system

Abstract

The system according to this embodiment aims to learn the user's preferences and purchase history, and to suggest and support the purchase of the most suitable products. [Solution] The system according to the embodiment comprises a collection unit, a learning unit, a suggestion unit, and a support unit. The collection unit collects user data. The learning unit analyzes the data collected by the collection unit and learns the user's preferences and purchase history. The suggestion unit suggests the most suitable product based on the information learned by the learning unit. The support unit provides support for purchasing the product suggested by the suggestion 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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, it has not been fully carried out to propose optimal products based on the user's preferences and purchase history, and there is room for improvement.

[0005] The system according to the embodiment aims to learn the user's preferences and purchase history and propose and support the purchase of optimal products.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a learning unit, a suggestion unit, and a support unit. The data collection unit collects user data. The learning unit analyzes the data collected by the data collection unit and learns the user's preferences and purchase history. The suggestion unit suggests the most suitable products based on the information learned by the learning unit. The support unit provides support for purchasing the products suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can learn the user's preferences and purchase history, and then suggest and support the purchase of the most suitable products. [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 controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that learns the user's preferences and past purchase history and proposes and supports the purchase of necessary and desired items. This AI agent system learns the user's preferences and past purchase history and proposes and supports the purchase of necessary and desired items. For example, the AI ​​agent system learns the user's preferences and past purchase history. In this process, data such as SNS posts, search history, and purchase history from e-commerce sites are collected and analyzed by the AI. For example, if a user posts on SNS that they "want a new smartphone," the AI ​​learns this information and understands that the user is interested in smartphones. Also, if a user frequently purchases products of a particular brand from an e-commerce site, the AI ​​learns the user's preference for that brand. Next, based on the learned information, the AI ​​proposes the most suitable products to the user. For example, if the user is interested in smartphones, the AI ​​proposes the latest smartphones and related accessories. Also, if the user prefers products from a particular brand, the AI ​​proposes new products and sale information from that brand. Furthermore, based on the user's purchase history and hobbies, the AI ​​proposes products at the optimal timing, tailored to the season and sale periods. Furthermore, the AI ​​provides support for purchasing the proposed products. Specifically, the AI ​​compares and considers products across e-commerce sites used and selects the most suitable product. Selected products are automatically added to the user's shopping cart on the e-commerce site they are using, allowing them to complete the purchase with a single click. For example, when a user purchases daily necessities, the AI ​​suggests products at the optimal time based on sales information and the season, and adds them to the cart. This system makes it easy for users to find and purchase what they need and want. In particular, it saves busy people and those who dislike shopping the trouble of purchasing daily necessities at the right time and searching for what they want. Furthermore, because the AI ​​learns the user's preferences and suggests the most suitable products, users can easily find products that suit their tastes and budget. In this way, the AI ​​agent system can learn the user's preferences and purchase history, suggest the most suitable products, and support their purchase.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, a learning unit, a suggestion unit, and a support unit. The collection unit collects user data. User data includes, but is not limited to, personal information, behavioral history, and purchase history. The collection unit collects data such as SNS posts, search history, and purchase history from e-commerce sites. The collection unit collects SNS posts, search history, and purchase history from e-commerce sites to understand the user's preferences and interests. For example, if a user posts on SNS that they "want a new smartphone," the collection unit collects that information. The collection unit also collects purchase history if a user frequently purchases products of a particular brand from an e-commerce site. The learning unit analyzes the data collected by the collection unit and learns the user's preferences and purchase history. The learning unit analyzes the data using methods such as data mining, statistical analysis, and machine learning. The learning unit learns the user's preferences and purchase history to understand the products and brands that the user is interested in. For example, the learning unit analyzes information such as a user's social media post stating "I want a new smartphone" and learns that the user is interested in smartphones. The learning unit also learns the user's preference for a particular brand if the user frequently purchases products from that brand on an e-commerce site. The suggestion unit proposes the most suitable products based on the information learned by the learning unit. The suggestion unit selects the best products based on criteria such as user preferences, price, and quality. The suggestion unit also suggests products and brands that the user is interested in. For example, if the user is interested in smartphones, the suggestion unit suggests the latest smartphones and related accessories. Furthermore, if the user prefers a particular brand, the suggestion unit suggests new products and sales information from that brand. In addition, the suggestion unit suggests products at the optimal time, tailored to the season and sales periods, based on the user's purchase history and preferences. The support unit provides support for purchasing the products suggested by the suggestion unit. The support unit provides support through methods such as assisting with the purchase process and customer support. The support unit compares and selects the most suitable products across different e-commerce sites.The support unit automatically adds selected products to the user's shopping cart on the e-commerce site. The support unit provides assistance to help the user complete the purchase with a single click. For example, when a user purchases daily necessities, the support unit suggests products at the optimal time based on sales information and the season, and adds them to the cart. In this way, the AI ​​agent system according to the embodiment can learn the user's preferences and purchase history, suggest the most suitable products, and support the purchase.

[0030] The data collection unit collects user data. User data includes, but is not limited to, personal information, behavioral history, and purchase history. The data collection unit collects data such as social media activity, search history, and e-commerce site purchase history. Specifically, the data collection unit collects the content of posts made by users on social media, search keywords used in search engines, and purchase history on e-commerce sites. This allows the unit to understand the user's preferences and interests. For example, if a user posts on social media that they "want a new smartphone," the data collection unit collects that information. The data collection unit also collects the purchase history of users who frequently purchase products of a particular brand on e-commerce sites. Furthermore, the data collection unit also collects the user's browsing history and click history to understand in detail what kinds of products the user is interested in. The data collection unit centrally manages this data and stores it in a database. Since the collected data is used by the subsequent learning and suggestion units, it is important to maintain the accuracy and timeliness of the data. The data collection unit can flexibly adjust the frequency and method of data collection, and can quickly respond to changes in user behavior patterns and interests. For example, during specific events or campaigns, increasing the frequency of data collection allows for the acquisition of more detailed user behavior data. This enables the data collection unit to efficiently collect diverse user data and improve the overall system performance.

[0031] The learning unit analyzes data collected by the collection unit to learn user preferences and purchase history. The learning unit analyzes data using methods such as data mining, statistical analysis, and machine learning. Specifically, the learning unit preprocesses the collected data, performing data cleansing and normalization. Next, it uses machine learning algorithms to analyze user behavior patterns and interest trends. For example, it uses clustering techniques to group users based on their interests and purchase history, and extracts the characteristics of each group. It also uses collaborative filtering and content-based recommendation systems to recommend relevant products based on products the user has previously purchased or viewed. The learning unit learns user preferences and purchase history to understand the products and brands that users are interested in. For example, the learning unit analyzes information such as a user posting "I want a new smartphone" on social media, learning that the user is interested in smartphones. Furthermore, if a user frequently purchases products from a specific brand on an e-commerce site, the learning unit learns the user's preference for that brand. In addition, the learning unit can analyze user behavior data over time to understand changes in user interests and purchase trends. This allows the learning unit to accurately understand the user's preferences and interests and provide the proposal unit with information to make optimal product recommendations.

