system

Abstract

The system according to this embodiment aims to optimize the matching of supply and demand by providing each user with the most suitable advertisements and discount information. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a provision unit, and a pricing unit. The collection unit collects the user's location information and consumption behavior. The generation unit analyzes the data collected by the collection unit and generates optimal advertisements and discount information for each user. The provision unit provides the information generated by the generation unit via push notifications. The pricing unit performs dynamic pricing based on the information generated by the generation 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide optimal advertisement or discount information for each user, and there is room for improvement in matching demand and supply.

[0005] The system according to the embodiment aims to provide optimal advertisement or discount information for each user and optimize the matching of demand and supply.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, a provision unit, and a pricing unit. The collection unit collects the user's location information and consumption behavior. The generation unit analyzes the data collected by the collection unit and generates optimal advertisements and discount information for each user. The provision unit provides the information generated by the generation unit via push notifications. The pricing unit performs dynamic pricing based on the information generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide optimal advertisements and discount information to each user, and optimize the matching of supply and demand. [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 that includes a communication processor, an antenna, and the like. The communication I / F manages communication between multiple 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 personalized advertising and dynamic pricing system according to an embodiment of the present invention is a system that provides personalized advertising and dynamic pricing for each user by utilizing generative AI. This system performs detailed segmentation based on the user's location information and purchasing behavior, and the generative AI analyzes this data to generate optimal advertising and discount information for each user. The generated information is provided to the user via push notification. As a result, users can purchase products at the discount and price best suited to them, and participating stores can maximize their sales. Furthermore, by providing discount information tailored to off-peak hours or low-occupancy times, it is possible to increase customer traffic for participating stores. In addition, by utilizing generative AI, it is expected that marketing campaigns will become more efficient and operating costs will be reduced. For example, if a user is near a specific store, discount information for that store can be provided via push notification. Furthermore, dynamic pricing that sets the optimal price for each user based on the user's past purchase history and behavior patterns is also realized. In this way, the personalized advertising and dynamic pricing system can provide personalized advertising and dynamic pricing based on the user's location information and purchasing behavior.

[0029] The personalized advertising and dynamic pricing system according to the embodiment comprises a collection unit, a generation unit, a provision unit, and a pricing unit. The collection unit collects the user's location information and consumption behavior. The collection unit collects the user's location information using, for example, GPS data or Wi-Fi location information. The collection unit can also collect the user's purchase history and browsing history. For example, the collection unit collects data on products the user has purchased in the past to understand their consumption behavior. Furthermore, the collection unit can also collect the user's website browsing history and analyze their interests. The generation unit analyzes the data collected by the collection unit and generates optimal advertisements and discount information for each user. The generation unit generates advertisements and discount information based on the user's interests and past behavior data, for example, using a generation AI. The generation unit can generate optimal ad copy for the user using a text generation AI (e.g., LLM). The generation unit can also generate advertisements that combine images and text using a multimodal generation AI. The generation unit can also customize the content of advertisements based on the user's interests. The provision unit provides the information generated by the generation unit via push notifications. The service provider, for example, uses smartphone notification functions to provide users with advertisements and discount information. The service provider can customize the timing and content of notifications. For example, if a user is near a specific store, the service provider can provide discount information for that store via push notification. The service provider can also send notifications at the optimal time based on the user's behavior patterns. The pricing unit performs dynamic pricing based on the information generated by the generation unit. For example, the pricing unit sets prices based on the user's past purchase history and behavior patterns. The pricing unit can use AI to execute a price fluctuation algorithm and calculate the optimal price. The pricing unit can also adjust prices considering the balance of user supply and demand. For example, the pricing unit can provide discount information specifically for off-peak hours or hours with low utilization rates. As a result, the personalized advertising and dynamic pricing system according to the embodiment can provide personalized advertisements and dynamic pricing based on the user's location information and consumption behavior.

[0030] The data collection unit collects user location information and consumer behavior. For example, it collects user location information using GPS data and Wi-Fi location information. Specifically, it obtains real-time location information from mobile devices such as smartphones and tablets to understand user movement patterns and destinations. The data collection unit can also collect user purchase and browsing history. For example, it collects data on products previously purchased by users from online shopping sites and e-commerce platforms to understand consumer behavior. Furthermore, the data collection unit can collect users' website browsing history and analyze their interests. This includes methods such as using browser cookies and tracking pixels to collect information on pages visited and links clicked. This allows the data collection unit to centrally manage detailed data on user location information and consumer behavior, which can then be used in subsequent analysis and generation processes. Additionally, the data collection unit can collect social media activity and user feedback. For example, it collects posts, comments, and likes shared by users on social media to understand user interests and trends. This allows the data collection unit to gather diverse user data and build a foundation for more accurate personalized advertising and dynamic pricing.

[0031] The generation unit analyzes the data collected by the collection unit and generates optimal advertisements and discount information for each user. For example, the generation unit uses a generation AI to generate advertisements and discount information based on the user's interests and past behavior data. Specifically, the generation AI receives data such as the user's location information, purchase history, and browsing history as input, and analyzes this data to identify the user's preferences and needs. The generation AI can use natural language processing technology to generate advertisements that are optimal for the user. For example, a text generation AI (LLM) generates advertisements about products and services that the user is likely to be interested in, based on the user's past purchase and browsing history. The generation unit can also use a multimodal generation AI to generate advertisements that combine images and text. This allows for the provision of visually appealing advertisements to users. The generation unit can also customize the content of advertisements based on the user's interests. For example, if a user has previously purchased products from a particular brand, the generation unit will generate advertisements about new products or related products from that brand. The generation unit can also generate advertisements that are appropriate for the season or event. For example, during the Christmas season, it will generate and provide advertisements for Christmas-related products and services to the user. This allows the generation unit to generate optimal advertisements and discount information for each user, providing personalized advertisements tailored to the user's interests.

[0032] The service provider delivers information generated by the generation unit via push notifications. For example, the service provider uses smartphone notification functions to provide users with advertisements and discount information. Specifically, the service provider can deliver notifications at the optimal time based on the user's location and behavior patterns. For example, if a user is near a specific store, it can deliver discount information for that store via push notification. The service provider can also customize the content of notifications. For example, it can notify users about new products related to items they have previously purchased. The service provider can also deliver notifications at the optimal time based on the user's behavior patterns. For example, if a user frequently visits a specific place at a specific time of day, it can deliver notifications at that time. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of notification content. For example, by analyzing the user's behavior after receiving a notification and adjusting the notification content and timing, it can deliver more effective notifications. In addition, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, email, etc. This allows the service provider to deliver advertisements and discount information to users quickly and reliably, and to provide personalized information tailored to the user's interests.

[0033] The pricing unit performs dynamic pricing based on information generated by the generation unit. For example, the pricing unit sets prices based on a user's past purchase history and behavioral patterns. Specifically, the pricing unit can use AI to execute price fluctuation algorithms and calculate the optimal price. The AI ​​can also adjust prices considering the balance of user supply and demand. For example, the pricing unit can provide discount information specifically for off-peak or low-occupancy periods. This allows the pricing unit to maximize sales even during periods of low demand. The pricing unit can also monitor competitor pricing information and market trends in real time and adjust prices accordingly. For example, if a competitor offers a particular product at a discounted price, the pricing unit can maintain its competitiveness by offering a similar product at an even lower price. Furthermore, the pricing unit can offer limited discounts and benefits to increase user purchasing intent. For example, it can offer discounts that apply only during specific times or when specific conditions are met to stimulate user purchasing intent. This allows the pricing unit to set flexible prices according to user demand and maximize sales. Furthermore, the pricing department can predict future price fluctuations based on historical data. This allows the pricing department to formulate long-term pricing strategies and maintain sustainable competitiveness.

[0034] The data collection unit can collect user location information and consumer behavior in real time. For example, the data collection unit can collect user location information in real time using GPS data. The data collection unit can obtain the user's current location using the location information service of the user's smartphone. The data collection unit can also collect user location information in real time using Wi-Fi location information. For example, the data collection unit can identify the user's location based on information from Wi-Fi access points. Furthermore, the data collection unit can collect user consumer behavior in real time. For example, the data collection unit can collect data on products purchased by the user on online shopping sites in real time. This allows for the provision of advertisements and discount information based on the latest information through real-time data collection. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can acquire user location information in real time and input that data into AI for analysis.

