system

Abstract

The system according to this embodiment aims to enable users to purchase products efficiently. [Solution] The system according to the embodiment comprises a reception unit, a proposal unit, a discovery unit, a settlement unit, and a confirmation unit. The reception unit receives input from the user. The proposal unit proposes appropriate products based on the information received by the reception unit. The discovery unit automatically finds the lowest price based on the products proposed by the proposal unit. The settlement unit settles the sale based on the lowest price found by the discovery unit. The confirmation unit confirms the delivery date and time and the method of receipt based on the sale settled by the settlement 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process in which a user searches for products, compares prices, and purchases them by himself / herself is complicated and not efficient.

[0005] The system according to the embodiment aims to enable a user to efficiently purchase products.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a proposal unit, a discovery unit, a settlement unit, and a confirmation unit. The reception unit receives input from the user. The proposal unit proposes appropriate products based on the information received by the reception unit. The discovery unit automatically finds the lowest price based on the products proposed by the proposal unit. The settlement unit settles the sale based on the lowest price found by the discovery unit. The confirmation unit confirms the delivery date and time and the method of receipt based on the sale settled by the settlement unit. [Effects of the Invention]

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

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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 reception 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 reception 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 conversational smart shopping agent system according to an embodiment of the present invention is a system that supports the purchase of goods by eliminating the need for users to actively search for products and compare prices. This system allows users to initiate a conversation with an agent who supports the purchase of goods via messenger, thereby suggesting the most suitable product and automatically finding the lowest price. Furthermore, the agent proposes, supports, and completes the transaction solely on the messenger screen, without going through an e-commerce site. This mechanism allows users to easily purchase goods. For example, a user initiates a conversation with an agent via messenger and enters "I want to buy XX." This information is entered into the agent. Next, the agent proposes the most suitable product based on the user's needs. The agent automatically finds the lowest price by linking with shopping, reuse, and travel-related assets within the group. For example, if a user enters "I want to buy XX," the agent proposes the most suitable product and presents the lowest price. Furthermore, the agent proposes, supports, and completes the transaction solely on the messenger screen, without going through an e-commerce site. For example, if a user purchases a suggested product, the agent supports the purchase process and confirms the purchase on the messenger. Furthermore, the agent will collaborate with delivery companies to provide a function to confirm delivery dates, times, and delivery methods. This allows users to check the delivery status of their purchased items and select their preferred delivery method. Basic functions will be provided free of charge, while convenient features that anticipate user needs will be offered as paid features. For example, a function that predicts when an item will be consumed and prompts the user, as well as the ability to turn recommendations and bundled product suggestions on or off, will be offered as paid features. This system allows users to easily purchase products and can also be used for monitoring and understanding the situation of the elderly. For example, when elderly people purchase items they need on a daily basis, the agent can support them, allowing them to shop with peace of mind. In this way, the conversational smart shopping agent system can support users in purchasing goods without the hassle of actively searching for products and comparing prices.

[0029] The conversational smart shopping agent system according to this embodiment comprises a reception unit, a suggestion unit, a discovery unit, a completion unit, and a confirmation unit. The reception unit receives input from the user. For example, the reception unit can receive the user's needs when the user inputs "I want to buy XX" on the messenger. The suggestion unit suggests the most suitable product based on the information received by the reception unit. For example, the suggestion unit can suggest the most suitable product by considering the user's preferences, past purchase history, current trends, etc. The discovery unit automatically finds the lowest price based on the product suggested by the suggestion unit. For example, the discovery unit can find the lowest price using price comparison sites or specific algorithms. The completion unit completes the sale based on the lowest price found by the discovery unit. For example, if the user purchases the suggested product, the completion unit supports the purchase procedure and allows the user to confirm the purchase on the messenger. The confirmation unit confirms the delivery date and time and receiving method based on the sale completed by the completion unit. For example, the confirmation unit can cooperate with a delivery company to provide a function to confirm the delivery date and time and receiving method. As a result, the conversational smart shopping agent system according to the embodiment can suggest the most suitable products based on user input, find the lowest price, complete the transaction, and confirm the delivery date and time and receiving method.

[0030] The reception desk receives input from users. For example, the reception desk can receive user needs when a user types "I want to buy XX" on the messenger. Specifically, the reception desk uses natural language processing (NLP) technology to analyze the user's input and accurately understand the user's intent. For example, if a user types "I want a new smartphone," the reception desk extracts the keyword "smartphone" and recognizes the user's intention to purchase a smartphone. The reception desk also processes user input in real time, allowing it to quickly move to the next step. Furthermore, the reception desk can save user input as a history and use it for future suggestions and support. For example, it can provide more personalized suggestions based on the user's past purchases and search history. This allows the reception desk to accurately understand user needs and respond quickly and effectively.

[0031] The suggestion department proposes the most suitable products based on the information received by the reception department. For example, the suggestion department can propose the most suitable products by considering the user's preferences, past purchase history, and current trends. Specifically, the suggestion department uses AI to analyze the user's preferences and needs and select the most suitable products. For example, it learns the user's preferences and tendencies based on products the user has purchased in the past and their search history, and proposes products that are suitable for the user. In addition, the suggestion department can provide users with the latest information by considering current trends and popular products. For example, the suggestion department collects trend information from social media and news sites and provides users with information on popular and new products. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy of its suggestions. For example, when users rate and comment on the suggested products, the suggestion department can learn from that feedback and reflect it in future suggestions. In this way, the suggestion department can propose the most suitable products that meet the user's needs and improve user satisfaction.

[0032] The discovery unit automatically finds the lowest price based on the products suggested by the suggestion unit. For example, the discovery unit can find the lowest price using price comparison sites or specific algorithms. Specifically, the discovery unit collects product information from multiple online shopping sites and price comparison sites and compares prices. For example, the discovery unit uses the APIs of each site to obtain product information and check prices and inventory status in real time. Furthermore, the discovery unit can efficiently find the lowest price using specific algorithms. For example, the discovery unit calculates the optimal price using algorithms such as Dijkstra's algorithm or dynamic programming. In addition, the discovery unit can suggest the optimal price according to the user's needs and conditions. For example, if the user specifies conditions such as "free shipping" or "same-day delivery," the discovery unit can find the lowest price that meets those conditions. In this way, the discovery unit can provide the user with the best price and improve cost performance.

[0033] The transaction completion unit completes the sale based on the lowest price found by the discovery unit. For example, if a user purchases a suggested product, the transaction completion unit supports the purchase process and allows the user to confirm the purchase on the messenger. Specifically, the transaction completion unit confirms the user's purchase intention and automates the purchase process. For example, if the user enters "I want to buy this product," the transaction completion unit starts the purchase process and collects the necessary information. The transaction completion unit also securely manages the user's payment and shipping information and can complete the purchase process quickly and reliably. Furthermore, the transaction completion unit notifies the user of the progress of the purchase process in real time, supporting the user so that they can proceed with the purchase with peace of mind. For example, the transaction completion unit notifies the user of each step of the purchase process on the messenger, keeping the user informed of the progress. In this way, the transaction completion unit can smoothly support the user's purchase process and improve user convenience.

[0034] The confirmation unit verifies the delivery date and time and receiving method based on the transaction concluded by the settlement unit. For example, the confirmation unit can collaborate with the delivery company to provide a function for confirming the delivery date and time and receiving method. Specifically, the confirmation unit proposes the optimal delivery date and time and receiving method based on the user's delivery address information. For example, the confirmation unit proposes the optimal delivery schedule considering the delivery address specified by the user and the desired receiving date and time. The confirmation unit can also collaborate with the delivery company's system to check the delivery status in real time. For example, the confirmation unit can use the delivery company's API to obtain the delivery status and scheduled delivery date and time and notify the user. Furthermore, the confirmation unit can collect user feedback and continuously improve the quality of the delivery service. For example, when users provide ratings and comments on delivery, the confirmation unit can use that feedback to strengthen collaboration with the delivery company and improve the quality of the service. As a result, the confirmation unit can provide users with fast and reliable delivery services and improve user satisfaction.

[0035] The consumption timing prediction function predicts when an item will be consumed and runs out, and includes a function to prompt the user via an agent. The consumption timing prediction function can predict consumption timing based on, for example, past consumption data, usage frequency, and consumption rate. For example, it can analyze the consumption rate of items a user has purchased in the past and predict when they will purchase them next. It can also predict consumption timing by considering the user's usage frequency. For example, it can analyze how often a user uses a particular item and predict when they will purchase it next. This allows the consumption timing prediction function to predict consumption timing and notify the user at the appropriate time. Some or all of the above processing in the consumption timing prediction function may be performed using, for example, AI, or not. For example, the consumption timing prediction function can input user consumption data into a generating AI and have the generating AI perform the consumption timing prediction.

[0036] The Recommended / Bundled Product ON / OFF function allows users to switch suggestions on or off according to their needs. For example, the Recommended / Bundled Product ON / OFF function can be turned on if the user wants suggestions and turned off if they do not. For example, the Recommended / Bundled Product ON / OFF function allows users to choose whether or not to suggest related products when purchasing a specific product. The Recommended / Bundled Product ON / OFF function can also automatically switch suggestions on or off based on the user's preferences and past purchase history. For example, the Recommended / Bundled Product ON / OFF function analyzes data on products the user has purchased in the past and determines whether or not to suggest related products. This allows the Recommended / Bundled Product ON / OFF function to switch suggestions on or off according to the user's needs. Some or all of the above processing in the Recommended / Bundled Product ON / OFF function may be performed using AI, for example, or without AI. For example, the Recommended / Bundled Product ON / OFF function can input the user's purchase history data into a generating AI and have the generating AI make the decision to turn suggestions on or off.