[0032] The suggestion unit proposes the most suitable products based on information learned by the learning unit. For example, the suggestion unit selects the best products based on criteria such as user preferences, price, and quality. Specifically, the suggestion unit considers the user's preferences and purchase history to suggest products and brands that the user is interested in. For example, if the user is interested in smartphones, the suggestion unit will suggest the latest smartphones and related accessories. Also, if the user prefers a particular brand, the suggestion unit will suggest new products and sales information from that brand. Furthermore, based on the user's purchase history and hobbies, the suggestion unit suggests products at the optimal time, tailored to the season and sales periods. The suggestion unit uses AI to analyze the user's interests and purchase history to select the most relevant products. For example, it uses natural language processing technology to analyze the user's social media posts and identify products and brands that the user is interested in. The suggestion unit also suggests related products and accessories based on the user's past purchase history. For example, if a user purchases a smartphone from a specific brand, it will suggest compatible cases, chargers, and other accessories for that smartphone. Furthermore, the suggestion unit can provide personalized product suggestions based on the user's interests and purchase history. This allows the proposal department to suggest the most suitable products for users, thereby increasing their desire to purchase.

[0033] The Support Department provides support for purchasing products recommended by the Proposal Department. This support includes methods such as assisting with the purchase process and providing customer support. Specifically, the Support Department assists with the purchase process to ensure users can smoothly purchase recommended products. For example, the Support Department compares products across different e-commerce sites and selects the most suitable product. The Support Department automatically adds the selected product to the user's shopping cart on the e-commerce site. This allows users to complete the purchase with a single click. The Support Department also provides real-time customer support for any questions or problems users may encounter during the purchase process. For example, it uses chatbots and voice assistants to quickly answer user questions and support the purchase process. Furthermore, the Support Department can suggest relevant products and services based on the user's purchase history and preferences. For example, if a user is purchasing daily necessities, the Support Department can suggest products at the optimal time based on sales information and the season, and add them to the cart. This allows the Support Department to improve the user's purchasing experience and increase user satisfaction. Additionally, the Support Department can continuously improve the overall system performance by collecting user feedback and providing it to the Proposal and Learning Departments. This allows the support department to provide users with prompt and reliable support, thereby improving the user's purchasing experience.

[0034] The data collection unit can collect data such as social media activity, search history, and e-commerce purchase history. For example, the data collection unit can collect social media posts. For instance, it can collect information about a user's social media post stating, "I want a new smartphone." The data collection unit can also collect search history. For example, it can collect a user's search history for "recommended smartphones" on a search engine. The data collection unit can also collect e-commerce purchase history. For example, if a user frequently purchases products from a particular brand on an e-commerce site, the data collection unit can collect that purchase history. This allows for more accurate learning by collecting diverse user data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media posts into a generating AI and have the generating AI collect data to understand the user's interests.

[0035] The suggestion unit can suggest products at the optimal time, tailored to the season and sales periods, based on the user's purchase history and preferences. For example, the suggestion unit can suggest seasonal products based on the user's purchase history. For instance, if the user has previously purchased winter clothing, the suggestion unit will suggest new winter clothing to coincide with the winter season. The suggestion unit can also suggest products tailored to sales periods based on the user's preferences. For example, if the user prefers products from a particular brand, the suggestion unit will suggest sales information for that brand. The suggestion unit can also suggest products at the optimal time, based on the user's purchase history and preferences. For example, if the user is interested in smartphones, the suggestion unit will suggest the latest smartphones and related accessories. By suggesting products at the optimal time, the suggestion unit can increase the user's desire to purchase. Some or all of the above processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's purchase history into a generating AI and have the generating AI generate product suggestions tailored to the season and sales periods.

[0036] The support department can compare and evaluate products across e-commerce sites and select the most suitable product. For example, the support department can perform price comparisons. For instance, it can compare the prices of the same product across multiple e-commerce sites and select the site with the lowest price. The support department can also select products based on customer reviews. For example, it can compare customer reviews across multiple e-commerce sites and select the product with the highest rating. Furthermore, the support department can compare product features. For example, it can compare the features of products across multiple e-commerce sites and select the product with the most comprehensive features. In this way, by comparing multiple e-commerce sites, the support department can select the most suitable product for the user. Some or all of the above processes performed by the support department may be carried out using AI, for example, or not. For example, the support department can input data from multiple e-commerce sites into a generating AI and have the generating AI perform the process of selecting the most suitable product based on price comparisons and customer reviews.

[0037] The support unit can automatically add selected products to the user's shopping cart on the e-commerce site they are using. For example, the support unit can add products to the cart with the user's permission. For example, the support unit can configure the system to allow the user to add selected products to their cart. The support unit can also automatically add products to the cart under specific conditions. For example, the support unit can configure the system to automatically add products that the user frequently purchases to the cart. The support unit can also add products to the cart based on conditions set by the user. For example, the support unit can add selected products to the cart within the user's set budget. This saves the user time and allows them to complete their purchase smoothly. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input selected products into a generating AI and have the generating AI execute the process of automatically adding them to the cart.

[0038] The support unit can provide assistance to users to complete purchases with a single click. The support unit can, for example, use pre-configured payment information. For example, the support unit can use the user's pre-configured credit card information to complete a purchase with a single click. The support unit can also build a system that allows users to complete a purchase simply by pressing a specific button. For example, the support unit can complete the purchase process simply by the user pressing a "Purchase" button. The support unit can also complete purchases based on conditions set by the user. For example, the support unit can complete a purchase within the user's budget. This allows users to easily complete purchases. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's payment information into a generating AI and have the generating AI execute the process of completing a purchase with a single click.

[0039] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize data collection methods that the user has frequently used in the past. For instance, if the user has frequently collected social media posts in the past, the unit will prioritize collecting social media posts. The data collection unit can also suggest the optimal collection method based on the types of data the user has collected in the past. For example, if the user has frequently collected e-commerce purchase history in the past, the unit will prioritize collecting e-commerce purchase history. The data collection unit can also select the optimal collection method for a specific time of day based on the user's past data collection history. For example, if the user has collected data at night in the past, the unit will collect data at night. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI perform the process of selecting the optimal collection method.

[0040] The data collection unit can filter data based on the user's current areas of interest and lifestyle. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. For example, the data collection unit can collect social media posts related to topics the user is currently interested in. The data collection unit can also filter data based on the user's lifestyle to determine its relevance. For example, the data collection unit can collect the user's purchase history from e-commerce sites related to their current lifestyle. The data collection unit can also exclude unnecessary data based on the user's current areas of interest. For example, the data collection unit can exclude data related to topics the user is not currently interested in. This enables data collection tailored to the user's areas of interest and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's current areas of interest and lifestyle into a generating AI and have the generating AI perform the filtering process.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, when the user is in a specific region, the data collection unit can collect social media posts related to that region. The data collection unit can also prioritize the collection of data related to the travel destination if the user is traveling. For example, when the user is traveling, the data collection unit can collect purchase history from e-commerce sites related to the travel destination. The data collection unit can also prioritize the collection of data around the user's home if the user is at home. For example, when the user is at home, the data collection unit can collect data related to the user's home. This allows for the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the process of prioritizing the collection of highly relevant data.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also collect relevant data based on the content of posts from accounts the user follows. The data collection unit can also collect relevant data based on the activities of groups and communities the user participates in. This allows for the collection of relevant data based on the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's social media activity into a generating AI and have the generating AI perform the process of collecting relevant data.

