores based on the user's past purchase history. Furthermore, the reception department can adjust the timing of request acceptance according to the user's purchase frequency. This allows the reception department to select the most suitable method for receiving requests by analyzing the user's past purchase history. Some or all of the above processing in the reception department may be performed using or without a generative AI. For example, the reception department can input the user's past purchase history into a generative AI, which can then select the most suitable method for receiving requests.

[0081] The reception desk can filter requests based on the user's current areas of interest. For example, the reception desk can prioritize requests based on product categories the user has recently searched for. It can also filter requests based on brands and stores the user follows on social media. Furthermore, it can filter requests based on the content of articles and videos the user has recently viewed. This allows the reception desk to prioritize requests that are highly relevant by filtering them based on the user's current areas of interest. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's current areas of interest into a generative AI, which can then filter the requests.

[0082] The reception desk can estimate the user's emotions and prioritize requests based on those emotions. For example, if the user is stressed, the reception desk will prioritize urgent requests. If the user is relaxed, the reception desk can prioritize detailed requests. Furthermore, if the user is in a hurry, the reception desk can prioritize requests that can be processed quickly. This allows the reception desk to process requests more appropriately by prioritizing them 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 may be, 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 generative AI or not. For example, the reception desk may have the generative AI perform facial recognition technology or voice analysis technology to estimate the user's emotions.

[0083] The reception desk can prioritize requests based on their relevance, taking into account the user's geographical location. For example, the reception desk can prioritize suggesting products from stores near the user's current location. Furthermore, if the user is traveling, the reception desk can prioritize suggesting stores and services in their travel destination. Additionally, if the user frequently visits a particular region, the reception desk can prioritize suggesting stores and services in that region. This allows the reception desk to prioritize requests based on their relevance by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's geographical location into a generative AI, which can then prioritize requests based on their relevance.

[0084] The reception desk can analyze a user's social media activity when receiving a request and accept relevant requests. For example, the reception desk can accept requests based on products or services that the user has shared on social media. It can also accept requests based on brands or stores that the user follows on social media. Furthermore, it can accept requests based on groups or communities that the user participates in on social media. This allows the reception desk to prioritize requests by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's social media activity into a generative AI, which can then accept relevant requests.

[0085] The data collection unit can estimate the user's emotions and adjust the type of information it collects based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect simple, minimal information. If the user is relaxed, the data collection unit can collect detailed product information and reviews. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting information that can be retrieved quickly. This allows the data collection unit to collect more appropriate information by adjusting the type of information it collects 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using generative AI or not. For example, the data collection unit may have the generative AI perform facial recognition technology or voice analysis technology to estimate the user's emotions.

[0086] The data collection unit can collect optimal information by referring to the user's past purchase history when collecting product information. For example, the data collection unit can collect relevant information based on the product categories the user has previously purchased. The data collection unit can also prioritize the collection of information on specific brands or stores from the user's past purchase history. Furthermore, the data collection unit can adjust the amount and type of information collected according to the user's purchase frequency. This allows the data collection unit to collect optimal information by referring to the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the user's past purchase history into a generation AI, which can then collect the optimal information.

[0087] The data collection unit can filter information based on the user's current areas of interest when collecting product information. For example, the data collection unit can collect relevant information based on the product categories the user has recently searched for. It can also prioritize collecting information on brands and stores the user follows on social media. Furthermore, the data collection unit can filter information based on the content of articles and videos the user has recently viewed. This allows the data collection unit to prioritize the collection of highly relevant information by filtering it based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's current areas of interest into a generative AI, which can then filter the information.

[0088] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting urgent information. If the user is relaxed, the data collection unit can prioritize collecting detailed information. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting information that can be collected quickly. In this way, the data collection unit can collect more appropriate information by determining the priority of information to collect 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, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using generative AI or not. For example, the data collection unit may have the generative AI perform facial recognition technology or voice analysis technology to estimate the user's emotions.