[0037] The suggestion unit can automatically find the lowest price by linking with multiple shopping, reuse, and travel-related assets. For example, the suggestion unit can automatically find the lowest price by linking with shopping, reuse, and travel-related assets. For example, when a user enters the product they want to buy, the suggestion unit searches these assets and presents the lowest price. The suggestion unit can also suggest the most suitable product based on the user's needs. For example, the suggestion unit suggests the most suitable product by considering the user's preferences and past purchase history. In this way, the suggestion unit can automatically find the lowest price by linking with assets within the group. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user needs data into a generating AI and have the generating AI perform the task of suggesting the most suitable product.

[0038] The confirmation unit can collaborate with multiple delivery companies to provide a function for confirming delivery dates and times and receiving methods. For example, the confirmation unit can collaborate with a delivery company such as Yamato Transport to provide a function for confirming delivery dates and times and receiving methods. For example, when a user selects a delivery date and time and receiving method for a purchased product, the confirmation unit provides information about the delivery company. Furthermore, when the user inputs their desired delivery date and time and receiving method, the confirmation unit can collaborate with the delivery company to perform confirmation. In this way, the confirmation unit can confirm delivery dates and times and receiving methods by collaborating with the delivery company. Some or all of the above-described processes in the confirmation unit may be performed using AI, for example, or without using AI. For example, the confirmation unit can input the user's delivery preference data into a generating AI and have the generating AI perform the confirmation of delivery dates and times and receiving methods.

[0039] The transaction completion unit can propose, support, and complete transactions solely on the messenger screen. For example, when a user purchases a proposed item on the messenger screen, the transaction completion unit supports the purchase process and completes the transaction. For example, when a user purchases a proposed item, the transaction completion unit supports the purchase process and confirms the purchase on the messenger. The transaction completion unit also provides the necessary information when the user is performing the purchase process, allowing the process to proceed smoothly. As a result, the transaction completion unit can complete transactions on the messenger screen. Some or all of the above-described processes in the transaction completion unit may be performed using AI, for example, or not using AI. For example, the transaction completion unit can input the user's purchase procedure data into a generating AI and have the generating AI execute the transaction completion.

[0040] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk may prioritize suggesting input methods that the user has frequently used in the past. For example, the reception desk may suggest the optimal reception method for a specific time period based on the user's past input history. The reception desk can also analyze the user's past input history and select the most efficient reception method. For example, the reception desk may suggest the optimal reception method based on the user's past input history. In this way, the reception desk can select the optimal reception method by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk may input the user's input history data into a generating AI and have the generating AI perform the selection of the optimal reception method.

[0041] The reception unit can filter input based on the user's current situation and areas of interest. For example, the reception unit can filter input based on the product categories the user is currently interested in. For example, the reception unit can filter input based on the user's current situation (e.g., traveling). The reception unit can also prioritize receiving input for relevant products based on the user's areas of interest. For example, the reception unit prioritizes receiving input for relevant products based on the user's areas of interest. This allows the reception unit to receive more relevant input by filtering input based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user situation data into a generating AI and have the generating AI perform the input filtering.

[0042] The reception unit can prioritize accepting inputs that are highly relevant, taking into account the user's geographical location. For example, the reception unit can prioritize accepting inputs of products related to the user's current location. For example, the reception unit can prioritize accepting inputs of products from nearby stores based on the user's geographical location. The reception unit can also prioritize accepting inputs of region-specific products, taking into account the user's geographical location. For example, the reception unit can prioritize accepting inputs of region-specific products, taking into account the user's geographical location. In this way, the reception unit can prioritize accepting inputs that are highly relevant by taking into account the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's location data into a generating AI and have the generating AI determine the priority of inputs.

[0043] The reception unit can analyze the user's social media activity and accept relevant inputs when receiving input. For example, the reception unit can accept inputs based on product categories that the user has shown interest in on social media. For example, the reception unit can prioritize accepting inputs related to the user's current interests from the user's social media activity. The reception unit can also analyze the user's social media activity and accept inputs related to products. For example, the reception unit can analyze the user's social media activity and accept inputs related to products. In this way, the reception unit can accept relevant inputs by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI perform input filtering.

[0044] The proposal unit can adjust the level of detail in its proposals based on the importance of the products. For example, it can provide detailed proposals for important products and concise proposals for less important products. The proposal unit can also adjust the level of detail in its proposals according to the importance of the products. This allows the proposal unit to provide more appropriate proposals by adjusting the level of detail according to the importance of the products. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input product importance data into a generating AI and have the generating AI adjust the level of detail in its proposals.

[0045] The suggestion unit can apply different suggestion algorithms depending on the product category when making suggestions. For example, in the case of electronic devices, the suggestion unit will make suggestions that emphasize technical details. For example, in the case of fashion items, the suggestion unit will make suggestions that emphasize trend information. Furthermore, in the case of food products, the suggestion unit can also make suggestions that emphasize health information. For example, in the case of food products, the suggestion unit will make suggestions that emphasize health information. In this way, the suggestion unit can make more appropriate suggestions by applying different suggestion algorithms depending on the product category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input product category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0046] The proposal department can determine the priority of proposals based on the product submission timing when submitting proposals. For example, the proposal department will prioritize new products. For example, the proposal department will propose seasonal products according to the season. The proposal department can also determine the priority of proposals based on the submission timing. For example, the proposal department will determine the priority of proposals based on the submission timing. This allows the proposal department to make more appropriate proposals by determining the priority of proposals based on the product submission timing. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input product submission timing data into a generating AI and have the generating AI perform the determination of proposal priorities.

[0047] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion unit may prioritize suggesting highly relevant products. For example, the suggestion unit may postpone suggesting less relevant products. The suggestion unit can also adjust the order of suggestions based on the relevance of the products. For example, the suggestion unit can adjust the order of suggestions based on the relevance of the products. This allows the suggestion unit to make more appropriate suggestions by adjusting the order of suggestions based on the relevance of the products. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input product relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0048] The discovery unit can improve the accuracy of its search by considering the interrelationships between products when finding the lowest price. For example, the discovery unit finds the lowest price by comparing the prices of related products. For example, the discovery unit proposes the optimal price by considering the interrelationships between products. The discovery unit can also find the lowest price by analyzing the interrelationships between products. For example, the discovery unit finds the lowest price by analyzing the interrelationships between products. In this way, the discovery unit can improve the accuracy of finding the lowest price by considering the interrelationships between products. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input interrelationship data between products into a generating AI and have the generating AI perform the task of improving the accuracy of finding the lowest price.

[0049] The discovery unit can perform its search for the lowest price by considering the attribute information of the product submitter. For example, the discovery unit can find the lowest price by considering the reliability of the submitter. For example, the discovery unit can find the lowest price by considering the submitter's past transaction history. The discovery unit can also find the lowest price by analyzing the submitter's attribute information. For example, the discovery unit can find the lowest price by analyzing the submitter's attribute information. In this way, the discovery unit can find a more reliable lowest price by considering the attribute information of the product submitter. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without using AI. For example, the discovery unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the lowest price discovery.

[0050] The discovery unit can perform price searches while considering the geographical distribution of products. For example, the discovery unit may prioritize finding the lowest prices of products that are geographically close. For example, the discovery unit may propose the optimal price by considering the geographical distribution. The discovery unit can also find the lowest price by analyzing the geographical distribution. For example, the discovery unit may find the lowest price by analyzing the geographical distribution. In this way, the discovery unit can find a more appropriate lowest price by considering the geographical distribution of products. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input geographical distribution data of products into a generating AI and have the generating AI perform price search.

[0051] The discovery unit can improve the accuracy of its search by referring to relevant literature on the product when finding the lowest price. For example, the discovery unit finds the lowest price by referring to relevant literature. For example, the discovery unit analyzes relevant literature on the product and proposes the optimal price. The discovery unit can also find the lowest price based on relevant literature. For example, the discovery unit finds the lowest price based on relevant literature. In this way, the discovery unit can improve the accuracy of finding the lowest price by referring to relevant literature on the product. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input relevant literature data on the product into a generating AI and have the generating AI perform the task of improving the accuracy of finding the lowest price.

[0052] The transaction completion unit can analyze the user's past purchase history and select the optimal transaction method when a transaction is completed. For example, the transaction completion unit can propose the optimal transaction procedure based on the user's past purchase history. For example, the transaction completion unit can prioritize proposing the transaction procedure for a specific product based on the user's past purchase history. The transaction completion unit can also analyze the user's past purchase history and select the most efficient transaction procedure. For example, the transaction completion unit analyzes the user's past purchase history and selects the most efficient transaction procedure. In this way, the transaction completion unit can provide the optimal transaction procedure by analyzing the user's past purchase history. Some or all of the above processing in the transaction completion unit may be performed using AI, for example, or without AI. For example, the transaction completion unit can input the user's purchase history data into a generating AI and have the generating AI select the optimal transaction method.

[0053] The transaction execution unit can customize the transaction execution method based on the user's current living situation when a transaction is completed. For example, if the user is busy, the transaction execution unit can provide a simple transaction procedure. For example, if the user is relaxed, the transaction execution unit can provide a detailed transaction procedure. The transaction execution unit can also suggest the optimal transaction procedure based on the user's living situation. For example, the transaction execution unit can suggest the optimal transaction procedure based on the user's living situation. In this way, the transaction execution unit can provide a more appropriate transaction procedure by customizing the transaction procedure based on the user's current living situation. Some or all of the above processing in the transaction execution unit may be performed using AI, for example, or without AI. For example, the transaction execution unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the transaction execution method.