[0043] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and select the optimal learning algorithm. The learning unit can also analyze patterns in past learning data and adjust the learning algorithm. For example, the learning unit can analyze patterns in past learning data and adjust the learning algorithm. The learning unit can also evaluate the accuracy of past learning data and improve the learning algorithm. For example, the learning unit can evaluate the accuracy of past learning data and improve the learning algorithm. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the process of optimizing the learning algorithm.

[0044] The learning unit can improve the accuracy of its learning based on the user's purchase history and preferences during the learning process. For example, the learning unit can prioritize learning relevant data based on the user's purchase history. The learning unit can also customize the learning data based on the user's preferences. The learning unit can also improve the accuracy of its learning by combining the user's purchase history and preferences. This allows the learning unit to improve the accuracy of its learning based on the user's purchase history and preferences. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on the user's purchase history and preferences into a generating AI and have the generating AI perform processes to improve the accuracy of its learning.

[0045] The learning unit can select the optimal learning method during learning, taking into account the user's device information. For example, if the user is using a smartphone, the learning unit will select a learning method optimized for smartphones. The learning unit can also select a learning method optimized for tablets if the user is using a tablet. The learning unit can also select a learning method optimized for desktops if the user is using a desktop. This allows the learning unit to select the optimal learning method based on the user's device information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's device information into a generating AI and have the generating AI perform the process of selecting the optimal learning method.

[0046] The learning unit can analyze the user's social media activity during training and incorporate relevant data into its learning. For example, the learning unit can incorporate information shared by the user on social media into its learning data. The learning unit can also incorporate posts from accounts the user follows into its learning data. The learning unit can also incorporate activities from groups and communities the user participates in into its learning data. This allows the learning unit to incorporate relevant data based on the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data about the user's social media activity into a generating AI and have the generating AI perform the process of incorporating relevant data into its learning.

[0047] The proposal unit can adjust the level of detail in its proposals based on the importance of the products. For example, the proposal unit can provide detailed information for important products. The proposal unit can also provide concise information for less important products. The proposal unit can also adjust the level of detail in its proposals according to the importance of the products. By adjusting the level of detail in proposals according to the importance of the products, it can provide users with information that is important to them. 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 data on the importance of products into a generating AI and have the generating AI perform the process of adjusting the level of detail in the proposals.

[0048] 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 can apply a suggestion algorithm that provides technical details. For example, the suggestion unit can apply a suggestion algorithm that provides technical details about electronic devices. The suggestion unit can also apply a visually-oriented suggestion algorithm for fashion items. For example, the suggestion unit can apply a visually-oriented suggestion algorithm for fashion items. The suggestion unit can also apply a practicality-oriented suggestion algorithm for everyday goods. For example, the suggestion unit can apply a practicality-oriented suggestion algorithm for everyday goods. By applying a suggestion algorithm according to the product category, more appropriate suggestions can be made. 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 data about the product category into a generating AI and have the generating AI execute the process of applying different suggestion algorithms.

[0049] The proposal department can determine the priority of proposals based on the timing of product submission. For example, the proposal department may prioritize new products. The proposal department may also prioritize products that are on sale. The proposal department may also prioritize seasonal products according to the season. By determining the priority of proposals based on the timing of product submission, important information can be provided to the user. 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 data on the timing of product submission into a generating AI and have the generating AI perform the process of determining the priority of proposals.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the products. For example, the suggestion unit might suggest the product most relevant to the user's interests first. The suggestion unit can also prioritize suggesting highly relevant products based on the user's purchase history. The suggestion unit can also prioritize suggesting highly relevant products based on the user's preferences. By adjusting the order of suggestions based on the relevance of the products, the suggestion unit can provide the user with important information. 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 data on product relevance into a generating AI and have the generating AI perform the process of adjusting the order of suggestions.

[0051] The support department can analyze the user's past purchasing behavior and select the optimal support method when providing purchase support. For example, the support department may prioritize purchase support methods the user has used in the past. The support department can also propose the optimal support method based on the user's past purchasing behavior. The support department can also analyze the user's past purchasing behavior and select the optimal support method. This allows the support department to select the optimal support method by analyzing the user's past purchasing behavior. Some or all of the above processes in the support department may be performed using AI, for example, or without AI. For example, the support department can input data on the user's past purchasing behavior into a generating AI and have the generating AI perform the process of selecting the optimal support method.

[0052] The support unit can customize the means of support provided during purchase support based on the user's current living situation. For example, if the user is busy, the support unit can provide concise support. For example, if the support unit provides concise support when the user is busy, the support unit can provide detailed support when the user is relaxed, for example, if the user is stressed, the support unit can provide support that helps reduce stress. For example, if the support unit provides support that helps reduce stress when the user is stressed, for example, the support unit can provide support that helps reduce stress when the user is stressed. By customizing the means of support based on the user's current living situation, more appropriate support becomes possible. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input data about the user's current living situation into a generating AI and have the generating AI perform the process of customizing the means of support.

[0053] The support unit can select the optimal support method when providing purchase support, taking into account the user's geographical location information. For example, if the user is in a specific region, the support unit can provide support methods relevant to that region. The support unit can also provide support methods relevant to the user's travel destination if the user is traveling. The support unit can also provide support methods relevant to the user's travel destination if the user is at home. This allows the support unit to select the optimal support method based on the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information into a generating AI and have the generating AI perform the process of selecting the optimal support method.

[0054] The support department can analyze a user's social media activity and propose support measures when providing purchase support. For example, the support department can propose relevant support measures based on information shared by the user on social media. The support department can also propose relevant support measures based on the content of posts from accounts the user follows. The support department can also propose relevant support measures based on the activities of groups and communities the user participates in. This allows the support department to propose relevant support measures based on the user's social media activity. Some or all of the above processing in the support department may be performed using AI, for example, or without AI. For example, the support department can input data on the user's social media activity into a generating AI and have the generating AI perform the process of proposing support measures.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can adjust the timing of data collection based on the user's device usage. For example, if the user is using a smartphone, the unit can collect data at that time. Similarly, if the user is using a PC, the unit can collect data at that time. Furthermore, if the user is using a tablet, the unit can collect data at that time. This allows for data collection at the optimal time according to the user's device usage, resulting in more accurate data collection.

[0057] The support department can analyze a user's past purchase history and select the most suitable purchase support method. For example, it can prioritize purchase support methods that the user has used in the past. It can also suggest the most suitable purchase support method based on the user's past purchase history. Furthermore, by analyzing the user's past purchase history and selecting the most suitable purchase support method, it is possible to select the most suitable purchase support method and increase the user's willingness to purchase.