[0089] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting product information. For example, the data collection unit can prioritize the collection of product information from stores near the user's current location. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of information from stores and services in their travel destination. Additionally, if the user frequently visits a particular region, the data collection unit can prioritize the collection of information from stores and services in that region. In this way, the data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant information.

[0090] The data collection unit can analyze the user's social media activity and collect relevant information when collecting product information. For example, the data collection unit can collect information based on products and services that the user has shared on social media. The data collection unit can also prioritize collecting information on brands and stores that the user follows on social media. Furthermore, the data collection unit can collect information based on groups and communities that the user participates in on social media. In this way, the data collection unit can prioritize collecting relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's social media activity into a generative AI, and the generative AI can collect relevant information.

[0091] The comparison unit can estimate the user's emotions and adjust the comparison criteria based on the estimated emotions. For example, if the user is stressed, the comparison unit can provide simple, minimal comparison criteria. If the user is relaxed, the comparison unit can provide more detailed comparison criteria. Furthermore, if the user is in a hurry, the comparison unit can prioritize criteria that allow for quick comparison. In this way, the comparison unit can make more appropriate comparisons by adjusting the comparison criteria 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the comparison unit may be performed using generative AI or not. For example, the comparison unit may have the generative AI perform facial recognition technology or speech analysis technology to estimate the user's emotions.

[0092] The comparison unit can select the optimal comparison criteria by referring to the user's past purchase history when comparing product information. For example, the comparison unit can provide relevant comparison criteria based on the product categories the user has previously purchased. The comparison unit can also prioritize providing comparison criteria for specific brands or stores based on the user's past purchase history. Furthermore, the comparison unit can adjust the level of detail of the comparison criteria according to the user's purchase frequency. This allows the comparison unit to select the optimal comparison criteria by referring to the user's past purchase history. Some or all of the above processing in the comparison unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the comparison unit can input the user's past purchase history into a generating AI, which can then select the optimal comparison criteria.

[0093] The comparison unit can filter comparison criteria based on the user's current areas of interest when comparing product information. For example, the comparison unit can provide relevant comparison criteria based on product categories the user has recently searched for. It can also prioritize providing comparison criteria for brands and stores the user follows on social media. Furthermore, the comparison unit can filter comparison criteria based on the content of articles and videos the user has recently viewed. This allows the comparison unit to perform highly relevant comparisons by filtering comparison criteria based on the user's current areas of interest. Some or all of the above processing in the comparison unit may be performed using or without a generating AI. For example, the comparison unit can input the user's current areas of interest into a generating AI, which can then filter the comparison criteria.

[0094] The comparison unit can estimate the user's emotions and adjust the display method of the comparison results based on the estimated user emotions. For example, if the user is stressed, the comparison unit can provide a simple and highly visible display method. If the user is relaxed, the comparison unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the comparison unit can provide a display method that gets straight to the point. In this way, the comparison unit can provide a more appropriate display by adjusting the display method of the comparison results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the comparison unit may be performed using a generative AI or not. For example, the comparison unit can have the generative AI perform facial recognition technology or voice analysis technology to estimate the user's emotions.

[0095] The comparison unit can prioritize comparing highly relevant information by considering the user's geographical location when comparing product information. For example, the comparison unit can prioritize comparing product information from stores near the user's current location. Furthermore, if the user is traveling, the comparison unit can prioritize comparing information from stores and services in their travel destination. Additionally, if the user frequently visits a particular region, the comparison unit can prioritize comparing information from stores and services in that region. In this way, the comparison unit can prioritize comparing highly relevant information by considering the user's geographical location. Some or all of the above processing in the comparison unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the comparison unit can input the user's geographical location information into a generating AI, which can then prioritize comparing highly relevant information.

[0096] The comparison unit can analyze a user's social media activity and compare relevant information when comparing product information. For example, the comparison unit can compare information based on products and services shared by the user on social media. It can also prioritize the comparison of information on brands and stores that the user follows on social media. Furthermore, the comparison unit can compare information based on groups and communities that the user participates in on social media. In this way, the comparison unit can prioritize the comparison of relevant information by analyzing the user's social media activity. Some or all of the above processing in the comparison unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the comparison unit can input the user's social media activity into a generative AI, which can then compare relevant information.