[0054] The transaction completion unit can select the optimal transaction method when a transaction is completed, taking into account the user's geographical location information. For example, the transaction completion unit may prioritize the completion of transactions for products related to the user's current location. For example, the transaction completion unit may prioritize the completion of transactions for products at nearby stores based on the user's geographical location information. The transaction completion unit may also prioritize the completion of transactions for region-specific products, taking into account the user's geographical location information. For example, the transaction completion unit may prioritize the completion of transactions for region-specific products, taking into account the user's geographical location information. In this way, the transaction completion unit can provide a more appropriate transaction procedure by taking into account the user's geographical location information. Some or all of the above processing in the transaction completion unit may be performed using AI, for example, or without AI. For example, the transaction completion unit may input the user's location information data into a generating AI and have the generating AI perform the selection of the transaction method.

[0055] The transaction completion unit can analyze the user's social media activity and propose means of completion when a transaction is completed. For example, the transaction completion unit can complete a transaction based on the product categories the user has shown interest in on social media. For example, the transaction completion unit can prioritize transactions related to the user's current interests based on their social media activity. The transaction completion unit can also analyze the user's social media activity and complete transactions for related products. For example, the transaction completion unit can analyze the user's social media activity and complete transactions for related products. In this way, the transaction completion unit can provide more appropriate transaction procedures by analyzing the user's social media activity. Some or all of the above processing in the transaction completion unit may be performed using AI, for example, or without AI. For example, the transaction completion unit can input the user's social media data into a generating AI and have the generating AI execute proposals for means of completion.

[0056] The verification unit can provide the optimal verification method by referring to the user's past verification history during verification. For example, the verification unit can propose the optimal verification method based on the user's past verification history. For example, the verification unit can prioritize proposing the verification method for a specific product based on the user's past verification history. The verification unit can also provide the most efficient verification method by analyzing the user's past verification history. For example, the verification unit can provide the most efficient verification method by analyzing the user's past verification history. In this way, the verification unit can provide the optimal verification procedure by referring to the user's past verification history. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's verification history data into a generating AI and have the generating AI perform the task of providing the optimal verification method.

[0057] The verification unit can customize the verification process based on the user's current living situation. For example, if the user is busy, the verification unit can provide a simple verification method. For example, if the user is relaxed, the verification unit can provide a detailed verification method. The verification unit can also suggest the most suitable verification method based on the user's living situation. For example, the verification unit can suggest the most suitable verification method based on the user's living situation. This allows the verification unit to provide a more appropriate verification procedure by customizing the verification procedure based on the user's current living situation. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input user living situation data into a generating AI and have the generating AI perform the customization of the verification method.

[0058] The verification unit can provide the optimal verification method during verification, taking into account the user's geographical location information. For example, the verification unit may prioritize verifying products related to the user's current location. For example, the verification unit may prioritize verifying products at nearby stores based on the user's geographical location information. The verification unit may also prioritize verifying region-specific products, taking into account the user's geographical location information. For example, the verification unit may prioritize verifying region-specific products, taking into account the user's geographical location information. In this way, the verification unit can provide a more appropriate verification procedure by taking into account the user's geographical location information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit may input the user's location information data into a generating AI and have the generating AI perform the task of providing a verification method.

[0059] The verification unit can analyze the user's social media activity during verification and propose verification methods. For example, the verification unit can perform verification based on product categories that the user has shown interest in on social media. For example, the verification unit can prioritize verifications related to the user's current interests based on the user's social media activity. The verification unit can also analyze the user's social media activity and perform verifications of related products. For example, the verification unit can analyze the user's social media activity and perform verifications of related products. In this way, the verification unit can provide more appropriate verification procedures by analyzing the user's social media activity. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of verification methods.

[0060] The consumption timing prediction function can analyze the user's past consumption history to select the optimal prediction method when predicting consumption timing. For example, the consumption timing prediction function predicts the optimal consumption timing based on the user's past consumption history. For example, the consumption timing prediction function predicts the consumption timing of a specific product from the user's past consumption history. The consumption timing prediction function can also analyze the user's past consumption history to predict the most efficient consumption timing. For example, the consumption timing prediction function analyzes the user's past consumption history to predict the most efficient consumption timing. In this way, the consumption timing prediction function can predict the optimal consumption timing by analyzing the user's past consumption history. Some or all of the above processing in the consumption timing prediction function may be performed using AI, for example, or without AI. For example, the consumption timing prediction function can input the user's consumption history data into a generating AI and have the generating AI select the optimal prediction method.

[0061] The consumption timing prediction function can provide an optimal prediction method by considering the user's geographical location when predicting consumption timing. For example, the consumption timing prediction function can prioritize predicting the consumption timing of products related to the user's current location. For example, based on the user's geographical location, the consumption timing prediction function can prioritize predicting the consumption timing of products at nearby stores. The consumption timing prediction function can also prioritize predicting the consumption timing of region-specific products by considering the user's geographical location. For example, the consumption timing prediction function can prioritize predicting the consumption timing of region-specific products by considering the user's geographical location. In this way, the consumption timing prediction function can predict a more appropriate consumption timing by considering the user's geographical location. Some or all of the above processing in the consumption timing prediction function may be performed using AI, for example, or without AI. For example, the consumption timing prediction function can input the user's location data into a generating AI and have the generating AI perform the task of providing an optimal prediction method.

[0062] The Recommended / Bundled Suggestion ON / OFF function can provide the optimal ON / OFF method by referring to the user's past suggestion history when suggesting ON / OFF. For example, the Recommended / Bundled Suggestion ON / OFF function suggests the optimal suggestion ON / OFF method based on the user's past suggestion history. For example, the Recommended / Bundled Suggestion ON / OFF function prioritizes suggesting ON / OFF methods for specific products based on the user's past suggestion history. The Recommended / Bundled Suggestion ON / OFF function can also provide the most efficient suggestion ON / OFF method by analyzing the user's past suggestion history. For example, the Recommended / Bundled Suggestion ON / OFF function analyzes the user's past suggestion history to provide the most efficient suggestion ON / OFF method. In this way, the Recommended / Bundled Suggestion ON / OFF function can provide the optimal suggestion ON / OFF method by referring to the user's past suggestion history. Some or all of the above processing in the Recommended / Bundled Suggestion ON / OFF function may be performed using AI, for example, or without using AI. For example, the recommendation / bundling suggestion ON / OFF function allows the user's suggestion history data to be input into the generating AI, which then performs the task of providing the optimal ON / OFF method.

[0063] The recommendation / bundle suggestion ON / OFF function can provide the optimal ON / OFF method by considering the user's geographical location when suggesting suggestions. For example, the recommendation / bundle suggestion ON / OFF function prioritizes suggesting products related to the user's current location. For example, based on the user's geographical location, the recommendation / bundle suggestion ON / OFF function prioritizes suggesting products from nearby stores. Furthermore, the recommendation / bundle suggestion ON / OFF function can also prioritize suggesting region-specific products by considering the user's geographical location. For example, the recommendation / bundle suggestion ON / OFF function prioritizes suggesting region-specific products by considering the user's geographical location. In this way, the recommendation / bundle suggestion ON / OFF function can provide a more appropriate suggestion ON / OFF method by considering the user's geographical location. Some or all of the above processing in the recommendation / bundle suggestion ON / OFF function may be performed using AI, for example, or without using AI. For example, the recommendation / bundling suggestion ON / OFF function can input the user's location data into a generating AI and have the AI ​​provide the optimal ON / OFF method.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The suggestion unit can analyze the user's past purchase history and select the optimal suggestion method. For example, the suggestion unit can suggest the most suitable products based on the user's past purchase history. Furthermore, the suggestion unit can prioritize suggestion methods for specific products based on the user's past purchase history. In addition, the suggestion unit can analyze the user's past purchase history to select the most efficient suggestion method. Thus, the suggestion unit can provide the optimal suggestion method by analyzing the user's past purchase history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's purchase history data into a generating AI and have the generating AI select the optimal suggestion method.

[0066] The discovery unit can improve the accuracy of its search by considering the interrelationships between products when finding the lowest price. For example, the discovery unit can find the lowest price by comparing the prices of related products. It can also propose the optimal price by considering the interrelationships between products. Furthermore, the discovery unit can find the lowest price by analyzing the interrelationships between products. In this way, the discovery unit can improve the accuracy of finding the lowest price by considering the interrelationships between products. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input interrelationship data between products into a generating AI and have the generating AI perform the task of improving the accuracy of finding the lowest price.

[0067] The transaction completion unit can analyze the user's past purchase history and select the optimal transaction method when a transaction is completed. For example, the transaction completion unit can propose the optimal transaction procedure based on the user's past purchase history. Furthermore, the transaction completion unit can prioritize suggesting transaction procedures for specific products based on the user's past purchase history. In addition, the transaction completion unit can analyze the user's past purchase history and select the most efficient transaction procedure. Thus, the transaction completion unit can provide the optimal transaction procedure by analyzing the user's past purchase history. Some or all of the above-described processes in the transaction completion unit may be performed using AI, for example, or without AI. For example, the transaction completion unit can input the user's purchase history data into a generating AI and have the generating AI select the optimal transaction method.

[0068] The verification unit can provide the optimal verification method by referring to the user's past verification history during the verification process. For example, the verification unit can propose the optimal verification method based on the user's past verification history. Furthermore, the verification unit can prioritize suggesting verification methods for specific products based on the user's past verification history. In addition, the verification unit can analyze the user's past verification history to provide the most efficient verification method. Thus, the verification unit can provide the optimal verification procedure by referring to the user's past verification history. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's verification history data into a generating AI and have the generating AI perform the task of providing the optimal verification method.