[0035] The generation unit can analyze collected data and generate optimal advertisements and discount information for each user. For example, the generation unit uses a generation AI to analyze the collected data. The generation unit generates optimal advertisements and discount information based on the user's interests and past behavior data. For example, the generation unit uses a text generation AI (e.g., LLM) to generate advertisement copy that is optimal for the user. The generation unit can also use a multimodal generation AI to generate advertisements that combine images and text. The generation unit can also customize the content of advertisements based on the user's interests. For example, the generation unit generates relevant advertisements based on data of products the user has purchased in the past. This improves user satisfaction by providing optimal advertisements and discount information for each user. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can input collected data into a generation AI and have the generation AI execute the generation of optimal advertisements and discount information.

[0036] The service provider can deliver the generated information to users via push notifications. For example, the service provider can use the notification function of a smartphone to provide users with advertisements and discount information. The service provider can customize the timing and content of notifications. For example, if a user is near a specific store, the service provider can provide discount information for that store via push notification. The service provider can also send notifications at the optimal time based on the user's behavior patterns. For example, the service provider can send notifications at a specific time based on data of products the user has purchased in the past during a particular time period. This allows information to be delivered to the user quickly via push notifications. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the generated information into AI and have the AI ​​determine the optimal notification timing.

[0037] The pricing unit can perform dynamic pricing based on the user's past purchase history and behavioral patterns. For example, the pricing unit can analyze the user's past purchase history and set the optimal price. The pricing unit can use AI to execute price fluctuation algorithms and calculate the optimal price. The pricing unit can also adjust prices considering the balance of user supply and demand. For example, the pricing unit can set the price of related products based on data of products the user has purchased in the past. This maximizes sales by providing the optimal price based on the user's past behavior. Some or all of the above processes in the pricing unit may be performed using AI or not. For example, the pricing unit can input the user's past purchase history into AI and have the AI ​​perform the optimal price setting.

[0038] The service provider can offer discount information specifically tailored to off-peak hours or periods of low occupancy. For example, the service provider can generate and provide discount information tailored to the off-peak hours of a store. The service provider can use AI to analyze the store's occupancy rate and generate optimal discount information. For example, the service provider can generate and provide discount information tailored to the store's low occupancy rate. By providing discount information tailored to off-peak hours or periods of low occupancy, it is possible to increase customer traffic. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input store occupancy rate data into AI and have the AI ​​generate optimal discount information.

[0039] The generation unit can generate information that improves the efficiency of marketing campaigns and reduces operating costs. For example, the generation unit can use a generation AI to generate information on improving the efficiency of marketing campaigns. The generation unit can analyze user behavior data and propose optimal marketing strategies. For example, the generation unit can propose effective advertising campaigns based on user interests. The generation unit can also generate information on reducing operating costs. For example, the generation unit can generate information on reducing advertising costs and personnel costs. This allows for economic benefits by improving the efficiency of marketing campaigns and reducing operating costs. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can input user behavior data into a generation AI and have the generation AI propose optimal marketing strategies.

[0040] The data collection unit can analyze the user's past location information and consumption behavior to select the optimal data collection method. For example, the data collection unit can determine the priority of location information to collect based on past location information. The data collection unit can analyze data on places the user has visited in the past and prioritize the collection of important location information. The data collection unit can also select the types of data to collect based on past consumption behavior. For example, the data collection unit can analyze the user's past purchase history and concentrate data collection during specific time periods. Furthermore, the data collection unit can customize the data collection method based on past behavior patterns. For example, the data collection unit can analyze the user's past behavior patterns and select the optimal data collection method. This enables efficient data collection by selecting the optimal data collection method based on past data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past location information into AI and have the AI ​​select the optimal data collection method.

[0041] The data collection unit can filter data based on the user's current lifestyle and areas of interest during collection. For example, the data collection unit can filter the data to be collected according to the user's lifestyle. The data collection unit can prioritize the collection of highly relevant data based on data such as the user's occupation and family structure. The data collection unit can also select the types of data to be collected based on the user's areas of interest. For example, the data collection unit can filter the data to be collected based on the user's hobbies and topics of interest. Furthermore, the data collection unit can determine the priority of the data to be collected, taking into account the user's lifestyle and areas of interest. For example, the data collection unit can prioritize the collection of important data according to the user's lifestyle. This allows for the collection of highly relevant data by filtering data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's lifestyle data into AI and have the AI ​​perform optimal data filtering.

[0042] 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 will prioritize the collection of data related to that region. The data collection unit can collect highly relevant data based on the user's current location. Furthermore, if the user is on the move, the data collection unit can prioritize the collection of data related to their destination. For example, the data collection unit will prioritize the collection of information the user needs at their destination. In addition, if the user is staying in a specific location for an extended period, the data collection unit can prioritize the collection of data related to that location. For example, if the user is staying in a specific store for an extended period, the data collection unit will collect data related to that store. This allows for the efficient collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data during the collection process. 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 analyze the content of user posts and the number of likes, and collect relevant data. The data collection unit can also analyze the user's social media activity patterns and select the types of data to collect. For example, the data collection unit can filter the data to be collected based on topics the user has shown interest in. Furthermore, the data collection unit can determine the priority of the data to be collected based on the user's social media activity history. For example, the data collection unit prioritizes collecting data related to topics that the user frequently shows interest in. This allows for the efficient collection of user-related data by analyzing social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into an AI and have the AI ​​collect the relevant data.

[0044] The generation unit can adjust the level of detail in the generated information based on the importance of the advertisements and discount information. For example, the generation unit can evaluate the importance of the advertisements and discount information and adjust the level of detail in the generated information. The generation unit can generate advertisements and discount information with detailed descriptions for those with high importance. The generation unit can also generate advertisements and discount information with concise content for those with low importance. For example, the generation unit can generate advertisements with detailed product descriptions and usage examples for those with high importance. The generation unit can also generate advertisements with only concise catchphrases and key features for those with low importance. Furthermore, the generation unit can adjust visual effects according to importance. For example, the generation unit can add visually striking effects to advertisements with high importance. This allows for efficient information provision by adjusting the level of detail in the generated information according to its importance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the importance data of the advertisements and discount information into a generation AI and have the generation AI perform the adjustment of the level of detail in the generated information.

[0045] The generation unit can apply different generation algorithms depending on the category of the advertisement or discount information during generation. For example, the generation unit can apply a generation algorithm that uses visually appealing images to food-related advertisements and discount information. For electronic device-related advertisements and discount information, it can apply a generation algorithm that emphasizes technical details. Furthermore, the generation unit can apply a generation algorithm that reflects trends to fashion-related advertisements and discount information. For example, the generation unit can apply a generation algorithm that uses images with vibrant colors and beautiful presentation to food-related advertisements. Furthermore, the generation unit can apply a generation algorithm that emphasizes the technical features and performance of products to electronic device-related advertisements. In addition, the generation unit can apply a generation algorithm that reflects the latest trends and styles to fashion-related advertisements. This allows for the provision of more effective advertisements and discount information by applying a generation algorithm appropriate to the category. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input category data of advertisements and discount information into a generation AI and have the generation AI execute the application of an appropriate generation algorithm.

[0046] The generation unit can determine the generation priority based on the submission timing of advertisements and discount information during the generation process. For example, the generation unit can prioritize generating advertisements and discount information with approaching submission deadlines. It can also postpone the generation of advertisements and discount information with ample time before submission. Furthermore, the generation unit can adjust the generation schedule based on the submission timing. For example, the generation unit can prioritize generating advertisements and discount information with imminent submission deadlines. It can also postpone the generation of advertisements and discount information with ample time before submission. In addition, the generation unit can adjust the generation schedule based on the submission timing. This enables efficient information provision by determining the generation priority based on the submission timing. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input advertisement and discount information submission timing data into a generation AI and have the generation AI determine the generation priority.