[0058] The data collection unit can adjust the timing of data collection considering the user's geographical location. For example, if the user is in a specific region, it can prioritize collecting data related to that region. Similarly, if the user is traveling, it can prioritize collecting data related to their travel destination. Furthermore, if the user is at home, it can prioritize collecting data around their home. This allows for data collection at the optimal time based on the user's geographical location, resulting in more accurate data collection.

[0059] The suggestion function can suggest relevant products based on the user's purchase history. For example, it can suggest products related to items the user has previously purchased. It can also suggest relevant products based on the user's purchase history. Furthermore, it can analyze the user's purchase history and suggest relevant products. This allows for increased purchase intent by suggesting relevant products based on the user's purchase history.

[0060] The suggestion department can suggest relevant products based on the user's preferences. For example, if a user prefers a particular brand, it can suggest products from that brand. It can also suggest relevant products based on the user's preferences. Furthermore, it can analyze the user's preferences and suggest relevant products. By suggesting relevant products based on the user's preferences, it is possible to increase their purchase intent.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects user data. User data includes, for example, personal information, behavioral history, and purchase history. The data collection unit collects data such as social media activity, search history, and purchase history from e-commerce sites to understand user preferences and interests. For example, if a user posts on social media that they "want a new smartphone," that information is collected. Also, if a user frequently purchases products from a particular brand on an e-commerce site, that purchase history is collected. Step 2: The learning unit analyzes the data collected by the collection unit to learn user preferences and purchase history. The learning unit analyzes the data using methods such as data mining, statistical analysis, and machine learning to understand the products and brands that users are interested in. For example, it analyzes information such as a user posting "I want a new smartphone" on social media to learn that the user is interested in smartphones. Also, if a user frequently purchases products from a particular brand on an e-commerce site, it learns the user's preference for that brand. Step 3: The suggestion unit proposes the most suitable products based on the information learned by the learning unit. The suggestion unit selects the most suitable products based on criteria such as user preferences, price, and quality, and proposes products and brands that the user is interested in. For example, if the user is interested in smartphones, it will propose the latest smartphones and related accessories. Also, if the user prefers products from a particular brand, it will propose new products and sales information from that brand. Furthermore, based on the user's purchase history and hobbies, it proposes products at the optimal time, tailored to the season and sales periods. Step 4: The support department provides support for purchasing the products suggested by the proposal department. The support department provides support through methods such as assisting with the purchase process and customer support. The support department compares products across the e-commerce sites used, selects the most suitable product, and automatically adds the selected product to the user's shopping cart on the e-commerce site they are using. They provide support to enable the user to complete the purchase with one click. For example, if a user is purchasing daily necessities, the support department will suggest products at the optimal time based on sales information and the season, and add them to the cart.

[0063] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that learns the user's preferences and past purchase history and proposes and supports the purchase of necessary and desired items. This AI agent system learns the user's preferences and past purchase history and proposes and supports the purchase of necessary and desired items. For example, the AI ​​agent system learns the user's preferences and past purchase history. In this process, data such as SNS posts, search history, and purchase history from e-commerce sites are collected and analyzed by the AI. For example, if a user posts on SNS that they "want a new smartphone," the AI ​​learns this information and understands that the user is interested in smartphones. Also, if a user frequently purchases products of a particular brand from an e-commerce site, the AI ​​learns the user's preference for that brand. Next, based on the learned information, the AI ​​proposes the most suitable products to the user. For example, if the user is interested in smartphones, the AI ​​proposes the latest smartphones and related accessories. Also, if the user prefers products from a particular brand, the AI ​​proposes new products and sale information from that brand. Furthermore, based on the user's purchase history and hobbies, the AI ​​proposes products at the optimal timing, tailored to the season and sale periods. Furthermore, the AI ​​provides support for purchasing the proposed products. Specifically, the AI ​​compares and considers products across e-commerce sites used and selects the most suitable product. Selected products are automatically added to the user's shopping cart on the e-commerce site they are using, allowing them to complete the purchase with a single click. For example, when a user purchases daily necessities, the AI ​​suggests products at the optimal time based on sales information and the season, and adds them to the cart. This system makes it easy for users to find and purchase what they need and want. In particular, it saves busy people and those who dislike shopping the trouble of purchasing daily necessities at the right time and searching for what they want. Furthermore, because the AI ​​learns the user's preferences and suggests the most suitable products, users can easily find products that suit their tastes and budget. In this way, the AI ​​agent system can learn the user's preferences and purchase history, suggest the most suitable products, and support their purchase.

[0064] The AI ​​agent system according to this embodiment comprises a collection unit, a learning unit, a suggestion unit, and a support unit. The collection unit collects user data. User data includes, but is not limited to, personal information, behavioral history, and purchase history. The collection unit collects data such as SNS posts, search history, and purchase history from e-commerce sites. The collection unit collects SNS posts, search history, and purchase history from e-commerce sites to understand the user's preferences and interests. For example, if a user posts on SNS that they "want a new smartphone," the collection unit collects that information. The collection unit also collects purchase history if a user frequently purchases products of a particular brand from an e-commerce site. The learning unit analyzes the data collected by the collection unit and learns the user's preferences and purchase history. The learning unit analyzes the data using methods such as data mining, statistical analysis, and machine learning. The learning unit learns the user's preferences and purchase history to understand the products and brands that the user is interested in. For example, the learning unit analyzes information such as a user's social media post stating "I want a new smartphone" and learns that the user is interested in smartphones. The learning unit also learns the user's preference for a particular brand if the user frequently purchases products from that brand on an e-commerce site. The suggestion unit proposes the most suitable products based on the information learned by the learning unit. The suggestion unit selects the best products based on criteria such as user preferences, price, and quality. The suggestion unit also suggests products and brands that the user is interested in. For example, if the user is interested in smartphones, the suggestion unit suggests the latest smartphones and related accessories. Furthermore, if the user prefers a particular brand, the suggestion unit suggests new products and sales information from that brand. In addition, the suggestion unit suggests products at the optimal time, tailored to the season and sales periods, based on the user's purchase history and preferences. The support unit provides support for purchasing the products suggested by the suggestion unit. The support unit provides support through methods such as assisting with the purchase process and customer support. The support unit compares and selects the most suitable products across different e-commerce sites.The support unit automatically adds selected products to the user's shopping cart on the e-commerce site. The support unit provides assistance to help the user complete the purchase with a single click. For example, when a user purchases daily necessities, the support unit suggests products at the optimal time based on sales information and the season, and adds them to the cart. In this way, the AI ​​agent system according to the embodiment can learn the user's preferences and purchase history, suggest the most suitable products, and support the purchase.