[0097] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide a simple and easily understandable suggestion. If the user is relaxed, it can provide a suggestion that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise suggestion. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way it presents suggestions 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can have the generative AI perform facial recognition or speech analysis to estimate the user's emotions.

[0098] The suggestion unit can select the optimal suggestion method by referring to the user's past purchase history when suggesting products. For example, the suggestion unit can suggest related products based on the product categories the user has previously purchased. Furthermore, the suggestion unit can prioritize suggesting products from specific brands or stores based on the user's past purchase history. In addition, the suggestion unit can adjust the quantity and type of products suggested according to the user's purchase frequency. This allows the suggestion unit to select the optimal suggestion method by referring to the user's past purchase history. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or without one. For example, the suggestion unit can input the user's past purchase history into a generation AI, which can then select the optimal suggestion method.

[0099] The suggestion unit can filter product suggestions based on the user's current areas of interest. For example, it can suggest relevant products based on product categories the user has recently searched for. It can also prioritize suggesting products from brands and stores the user follows on social media. Furthermore, it can filter suggestions based on the content of articles and videos the user has recently viewed. This allows the suggestion unit to provide highly relevant suggestions by filtering suggestions based on the user's current areas of interest. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input the user's current areas of interest into a generative AI, which can then filter the suggestions.

[0100] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize urgent suggestions. If the user is relaxed, the suggestion unit will prioritize detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit will prioritize suggesting products that can be provided quickly. In this way, the suggestion unit can provide more appropriate suggestions by prioritizing suggestions 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit may have the generative AI perform facial recognition technology or voice analysis technology to estimate the user's emotions.

[0101] The suggestion unit can prioritize suggesting highly relevant products by considering the user's geographical location when suggesting products. For example, the suggestion unit can prioritize suggesting products from stores near the user's current location. Furthermore, if the user is traveling, the suggestion unit can prioritize suggesting products from stores and services in their travel destination. Additionally, if the user frequently visits a particular region, the suggestion unit can prioritize suggesting products from stores and services in that region. In this way, the suggestion unit can prioritize suggesting highly relevant products by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI, which can then prioritize suggesting highly relevant products.

[0102] The suggestion unit can analyze a user's social media activity and suggest relevant products when making product recommendations. For example, the suggestion unit can suggest products based on products and services that the user has shared on social media. It can also prioritize suggesting products from brands and stores that the user follows on social media. Furthermore, the suggestion unit can suggest products based on groups and communities that the user participates in on social media. In this way, the suggestion unit can prioritize suggesting relevant products by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using generative AI, or it may be performed without generative AI. For example, the suggestion unit can input the user's social media activity into a generative AI, which can then suggest relevant products.

[0103] The procedure unit can estimate the user's emotions and adjust the purchase procedure based on the estimated emotions. For example, if the user is stressed, the procedure unit can provide a simple and quick purchase procedure. If the user is relaxed, the procedure unit can provide a purchase procedure that includes detailed confirmation steps. Furthermore, if the user is in a hurry, the procedure unit can allow the purchase procedure to be completed with a single click. In this way, the procedure unit can provide a more appropriate purchase procedure by adjusting the purchase procedure 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, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the procedure unit may be performed using generative AI or not. For example, the procedure unit can have the generative AI perform facial recognition technology or speech analysis technology to estimate the user's emotions.

[0104] The procedure unit can select the optimal procedure method by referring to the user's past purchase history during the purchase process. For example, the procedure unit may prioritize suggesting payment methods the user has used in the past. It can also prioritize suggesting specific shipping methods based on the user's past purchase history. Furthermore, the procedure unit can adjust the level of detail in the procedure according to the user's purchase frequency. This allows the procedure unit to select the optimal procedure method by referring to the user's past purchase history. Some or all of the above processing in the procedure unit may be performed using a generating AI, or not. For example, the procedure unit can input the user's past purchase history into a generating AI, which can then select the optimal procedure method.