[0047] The generation unit can adjust the generation order based on the relevance of advertisements and discount information during generation. For example, the generation unit can prioritize generating advertisements and discount information that are highly relevant to the user. The generation unit can postpone the generation of advertisements and discount information that are less relevant. Furthermore, the generation unit can optimize the generation order based on relevance. For example, the generation unit can prioritize generating advertisements and discount information that are highly relevant based on the user's interests and past behavior data. Furthermore, the generation unit can postpone the generation of advertisements and discount information that are less relevant. In addition, the generation unit can optimize the generation order based on relevance. This makes it possible to provide the user with the most optimal information by adjusting the generation order based on relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input relevance data of advertisements and discount information into a generation AI and have the generation AI perform the adjustment of the generation order.

[0048] The delivery unit can select the optimal delivery method by referring to the user's past response history at the time of delivery. For example, the delivery unit may prioritize delivery methods in which the user has shown a favorable response in the past. The delivery unit can analyze the user's past response history and select the optimal delivery method. Furthermore, the delivery unit can customize the delivery method based on the user's past response history. For example, the delivery unit may prioritize delivery methods in which the user has had a high click-through rate in the past. The delivery unit can also select the optimal delivery method based on the user's past purchase history. In addition, the delivery unit can customize the delivery method based on the user's past response history. This improves user satisfaction by selecting the optimal delivery method based on past response history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's past response history data into AI and have the AI ​​select the optimal delivery method.

[0049] The service provider can customize the content offered based on the user's current lifestyle at the time of delivery. For example, if the user is busy, the service provider can select concise content. If the user is relaxed, the service provider can select detailed content. The service provider can also customize the content according to the user's lifestyle. For example, the service provider can select highly relevant content based on data such as the user's occupation and family structure. The service provider can also customize the content based on the user's lifestyle. Furthermore, the service provider can determine the priority of the content offered according to the user's lifestyle. This allows for the provision of more appropriate information by customizing the content according to the user's lifestyle. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's lifestyle data into AI and have the AI ​​perform the customization of the content offered.

[0050] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can select a service delivery method relevant to that region. The service provider can select a highly relevant service delivery method based on the user's current location. Furthermore, if the user is on the move, the service provider can select a service delivery method relevant to their destination. For example, the service provider can prioritize providing information the user needs at their destination. In addition, if the user is staying in a specific location for an extended period, the service provider can select a service delivery method relevant to that location. For example, if the user is staying in a specific store for an extended period, the service provider can provide information relevant to that store. This allows the service provider to provide highly relevant information by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location information into AI and have the AI ​​select the optimal service delivery method.

[0051] The service provider can analyze the user's social media activity and adjust the content offered at the time of delivery. For example, the service provider can adjust the content based on information shared by the user on social media. The service provider can analyze the content of user posts and the number of likes to adjust the content. The service provider can also analyze the user's social media activity patterns and select content to offer. For example, the service provider can customize content based on topics the user has shown interest in. Furthermore, the service provider can determine the priority of content based on the user's social media activity history. For example, the service provider can prioritize providing information related to topics the user frequently shows interest in. This allows the service provider to provide information relevant to the user by analyzing social media activity. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input user social media activity data into AI and have the AI ​​perform the adjustment of content.

[0052] The pricing unit can analyze a user's past purchase history to select the optimal pricing method when setting prices. For example, the pricing unit can set the optimal price based on the prices of products the user has purchased in the past. The pricing unit can analyze the price sensitivity of specific products from the user's past purchase history and adjust the pricing accordingly. The pricing unit can also consider the user's purchase frequency and set prices for repeat customers. For example, the pricing unit can set prices for related products based on data of products the user has purchased in the past. The pricing unit can also set discounted prices for repeat customers based on the user's purchase frequency. Furthermore, the pricing unit can select the optimal pricing method based on the user's past purchase history. This allows the system to provide the user with the best possible price by selecting the optimal pricing method based on past purchase history. Some or all of the above processes in the pricing unit may be performed using AI or not. For example, the pricing unit can input the user's past purchase history data into AI and have the AI ​​select the optimal pricing method.

[0053] The pricing unit can customize its pricing methods based on the user's current living situation when setting prices. For example, if the user is financially well-off, the pricing unit can set a premium price. If the user is in a financially difficult situation, the pricing unit can set a discounted price. The pricing unit can also customize its pricing methods according to the user's living situation. For example, the pricing unit can set highly relevant prices based on data such as the user's occupation and family structure. Furthermore, the pricing unit can also customize its pricing methods based on the user's living situation. In addition, the pricing unit can determine the priority of pricing according to the user's living situation. This allows for the provision of more appropriate prices by customizing the pricing methods according to the user's living situation. Some or all of the above processes in the pricing unit may be performed using AI or not. For example, the pricing unit can input user living situation data into AI and have the AI ​​perform the customization of pricing methods.

[0054] The pricing unit can select the optimal pricing method when setting prices, taking into account the user's geographical location information. For example, if the user is in a specific region, the pricing unit will set prices based on the market price of that region. If the user is on the move, the pricing unit can set prices considering the market price of the destination. Furthermore, if the user is staying in a specific location for an extended period, the pricing unit can also set prices based on the market price of that location. This makes it possible to set prices that are more relevant by taking into account the user's geographical location information. Some or all of the above processing in the pricing unit may be performed using AI, or not. For example, the pricing unit can input the user's geographical location information into AI and have the AI ​​select the optimal pricing method.

[0055] The pricing unit can analyze a user's social media activity and propose pricing methods when setting prices. For example, the pricing unit can propose pricing methods based on information shared by the user on social media. The pricing unit can analyze the content of user posts and the number of likes to propose pricing methods. The pricing unit can also analyze a user's social media activity patterns and select pricing methods. For example, the pricing unit can customize pricing methods based on topics the user has shown interest in. Furthermore, the pricing unit can determine pricing priorities based on the user's social media activity history. For example, the pricing unit can prioritize proposing pricing methods related to topics the user frequently shows interest in. In this way, by analyzing social media activity, the pricing unit can propose the most suitable pricing method for the user. Some or all of the above processes in the pricing unit may be performed using AI or not. For example, the pricing unit can input user social media activity data into AI and have the AI ​​propose pricing methods.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The generation unit can analyze users' social media activity in addition to their location information and consumption behavior. For example, it can analyze information and posts shared by users on social media to understand their interests. This allows it to generate advertisements and discount information related to topics that users are interested in. For instance, if a user mentions a specific brand or product on social media, the generation unit will generate advertisements for that brand or product. It can also analyze photos and videos shared by users on social media and generate advertisements for related products. Furthermore, the generation unit can determine the optimal timing for advertisements based on the user's social media activity patterns. This makes it easier to attract user interest by providing advertisements and discount information based on the user's social media activity.

[0058] The pricing unit can set prices based on the user's location information, purchasing behavior, and past purchase history. For example, the pricing unit can analyze data on products the user has purchased in the past and set prices for related products. It can also set discounted prices for repeat customers, taking into account the user's purchase frequency. Furthermore, the pricing unit can provide discount information tailored to specific time periods based on the user's past purchase history. This maximizes sales by providing optimal prices based on the user's past behavior. Some or all of the above processes in the pricing unit may be performed using AI or not. For example, the pricing unit can input the user's past purchase history into AI and have the AI ​​set the optimal price.

[0059] The data collection unit can collect data considering the user's lifestyle, in addition to the user's location and consumption behavior. For example, the data collection unit can prioritize collecting highly relevant data based on data such as the user's occupation and family structure. It can also select the type of data to collect based on the user's lifestyle. For example, the data collection unit can collect concise data when the user is busy and detailed data when the user is relaxed. Furthermore, the data collection unit can determine the priority of the data to collect according to the user's lifestyle. This allows for the efficient collection of highly relevant data based on the user's lifestyle. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's lifestyle data into AI and have the AI ​​perform the optimal data collection.

[0060] The delivery unit can select the optimal delivery method by referring to the user's past response history in addition to the user's location information and consumption behavior. For example, the delivery unit will prioritize delivery methods that the user has responded favorably to in the past. It can also analyze the user's past response history and select the optimal delivery method. Furthermore, it can customize the delivery method based on the user's past response history. For example, the delivery unit will prioritize delivery methods that the user has clicked on in the past. It can also select the optimal delivery method based on the user's past purchase history. In this way, user satisfaction can be improved by selecting the optimal delivery method based on past response history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's past response history data into AI and have the AI ​​select the optimal delivery method.