[0069] The consumption timing prediction function can analyze the user's past consumption history to select the optimal prediction method when predicting consumption timing. For example, the consumption timing prediction function can predict the optimal consumption timing based on the user's past consumption history. Furthermore, the consumption timing prediction function can predict the consumption timing of a specific product based on the user's past consumption history. In addition, the consumption timing prediction function can analyze the user's past consumption history to predict the most efficient consumption timing. Thus, the consumption timing prediction function can predict the optimal consumption timing by analyzing the user's past consumption history. Some or all of the above-described processes in the consumption timing prediction function may be performed using AI, for example, or without AI. For example, the consumption timing prediction function can input the user's consumption history data into a generating AI and have the generating AI select the optimal prediction method.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The reception desk receives input from users. For example, a user can input "I want to buy XX" on the messenger to receive their needs. Step 2: The proposal department proposes the most suitable products based on the information received by the reception department. For example, the proposal department can propose the most suitable products by considering the user's preferences, past purchase history, current trends, etc. Step 3: The discovery unit automatically finds the lowest price based on the products suggested by the suggestion unit. For example, the discovery unit can find the lowest price using price comparison sites or specific algorithms. Step 4: The settlement unit completes the transaction based on the lowest price found by the discovery unit. For example, if the user decides to purchase the proposed product, the settlement unit can support the purchase process and confirm the purchase on the messenger. Step 5: The confirmation unit verifies the delivery date and time and delivery method based on the sale concluded by the settlement unit. For example, the confirmation unit can cooperate with the delivery company to provide a function for confirming the delivery date and time and delivery method.

[0072] (Example of form 2) The conversational smart shopping agent system according to an embodiment of the present invention is a system that supports the purchase of goods by eliminating the need for users to actively search for products and compare prices. This system allows users to initiate a conversation with an agent who supports the purchase of goods via messenger, thereby suggesting the most suitable product and automatically finding the lowest price. Furthermore, the agent proposes, supports, and completes the transaction solely on the messenger screen, without going through an e-commerce site. This mechanism allows users to easily purchase goods. For example, a user initiates a conversation with an agent via messenger and enters "I want to buy XX." This information is entered into the agent. Next, the agent proposes the most suitable product based on the user's needs. The agent automatically finds the lowest price by linking with shopping, reuse, and travel-related assets within the group. For example, if a user enters "I want to buy XX," the agent proposes the most suitable product and presents the lowest price. Furthermore, the agent proposes, supports, and completes the transaction solely on the messenger screen, without going through an e-commerce site. For example, if a user purchases a suggested product, the agent supports the purchase process and confirms the purchase on the messenger. Furthermore, the agent will collaborate with delivery companies to provide a function to confirm delivery dates, times, and delivery methods. This allows users to check the delivery status of their purchased items and select their preferred delivery method. Basic functions will be provided free of charge, while convenient features that anticipate user needs will be offered as paid features. For example, a function that predicts when an item will be consumed and prompts the user, as well as the ability to turn recommendations and bundled product suggestions on or off, will be offered as paid features. This system allows users to easily purchase products and can also be used for monitoring and understanding the situation of the elderly. For example, when elderly people purchase items they need on a daily basis, the agent can support them, allowing them to shop with peace of mind. In this way, the conversational smart shopping agent system can support users in purchasing goods without the hassle of actively searching for products and comparing prices.

[0073] The conversational smart shopping agent system according to this embodiment comprises a reception unit, a suggestion unit, a discovery unit, a completion unit, and a confirmation unit. The reception unit receives input from the user. For example, the reception unit can receive the user's needs when the user inputs "I want to buy XX" on the messenger. The suggestion unit suggests the most suitable product based on the information received by the reception unit. For example, the suggestion unit can suggest the most suitable product by considering the user's preferences, past purchase history, current trends, etc. The discovery unit automatically finds the lowest price based on the product suggested by the suggestion unit. For example, the discovery unit can find the lowest price using price comparison sites or specific algorithms. The completion unit completes the sale based on the lowest price found by the discovery unit. For example, if the user purchases the suggested product, the completion unit supports the purchase procedure and allows the user to confirm the purchase on the messenger. The confirmation unit confirms the delivery date and time and receiving method based on the sale completed by the completion unit. For example, the confirmation unit can cooperate with a delivery company to provide a function to confirm the delivery date and time and receiving method. As a result, the conversational smart shopping agent system according to the embodiment can suggest the most suitable products based on user input, find the lowest price, complete the transaction, and confirm the delivery date and time and receiving method.

[0074] The reception desk receives input from users. For example, the reception desk can receive user needs when a user types "I want to buy XX" on the messenger. Specifically, the reception desk uses natural language processing (NLP) technology to analyze the user's input and accurately understand the user's intent. For example, if a user types "I want a new smartphone," the reception desk extracts the keyword "smartphone" and recognizes the user's intention to purchase a smartphone. The reception desk also processes user input in real time, allowing it to quickly move to the next step. Furthermore, the reception desk can save user input as a history and use it for future suggestions and support. For example, it can provide more personalized suggestions based on the user's past purchases and search history. This allows the reception desk to accurately understand user needs and respond quickly and effectively.

[0075] The suggestion department proposes the most suitable products based on the information received by the reception department. For example, the suggestion department can propose the most suitable products by considering the user's preferences, past purchase history, and current trends. Specifically, the suggestion department uses AI to analyze the user's preferences and needs and select the most suitable products. For example, it learns the user's preferences and tendencies based on products the user has purchased in the past and their search history, and proposes products that are suitable for the user. In addition, the suggestion department can provide users with the latest information by considering current trends and popular products. For example, the suggestion department collects trend information from social media and news sites and provides users with information on popular and new products. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy of its suggestions. For example, when users rate and comment on the suggested products, the suggestion department can learn from that feedback and reflect it in future suggestions. In this way, the suggestion department can propose the most suitable products that meet the user's needs and improve user satisfaction.

[0076] The discovery unit automatically finds the lowest price based on the products suggested by the suggestion unit. For example, the discovery unit can find the lowest price using price comparison sites or specific algorithms. Specifically, the discovery unit collects product information from multiple online shopping sites and price comparison sites and compares prices. For example, the discovery unit uses the APIs of each site to obtain product information and check prices and inventory status in real time. Furthermore, the discovery unit can efficiently find the lowest price using specific algorithms. For example, the discovery unit calculates the optimal price using algorithms such as Dijkstra's algorithm or dynamic programming. In addition, the discovery unit can suggest the optimal price according to the user's needs and conditions. For example, if the user specifies conditions such as "free shipping" or "same-day delivery," the discovery unit can find the lowest price that meets those conditions. In this way, the discovery unit can provide the user with the best price and improve cost performance.

[0077] The transaction completion unit completes the sale based on the lowest price found by the discovery unit. For example, if a user purchases a suggested product, the transaction completion unit supports the purchase process and allows the user to confirm the purchase on the messenger. Specifically, the transaction completion unit confirms the user's purchase intention and automates the purchase process. For example, if the user enters "I want to buy this product," the transaction completion unit starts the purchase process and collects the necessary information. The transaction completion unit also securely manages the user's payment and shipping information and can complete the purchase process quickly and reliably. Furthermore, the transaction completion unit notifies the user of the progress of the purchase process in real time, supporting the user so that they can proceed with the purchase with peace of mind. For example, the transaction completion unit notifies the user of each step of the purchase process on the messenger, keeping the user informed of the progress. In this way, the transaction completion unit can smoothly support the user's purchase process and improve user convenience.

[0078] The confirmation unit verifies the delivery date and time and receiving method based on the transaction concluded by the settlement unit. For example, the confirmation unit can collaborate with the delivery company to provide a function for confirming the delivery date and time and receiving method. Specifically, the confirmation unit proposes the optimal delivery date and time and receiving method based on the user's delivery address information. For example, the confirmation unit proposes the optimal delivery schedule considering the delivery address specified by the user and the desired receiving date and time. The confirmation unit can also collaborate with the delivery company's system to check the delivery status in real time. For example, the confirmation unit can use the delivery company's API to obtain the delivery status and scheduled delivery date and time and notify the user. Furthermore, the confirmation unit can collect user feedback and continuously improve the quality of the delivery service. For example, when users provide ratings and comments on delivery, the confirmation unit can use that feedback to strengthen collaboration with the delivery company and improve the quality of the service. As a result, the confirmation unit can provide users with fast and reliable delivery services and improve user satisfaction.

[0079] The consumption timing prediction function predicts when an item will be consumed and runs out, and includes a function to prompt the user via an agent. The consumption timing prediction function can predict consumption timing based on, for example, past consumption data, usage frequency, and consumption rate. For example, it can analyze the consumption rate of items a user has purchased in the past and predict when they will purchase them next. It can also predict consumption timing by considering the user's usage frequency. For example, it can analyze how often a user uses a particular item and predict when they will purchase it next. This allows the consumption timing prediction function to predict consumption timing and notify the user at the appropriate time. Some or all of the above processing in the consumption timing prediction function may be performed using, for example, AI, or not. For example, the consumption timing prediction function can input user consumption data into a generating AI and have the generating AI perform the consumption timing prediction.

[0080] The Recommended / Bundled Product ON / OFF function allows users to switch suggestions on or off according to their needs. For example, the Recommended / Bundled Product ON / OFF function can be turned on if the user wants suggestions and turned off if they do not. For example, the Recommended / Bundled Product ON / OFF function allows users to choose whether or not to suggest related products when purchasing a specific product. The Recommended / Bundled Product ON / OFF function can also automatically switch suggestions on or off based on the user's preferences and past purchase history. For example, the Recommended / Bundled Product ON / OFF function analyzes data on products the user has purchased in the past and determines whether or not to suggest related products. This allows the Recommended / Bundled Product ON / OFF function to switch suggestions on or off according to the user's needs. Some or all of the above processing in the Recommended / Bundled Product ON / OFF function may be performed using AI, for example, or without AI. For example, the Recommended / Bundled Product ON / OFF function can input the user's purchase history data into a generating AI and have the generating AI make the decision to turn suggestions on or off.