[0105] The procedure unit can filter the procedure content based on the user's current areas of interest during the purchase process. For example, the procedure unit can suggest relevant procedures based on the product categories the user has recently searched for. It can also prioritize suggesting procedures from brands and stores the user follows on social media. Furthermore, the procedure unit can filter the procedure content based on the content of articles and videos the user has recently viewed. In this way, the procedure unit can prioritize highly relevant procedures by filtering the procedure content based on the user's current areas of interest. Some or all of the above processing in the procedure unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the procedure unit can input the user's current areas of interest into a generative AI, which can then filter the procedure content.

[0106] The procedure unit can estimate the user's emotions and determine the priority of the purchase process based on the estimated emotions. For example, if the user is stressed, the procedure unit will prioritize urgent procedures. If the user is relaxed, the procedure unit will prioritize detailed procedures. Furthermore, if the user is in a hurry, the procedure unit can ensure that the procedure is completed quickly. In this way, the procedure unit can provide a more appropriate service by prioritizing the purchase process 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the procedure unit may be performed using generative AI or not. For example, the procedure unit may have the generative AI perform facial recognition technology or speech analysis technology to estimate the user's emotions.

[0107] The procedure unit can prioritize highly relevant procedures during the purchase process, taking into account the user's geographical location. For example, the procedure unit can prioritize purchasing products from stores near the user's current location. Furthermore, if the user is traveling, the procedure unit can prioritize procedures for stores and services in the user's travel destination. Additionally, if the user frequently visits a particular region, the procedure unit can prioritize procedures for stores and services in that region. In this way, the procedure unit can prioritize highly relevant procedures by considering the user's geographical location. Some or all of the above processing in the procedure unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the procedure unit can input the user's geographical location information into a generative AI, which can then prioritize highly relevant procedures.

[0108] The processing unit can analyze the user's social media activity during the purchase process and perform relevant procedures. For example, the processing unit can perform procedures based on products and services shared by the user on social media. It can also prioritize procedures for brands and stores that the user follows on social media. Furthermore, the processing unit can perform procedures based on groups and communities that the user participates in on social media. In this way, the processing unit can prioritize relevant procedures by analyzing the user's social media activity. Some or all of the above processing in the processing unit may be performed using generative AI, or not. For example, the processing unit can input the user's social media activity into generative AI, which can then perform the relevant procedures.

[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] Shopping automation systems can analyze a user's purchase history and suggest related products based on items and services the user has previously purchased. For example, they can suggest new or related products in the same category based on the product categories the user has previously purchased. Furthermore, if a user frequently purchases from a particular brand or store, the system can prioritize suggesting new products and sales information from that brand or store. It can also analyze the user's purchase frequency and seasonal purchasing patterns to suggest products at the appropriate time. In this way, shopping automation systems can leverage a user's purchase history to provide more personalized product suggestions.

[0111] An automated shopping system can estimate a user's emotions and adjust the timing of product recommendations based on those emotions. For example, if a user is stressed, it can suggest products or services that help them relax. If a user is happy, it can suggest products suitable for special events or anniversaries. Furthermore, if a user is in a hurry, it can suggest products or services that can be purchased quickly. In this way, an automated shopping system can make more appropriate recommendations by suggesting products at the right time according to the user's emotions.

[0112] An automated shopping system can leverage a user's geographical location to prioritize suggesting nearby stores and services. For example, it can suggest products from stores close to the user's current location, providing items that can be purchased or picked up immediately. If the user is traveling, it can suggest stores and services in their destination, supporting their shopping during their trip. Furthermore, if the user frequently visits a particular region, the system can prioritize suggesting information about stores and services in that area. In this way, an automated shopping system can provide more convenient product suggestions by utilizing the user's geographical location.