[0061] The generation unit can generate advertisements and discount information based on the user's location information, purchasing behavior, and past purchase history. For example, the generation unit can analyze data on products the user has purchased in the past and generate advertisements for related products. It can also generate advertisements for repeat customers, taking into account the user's purchase frequency. Furthermore, the generation unit can generate advertisements and discount information tailored to specific time periods based on the user's past purchase history. This makes it easier to attract the user's attention by providing optimal advertisements and discount information based on the user's past behavior. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's past purchase history into AI and have the AI ​​generate optimal advertisements and discount information.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects user location information and consumer behavior. For example, it can collect user location information using GPS data or Wi-Fi location information, and also collect user purchase history and browsing history. This allows the system to understand data on products the user has purchased in the past and website browsing history, and to analyze their interests. Step 2: The generation unit analyzes the data collected by the collection unit and generates optimal advertisements and discount information for each user. For example, it can use a generation AI to generate advertisements and discount information based on the user's interests and past behavior data, and a text generation AI (e.g., LLM) to generate advertisement copy that is optimal for the user. It can also use a multimodal generation AI to generate advertisements that combine images and text. Step 3: The delivery unit provides the information generated by the generation unit via push notifications. For example, it can use the smartphone's notification function to provide users with advertisements and discount information, and customize the timing and content of the notifications. If a user is near a specific store, it can also provide discount information for that store via push notifications. Step 4: The pricing unit performs dynamic pricing based on the information generated by the generation unit. For example, it sets prices based on the user's past purchase history and behavior patterns, and uses AI to execute a price fluctuation algorithm to calculate the optimal price. The pricing unit adjusts prices considering the balance of user supply and demand, and provides discount information specifically for off-peak hours and low-occupancy periods.

[0064] (Example of form 2) The personalized advertising and dynamic pricing system according to an embodiment of the present invention is a system that provides personalized advertising and dynamic pricing for each user by utilizing generative AI. This system performs detailed segmentation based on the user's location information and purchasing behavior, and the generative AI analyzes this data to generate optimal advertising and discount information for each user. The generated information is provided to the user via push notification. As a result, users can purchase products at the discount and price best suited to them, and participating stores can maximize their sales. Furthermore, by providing discount information tailored to off-peak hours or low-occupancy times, it is possible to increase customer traffic for participating stores. In addition, by utilizing generative AI, it is expected that marketing campaigns will become more efficient and operating costs will be reduced. For example, if a user is near a specific store, discount information for that store can be provided via push notification. Furthermore, dynamic pricing that sets the optimal price for each user based on the user's past purchase history and behavior patterns is also realized. In this way, the personalized advertising and dynamic pricing system can provide personalized advertising and dynamic pricing based on the user's location information and purchasing behavior.

[0065] The personalized advertising and dynamic pricing system according to the embodiment comprises a collection unit, a generation unit, a provision unit, and a pricing unit. The collection unit collects the user's location information and consumption behavior. The collection unit collects the user's location information using, for example, GPS data or Wi-Fi location information. The collection unit can also collect the user's purchase history and browsing history. For example, the collection unit collects data on products the user has purchased in the past to understand their consumption behavior. Furthermore, the collection unit can also collect the user's website browsing history and analyze their interests. The generation unit analyzes the data collected by the collection unit and generates optimal advertisements and discount information for each user. The generation unit generates advertisements and discount information based on the user's interests and past behavior data, for example, using a generation AI. The generation unit can generate optimal ad copy for the user using a text generation AI (e.g., LLM). The generation unit can also generate advertisements that combine images and text using a multimodal generation AI. The generation unit can also customize the content of advertisements based on the user's interests. The provision unit provides the information generated by the generation unit via push notifications. The service provider, for example, uses smartphone notification functions to provide users with advertisements and discount information. The service provider can customize the timing and content of notifications. For example, if a user is near a specific store, the service provider can provide discount information for that store via push notification. The service provider can also send notifications at the optimal time based on the user's behavior patterns. The pricing unit performs dynamic pricing based on the information generated by the generation unit. For example, the pricing unit sets prices based on the user's past purchase history and behavior patterns. The pricing unit can use AI to execute a price fluctuation algorithm and calculate the optimal price. The pricing unit can also adjust prices considering the balance of user supply and demand. For example, the pricing unit can provide discount information specifically for off-peak hours or hours with low utilization rates. As a result, the personalized advertising and dynamic pricing system according to the embodiment can provide personalized advertisements and dynamic pricing based on the user's location information and consumption behavior.

[0066] The data collection unit collects user location information and consumer behavior. For example, it collects user location information using GPS data and Wi-Fi location information. Specifically, it obtains real-time location information from mobile devices such as smartphones and tablets to understand user movement patterns and destinations. The data collection unit can also collect user purchase and browsing history. For example, it collects data on products previously purchased by users from online shopping sites and e-commerce platforms to understand consumer behavior. Furthermore, the data collection unit can collect users' website browsing history and analyze their interests. This includes methods such as using browser cookies and tracking pixels to collect information on pages visited and links clicked. This allows the data collection unit to centrally manage detailed data on user location information and consumer behavior, which can then be used in subsequent analysis and generation processes. Additionally, the data collection unit can collect social media activity and user feedback. For example, it collects posts, comments, and likes shared by users on social media to understand user interests and trends. This allows the data collection unit to gather diverse user data and build a foundation for more accurate personalized advertising and dynamic pricing.

[0067] The generation unit analyzes the data collected by the collection unit and generates optimal advertisements and discount information for each user. For example, the generation unit uses a generation AI to generate advertisements and discount information based on the user's interests and past behavior data. Specifically, the generation AI receives data such as the user's location information, purchase history, and browsing history as input, and analyzes this data to identify the user's preferences and needs. The generation AI can use natural language processing technology to generate advertisements that are optimal for the user. For example, a text generation AI (LLM) generates advertisements about products and services that the user is likely to be interested in, based on the user's past purchase and browsing history. The generation unit can also use a multimodal generation AI to generate advertisements that combine images and text. This allows for the provision of visually appealing advertisements to users. The generation unit can also customize the content of advertisements based on the user's interests. For example, if a user has previously purchased products from a particular brand, the generation unit will generate advertisements about new products or related products from that brand. The generation unit can also generate advertisements that are appropriate for the season or event. For example, during the Christmas season, it will generate and provide advertisements for Christmas-related products and services to the user. This allows the generation unit to generate optimal advertisements and discount information for each user, providing personalized advertisements tailored to the user's interests.

[0068] The service provider delivers information generated by the generation unit via push notifications. For example, the service provider uses smartphone notification functions to provide users with advertisements and discount information. Specifically, the service provider can deliver notifications at the optimal time based on the user's location and behavior patterns. For example, if a user is near a specific store, it can deliver discount information for that store via push notification. The service provider can also customize the content of notifications. For example, it can notify users about new products related to items they have previously purchased. The service provider can also deliver notifications at the optimal time based on the user's behavior patterns. For example, if a user frequently visits a specific place at a specific time of day, it can deliver notifications at that time. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of notification content. For example, by analyzing the user's behavior after receiving a notification and adjusting the notification content and timing, it can deliver more effective notifications. In addition, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, email, etc. This allows the service provider to deliver advertisements and discount information to users quickly and reliably, and to provide personalized information tailored to the user's interests.

[0069] The pricing unit performs dynamic pricing based on information generated by the generation unit. For example, the pricing unit sets prices based on a user's past purchase history and behavioral patterns. Specifically, the pricing unit can use AI to execute price fluctuation algorithms and calculate the optimal price. The AI ​​can also adjust prices considering the balance of user supply and demand. For example, the pricing unit can provide discount information specifically for off-peak or low-occupancy periods. This allows the pricing unit to maximize sales even during periods of low demand. The pricing unit can also monitor competitor pricing information and market trends in real time and adjust prices accordingly. For example, if a competitor offers a particular product at a discounted price, the pricing unit can maintain its competitiveness by offering a similar product at an even lower price. Furthermore, the pricing unit can offer limited discounts and benefits to increase user purchasing intent. For example, it can offer discounts that apply only during specific times or when specific conditions are met to stimulate user purchasing intent. This allows the pricing unit to set flexible prices according to user demand and maximize sales. Furthermore, the pricing department can predict future price fluctuations based on historical data. This allows the pricing department to formulate long-term pricing strategies and maintain sustainable competitiveness.