[0081] The suggestion unit can automatically find the lowest price by linking with multiple shopping, reuse, and travel-related assets. For example, the suggestion unit can automatically find the lowest price by linking with shopping, reuse, and travel-related assets. For example, when a user enters the product they want to buy, the suggestion unit searches these assets and presents the lowest price. The suggestion unit can also suggest the most suitable product based on the user's needs. For example, the suggestion unit suggests the most suitable product by considering the user's preferences and past purchase history. In this way, the suggestion unit can automatically find the lowest price by linking with assets within the group. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user needs data into a generating AI and have the generating AI perform the task of suggesting the most suitable product.

[0082] The confirmation unit can collaborate with multiple delivery companies to provide a function for confirming delivery dates and times and receiving methods. For example, the confirmation unit can collaborate with a delivery company such as Yamato Transport to provide a function for confirming delivery dates and times and receiving methods. For example, when a user selects a delivery date and time and receiving method for a purchased product, the confirmation unit provides information about the delivery company. Furthermore, when the user inputs their desired delivery date and time and receiving method, the confirmation unit can collaborate with the delivery company to perform confirmation. In this way, the confirmation unit can confirm delivery dates and times and receiving methods by collaborating with the delivery company. Some or all of the above-described processes in the confirmation unit may be performed using AI, for example, or without using AI. For example, the confirmation unit can input the user's delivery preference data into a generating AI and have the generating AI perform the confirmation of delivery dates and times and receiving methods.

[0083] The transaction completion unit can propose, support, and complete transactions solely on the messenger screen. For example, when a user purchases a proposed item on the messenger screen, the transaction completion unit supports the purchase process and completes the transaction. For example, when a user purchases a proposed item, the transaction completion unit supports the purchase process and confirms the purchase on the messenger. The transaction completion unit also provides the necessary information when the user is performing the purchase process, allowing the process to proceed smoothly. As a result, the transaction completion unit can complete transactions on the messenger screen. Some or all of the above-described processes in the transaction completion unit may be performed using AI, for example, or not using AI. For example, the transaction completion unit can input the user's purchase procedure data into a generating AI and have the generating AI execute the transaction completion.

[0084] The reception unit can estimate the user's emotions and adjust the timing of input requests based on the estimated emotions. For example, if the user is stressed, the reception unit will prompt for input at a time when the user can relax. For example, if the user is busy, the reception unit will prompt for input when there is free time. Also, if the user is relaxed, the reception unit will prompt for input immediately. In this way, the reception unit can prompt for input at a more appropriate time by adjusting the timing of input requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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 unit may be performed using AI, or not using AI. For example, the reception unit can input user emotion data into the generative AI and have the generative AI adjust the timing of input requests.

[0085] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk may prioritize suggesting input methods that the user has frequently used in the past. For example, the reception desk may suggest the optimal reception method for a specific time period based on the user's past input history. The reception desk can also analyze the user's past input history and select the most efficient reception method. For example, the reception desk may suggest the optimal reception method based on the user's past input history. In this way, the reception desk can select the optimal reception method by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk may input the user's input history data into a generating AI and have the generating AI perform the selection of the optimal reception method.

[0086] The reception unit can filter input based on the user's current situation and areas of interest. For example, the reception unit can filter input based on the product categories the user is currently interested in. For example, the reception unit can filter input based on the user's current situation (e.g., traveling). The reception unit can also prioritize receiving input for relevant products based on the user's areas of interest. For example, the reception unit prioritizes receiving input for relevant products based on the user's areas of interest. This allows the reception unit to receive more relevant input by filtering input based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user situation data into a generating AI and have the generating AI perform the input filtering.

[0087] The reception unit can estimate the user's emotions and determine the priority of inputs to be received based on the estimated emotions. For example, if the user is in a hurry, the reception unit will prioritize important inputs. For example, if the user is relaxed, the reception unit will prioritize detailed inputs. The reception unit can also prioritize simple inputs if the user is stressed. For example, if the reception unit is stressed, the reception unit will prioritize simple inputs. In this way, the reception unit can prioritize more appropriate inputs by determining the priority of inputs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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 unit may be performed using AI, for example, or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI determine the priority of inputs.

[0088] The reception unit can prioritize accepting inputs that are highly relevant, taking into account the user's geographical location. For example, the reception unit can prioritize accepting inputs of products related to the user's current location. For example, the reception unit can prioritize accepting inputs of products from nearby stores based on the user's geographical location. The reception unit can also prioritize accepting inputs of region-specific products, taking into account the user's geographical location. For example, the reception unit can prioritize accepting inputs of region-specific products, taking into account the user's geographical location. In this way, the reception unit can prioritize accepting inputs that are highly relevant by taking into account the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's location data into a generating AI and have the generating AI determine the priority of inputs.

[0089] The reception unit can analyze the user's social media activity and accept relevant inputs when receiving input. For example, the reception unit can accept inputs based on product categories that the user has shown interest in on social media. For example, the reception unit can prioritize accepting inputs related to the user's current interests from the user's social media activity. The reception unit can also analyze the user's social media activity and accept inputs related to products. For example, the reception unit can analyze the user's social media activity and accept inputs related to products. In this way, the reception unit can accept relevant inputs by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI perform input filtering.

[0090] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, if the user is in a hurry, the suggestion unit can provide concise suggestions. The suggestion unit can also provide simple suggestions if the user is stressed. For example, if the suggestion unit is stressed, the suggestion unit can provide simple suggestions. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the way it presents its 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 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the way it presents its suggestions.

[0091] The proposal unit can adjust the level of detail in its proposals based on the importance of the products. For example, it can provide detailed proposals for important products and concise proposals for less important products. The proposal unit can also adjust the level of detail in its proposals according to the importance of the products. This allows the proposal unit to provide more appropriate proposals by adjusting the level of detail according to the importance of the products. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input product importance data into a generating AI and have the generating AI adjust the level of detail in its proposals.

[0092] The suggestion unit can apply different suggestion algorithms depending on the product category when making suggestions. For example, in the case of electronic devices, the suggestion unit will make suggestions that emphasize technical details. For example, in the case of fashion items, the suggestion unit will make suggestions that emphasize trend information. Furthermore, in the case of food products, the suggestion unit can also make suggestions that emphasize health information. For example, in the case of food products, the suggestion unit will make suggestions that emphasize health information. In this way, the suggestion unit can make more appropriate suggestions by applying different suggestion algorithms depending on the product category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input product category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0093] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will make a short suggestion. For example, if the user is relaxed, the suggestion unit will make a long suggestion. The suggestion unit can also make a concise suggestion if the user is stressed. For example, if the suggestion unit is stressed, the suggestion unit will make a concise suggestion. In this way, the suggestion unit can make more appropriate suggestions by adjusting the length of the suggestion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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 AI or not using AI. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the length of the suggestion.

[0094] The proposal department can determine the priority of proposals based on the product submission timing when submitting proposals. For example, the proposal department will prioritize new products. For example, the proposal department will propose seasonal products according to the season. The proposal department can also determine the priority of proposals based on the submission timing. For example, the proposal department will determine the priority of proposals based on the submission timing. This allows the proposal department to make more appropriate proposals by determining the priority of proposals based on the product submission timing. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input product submission timing data into a generating AI and have the generating AI perform the determination of proposal priorities.

[0095] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion unit may prioritize suggesting highly relevant products. For example, the suggestion unit may postpone suggesting less relevant products. The suggestion unit can also adjust the order of suggestions based on the relevance of the products. For example, the suggestion unit can adjust the order of suggestions based on the relevance of the products. This allows the suggestion unit to make more appropriate suggestions by adjusting the order of suggestions based on the relevance of the products. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input product relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0096] The discovery unit can estimate the user's emotions and adjust the criteria for finding the lowest price based on the estimated emotions. For example, if the user is relaxed, the discovery unit performs a detailed price comparison. For example, if the user is in a hurry, the discovery unit performs a concise price comparison. The discovery unit can also perform a simple price comparison if the user is stressed. For example, if the discovery unit is stressed, the discovery unit performs a simple price comparison. In this way, the discovery unit can perform a more appropriate price comparison by adjusting the criteria for finding the lowest price according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the discovery unit may be performed using AI, for example, or not using AI. For example, the discovery unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the criteria for finding the lowest price.

[0097] The discovery unit can improve the accuracy of its search by considering the interrelationships between products when finding the lowest price. For example, the discovery unit finds the lowest price by comparing the prices of related products. For example, the discovery unit proposes the optimal price by considering the interrelationships between products. The discovery unit can also find the lowest price by analyzing the interrelationships between products. For example, the discovery unit finds the lowest price by analyzing the interrelationships between products. In this way, the discovery unit can improve the accuracy of finding the lowest price by considering the interrelationships between products. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input interrelationship data between products into a generating AI and have the generating AI perform the task of improving the accuracy of finding the lowest price.

[0098] The discovery unit can perform its search for the lowest price by considering the attribute information of the product submitter. For example, the discovery unit can find the lowest price by considering the reliability of the submitter. For example, the discovery unit can find the lowest price by considering the submitter's past transaction history. The discovery unit can also find the lowest price by analyzing the submitter's attribute information. For example, the discovery unit can find the lowest price by analyzing the submitter's attribute information. In this way, the discovery unit can find a more reliable lowest price by considering the attribute information of the product submitter. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without using AI. For example, the discovery unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the lowest price discovery.