[0065] The data collection unit collects user data. User data includes, but is not limited to, personal information, behavioral history, and purchase history. The data collection unit collects data such as social media activity, search history, and e-commerce site purchase history. Specifically, the data collection unit collects the content of posts made by users on social media, search keywords used in search engines, and purchase history on e-commerce sites. This allows the unit to understand the user's preferences and interests. For example, if a user posts on social media that they "want a new smartphone," the data collection unit collects that information. The data collection unit also collects the purchase history of users who frequently purchase products of a particular brand on e-commerce sites. Furthermore, the data collection unit also collects the user's browsing history and click history to understand in detail what kinds of products the user is interested in. The data collection unit centrally manages this data and stores it in a database. Since the collected data is used by the subsequent learning and suggestion units, it is important to maintain the accuracy and timeliness of the data. The data collection unit can flexibly adjust the frequency and method of data collection, and can quickly respond to changes in user behavior patterns and interests. For example, during specific events or campaigns, increasing the frequency of data collection allows for the acquisition of more detailed user behavior data. This enables the data collection unit to efficiently collect diverse user data and improve the overall system performance.

[0066] The learning unit analyzes data collected by the collection unit to learn user preferences and purchase history. The learning unit analyzes data using methods such as data mining, statistical analysis, and machine learning. Specifically, the learning unit preprocesses the collected data, performing data cleansing and normalization. Next, it uses machine learning algorithms to analyze user behavior patterns and interest trends. For example, it uses clustering techniques to group users based on their interests and purchase history, and extracts the characteristics of each group. It also uses collaborative filtering and content-based recommendation systems to recommend relevant products based on products the user has previously purchased or viewed. The learning unit learns user preferences and purchase history to understand the products and brands that users are interested in. For example, the learning unit analyzes information such as a user posting "I want a new smartphone" on social media, learning that the user is interested in smartphones. Furthermore, if a user frequently purchases products from a specific brand on an e-commerce site, the learning unit learns the user's preference for that brand. In addition, the learning unit can analyze user behavior data over time to understand changes in user interests and purchase trends. This allows the learning unit to accurately understand the user's preferences and interests and provide the proposal unit with information to make optimal product recommendations.

[0067] The suggestion unit proposes the most suitable products based on information learned by the learning unit. For example, the suggestion unit selects the best products based on criteria such as user preferences, price, and quality. Specifically, the suggestion unit considers the user's preferences and purchase history to suggest products and brands that the user is interested in. For example, if the user is interested in smartphones, the suggestion unit will suggest the latest smartphones and related accessories. Also, if the user prefers a particular brand, the suggestion unit will suggest new products and sales information from that brand. Furthermore, based on the user's purchase history and hobbies, the suggestion unit suggests products at the optimal time, tailored to the season and sales periods. The suggestion unit uses AI to analyze the user's interests and purchase history to select the most relevant products. For example, it uses natural language processing technology to analyze the user's social media posts and identify products and brands that the user is interested in. The suggestion unit also suggests related products and accessories based on the user's past purchase history. For example, if a user purchases a smartphone from a specific brand, it will suggest compatible cases, chargers, and other accessories for that smartphone. Furthermore, the suggestion unit can provide personalized product suggestions based on the user's interests and purchase history. This allows the proposal department to suggest the most suitable products for users, thereby increasing their desire to purchase.

[0068] The Support Department provides support for purchasing products recommended by the Proposal Department. This support includes methods such as assisting with the purchase process and providing customer support. Specifically, the Support Department assists with the purchase process to ensure users can smoothly purchase recommended products. For example, the Support Department compares products across different e-commerce sites and selects the most suitable product. The Support Department automatically adds the selected product to the user's shopping cart on the e-commerce site. This allows users to complete the purchase with a single click. The Support Department also provides real-time customer support for any questions or problems users may encounter during the purchase process. For example, it uses chatbots and voice assistants to quickly answer user questions and support the purchase process. Furthermore, the Support Department can suggest relevant products and services based on the user's purchase history and preferences. For example, if a user is purchasing daily necessities, the Support Department can suggest products at the optimal time based on sales information and the season, and add them to the cart. This allows the Support Department to improve the user's purchasing experience and increase user satisfaction. Additionally, the Support Department can continuously improve the overall system performance by collecting user feedback and providing it to the Proposal and Learning Departments. This allows the support department to provide users with prompt and reliable support, thereby improving the user's purchasing experience.

[0069] The data collection unit can collect data such as social media activity, search history, and e-commerce purchase history. For example, the data collection unit can collect social media posts. For instance, it can collect information about a user's social media post stating, "I want a new smartphone." The data collection unit can also collect search history. For example, it can collect a user's search history for "recommended smartphones" on a search engine. The data collection unit can also collect e-commerce purchase history. For example, if a user frequently purchases products from a particular brand on an e-commerce site, the data collection unit can collect that purchase history. This allows for more accurate learning by collecting diverse user data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media posts into a generating AI and have the generating AI collect data to understand the user's interests.

[0070] The suggestion unit can suggest products at the optimal time, tailored to the season and sales periods, based on the user's purchase history and preferences. For example, the suggestion unit can suggest seasonal products based on the user's purchase history. For instance, if the user has previously purchased winter clothing, the suggestion unit will suggest new winter clothing to coincide with the winter season. The suggestion unit can also suggest products tailored to sales periods based on the user's preferences. For example, if the user prefers products from a particular brand, the suggestion unit will suggest sales information for that brand. The suggestion unit can also suggest products at the optimal time, based on the user's purchase history and preferences. For example, if the user is interested in smartphones, the suggestion unit will suggest the latest smartphones and related accessories. By suggesting products at the optimal time, the suggestion unit can increase the user's desire to purchase. Some or all of the above processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's purchase history into a generating AI and have the generating AI generate product suggestions tailored to the season and sales periods.

[0071] The support department can compare and evaluate products across e-commerce sites and select the most suitable product. For example, the support department can perform price comparisons. For instance, it can compare the prices of the same product across multiple e-commerce sites and select the site with the lowest price. The support department can also select products based on customer reviews. For example, it can compare customer reviews across multiple e-commerce sites and select the product with the highest rating. Furthermore, the support department can compare product features. For example, it can compare the features of products across multiple e-commerce sites and select the product with the most comprehensive features. In this way, by comparing multiple e-commerce sites, the support department can select the most suitable product for the user. Some or all of the above processes performed by the support department may be carried out using AI, for example, or not. For example, the support department can input data from multiple e-commerce sites into a generating AI and have the generating AI perform the process of selecting the most suitable product based on price comparisons and customer reviews.

[0072] The support unit can automatically add selected products to the user's shopping cart on the e-commerce site they are using. For example, the support unit can add products to the cart with the user's permission. For example, the support unit can configure the system to allow the user to add selected products to their cart. The support unit can also automatically add products to the cart under specific conditions. For example, the support unit can configure the system to automatically add products that the user frequently purchases to the cart. The support unit can also add products to the cart based on conditions set by the user. For example, the support unit can add selected products to the cart within the user's set budget. This saves the user time and allows them to complete their purchase smoothly. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input selected products into a generating AI and have the generating AI execute the process of automatically adding them to the cart.