[0070] The data collection unit can collect user location information and consumer behavior in real time. For example, the data collection unit can collect user location information in real time using GPS data. The data collection unit can obtain the user's current location using the location information service of the user's smartphone. The data collection unit can also collect user location information in real time using Wi-Fi location information. For example, the data collection unit can identify the user's location based on information from Wi-Fi access points. Furthermore, the data collection unit can collect user consumer behavior in real time. For example, the data collection unit can collect data on products purchased by the user on online shopping sites in real time. This allows for the provision of advertisements and discount information based on the latest information through real-time data collection. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can acquire user location information in real time and input that data into AI for analysis.

[0071] The generation unit can analyze collected data and generate optimal advertisements and discount information for each user. For example, the generation unit uses a generation AI to analyze the collected data. The generation unit generates optimal advertisements and discount information based on the user's interests and past behavior data. For example, the generation unit uses a text generation AI (e.g., LLM) to generate advertisement copy that is optimal for the user. The generation unit can also use a multimodal generation AI to generate advertisements that combine images and text. The generation unit can also customize the content of advertisements based on the user's interests. For example, the generation unit generates relevant advertisements based on data of products the user has purchased in the past. This improves user satisfaction by providing optimal advertisements and discount information for each user. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can input collected data into a generation AI and have the generation AI execute the generation of optimal advertisements and discount information.

[0072] The service provider can deliver the generated information to users via push notifications. For example, the service provider can use the notification function of a smartphone to provide users with advertisements and discount information. The service provider can customize the timing and content of notifications. For example, if a user is near a specific store, the service provider can provide discount information for that store via push notification. The service provider can also send notifications at the optimal time based on the user's behavior patterns. For example, the service provider can send notifications at a specific time based on data of products the user has purchased in the past during a particular time period. This allows information to be delivered to the user quickly via push notifications. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the generated information into AI and have the AI ​​determine the optimal notification timing.

[0073] The pricing unit can perform dynamic pricing based on the user's past purchase history and behavioral patterns. For example, the pricing unit can analyze the user's past purchase history and set the optimal price. The pricing unit can use AI to execute price fluctuation algorithms and calculate the optimal price. The pricing unit can also adjust prices considering the balance of user supply and demand. For example, the pricing unit can set the price of related products based on data of products the user has purchased in the past. This maximizes sales by providing the optimal price based on the user's past behavior. Some or all of the above processes in the pricing unit may be performed using AI or not. For example, the pricing unit can input the user's past purchase history into AI and have the AI ​​perform the optimal price setting.

[0074] The service provider can offer discount information specifically tailored to off-peak hours or periods of low occupancy. For example, the service provider can generate and provide discount information tailored to the off-peak hours of a store. The service provider can use AI to analyze the store's occupancy rate and generate optimal discount information. For example, the service provider can generate and provide discount information tailored to the store's low occupancy rate. By providing discount information tailored to off-peak hours or periods of low occupancy, it is possible to increase customer traffic. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input store occupancy rate data into AI and have the AI ​​generate optimal discount information.

[0075] The generation unit can generate information that improves the efficiency of marketing campaigns and reduces operating costs. For example, the generation unit can use a generation AI to generate information on improving the efficiency of marketing campaigns. The generation unit can analyze user behavior data and propose optimal marketing strategies. For example, the generation unit can propose effective advertising campaigns based on user interests. The generation unit can also generate information on reducing operating costs. For example, the generation unit can generate information on reducing advertising costs and personnel costs. This allows for economic benefits by improving the efficiency of marketing campaigns and reducing operating costs. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can input user behavior data into a generation AI and have the generation AI propose optimal marketing strategies.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of location data and consumer behavior collection based on the estimated emotions. For example, the data collection unit can estimate the user's emotions using an emotion estimation algorithm. The data collection unit can also estimate emotions by analyzing the user's facial expressions and voice data. For example, the data collection unit can estimate emotions based on changes in the user's facial expressions. Furthermore, the data collection unit can also estimate emotions by analyzing the tone and speed of the user's voice. Based on the estimated emotions, the data collection unit adjusts the timing of location data and consumer behavior collection. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. Conversely, if the user is relaxed, the data collection unit can speed up the collection timing to collect more data. Additionally, if the user is in a hurry, the data collection unit can optimize the collection timing to quickly collect only the minimum necessary data. This reduces the user's burden by adjusting the collection timing according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using AI or not using AI. For example, the collection unit can input user facial expression data into the generating AI and have the generating AI perform emotion estimation.

[0077] The data collection unit can analyze the user's past location information and consumption behavior to select the optimal data collection method. For example, the data collection unit can determine the priority of location information to collect based on past location information. The data collection unit can analyze data on places the user has visited in the past and prioritize the collection of important location information. The data collection unit can also select the types of data to collect based on past consumption behavior. For example, the data collection unit can analyze the user's past purchase history and concentrate data collection during specific time periods. Furthermore, the data collection unit can customize the data collection method based on past behavior patterns. For example, the data collection unit can analyze the user's past behavior patterns and select the optimal data collection method. This enables efficient data collection by selecting the optimal data collection method based on past data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past location information into AI and have the AI ​​select the optimal data collection method.

[0078] The data collection unit can filter data based on the user's current lifestyle and areas of interest during collection. For example, the data collection unit can filter the data to be collected according to the user's lifestyle. The data collection unit can prioritize the collection of highly relevant data based on data such as the user's occupation and family structure. The data collection unit can also select the types of data to be collected based on the user's areas of interest. For example, the data collection unit can filter the data to be collected based on the user's hobbies and topics of interest. Furthermore, the data collection unit can determine the priority of the data to be collected, taking into account the user's lifestyle and areas of interest. For example, the data collection unit can prioritize the collection of important data according to the user's lifestyle. This allows for the collection of highly relevant data by filtering data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's lifestyle data into AI and have the AI ​​perform optimal data filtering.

[0079] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, the data collection unit can estimate the user's emotions using an emotion estimation algorithm. The data collection unit can also estimate emotions by analyzing the user's facial expressions and voice data. For example, the data collection unit can estimate emotions based on changes in the user's facial expressions. Furthermore, the data collection unit can also estimate emotions by analyzing the tone and speed of the user's voice. Based on the estimated emotions, the data collection unit prioritizes the data to collect. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. Conversely, if the user is relaxed, the data collection unit can prioritize the collection of more important data. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of only the minimum necessary data. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0080] 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 will prioritize the collection of data related to that region. The data collection unit can collect highly relevant data based on the user's current location. Furthermore, if the user is on the move, the data collection unit can prioritize the collection of data related to their destination. For example, the data collection unit will prioritize the collection of information the user needs at their destination. In addition, if the user is staying in a specific location for an extended period, the data collection unit can prioritize the collection of data related to that location. For example, if the user is staying in a specific store for an extended period, the data collection unit will collect data related to that store. This allows for the efficient collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0081] The data collection unit can analyze a user's social media activity and collect relevant data during the collection process. 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 analyze the content of user posts and the number of likes, and collect relevant data. The data collection unit can also analyze the user's social media activity patterns and select the types of data to collect. For example, the data collection unit can filter the data to be collected based on topics the user has shown interest in. Furthermore, the data collection unit can determine the priority of the data to be collected based on the user's social media activity history. For example, the data collection unit prioritizes collecting data related to topics that the user frequently shows interest in. This allows for the efficient collection of user-related data by analyzing social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into an AI and have the AI ​​collect the relevant data.