[0099] The discovery unit can estimate the user's emotions and adjust the order in which it displays the lowest price discovery results based on the estimated emotions. For example, if the user is in a hurry, the discovery unit can display the lowest price first. For example, if the user is relaxed, the discovery unit can display detailed price comparison results. The discovery unit can also display simple price comparison results if the user is stressed. For example, if the discovery unit is stressed, it can display simple price comparison results. In this way, the discovery unit can provide more appropriate price comparison results by adjusting the order in which it displays the lowest price discovery results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the discovery unit may be performed using AI, for example, or not using AI. For example, the discovery unit can input user emotion data into the generative AI and have the generative AI adjust the order in which the results are displayed.

[0100] The discovery unit can perform price searches while considering the geographical distribution of products. For example, the discovery unit may prioritize finding the lowest prices of products that are geographically close. For example, the discovery unit may propose the optimal price by considering the geographical distribution. The discovery unit can also find the lowest price by analyzing the geographical distribution. For example, the discovery unit may find the lowest price by analyzing the geographical distribution. In this way, the discovery unit can find a more appropriate lowest price by considering the geographical distribution of products. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input geographical distribution data of products into a generating AI and have the generating AI perform price search.

[0101] The discovery unit can improve the accuracy of its search by referring to relevant literature on the product when finding the lowest price. For example, the discovery unit finds the lowest price by referring to relevant literature. For example, the discovery unit analyzes relevant literature on the product and proposes the optimal price. The discovery unit can also find the lowest price based on relevant literature. For example, the discovery unit finds the lowest price based on relevant literature. In this way, the discovery unit can improve the accuracy of finding the lowest price by referring to relevant literature on the product. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input relevant literature data on the product into a generating AI and have the generating AI perform the task of improving the accuracy of finding the lowest price.

[0102] The transaction execution unit can estimate the user's emotions and adjust the transaction execution method based on the estimated emotions. For example, if the user is relaxed, the transaction execution unit can provide a detailed transaction procedure. For example, if the user is in a hurry, the transaction execution unit can provide a concise transaction procedure. The transaction execution unit can also provide a simple transaction procedure if the user is stressed. For example, if the transaction execution unit is stressed, the transaction execution unit can provide a simple transaction procedure. In this way, the transaction execution unit can provide a more appropriate transaction procedure by adjusting the transaction execution method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the transaction execution unit may be performed using AI, for example, or without AI. For example, the transaction execution unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the transaction execution method.

[0103] The transaction completion unit can analyze the user's past purchase history and select the optimal transaction method when a transaction is completed. For example, the transaction completion unit can propose the optimal transaction procedure based on the user's past purchase history. For example, the transaction completion unit can prioritize proposing the transaction procedure for a specific product based on the user's past purchase history. The transaction completion unit can also analyze the user's past purchase history and select the most efficient transaction procedure. For example, the transaction completion unit analyzes the user's past purchase history and selects the most efficient transaction procedure. In this way, the transaction completion unit can provide the optimal transaction procedure by analyzing the user's past purchase history. Some or all of the above processing in the transaction completion unit may be performed using AI, for example, or without AI. For example, the transaction completion unit can input the user's purchase history data into a generating AI and have the generating AI select the optimal transaction method.

[0104] The transaction execution unit can customize the transaction execution method based on the user's current living situation when a transaction is completed. For example, if the user is busy, the transaction execution unit can provide a simple transaction procedure. For example, if the user is relaxed, the transaction execution unit can provide a detailed transaction procedure. The transaction execution unit can also suggest the optimal transaction procedure based on the user's living situation. For example, the transaction execution unit can suggest the optimal transaction procedure based on the user's living situation. In this way, the transaction execution unit can provide a more appropriate transaction procedure by customizing the transaction procedure based on the user's current living situation. Some or all of the above processing in the transaction execution unit may be performed using AI, for example, or without AI. For example, the transaction execution unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the transaction execution method.

[0105] The transaction execution unit can estimate the user's emotions and determine the priority of trade executions based on the estimated emotions. For example, if the user is in a hurry, the transaction execution unit will prioritize important trades. For example, if the user is relaxed, the transaction execution unit will prioritize detailed trades. The transaction execution unit can also prioritize simple trades if the user is stressed. For example, if the transaction execution unit is stressed, the transaction execution unit will prioritize simple trades. In this way, the transaction execution unit can provide a more appropriate trading procedure by determining the priority of trade executions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the transaction execution unit may be performed using AI, for example, or not using AI. For example, the transaction execution unit can input user emotion data into a generative AI and have the generative AI perform the determination of trade execution priorities.

[0106] The transaction completion unit can select the optimal transaction method when a transaction is completed, taking into account the user's geographical location information. For example, the transaction completion unit may prioritize the completion of transactions for products related to the user's current location. For example, the transaction completion unit may prioritize the completion of transactions for products at nearby stores based on the user's geographical location information. The transaction completion unit may also prioritize the completion of transactions for region-specific products, taking into account the user's geographical location information. For example, the transaction completion unit may prioritize the completion of transactions for region-specific products, taking into account the user's geographical location information. In this way, the transaction completion unit can provide a more appropriate transaction procedure by taking into account the user's geographical location information. Some or all of the above processing in the transaction completion unit may be performed using AI, for example, or without AI. For example, the transaction completion unit may input the user's location information data into a generating AI and have the generating AI perform the selection of the transaction method.

[0107] The transaction completion unit can analyze the user's social media activity and propose means of completion when a transaction is completed. For example, the transaction completion unit can complete a transaction based on the product categories the user has shown interest in on social media. For example, the transaction completion unit can prioritize transactions related to the user's current interests based on their social media activity. The transaction completion unit can also analyze the user's social media activity and complete transactions for related products. For example, the transaction completion unit can analyze the user's social media activity and complete transactions for related products. In this way, the transaction completion unit can provide more appropriate transaction procedures by analyzing the user's social media activity. Some or all of the above processing in the transaction completion unit may be performed using AI, for example, or without AI. For example, the transaction completion unit can input the user's social media data into a generating AI and have the generating AI execute proposals for means of completion.

[0108] The verification unit can estimate the user's emotions and adjust the verification method based on the estimated emotions. For example, if the user is relaxed, the verification unit can provide a detailed verification method. For example, if the user is in a hurry, the verification unit can provide a concise verification method. The verification unit can also provide a simple verification method if the user is stressed. For example, if the verification unit is stressed, the verification unit can provide a simple verification method. In this way, the verification unit can provide a more appropriate verification procedure by adjusting the verification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the verification method.

[0109] The verification unit can provide the optimal verification method by referring to the user's past verification history during verification. For example, the verification unit can propose the optimal verification method based on the user's past verification history. For example, the verification unit can prioritize proposing the verification method for a specific product based on the user's past verification history. The verification unit can also provide the most efficient verification method by analyzing the user's past verification history. For example, the verification unit can provide the most efficient verification method by analyzing the user's past verification history. In this way, the verification unit can provide the optimal verification procedure by referring to the user's past verification history. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's verification history data into a generating AI and have the generating AI perform the task of providing the optimal verification method.

[0110] The verification unit can customize the verification process based on the user's current living situation. For example, if the user is busy, the verification unit can provide a simple verification method. For example, if the user is relaxed, the verification unit can provide a detailed verification method. The verification unit can also suggest the most suitable verification method based on the user's living situation. For example, the verification unit can suggest the most suitable verification method based on the user's living situation. This allows the verification unit to provide a more appropriate verification procedure by customizing the verification procedure based on the user's current living situation. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input user living situation data into a generating AI and have the generating AI perform the customization of the verification method.

[0111] The verification unit can estimate the user's emotions and determine the priority of verifications based on the estimated emotions. For example, if the user is in a hurry, the verification unit will prioritize important verifications. For example, if the user is relaxed, the verification unit will prioritize detailed verifications. The verification unit can also prioritize simple verifications if the user is stressed. For example, if the verification unit is stressed, the verification unit will prioritize simple verifications. In this way, the verification unit can provide a more appropriate verification procedure by determining the priority of verifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input user emotion data into a generative AI and have the generative AI perform the determination of verification priorities.

[0112] The verification unit can provide the optimal verification method during verification, taking into account the user's geographical location information. For example, the verification unit may prioritize verifying products related to the user's current location. For example, the verification unit may prioritize verifying products at nearby stores based on the user's geographical location information. The verification unit may also prioritize verifying region-specific products, taking into account the user's geographical location information. For example, the verification unit may prioritize verifying region-specific products, taking into account the user's geographical location information. In this way, the verification unit can provide a more appropriate verification procedure by taking into account the user's geographical location information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit may input the user's location information data into a generating AI and have the generating AI perform the task of providing a verification method.

[0113] The verification unit can analyze the user's social media activity during verification and propose verification methods. For example, the verification unit can perform verification based on product categories that the user has shown interest in on social media. For example, the verification unit can prioritize verifications related to the user's current interests based on the user's social media activity. The verification unit can also analyze the user's social media activity and perform verifications of related products. For example, the verification unit can analyze the user's social media activity and perform verifications of related products. In this way, the verification unit can provide more appropriate verification procedures by analyzing the user's social media activity. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of verification methods.