[0113] Shopping automation systems can analyze a user's social media activity and suggest relevant products and services. For example, they can suggest similar products based on what the user has shared on social media. They can also prioritize suggesting new products and sales information from brands and stores the user follows. Furthermore, they can suggest relevant products and services based on the activities of groups and communities the user participates in. In this way, shopping automation systems can leverage a user's social media activity to provide more personalized product suggestions.

[0114] The automated shopping system can estimate the user's emotions and filter product reviews and ratings based on those emotions. For example, if the user is stressed, it will prioritize displaying products with positive reviews and high ratings. If the user is relaxed, it can display detailed reviews and ratings. Furthermore, if the user is in a hurry, it can display concise reviews and ratings that get straight to the point. In this way, the automated shopping system can provide more relevant information by adjusting how reviews and ratings are displayed according to the user's emotions.

[0115] An automated shopping system can analyze a user's past purchase history and suggest repeat purchases based on purchase frequency and patterns. For example, it can suggest consumables and food items that the user regularly buys as repeat purchases. It can also suggest products that the user purchases in accordance with specific seasons or events. Furthermore, it can suggest repeat purchases at the appropriate time based on the user's purchase frequency. In this way, an automated shopping system can support repeat purchases by utilizing the user's past purchase history.

[0116] An automated shopping system can estimate a user's emotions and adjust the product description and presentation based on those emotions. For example, if a user is stressed, it can provide a simple and easy-to-understand description. If the user is relaxed, it can provide a description that includes detailed information and background stories. Furthermore, if the user is in a hurry, it can provide a concise and to-the-point description. In this way, the automated shopping system can provide more appropriate information by adjusting the product description and presentation based on the user's emotions.

[0117] Shopping automation systems can analyze a user's current areas of interest and suggest relevant products and services. For example, they can suggest similar products based on product categories the user has recently searched for. They can also prioritize suggesting products from brands and stores the user follows on social media. Furthermore, they can suggest relevant products and services based on the content of articles and videos the user has recently viewed. In this way, shopping automation systems can leverage a user's current areas of interest to provide more personalized product suggestions.

[0118] Shopping automation systems can estimate a user's emotions and adjust the steps of the purchase process based on those emotions. For example, if a user is stressed, they can be offered a simple and quick purchase process. If the user is relaxed, they can be offered a purchase process that includes detailed confirmation steps. Furthermore, if the user is in a hurry, it may be possible to complete the purchase process with a single click. In this way, shopping automation systems enable a more appropriate purchase process by adjusting the steps of the purchase process according to the user's emotions.

[0119] The shopping automation system can analyze a user's social media activity and prioritize displaying reviews and ratings of relevant products. For example, it can prioritize displaying reviews of products and services that the user has shared on social media. It can also prioritize product reviews from brands and stores that the user follows. Furthermore, it can prioritize displaying reviews posted by members of groups and communities that the user belongs to. In this way, the shopping automation system can leverage the user's social media activity to provide more relevant reviews and ratings.

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

[0121] Step 1: The reception desk receives user requests. User requests include product type, price range, and features. For example, the user decides on an image of the product they want and inputs it into the generation AI. Step 2: The collection department collects product information based on requests received by the reception department. For example, they perform web searches to collect product information, images, and videos. The collection department collects product images and videos from e-commerce services and social media, and compares product specifications and reviews. Step 3: The comparison unit compares the information collected by the collection unit. For example, it compares product specifications and reviews, and compares product performance, functions, size, weight, etc. Step 4: The proposal department proposes the optimal product based on the comparison results from the comparison department. For example, it proposes the best product for the user, suggesting the product that best meets the user's needs or offers the best cost performance. Step 5: The Procedure Department carries out the process of purchasing the products proposed by the Proposal Department. For example, it adds products to the cart, enters the address and payment information, and confirms the purchase. The Procedure Department automatically performs the operations of adding items to the cart and entering the address and payment information.