[0082] The generation unit can estimate the user's emotions and adjust the presentation of advertisements and discount information based on the estimated emotions. For example, the generation unit can estimate the user's emotions using an emotion estimation algorithm. It can also estimate emotions by analyzing the user's facial expressions and voice data. For example, it can estimate emotions based on changes in the user's facial expressions. Furthermore, it can estimate emotions by analyzing the tone and speed of the user's voice. Based on the estimated emotions, the generation unit adjusts the presentation of advertisements and discount information. For example, if the user is relaxed, the generation unit generates advertisements and discount information in a soft tone. If the user is in a hurry, the generation unit can generate concise and to-the-point advertisements and discount information. Furthermore, if the user is excited, the generation unit can generate visually stimulating advertisements and discount information. This makes it easier to attract the user's attention by adjusting the presentation of advertisements and discount information according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to such examples. Some or all of the processing described above in the generation unit may be performed using the generative AI or not. For example, the generation unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0083] The generation unit can adjust the level of detail in the generated information based on the importance of the advertisements and discount information. For example, the generation unit can evaluate the importance of the advertisements and discount information and adjust the level of detail in the generated information. The generation unit can generate advertisements and discount information with detailed descriptions for those with high importance. The generation unit can also generate advertisements and discount information with concise content for those with low importance. For example, the generation unit can generate advertisements with detailed product descriptions and usage examples for those with high importance. The generation unit can also generate advertisements with only concise catchphrases and key features for those with low importance. Furthermore, the generation unit can adjust visual effects according to importance. For example, the generation unit can add visually striking effects to advertisements with high importance. This allows for efficient information provision by adjusting the level of detail in the generated information according to its importance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the importance data of the advertisements and discount information into a generation AI and have the generation AI perform the adjustment of the level of detail in the generated information.

[0084] The generation unit can apply different generation algorithms depending on the category of the advertisement or discount information during generation. For example, the generation unit can apply a generation algorithm that uses visually appealing images to food-related advertisements and discount information. For electronic device-related advertisements and discount information, it can apply a generation algorithm that emphasizes technical details. Furthermore, the generation unit can apply a generation algorithm that reflects trends to fashion-related advertisements and discount information. For example, the generation unit can apply a generation algorithm that uses images with vibrant colors and beautiful presentation to food-related advertisements. Furthermore, the generation unit can apply a generation algorithm that emphasizes the technical features and performance of products to electronic device-related advertisements. In addition, the generation unit can apply a generation algorithm that reflects the latest trends and styles to fashion-related advertisements. This allows for the provision of more effective advertisements and discount information by applying a generation algorithm appropriate to the category. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input category data of advertisements and discount information into a generation AI and have the generation AI execute the application of an appropriate generation algorithm.

[0085] The generation unit can estimate the user's emotions and adjust the length of the information it generates based on the estimated emotions. For example, the generation unit can estimate the user's emotions using an emotion estimation algorithm. The generation unit can also estimate emotions by analyzing the user's facial expressions and voice data. For example, the generation unit can estimate emotions based on changes in the user's facial expressions. Furthermore, the generation unit can estimate emotions by analyzing the tone and speed of the user's voice. Based on the estimated emotions, the generation unit adjusts the length of the information it generates. For example, if the user is in a hurry, the generation unit generates short, concise information. If the user is relaxed, the generation unit can generate longer information with detailed explanations. Furthermore, if the user is excited, the generation unit can generate information with visually stimulating effects. This allows for optimal information provision by adjusting the length of information according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generation AI. Generation AI includes, 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 generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input user facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0086] The generation unit can determine the generation priority based on the submission timing of advertisements and discount information during the generation process. For example, the generation unit can prioritize generating advertisements and discount information with approaching submission deadlines. It can also postpone the generation of advertisements and discount information with ample time before submission. Furthermore, the generation unit can adjust the generation schedule based on the submission timing. For example, the generation unit can prioritize generating advertisements and discount information with imminent submission deadlines. It can also postpone the generation of advertisements and discount information with ample time before submission. In addition, the generation unit can adjust the generation schedule based on the submission timing. This enables efficient information provision by determining the generation priority based on the submission timing. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input advertisement and discount information submission timing data into a generation AI and have the generation AI determine the generation priority.

[0087] The generation unit can adjust the generation order based on the relevance of advertisements and discount information during generation. For example, the generation unit can prioritize generating advertisements and discount information that are highly relevant to the user. The generation unit can postpone the generation of advertisements and discount information that are less relevant. Furthermore, the generation unit can optimize the generation order based on relevance. For example, the generation unit can prioritize generating advertisements and discount information that are highly relevant based on the user's interests and past behavior data. Furthermore, the generation unit can postpone the generation of advertisements and discount information that are less relevant. In addition, the generation unit can optimize the generation order based on relevance. This makes it possible to provide the user with the most optimal information by adjusting the generation order based on relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input relevance data of advertisements and discount information into a generation AI and have the generation AI perform the adjustment of the generation order.

[0088] The service provider can estimate the user's emotions and adjust the timing of push notifications based on those emotions. For example, the service provider can estimate the user's emotions using an emotion estimation algorithm. The service provider can also estimate emotions by analyzing the user's facial expressions and voice data. For example, the service provider can estimate emotions based on changes in the user's facial expressions. Furthermore, the service provider can estimate emotions by analyzing the tone and speed of the user's voice. Based on the estimated emotions, the service provider adjusts the timing of push notifications. For example, if the user is relaxed, the service provider can speed up the timing of push notifications. Conversely, if the user is stressed, the service provider can delay the timing of push notifications. Furthermore, if the user is in a hurry, the service provider can optimize the timing of push notifications. This reduces the user's burden by adjusting the timing of push notifications according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provision unit may be performed using AI or not using AI. For example, the service provision unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0089] The delivery unit can select the optimal delivery method by referring to the user's past response history at the time of delivery. For example, the delivery unit may prioritize delivery methods in which the user has shown a favorable response in the past. The delivery unit can analyze the user's past response history and select the optimal delivery method. Furthermore, the delivery unit can customize the delivery method based on the user's past response history. For example, the delivery unit may prioritize delivery methods in which the user has had a high click-through rate in the past. The delivery unit can also select the optimal delivery method based on the user's past purchase history. In addition, the delivery unit can customize the delivery method based on the user's past response history. This improves user satisfaction by selecting the optimal delivery method based on past response history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's past response history data into AI and have the AI ​​select the optimal delivery method.

[0090] The service provider can customize the content offered based on the user's current lifestyle at the time of delivery. For example, if the user is busy, the service provider can select concise content. If the user is relaxed, the service provider can select detailed content. The service provider can also customize the content according to the user's lifestyle. For example, the service provider can select highly relevant content based on data such as the user's occupation and family structure. The service provider can also customize the content based on the user's lifestyle. Furthermore, the service provider can determine the priority of the content offered according to the user's lifestyle. This allows for the provision of more appropriate information by customizing the content according to the user's lifestyle. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's lifestyle data into AI and have the AI ​​perform the customization of the content offered.

[0091] The service provider can estimate the user's emotions and determine the priority of push notifications based on those estimated emotions. For example, the service provider can estimate the user's emotions using an emotion estimation algorithm. The service provider can also estimate emotions by analyzing the user's facial expressions and voice data. For example, the service provider can estimate emotions based on changes in the user's facial expressions. Furthermore, the service provider can estimate emotions by analyzing the tone and speed of the user's voice. Based on the estimated emotions, the service provider determines the priority of push notifications. For example, if the user is relaxed, the service provider will prioritize high-priority push notifications. Also, if the user is stressed, the service provider can postpone lower-priority push notifications. Additionally, if the user is in a hurry, the service provider can prioritize only the essential push notifications. This allows for the priority delivery of important information by determining the priority of push notifications according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generating AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data into the generating AI and have the generating AI perform emotion estimation.

[0092] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can select a service delivery method relevant to that region. The service provider can select a highly relevant service delivery method based on the user's current location. Furthermore, if the user is on the move, the service provider can select a service delivery method relevant to their destination. For example, the service provider can prioritize providing information the user needs at their destination. In addition, if the user is staying in a specific location for an extended period, the service provider can select a service delivery method relevant to that location. For example, if the user is staying in a specific store for an extended period, the service provider can provide information relevant to that store. This allows the service provider to provide highly relevant information by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location information into AI and have the AI ​​select the optimal service delivery method.

[0093] The service provider can analyze the user's social media activity and adjust the content offered at the time of delivery. For example, the service provider can adjust the content based on information shared by the user on social media. The service provider can analyze the content of user posts and the number of likes to adjust the content. The service provider can also analyze the user's social media activity patterns and select content to offer. For example, the service provider can customize content based on topics the user has shown interest in. Furthermore, the service provider can determine the priority of content based on the user's social media activity history. For example, the service provider can prioritize providing information related to topics the user frequently shows interest in. This allows the service provider to provide information relevant to the user by analyzing social media activity. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input user social media activity data into AI and have the AI ​​perform the adjustment of content.