[0114] The consumption timing prediction function can estimate the user's emotions and adjust the consumption timing prediction method based on the estimated emotions. For example, if the user is relaxed, the consumption timing prediction function can provide a detailed consumption timing prediction. For example, if the user is in a hurry, the consumption timing prediction function can provide a concise consumption timing prediction. Furthermore, if the user is stressed, the consumption timing prediction function can provide a simple consumption timing prediction. For example, if the user is stressed, the consumption timing prediction function can provide a simple consumption timing prediction. In this way, the consumption timing prediction function can predict more appropriate consumption timing by adjusting the consumption timing prediction method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 consumption timing prediction function may be performed using AI or not using AI. For example, the consumption timing prediction function can input user emotion data into a generative AI and have the generative AI adjust the consumption timing prediction method.

[0115] The consumption timing prediction function can analyze the user's past consumption history to select the optimal prediction method when predicting consumption timing. For example, the consumption timing prediction function predicts the optimal consumption timing based on the user's past consumption history. For example, the consumption timing prediction function predicts the consumption timing of a specific product from the user's past consumption history. The consumption timing prediction function can also analyze the user's past consumption history to predict the most efficient consumption timing. For example, the consumption timing prediction function analyzes the user's past consumption history to predict the most efficient consumption timing. In this way, the consumption timing prediction function can predict the optimal consumption timing by analyzing the user's past consumption history. Some or all of the above processing in the consumption timing prediction function may be performed using AI, for example, or without AI. For example, the consumption timing prediction function can input the user's consumption history data into a generating AI and have the generating AI select the optimal prediction method.

[0116] The consumption timing prediction function can estimate the user's emotions and determine the priority of consumption timing based on the estimated emotions. For example, if the user is in a hurry, the consumption timing prediction function will prioritize important consumption timings. For example, if the user is relaxed, the consumption timing prediction function will prioritize detailed consumption timings. Also, if the user is stressed, the consumption timing prediction function can prioritize simple consumption timings. For example, if the user is stressed, the consumption timing prediction function will prioritize simple consumption timings. In this way, the consumption timing prediction function can predict more appropriate consumption timings by determining the priority of consumption timings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consumption timing prediction function may be performed using AI or not using AI. For example, the consumption timing prediction function can input user emotion data into a generative AI and have the generative AI perform the determination of consumption timing priorities.

[0117] The consumption timing prediction function can provide an optimal prediction method by considering the user's geographical location when predicting consumption timing. For example, the consumption timing prediction function can prioritize predicting the consumption timing of products related to the user's current location. For example, based on the user's geographical location, the consumption timing prediction function can prioritize predicting the consumption timing of products at nearby stores. The consumption timing prediction function can also prioritize predicting the consumption timing of region-specific products by considering the user's geographical location. For example, the consumption timing prediction function can prioritize predicting the consumption timing of region-specific products by considering the user's geographical location. In this way, the consumption timing prediction function can predict a more appropriate consumption timing by considering the user's geographical location. Some or all of the above processing in the consumption timing prediction function may be performed using AI, for example, or without AI. For example, the consumption timing prediction function can input the user's location data into a generating AI and have the generating AI perform the task of providing an optimal prediction method.

[0118] The recommendation / bundled suggestion ON / OFF function can estimate the user's emotions and adjust how suggestions are turned ON / OFF based on those emotions. For example, if the user is relaxed, the recommendation / bundled suggestion ON / OFF function will provide detailed suggestions. For example, if the user is in a hurry, the recommendation / bundled suggestion ON / OFF function will provide concise suggestions. Also, if the user is stressed, the recommendation / bundled suggestion ON / OFF function can provide simple suggestions. For example, if the user is stressed, the recommendation / bundled suggestion ON / OFF function will provide simple suggestions. In this way, the recommendation / bundled suggestion ON / OFF function can provide more appropriate suggestions by adjusting how suggestions are turned ON / OFF according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 recommendation / bundled suggestion ON / OFF function may be performed using AI, for example, or without AI. For example, the recommendation / bundling suggestion ON / OFF function allows the user's emotional data to be input into the generating AI, which then adjusts how the suggestions are turned ON or OFF.

[0119] The Recommended / Bundled Suggestion ON / OFF function can provide the optimal ON / OFF method by referring to the user's past suggestion history when suggesting ON / OFF. For example, the Recommended / Bundled Suggestion ON / OFF function suggests the optimal suggestion ON / OFF method based on the user's past suggestion history. For example, the Recommended / Bundled Suggestion ON / OFF function prioritizes suggesting ON / OFF methods for specific products based on the user's past suggestion history. The Recommended / Bundled Suggestion ON / OFF function can also provide the most efficient suggestion ON / OFF method by analyzing the user's past suggestion history. For example, the Recommended / Bundled Suggestion ON / OFF function analyzes the user's past suggestion history to provide the most efficient suggestion ON / OFF method. In this way, the Recommended / Bundled Suggestion ON / OFF function can provide the optimal suggestion ON / OFF method by referring to the user's past suggestion history. Some or all of the above processing in the Recommended / Bundled Suggestion ON / OFF function may be performed using AI, for example, or without using AI. For example, the recommendation / bundling suggestion ON / OFF function allows the user's suggestion history data to be input into the generating AI, which then performs the task of providing the optimal ON / OFF method.

[0120] The recommendation / bundled suggestion ON / OFF function can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is in a hurry, the recommendation / bundled suggestion ON / OFF function will prioritize important suggestions. For example, if the user is relaxed, the recommendation / bundled suggestion ON / OFF function will prioritize detailed suggestions. Also, if the user is stressed, the recommendation / bundled suggestion ON / OFF function can prioritize simple suggestions. For example, if the user is stressed, the recommendation / bundled suggestion ON / OFF function will prioritize simple suggestions. In this way, the recommendation / bundled suggestion ON / OFF function can provide more appropriate suggestions by determining the priority of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 recommendation / bundled suggestion ON / OFF function may be performed using AI, for example, or without AI. For example, the recommendation / bundle suggestion ON / OFF function allows the user's emotional data to be input into the generating AI, which then determines the priority of the suggestions.

[0121] The recommendation / bundle suggestion ON / OFF function can provide the optimal ON / OFF method by considering the user's geographical location when suggesting suggestions. For example, the recommendation / bundle suggestion ON / OFF function prioritizes suggesting products related to the user's current location. For example, based on the user's geographical location, the recommendation / bundle suggestion ON / OFF function prioritizes suggesting products from nearby stores. Furthermore, the recommendation / bundle suggestion ON / OFF function can also prioritize suggesting region-specific products by considering the user's geographical location. For example, the recommendation / bundle suggestion ON / OFF function prioritizes suggesting region-specific products by considering the user's geographical location. In this way, the recommendation / bundle suggestion ON / OFF function can provide a more appropriate suggestion ON / OFF method by considering the user's geographical location. Some or all of the above processing in the recommendation / bundle suggestion ON / OFF function may be performed using AI, for example, or without using AI. For example, the recommendation / bundling suggestion ON / OFF function can input the user's location data into a generating AI and have the AI ​​provide the optimal ON / OFF method.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is relaxed, it can offer suggestions that include detailed product descriptions. If the user is in a hurry, it can offer concise suggestions. Furthermore, if the user is stressed, it can offer simple and easy-to-understand suggestions. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the content of its 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-described processes in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the content of its suggestions.

[0124] The discovery unit can estimate the user's emotions and adjust the criteria for finding the lowest price based on the estimated emotions. For example, if the user is relaxed, a detailed price comparison can be performed. If the user is in a hurry, a concise price comparison can be performed. Furthermore, if the user is stressed, a simple price comparison can be performed. In this way, the discovery unit can perform a more appropriate price comparison by adjusting the criteria for finding the lowest price according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the discovery unit may be performed using AI, for example, or not using AI. For example, the discovery unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the criteria for finding the lowest price.

[0125] The transaction unit can estimate the user's emotions and adjust the transaction execution method based on the estimated emotions. For example, if the user is relaxed, it can provide a detailed transaction procedure. If the user is in a hurry, it can provide a concise transaction procedure. Furthermore, if the user is stressed, it can provide a simple transaction procedure. In this way, the transaction unit can provide a more appropriate transaction procedure by adjusting the transaction execution method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the transaction unit may be performed using AI, for example, or not using AI. For example, the transaction unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the transaction execution method.

[0126] The verification unit can estimate the user's emotions and adjust the verification method based on the estimated emotions. For example, if the user is relaxed, it can provide a detailed verification method. If the user is in a hurry, it can provide a concise verification method. Furthermore, if the user is stressed, it can provide a simple verification method. In this way, the verification unit can provide a more appropriate verification procedure by adjusting the verification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the verification method.

[0127] The consumption timing prediction function can estimate the user's emotions and adjust the consumption timing prediction method based on the estimated emotions. For example, if the user is relaxed, a detailed consumption timing prediction can be made. If the user is in a hurry, a concise consumption timing prediction can be made. Furthermore, if the user is stressed, a simple consumption timing prediction can be made. In this way, the consumption timing prediction function can predict more appropriate consumption timing by adjusting the consumption timing prediction method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consumption timing prediction function may be performed using AI or not using AI. For example, the consumption timing prediction function can input user emotion data into a generative AI and have the generative AI adjust the consumption timing prediction method.

[0128] The suggestion unit can analyze the user's past purchase history and select the optimal suggestion method. For example, the suggestion unit can suggest the most suitable products based on the user's past purchase history. Furthermore, the suggestion unit can prioritize suggestion methods for specific products based on the user's past purchase history. In addition, the suggestion unit can analyze the user's past purchase history to select the most efficient suggestion method. Thus, the suggestion unit can provide the optimal suggestion method by analyzing the user's past purchase history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's purchase history data into a generating AI and have the generating AI select the optimal suggestion method.