[0073] The support unit can provide assistance to users to complete purchases with a single click. The support unit can, for example, use pre-configured payment information. For example, the support unit can use the user's pre-configured credit card information to complete a purchase with a single click. The support unit can also build a system that allows users to complete a purchase simply by pressing a specific button. For example, the support unit can complete the purchase process simply by the user pressing a "Purchase" button. The support unit can also complete purchases based on conditions set by the user. For example, the support unit can complete a purchase within the user's budget. This allows users to easily complete purchases. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's payment information into a generating AI and have the generating AI execute the process of completing a purchase with a single click.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect data during times when the user is relaxed. For example, the data collection unit will collect data at night when the user is relaxed. The data collection unit can also collect data immediately and reflect it in real time if the user is excited. For example, the data collection unit will collect social media posts when the user is excited. The data collection unit can also collect data after the user has rested if the user is tired. For example, the data collection unit will collect purchase history from an e-commerce site after the user has rested. By adjusting the timing of data collection according to the user's emotions, more appropriate data collection becomes possible. 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI perform a process to adjust the timing of data collection.

[0075] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize data collection methods that the user has frequently used in the past. For instance, if the user has frequently collected social media posts in the past, the unit will prioritize collecting social media posts. The data collection unit can also suggest the optimal collection method based on the types of data the user has collected in the past. For example, if the user has frequently collected e-commerce purchase history in the past, the unit will prioritize collecting e-commerce purchase history. The data collection unit can also select the optimal collection method for a specific time of day based on the user's past data collection history. For example, if the user has collected data at night in the past, the unit will collect data at night. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI perform the process of selecting the optimal collection method.

[0076] The data collection unit can filter data based on the user's current areas of interest and lifestyle. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. For example, the data collection unit can collect social media posts related to topics the user is currently interested in. The data collection unit can also filter data based on the user's lifestyle to determine its relevance. For example, the data collection unit can collect the user's purchase history from e-commerce sites related to their current lifestyle. The data collection unit can also exclude unnecessary data based on the user's current areas of interest. For example, the data collection unit can exclude data related to topics the user is not currently interested in. This enables data collection tailored to the user's areas of interest and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's current areas of interest and lifestyle into a generating AI and have the generating AI perform the filtering process.

[0077] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting important data in real time. For example, if the user is excited, the data collection unit will prioritize collecting social media posts. The data collection unit can also prioritize collecting data that is important in the long term if the user is relaxed. For example, if the user is relaxed, the data collection unit will prioritize collecting purchase history from e-commerce sites. The data collection unit can also prioritize collecting data that helps reduce stress if the user is stressed. For example, if the data collection unit is stressed, the data collection unit will prioritize collecting data related to relaxation. In this way, important data can be prioritized by determining the priority of data 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI perform a process to determine the priority of the data to be collected.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, when the user is in a specific region, the data collection unit can collect social media posts related to that region. The data collection unit can also prioritize the collection of data related to the travel destination if the user is traveling. For example, when the user is traveling, the data collection unit can collect purchase history from e-commerce sites related to the travel destination. The data collection unit can also prioritize the collection of data around the user's home if the user is at home. For example, when the user is at home, the data collection unit can collect data related to the user's home. This allows for the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the process of prioritizing the collection of highly relevant data.

[0079] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also collect relevant data based on the content of posts from accounts the user follows. The data collection unit can also collect relevant data based on the activities of groups and communities the user participates in. This allows for the collection of relevant data based on the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's social media activity into a generating AI and have the generating AI perform the process of collecting relevant data.

[0080] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit will prioritize learning data related to relaxation. Similarly, if the user is excited, the learning unit can prioritize learning data related to excitement. Similarly, if the user is stressed, the learning unit can prioritize learning data that helps reduce stress. Similarly, if the user is stressed, the learning unit can prioritize learning data that helps reduce stress. This allows for more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into the generating AI and have the generating AI perform the process of selecting training data.

[0081] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and select the optimal learning algorithm. The learning unit can also analyze patterns in past learning data and adjust the learning algorithm. For example, the learning unit can analyze patterns in past learning data and adjust the learning algorithm. The learning unit can also evaluate the accuracy of past learning data and improve the learning algorithm. For example, the learning unit can evaluate the accuracy of past learning data and improve the learning algorithm. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the process of optimizing the learning algorithm.

[0082] The learning unit can improve the accuracy of its learning based on the user's purchase history and preferences during the learning process. For example, the learning unit can prioritize learning relevant data based on the user's purchase history. The learning unit can also customize the learning data based on the user's preferences. The learning unit can also improve the accuracy of its learning by combining the user's purchase history and preferences. This allows the learning unit to improve the accuracy of its learning based on the user's purchase history and preferences. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on the user's purchase history and preferences into a generating AI and have the generating AI perform processes to improve the accuracy of its learning.

[0083] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can increase the learning frequency when the user is relaxed. The learning unit can also decrease the learning frequency when the user is stressed. The learning unit can also adjust the learning frequency when the user is excited. This allows for more appropriate learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input user emotion data into the generative AI and have the generative AI perform the process of adjusting the learning frequency.

[0084] The learning unit can select the optimal learning method during learning, taking into account the user's device information. For example, if the user is using a smartphone, the learning unit will select a learning method optimized for smartphones. The learning unit can also select a learning method optimized for tablets if the user is using a tablet. The learning unit can also select a learning method optimized for desktops if the user is using a desktop. This allows the learning unit to select the optimal learning method based on the user's device information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's device information into a generating AI and have the generating AI perform the process of selecting the optimal learning method.

[0085] The learning unit can analyze the user's social media activity during training and incorporate relevant data into its learning. For example, the learning unit can incorporate information shared by the user on social media into its learning data. The learning unit can also incorporate posts from accounts the user follows into its learning data. The learning unit can also incorporate activities from groups and communities the user participates in into its learning data. This allows the learning unit to incorporate relevant data based on the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data about the user's social media activity into a generating AI and have the generating AI perform the process of incorporating relevant data into its learning.

[0086] 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 relaxed, the suggestion unit can provide detailed suggestions. If the user is excited, the suggestion unit can provide concise suggestions. For example, if the user is stressed, the suggestion unit can provide suggestions that help reduce stress. For example, if the user is stressed, the suggestion unit can provide suggestions that help reduce stress. By adjusting the way it presents its suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-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 the generative AI and have the generative AI perform the process of adjusting the way it presents its suggestions.

[0087] The proposal unit can adjust the level of detail in its proposals based on the importance of the products. For example, the proposal unit can provide detailed information for important products. The proposal unit can also provide concise information for less important products. The proposal unit can also adjust the level of detail in its proposals according to the importance of the products. By adjusting the level of detail in proposals according to the importance of the products, it can provide users with information that is important to them. 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 data on the importance of products into a generating AI and have the generating AI perform the process of adjusting the level of detail in the proposals.