[0094] The pricing unit can estimate the user's emotions and adjust the pricing method based on the estimated emotions. For example, the pricing unit can estimate the user's emotions using an emotion estimation algorithm. The pricing unit can also estimate emotions by analyzing the user's facial expressions and voice data. For example, the pricing unit can estimate emotions based on changes in the user's facial expressions. Furthermore, the pricing unit can estimate emotions by analyzing the tone and speed of the user's voice. Based on the estimated emotions, the pricing unit adjusts the pricing method. For example, if the user is relaxed, the pricing unit can set flexible pricing. If the user is stressed, the pricing unit can also set pricing that emphasizes discounts. Furthermore, if the user is in a hurry, the pricing unit can set pricing quickly. This allows for the provision of more appropriate prices by adjusting the pricing method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the pricing unit may be performed using AI or not. For example, the pricing unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0095] The pricing unit can analyze a user's past purchase history to select the optimal pricing method when setting prices. For example, the pricing unit can set the optimal price based on the prices of products the user has purchased in the past. The pricing unit can analyze the price sensitivity of specific products from the user's past purchase history and adjust the pricing accordingly. The pricing unit can also consider the user's purchase frequency and set prices for repeat customers. For example, the pricing unit can set prices for related products based on data of products the user has purchased in the past. The pricing unit can also set discounted prices for repeat customers based on the user's purchase frequency. Furthermore, the pricing unit can select the optimal pricing method based on the user's past purchase history. This allows the system to provide the user with the best possible price by selecting the optimal pricing method based on past purchase history. Some or all of the above processes in the pricing unit may be performed using AI or not. For example, the pricing unit can input the user's past purchase history data into AI and have the AI ​​select the optimal pricing method.

[0096] The pricing unit can customize its pricing methods based on the user's current living situation when setting prices. For example, if the user is financially well-off, the pricing unit can set a premium price. If the user is in a financially difficult situation, the pricing unit can set a discounted price. The pricing unit can also customize its pricing methods according to the user's living situation. For example, the pricing unit can set highly relevant prices based on data such as the user's occupation and family structure. Furthermore, the pricing unit can also customize its pricing methods based on the user's living situation. In addition, the pricing unit can determine the priority of pricing according to the user's living situation. This allows for the provision of more appropriate prices by customizing the pricing methods according to the user's living situation. Some or all of the above processes in the pricing unit may be performed using AI or not. For example, the pricing unit can input user living situation data into AI and have the AI ​​perform the customization of pricing methods.

[0097] The pricing unit can estimate the user's emotions and determine pricing priorities based on those emotions. For example, the pricing unit can estimate the user's emotions using an emotion estimation algorithm. It can also estimate emotions by analyzing the user's facial expressions and voice data. For instance, it can estimate emotions based on changes in the user's facial expressions. Furthermore, it can estimate emotions by analyzing the tone and speed of the user's voice. Based on the estimated emotions, the pricing unit determines pricing priorities. For example, if the user is relaxed, the pricing unit prioritizes pricing for high-priority items. If the user is stressed, the pricing unit can postpone pricing for less important items. Additionally, if the user is in a hurry, the pricing unit can prioritize pricing for essential items. This allows for prioritizing pricing of important items based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the pricing unit may be performed using AI or not. For example, the pricing unit may input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0098] The pricing unit can select the optimal pricing method when setting prices, taking into account the user's geographical location information. For example, if the user is in a specific region, the pricing unit will set prices based on the market price of that region. If the user is on the move, the pricing unit can set prices considering the market price of the destination. Furthermore, if the user is staying in a specific location for an extended period, the pricing unit can also set prices based on the market price of that location. This makes it possible to set prices that are more relevant by taking into account the user's geographical location information. Some or all of the above processing in the pricing unit may be performed using AI, or not. For example, the pricing unit can input the user's geographical location information into AI and have the AI ​​select the optimal pricing method.

[0099] The pricing unit can analyze a user's social media activity and propose pricing methods when setting prices. For example, the pricing unit can propose pricing methods based on information shared by the user on social media. The pricing unit can analyze the content of user posts and the number of likes to propose pricing methods. The pricing unit can also analyze a user's social media activity patterns and select pricing methods. For example, the pricing unit can customize pricing methods based on topics the user has shown interest in. Furthermore, the pricing unit can determine pricing priorities based on the user's social media activity history. For example, the pricing unit can prioritize proposing pricing methods related to topics the user frequently shows interest in. In this way, by analyzing social media activity, the pricing unit can propose the most suitable pricing method for the user. Some or all of the above processes in the pricing unit may be performed using AI or not. For example, the pricing unit can input user social media activity data into AI and have the AI ​​propose pricing methods.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The data collection unit can collect not only user location information and purchasing behavior, but also user health data. For example, it can collect heart rate, steps, and sleep data from the user's smartwatch or fitness tracker. This allows for more personalized advertising and discount information based on the user's health status. For instance, if the user is relaxed after exercise, the data collection unit can display advertisements for relaxation-related products and services. Similarly, if the user is stressed, it can provide discount information on products and services that help reduce stress. Furthermore, the data collection unit can generate advertisements for health-conscious products based on the user's health data. This allows for improved user satisfaction by providing advertisements and discount information tailored to the user's health status.

[0102] The generation unit can analyze users' social media activity in addition to their location information and consumption behavior. For example, it can analyze information and posts shared by users on social media to understand their interests. This allows it to generate advertisements and discount information related to topics that users are interested in. For instance, if a user mentions a specific brand or product on social media, the generation unit will generate advertisements for that brand or product. It can also analyze photos and videos shared by users on social media and generate advertisements for related products. Furthermore, the generation unit can determine the optimal timing for advertisements based on the user's social media activity patterns. This makes it easier to attract user interest by providing advertisements and discount information based on the user's social media activity.

[0103] The service provider can estimate the user's emotions in addition to their location and consumption behavior, and customize the content of push notifications based on those estimated emotions. For example, if the user is relaxed, the service provider can deliver advertisements and discount information in a soft tone. If the user is stressed, it can also deliver discount information on products and services that help reduce stress. Furthermore, if the user is in a hurry, the service provider can deliver concise and to-the-point advertisements and discount information. By customizing the content of push notifications according to the user's emotions, the burden on the user is reduced, and information can be delivered effectively. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The pricing unit can set prices based on the user's location information, purchasing behavior, and past purchase history. For example, the pricing unit can analyze data on products the user has purchased in the past and set prices for related products. It can also set discounted prices for repeat customers, taking into account the user's purchase frequency. Furthermore, the pricing unit can provide discount information tailored to specific time periods based on the user's past purchase history. This maximizes sales by providing optimal prices based on the user's past behavior. Some or all of the above processes in the pricing unit may be performed using AI or not. For example, the pricing unit can input the user's past purchase history into AI and have the AI ​​set the optimal price.

[0105] The data collection unit can collect data considering the user's lifestyle, in addition to the user's location and consumption behavior. For example, the data collection unit can prioritize collecting highly relevant data based on data such as the user's occupation and family structure. It can also select the type of data to collect based on the user's lifestyle. For example, the data collection unit can collect concise data when the user is busy and detailed data when the user is relaxed. Furthermore, the data collection unit can determine the priority of the data to collect according to the user's lifestyle. This allows for the efficient collection of highly relevant data based on the user's lifestyle. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's lifestyle data into AI and have the AI ​​perform the optimal data collection.

[0106] The generation unit can estimate the user's emotions in addition to their location and consumption behavior, and adjust the presentation of advertisements and discount information based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate advertisements and discount information in a soft tone. If the user is in a hurry, it can generate concise and to-the-point advertisements and discount information. Furthermore, if the user is excited, it can generate visually stimulating advertisements and discount information. By adjusting the presentation of advertisements and discount information according to the user's emotions, it becomes easier to attract the user's attention. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0107] The delivery unit can select the optimal delivery method by referring to the user's past response history in addition to the user's location information and consumption behavior. For example, the delivery unit will prioritize delivery methods that the user has responded favorably to in the past. It can also analyze the user's past response history and select the optimal delivery method. Furthermore, it can customize the delivery method based on the user's past response history. For example, the delivery unit will prioritize delivery methods that the user has clicked on in the past. It can also select the optimal delivery method based on the user's past purchase history. In this way, user satisfaction can be improved by selecting the optimal delivery method based on past response history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's past response history data into AI and have the AI ​​select the optimal delivery method.