[0129] The discovery unit can improve the accuracy of its search by considering the interrelationships between products when finding the lowest price. For example, the discovery unit can find the lowest price by comparing the prices of related products. It can also propose the optimal price by considering the interrelationships between products. Furthermore, the discovery unit can find the lowest price by analyzing the interrelationships between products. In this way, the discovery unit can improve the accuracy of finding the lowest price by considering the interrelationships between products. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input interrelationship data between products into a generating AI and have the generating AI perform the task of improving the accuracy of finding the lowest price.

[0130] The transaction completion unit can analyze the user's past purchase history and select the optimal transaction method when a transaction is completed. For example, the transaction completion unit can propose the optimal transaction procedure based on the user's past purchase history. Furthermore, the transaction completion unit can prioritize suggesting transaction procedures for specific products based on the user's past purchase history. In addition, the transaction completion unit can analyze the user's past purchase history and select the most efficient transaction procedure. Thus, the transaction completion unit can provide the optimal transaction procedure by analyzing the user's past purchase history. Some or all of the above-described processes in the transaction completion unit may be performed using AI, for example, or without AI. For example, the transaction completion unit can input the user's purchase history data into a generating AI and have the generating AI select the optimal transaction method.

[0131] The verification unit can provide the optimal verification method by referring to the user's past verification history during the verification process. For example, the verification unit can propose the optimal verification method based on the user's past verification history. Furthermore, the verification unit can prioritize suggesting verification methods for specific products based on the user's past verification history. In addition, the verification unit can analyze the user's past verification history to provide the most efficient verification method. Thus, the verification unit can provide the optimal verification procedure by referring to the user's past verification history. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's verification history data into a generating AI and have the generating AI perform the task of providing the optimal verification method.

[0132] The consumption timing prediction function can analyze the user's past consumption history to select the optimal prediction method when predicting consumption timing. For example, the consumption timing prediction function can predict the optimal consumption timing based on the user's past consumption history. Furthermore, the consumption timing prediction function can predict the consumption timing of a specific product based on the user's past consumption history. In addition, the consumption timing prediction function can analyze the user's past consumption history to predict the most efficient consumption timing. Thus, the consumption timing prediction function can predict the optimal consumption timing by analyzing the user's past consumption history. Some or all of the above-described processes in the consumption timing prediction function may be performed using AI, for example, or without AI. For example, the consumption timing prediction function can input the user's consumption history data into a generating AI and have the generating AI select the optimal prediction method.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The reception desk receives input from users. For example, a user can input "I want to buy XX" on the messenger to receive their needs. Step 2: The proposal department proposes the most suitable products based on the information received by the reception department. For example, the proposal department can propose the most suitable products by considering the user's preferences, past purchase history, current trends, etc. Step 3: The discovery unit automatically finds the lowest price based on the products suggested by the suggestion unit. For example, the discovery unit can find the lowest price using price comparison sites or specific algorithms. Step 4: The settlement unit completes the transaction based on the lowest price found by the discovery unit. For example, if the user decides to purchase the proposed product, the settlement unit can support the purchase process and confirm the purchase on the messenger. Step 5: The confirmation unit verifies the delivery date and time and delivery method based on the sale concluded by the settlement unit. For example, the confirmation unit can cooperate with the delivery company to provide a function for confirming the delivery date and time and delivery method.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the reception unit, proposal unit, discovery unit, completion unit, and confirmation unit, is implemented by, for example, at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives information entered by the user on the messenger. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing device 12 and proposes the optimal product based on the user's needs. The discovery unit is implemented by, for example, the specific processing unit 290 of the data processing device 12 and automatically finds the lowest price. The completion unit is implemented by, for example, the control unit 46A of the smart device 14 and completes the sale. The confirmation unit is implemented by, for example, the specific processing unit 290 of the data processing device 12 and confirms the delivery date and time and the method of receipt. 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.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the reception unit, proposal unit, discovery unit, completion unit, and confirmation unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives information entered by the user on the messenger. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing device 12 and proposes the optimal product based on the user's needs. The discovery unit is implemented by, for example, the specific processing unit 290 of the data processing device 12 and automatically finds the lowest price. The completion unit is implemented by, for example, the control unit 46A of the smart glasses 214 and completes the sale. The confirmation unit is implemented by, for example, the specific processing unit 290 of the data processing device 12 and confirms the delivery date and time and the method of receipt. 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.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the reception unit, proposal unit, discovery unit, completion unit, and confirmation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives information entered by the user on the messenger. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal product based on the user's needs. The discovery unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically finds the lowest price. The completion unit is implemented by, for example, the control unit 46A of the headset terminal 314 and completes the sale. The confirmation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and confirms the delivery date and time and the method of receipt. 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.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] Each of the multiple elements described above, including the reception unit, proposal unit, discovery unit, completion unit, and confirmation 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 information entered by the user on the messenger. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal product based on the user's needs. The discovery unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically finds the lowest price. The completion unit is implemented by, for example, the control unit 46A of the robot 414 and completes the sale. The confirmation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and confirms the delivery date and time and the method of receipt. 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] (Note 1) A system characterized by comprising: a reception unit that receives input from a user; a suggestion unit that proposes appropriate products based on the information received by the reception unit; a discovery unit that automatically finds the lowest price based on the products proposed by the suggestion unit; a settlement unit that settles a transaction based on the lowest price found by the discovery unit; and a confirmation unit that confirms the delivery date and time and receiving method based on the transaction settled by the settlement unit. (Note 2) It has a feature that predicts when an item will be consumed and runs out, and then has an agent contact the user. The system described in Appendix 1, characterized by the features described herein. (Note 3) Features an ON / OFF function for recommendations and bundled product suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The proposed unit is the system described in Appendix 1, characterized by its ability to automatically find the lowest price by linking with multiple shopping, reuse, and travel-related assets. (Note 5) The system described in Appendix 1 is characterized in that the confirmation unit cooperates with multiple delivery companies and provides a function to confirm the delivery date and time and the method of receipt. (Note 6) The aforementioned settlement unit is characterized by proposing, supporting, and settling transactions solely on the messenger screen, as described in Appendix 1. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving input, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, the system prioritizes accepting inputs 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 12) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the products are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned detection unit is We estimate user sentiment and adjust the criteria for finding the lowest price based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned detection unit is When finding the lowest price, we improve the accuracy of the search by considering the interrelationships between products. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned detection unit is When finding the lowest price, the system takes into account the attribute information of the person who submitted the product. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned detection unit is It estimates the user's sentiment and adjusts the order in which the results of the lowest price search are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned detection unit is When finding the lowest price, the geographical distribution of the product is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned detection unit is When finding the lowest price, we refer to related literature on the product to improve the accuracy of the search. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned building portion is, The system estimates user sentiment and adjusts the transaction process based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned building portion is, When a transaction is completed, the system analyzes the user's past purchase history to select the optimal transaction method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned building portion is, When a transaction is completed, the method of completion is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned building portion is, The system estimates user sentiment and determines the priority of transactions based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned building portion is, When a transaction is completed, the system selects the optimal method of completion, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned building portion is, When a transaction is completed, we analyze the user's social media activity and propose methods for completing the transaction. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned verification unit is We estimate the user's emotions and adjust the confirmation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned verification unit is During verification, the system refers to the user's past verification history to provide the most suitable verification method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned verification unit is During verification, the verification process is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned verification unit is The system estimates the user's emotions and determines the priority of confirmations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned verification unit is During verification, the system will provide the optimal verification method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned verification unit is During verification, we analyze the user's social media activity and suggest verification methods. The system described in Appendix 1, characterized by the features described herein. (Note 37) The consumption timing prediction function is We estimate the user's emotions and adjust the prediction method for consumption timing based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The consumption timing prediction function is When predicting consumption timing, the system analyzes the user's past consumption history to select the optimal prediction method. The system described in Appendix 2, characterized by the features described herein. (Note 39) The consumption timing prediction function is It estimates the user's emotions and determines the priority of consumption timing based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 40) The consumption timing prediction function is When predicting consumption timing, we provide the optimal prediction method by taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 41) The Recommended / Bundled Suggestion ON / OFF function is It estimates the user's emotions and adjusts how suggestions are turned on or off based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The Recommended / Bundled Suggestion ON / OFF function is When a user turns a suggestion ON / OFF, the system refers to their past suggestion history to provide the optimal ON / OFF method. The system described in Appendix 3, characterized by the features described herein. (Note 43) The Recommended / Bundled Suggestion ON / OFF function is It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 44) The Recommended / Bundled Suggestion ON / OFF function is When a suggestion is turned ON / OFF, the system provides the optimal ON / OFF method considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0207] 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 system characterized by comprising: a reception unit that receives input from a user; a suggestion unit that proposes appropriate products based on the information received by the reception unit; a discovery unit that automatically finds the lowest price based on the products proposed by the suggestion unit; a settlement unit that settles a transaction based on the lowest price found by the discovery unit; and a confirmation unit that confirms the delivery date and time and receiving method based on the transaction settled by the settlement unit.

2. It has a feature that predicts when an item will be consumed and runs out, and then has an agent contact the user. The system according to feature 1.

3. Features an ON / OFF function for recommendations and bundled product suggestions. The system according to feature 1.

4. The system according to claim 1, characterized in that the proposed unit links with multiple shopping, reuse, and travel-related assets and automatically finds the lowest price.

5. The system according to claim 1, characterized in that the confirmation unit cooperates with multiple delivery companies and provides a function to confirm the delivery date and time and the method of receipt.

6. The system according to claim 1, characterized in that the settlement unit proposes, supports, and settles trades only on the messenger screen.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system according to feature 1.

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

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

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