[0088] 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 can apply a suggestion algorithm that provides technical details. For example, the suggestion unit can apply a suggestion algorithm that provides technical details about electronic devices. The suggestion unit can also apply a visually-oriented suggestion algorithm for fashion items. For example, the suggestion unit can apply a visually-oriented suggestion algorithm for fashion items. The suggestion unit can also apply a practicality-oriented suggestion algorithm for everyday goods. For example, the suggestion unit can apply a practicality-oriented suggestion algorithm for everyday goods. By applying a suggestion algorithm according to the product category, more appropriate suggestions can be made. 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 data about the product category into a generating AI and have the generating AI execute the process of applying different suggestion algorithms.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, the suggestion unit can make longer suggestions when the user is relaxed. It can also make shorter suggestions when the user is in a hurry. It can also make concise suggestions when the user is excited. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be made. 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 perform the process of adjusting the length of the suggestions.

[0090] The proposal department can determine the priority of proposals based on the timing of product submission. For example, the proposal department may prioritize new products. The proposal department may also prioritize products that are on sale. The proposal department may also prioritize seasonal products according to the season. By determining the priority of proposals based on the timing of product submission, important information can be provided to the user. 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 data on the timing of product submission into a generating AI and have the generating AI perform the process of determining the priority of proposals.

[0091] The suggestion unit can adjust the order of suggestions based on the relevance of the products. For example, the suggestion unit might suggest the product most relevant to the user's interests first. The suggestion unit can also prioritize suggesting highly relevant products based on the user's purchase history. The suggestion unit can also prioritize suggesting highly relevant products based on the user's preferences. By adjusting the order of suggestions based on the relevance of the products, the suggestion unit can provide the user with important information. 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 data on product relevance into a generating AI and have the generating AI perform the process of adjusting the order of suggestions.

[0092] The support unit can estimate the user's emotions and adjust the purchase support method based on the estimated emotions. For example, the support unit can provide detailed purchase support when the user is relaxed. For example, the support unit can provide detailed purchase support when the user is relaxed. The support unit can also provide concise purchase support when the user is in a hurry. For example, the support unit can provide concise purchase support when the user is in a hurry. The support unit can also provide purchase support that helps reduce stress when the user is stressed. For example, the support unit can provide purchase support that helps reduce stress when the user is stressed. By adjusting the purchase support method according to the user's emotions, more appropriate support becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 support unit may be performed using AI, for example, or without AI. For example, the support department can input user emotion data into a generating AI and have the AI ​​perform a process to adjust the purchase support method.

[0093] The support department can analyze the user's past purchasing behavior and select the optimal support method when providing purchase support. For example, the support department may prioritize purchase support methods the user has used in the past. The support department can also propose the optimal support method based on the user's past purchasing behavior. The support department can also analyze the user's past purchasing behavior and select the optimal support method. This allows the support department to select the optimal support method by analyzing the user's past purchasing behavior. Some or all of the above processes in the support department may be performed using AI, for example, or without AI. For example, the support department can input data on the user's past purchasing behavior into a generating AI and have the generating AI perform the process of selecting the optimal support method.

[0094] The support unit can customize the means of support provided during purchase support based on the user's current living situation. For example, if the user is busy, the support unit can provide concise support. For example, if the support unit provides concise support when the user is busy, the support unit can provide detailed support when the user is relaxed, for example, if the user is stressed, the support unit can provide support that helps reduce stress. For example, if the support unit provides support that helps reduce stress when the user is stressed, for example, the support unit can provide support that helps reduce stress when the user is stressed. By customizing the means of support based on the user's current living situation, more appropriate support becomes possible. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input data about the user's current living situation into a generating AI and have the generating AI perform the process of customizing the means of support.

[0095] The support unit can estimate the user's emotions and prioritize purchase support based on the estimated emotions. For example, if the user is excited, the support unit can immediately provide purchase support. For example, the support unit can immediately provide purchase support when the user is excited. The support unit can also provide detailed purchase support when the user is relaxed. For example, the support unit can provide detailed purchase support when the user is relaxed. The support unit can also prioritize purchase support that helps reduce stress when the user is stressed. For example, the support unit prioritizes purchase support that helps reduce stress when the user is stressed. This allows for more appropriate support by prioritizing purchase support 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 support unit may be performed using AI, for example, or without AI. For example, the support department can input user emotion data into a generating AI and have the AI ​​perform a process to determine the priority of purchase support.

[0096] The support unit can select the optimal support method when providing purchase support, taking into account the user's geographical location information. For example, if the user is in a specific region, the support unit can provide support methods relevant to that region. The support unit can also provide support methods relevant to the user's travel destination if the user is traveling. The support unit can also provide support methods relevant to the user's travel destination if the user is at home. This allows the support unit to select the optimal support method based on the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information into a generating AI and have the generating AI perform the process of selecting the optimal support method.

[0097] The support department can analyze a user's social media activity and propose support measures when providing purchase support. For example, the support department can propose relevant support measures based on information shared by the user on social media. The support department can also propose relevant support measures based on the content of posts from accounts the user follows. The support department can also propose relevant support measures based on the activities of groups and communities the user participates in. This allows the support department to propose relevant support measures based on the user's social media activity. Some or all of the above processing in the support department may be performed using AI, for example, or without AI. For example, the support department can input data on the user's social media activity into a generating AI and have the generating AI perform the process of proposing support measures.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can suggest products at that time. If the user is stressed, the suggestion function can either refrain from making suggestions or suggest products that help reduce stress. Furthermore, if the user is excited, the suggestion function can leverage that excitement to make suggestions that encourage immediate purchase. This allows for suggesting products at the optimal time according to the user's emotions, thereby increasing their willingness to purchase.

[0100] The data collection unit can adjust the timing of data collection based on the user's device usage. For example, if the user is using a smartphone, the unit can collect data at that time. Similarly, if the user is using a PC, the unit can collect data at that time. Furthermore, if the user is using a tablet, the unit can collect data at that time. This allows for data collection at the optimal time according to the user's device usage, resulting in more accurate data collection.

[0101] The suggestion function can estimate the user's emotions and customize the content of suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can suggest products related to relaxation. If the user is excited, the suggestion function can suggest products related to excitement. Furthermore, if the user is stressed, the suggestion function can suggest products that help reduce stress. This allows for product suggestions tailored to the user's emotions, thereby increasing their willingness to purchase.

[0102] The support department can analyze a user's past purchase history and select the most suitable purchase support method. For example, it can prioritize purchase support methods that the user has used in the past. It can also suggest the most suitable purchase support method based on the user's past purchase history. Furthermore, by analyzing the user's past purchase history and selecting the most suitable purchase support method, it is possible to select the most suitable purchase support method and increase the user's willingness to purchase.

[0103] The support department can estimate the user's emotions and customize the purchase support based on those emotions. For example, if the user is relaxed, the support department can provide purchase support related to relaxation. If the user is excited, the support department can provide purchase support related to excitement. Furthermore, if the user is stressed, the support department can provide purchase support that helps reduce stress. This allows for purchase support tailored to the user's emotions, thereby increasing their willingness to purchase.