[0108] The data collection unit can estimate the user's emotions in addition to their location and consumption behavior, and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can postpone the collection of less important data. Conversely, if the user is relaxed, it can prioritize the collection of highly important data. Furthermore, if the user is in a hurry, it can prioritize the collection of only the essential data. In this way, by prioritizing data according to the user's emotions, important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0109] The generation unit can generate advertisements and discount information based on the user's location information, purchasing behavior, and past purchase history. For example, the generation unit can analyze data on products the user has purchased in the past and generate advertisements for related products. It can also generate advertisements for repeat customers, taking into account the user's purchase frequency. Furthermore, the generation unit can generate advertisements and discount information tailored to specific time periods based on the user's past purchase history. This makes it easier to attract the user's attention by providing optimal advertisements and discount information based on the user's past behavior. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's past purchase history into AI and have the AI ​​generate optimal advertisements and discount information.

[0110] The service provider can estimate the user's emotions in addition to their location and consumption behavior, and adjust the timing of push notifications based on the estimated emotions. For example, if the user is relaxed, the service provider can speed up the timing of push notifications. If the user is stressed, it can delay the timing of push notifications. Furthermore, if the user is in a hurry, it can optimize the timing of push notifications. This reduces the burden on the user by adjusting the timing of push notifications according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit collects user location information and consumer behavior. For example, it can collect user location information using GPS data or Wi-Fi location information, and also collect user purchase history and browsing history. This allows the system to understand data on products the user has purchased in the past and website browsing history, and to analyze their interests. Step 2: The generation unit analyzes the data collected by the collection unit and generates optimal advertisements and discount information for each user. For example, it can use a generation AI to generate advertisements and discount information based on the user's interests and past behavior data, and a text generation AI (e.g., LLM) to generate advertisement copy that is optimal for the user. It can also use a multimodal generation AI to generate advertisements that combine images and text. Step 3: The delivery unit provides the information generated by the generation unit via push notifications. For example, it can use the smartphone's notification function to provide users with advertisements and discount information, and customize the timing and content of the notifications. If a user is near a specific store, it can also provide discount information for that store via push notifications. Step 4: The pricing unit performs dynamic pricing based on the information generated by the generation unit. For example, it sets prices based on the user's past purchase history and behavior patterns, and uses AI to execute a price fluctuation algorithm to calculate the optimal price. The pricing unit adjusts prices considering the balance of user supply and demand, and provides discount information specifically for off-peak hours and low-occupancy periods.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, generation unit, provision unit, and pricing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user location information using GPS data and Wi-Fi location information from the smart device 14 and analyzes consumer behavior using the specific processing unit 290 of the data processing unit 12. The generation unit generates advertisements and discount information using generation AI, for example, using the specific processing unit 290 of the data processing unit 12. The provision unit provides the generated information to the user, for example, using the notification function of the smart device 14. The pricing unit performs dynamic pricing, for example, using the specific processing unit 290 of the data processing unit 12. The collection unit can estimate the user's emotions and adjust the timing of collecting location information and consumer behavior based on the estimated emotions. Emotion estimation is achieved, for example, by analyzing the user's facial expressions and voice data using the camera 42 and microphone 38B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, generation unit, provision unit, and pricing unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user location information using GPS data and Wi-Fi location information from the smart glasses 214 and analyzes consumer behavior using the identification processing unit 290 of the data processing unit 12. The generation unit generates advertisements and discount information using generation AI, for example, using the identification processing unit 290 of the data processing unit 12. The provision unit provides the generated information to the user, for example, using the notification function of the smart glasses 214. The pricing unit performs dynamic pricing, for example, using the identification processing unit 290 of the data processing unit 12. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting location information and consumer behavior based on the estimated emotions. Emotion estimation is achieved, for example, by analyzing the user's facial expressions and voice data using the camera 42 and microphone 238 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, generation unit, provision unit, and pricing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user location information using GPS data and Wi-Fi location information from the headset terminal 314 and analyzes consumer behavior using the specific processing unit 290 of the data processing unit 12. The generation unit generates advertisements and discount information using generation AI, for example, using the specific processing unit 290 of the data processing unit 12. The provision unit provides the generated information to the user, for example, using the notification function of the headset terminal 314. The pricing unit performs dynamic pricing, for example, using the specific processing unit 290 of the data processing unit 12. The collection unit can estimate the user's emotions and adjust the timing of collecting location information and consumer behavior based on the estimated emotions. Emotion estimation is achieved, for example, by analyzing the user's facial expressions and voice data using the camera 42 and microphone 238 of the headset terminal 314. The correspondence between each unit and the device and control unit is not limited to the examples described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, generation unit, provision unit, and pricing unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user location information using GPS data and Wi-Fi location information from the robot 414 and analyzes consumer behavior using the specific processing unit 290 of the data processing unit 12. The generation unit generates advertisements and discount information using generation AI, for example, by the specific processing unit 290 of the data processing unit 12. The provision unit provides the generated information to the user, for example, using the notification function of the robot 414. The pricing unit performs dynamic pricing, for example, by the specific processing unit 290 of the data processing unit 12. The collection unit can estimate the user's emotions and adjust the timing of collecting location information and consumer behavior based on the estimated emotions. Emotion estimation is achieved, for example, by analyzing the user's facial expressions and voice data using the camera 42 and microphone 238 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A data collection unit that collects user location information and consumption behavior, A generation unit analyzes the data collected by the aforementioned collection unit and generates optimal advertisements and discount information for each user, A providing unit that provides the information generated by the generation unit via push notification, The system includes a pricing unit that performs dynamic pricing based on the information generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects user location information and consumption behavior in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The collected data is analyzed to generate personalized advertisements and discount information for each user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, The generated information will be delivered to the user via push notification. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned pricing unit is Dynamic pricing is implemented based on the user's past purchase history and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We provide discount information specifically for off-peak hours and times with low occupancy rates. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is Generate information that helps streamline marketing campaigns and reduce operating costs. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of location data and consumer behavior collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze users' past location data and consumption behavior to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is We estimate user emotions and adjust how ads and discount information are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, adjust the level of detail based on the importance of the advertisements and discount information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, different generation algorithms are applied depending on the category of the advertisement or discount information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts the length of the information generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the generation priority is determined based on the timing of submission of advertisements and discount information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the order of generation is adjusted based on the relevance of advertisements and discount information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts the timing of push notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, the content will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and prioritizes push notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and adjust the content accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned pricing unit is It estimates user sentiment and adjusts pricing based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned pricing unit is When setting prices, the system analyzes the user's past purchase history to select the optimal pricing method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned pricing unit is When setting prices, customize the pricing method based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned pricing unit is The system estimates user sentiment and determines pricing priorities based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned pricing unit is When setting prices, the optimal pricing method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned pricing unit is When setting prices, we analyze users' social media activity and suggest pricing methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 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 location information and consumption behavior, A generation unit analyzes the data collected by the aforementioned collection unit and generates optimal advertisements and discount information for each user, A providing unit that provides the information generated by the generation unit via push notification, The system includes a pricing unit that performs dynamic pricing based on the information generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collects user location information and consumption behavior in real time. The system according to feature 1.

3. The generating unit is The collected data is analyzed to generate personalized advertisements and discount information for each user. The system according to feature 1.

4. The aforementioned supply unit is, The generated information will be delivered to the user via push notification. The system according to feature 1.

5. The aforementioned pricing unit is Dynamic pricing is implemented based on the user's past purchase history and behavioral patterns. The system according to feature 1.

6. The aforementioned supply unit is, We provide discount information specifically for off-peak hours and times with low occupancy rates. The system according to feature 1.

7. The generating unit is Generate information that helps streamline marketing campaigns and reduce operating costs. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of location data and consumer behavior collection based on those estimated emotions. The system according to feature 1.

Images