[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, comparison unit, proposal unit, and procedure 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 user requests. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects product information by performing a web search. The comparison unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and compares the collected information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the most suitable product. The procedure unit is implemented by, for example, the control unit 46A of the smart device 14 and performs the purchase procedure. 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, comparison unit, proposal unit, and procedure 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 the user's request. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects product information by performing a web search. The comparison unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and compares the collected information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the most suitable product. The procedure unit is implemented by, for example, the control unit 46A of the smart glasses 214 and performs the purchase procedure. 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, comparison unit, proposal unit, and procedure unit, is implemented by 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 user requests. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects product information by performing a web search. The comparison unit is implemented by the specific processing unit 290 of the data processing unit 12 and compares the collected information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable product. The procedure unit is implemented by the control unit 46A of the headset terminal 314 and performs the purchase procedure. 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, comparison unit, proposal unit, and procedure 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 user requests. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects product information by performing a web search. The comparison unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and compares the collected information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the most suitable product. The procedure unit is implemented by, for example, the control unit 46A of the robot 414 and performs the purchase procedure. 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 handles user requests, A collection unit that collects product information based on requests received by the aforementioned reception unit, A comparison unit that compares the information collected by the aforementioned collection unit, A proposal unit that proposes the optimal product based on the comparison results from the comparison unit, The system comprises a procedure unit that performs the procedure for purchasing the products proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Perform a web search to collect product information, images, and videos. The system described in Appendix 1, characterized by the features described herein. (Note 3) The comparison unit is, Compare the collected information, and compare product specifications and reviews. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose the most suitable products to our users. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned procedural unit, Add the item to your cart, enter your address and payment information, and confirm your purchase. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is We estimate the user's emotions and adjust the way we receive requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past purchase history to select the most suitable method for receiving requests. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving requests, filtering is performed based on the user's current areas of interest. 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 prioritizes requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving requests, we prioritize requests that are highly relevant, taking into account 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 receiving a request, we analyze the user's social media activity and accept related requests. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting product information, we refer to the user's past purchase history to gather the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting product information, filter the information based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is When collecting product information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When collecting product information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The comparison unit is, It estimates the user's emotions and adjusts the comparison criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The comparison unit is, When comparing product information, the system selects the optimal comparison criteria by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The comparison unit is, When comparing product information, filter the comparison criteria based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 21) The comparison unit is, It estimates the user's emotions and adjusts how comparison results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The comparison unit is, When comparing product information, the system prioritizes comparing highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The comparison unit is, When comparing product information, analyze users' social media activity and compare relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When proposing products, the system selects the most suitable proposal method by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When suggesting products, filter the suggestions based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When suggesting products, the system prioritizes suggesting highly relevant products by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When suggesting products, 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 30) The aforementioned procedural unit, It estimates the user's emotions and adjusts the purchase process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned procedural unit, During the purchase process, the system will refer to the user's past purchase history to select the most suitable procedure. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned procedural unit, During the purchase process, the process is filtered based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned procedural unit, It estimates the user's emotions and determines the priority of the purchase process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned procedural unit, During the purchase process, the system prioritizes relevant procedures based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned procedural unit, During the purchase process, we analyze the user's social media activity and perform relevant actions. 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 handles user requests, A collection unit that collects product information based on requests received by the aforementioned reception unit, A comparison unit that compares the information collected by the aforementioned collection unit, A proposal unit that proposes the optimal product based on the comparison results from the comparison unit, The system includes a procedure unit that performs the procedure for purchasing the products proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Perform a web search to collect product information, images, and videos. The system according to feature 1.

3. The comparison unit is, Compare the collected information, and compare product specifications and reviews. The system according to feature 1.

4. The aforementioned proposal section is, We propose the most suitable products to our users. The system according to feature 1.

5. The aforementioned procedural unit, Add the item to your cart, enter your address and payment information, and confirm your purchase. The system according to feature 1.

6. The aforementioned reception unit is We estimate the user's emotions and adjust the way we receive requests based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past purchase history to select the most suitable method for receiving requests. The system according to feature 1.

8. The aforementioned reception unit is When receiving requests, filtering is performed based on the user's current areas of interest. The system according to feature 1.

Images