[0104] The data collection unit can adjust the timing of data collection considering the user's geographical location. For example, if the user is in a specific region, it can prioritize collecting data related to that region. Similarly, if the user is traveling, it can prioritize collecting data related to their travel destination. Furthermore, if the user is at home, it can prioritize collecting data around their home. This allows for data collection at the optimal time based on the user's geographical location, resulting in more accurate data collection.

[0105] The learning unit can estimate the user's emotions and prioritize training data based on those emotions. For example, if the user is relaxed, it can prioritize learning data related to relaxation. Similarly, if the user is excited, it can prioritize learning data related to excitement. Furthermore, if the user is stressed, it can prioritize learning data that helps reduce stress. This allows for more appropriate learning by prioritizing training data according to the user's emotions.

[0106] The suggestion function can suggest relevant products based on the user's purchase history. For example, it can suggest products related to items the user has previously purchased. It can also suggest relevant products based on the user's purchase history. Furthermore, it can analyze the user's purchase history and suggest relevant products. This allows for increased purchase intent by suggesting relevant products based on the user's purchase history.

[0107] The support unit can estimate the user's emotions and adjust the timing of purchase support based on those emotions. For example, if the user is relaxed, the support unit can provide purchase support at that time. If the user is excited, the support unit can use that excitement to encourage immediate purchase. Furthermore, if the user is stressed, the support unit can provide purchase support that helps reduce stress. This allows for purchase support to be provided at the optimal time according to the user's emotions, thereby increasing their willingness to purchase.

[0108] The suggestion department can suggest relevant products based on the user's preferences. For example, if a user prefers a particular brand, it can suggest products from that brand. It can also suggest relevant products based on the user's preferences. Furthermore, it can analyze the user's preferences and suggest relevant products. By suggesting relevant products based on the user's preferences, it is possible to increase their purchase intent.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects user data. User data includes, for example, personal information, behavioral history, and purchase history. The data collection unit collects data such as social media activity, search history, and purchase history from e-commerce sites to understand user preferences and interests. For example, if a user posts on social media that they "want a new smartphone," that information is collected. Also, if a user frequently purchases products from a particular brand on an e-commerce site, that purchase history is collected. Step 2: The learning unit analyzes the data collected by the collection unit to learn user preferences and purchase history. The learning unit analyzes the data using methods such as data mining, statistical analysis, and machine learning to understand the products and brands that users are interested in. For example, it analyzes information such as a user posting "I want a new smartphone" on social media to learn that the user is interested in smartphones. Also, if a user frequently purchases products from a particular brand on an e-commerce site, it learns the user's preference for that brand. Step 3: The suggestion unit proposes the most suitable products based on the information learned by the learning unit. The suggestion unit selects the most suitable products based on criteria such as user preferences, price, and quality, and proposes products and brands that the user is interested in. For example, if the user is interested in smartphones, it will propose the latest smartphones and related accessories. Also, if the user prefers products from a particular brand, it will propose new products and sales information from that brand. Furthermore, based on the user's purchase history and hobbies, it proposes products at the optimal time, tailored to the season and sales periods. Step 4: The support department provides support for purchasing the products suggested by the proposal department. The support department provides support through methods such as assisting with the purchase process and customer support. The support department compares products across the e-commerce sites used, selects the most suitable product, and automatically adds the selected product to the user's shopping cart on the e-commerce site they are using. They provide support to enable the user to complete the purchase with one click. For example, if a user is purchasing daily necessities, the support department will suggest products at the optimal time based on sales information and the season, and add them to the cart.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the data collection unit, learning unit, suggestion unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user data using the camera 42 and microphone 38B of the smart device 14 and analyzes the data using the control unit 46A. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to learn the user's preferences and purchase history. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests the optimal product based on the learned information. The support unit is implemented in the specific processing unit 46A of the smart device 14 and supports the purchase of the suggested product. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the data collection unit, learning unit, suggestion unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user data using the camera 42 and microphone 238 of the smart glasses 214 and analyzes the data using the control unit 46A. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to learn the user's preferences and purchase history. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests the optimal product based on the learned information. The support unit is implemented in the specific processing unit 46A of the smart glasses 214 and supports the purchase of the suggested product. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the data collection unit, learning unit, suggestion unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user data using the camera 42 and microphone 238 of the headset terminal 314 and analyzes the data using the control unit 46A. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to learn the user's preferences and purchase history. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests the optimal product based on the learned information. The support unit is implemented in the specific processing unit 46A of the headset terminal 314 and supports the purchase of the suggested product. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the data collection unit, learning unit, suggestion unit, and support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user data using the camera 42 and microphone 238 of the robot 414 and analyzes the data using the control unit 46A. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and learns the user's preferences and purchase history. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12, which suggests the optimal product based on the learned information. The support unit is implemented in the control unit 46A of the robot 414, which supports the purchase of the suggested product. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A data collection unit that collects user data, A learning unit analyzes the data collected by the aforementioned collection unit and learns the user's preferences and purchase history, A proposal unit that proposes the optimal product based on the information learned by the aforementioned learning unit, The system includes a support unit that provides support for purchasing the products proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as social media activity, search history, and purchase history from e-commerce sites. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on the user's purchase history and preferences, we suggest products at the optimal time, tailored to the season and sales periods. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit is Compare and consider products across different e-commerce sites to select the best product. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit is The selected products are automatically added to the user's shopping cart on the e-commerce site they are using. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit is We provide support to help users complete purchases with a single click. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning unit, During training, the accuracy of the learning process is improved based on the user's purchase history and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, During learning, the optimal learning method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, During training, the system analyzes users' social media activity and incorporates relevant data into the learning process. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) 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 21) 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 22) 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 23) 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 24) 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 25) The aforementioned support unit is We estimate the user's emotions and adjust the purchase support methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is When providing purchase support, we analyze the user's past purchasing behavior to select the most suitable support method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is During purchase support, customize the support methods based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is The system estimates the user's emotions and prioritizes purchase support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit is When providing purchase support, the optimal support method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit is When providing purchase support, we analyze the user's social media activity and suggest support methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 data collection unit that collects user data, A learning unit analyzes the data collected by the aforementioned collection unit and learns the user's preferences and purchase history, A proposal unit that proposes the optimal product based on the information learned by the aforementioned learning unit, The system includes a support unit that provides support for purchasing the products proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as social media activity, search history, and purchase history from e-commerce sites. The system according to feature 1.

3. The aforementioned proposal section is, Based on the user's purchase history and preferences, we suggest products at the optimal time, tailored to the season and sales periods. The system according to feature 1.

4. The aforementioned support unit is Compare and consider products across different e-commerce sites to select the best product. The system according to feature 1.

5. The aforementioned support unit is The selected products are automatically added to the user's shopping cart on the e-commerce site they are using. The system according to feature 1.

6. The aforementioned support unit is We provide support to help users complete purchases with a single click. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

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

9. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest and lifestyle. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

Images