system

Abstract

The system according to this embodiment aims to integrate user data from multiple platforms and provide personalized recommendations. [Solution] The system according to the embodiment comprises a collection unit, an integration unit, a recommendation unit, an update unit, and an optimization unit. The collection unit collects user data. The integration unit integrates the data collected by the collection unit. The recommendation unit makes personalized recommendations based on the data integrated by the integration unit. The update unit updates the recommendations made by the recommendation unit in real time. The optimization unit optimizes cross-selling based on the recommendations made by the recommendation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, user data from multiple platforms has not been sufficiently integrated to perform personalized recommendations, and there is room for improvement.

[0005] The system according to the embodiment aims to integrate user data from multiple platforms and perform personalized recommendations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an integration unit, a recommendation unit, an update unit, and an optimization unit. The collection unit collects user data. The integration unit integrates the data collected by the collection unit. The recommendation unit makes personalized recommendations based on the data integrated by the integration unit. The update unit updates the recommendations made by the recommendation unit in real time. The optimization unit optimizes cross-selling based on the recommendations made by the recommendation unit. [Effects of the Invention]

[0007] The system according to this embodiment can integrate user data from multiple platforms and provide personalized recommendations. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between 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) An AI content recommendation system according to an embodiment of the present invention is an advanced system that integrates data from media and e-commerce platforms to provide hyper-personalized content and product recommendations. This system leverages cutting-edge AI algorithms and vast amounts of user data to significantly improve user engagement, increase cross-platform sales, and maximize the value of existing digital assets. For example, the AI ​​content recommendation system creates comprehensive user profiles across all platforms. Furthermore, it seamlessly integrates user data from various services to gain holistic insights. The AI ​​content recommendation system utilizes advanced machine learning to provide highly accurate recommendations. Additionally, it continuously updates recommendations based on user behavior and preferences. The AI ​​content recommendation system identifies and suggests relevant products and services across platforms. Through this, the AI ​​content recommendation system aims to transform the digital ecosystem into a highly personalized and interconnected user experience. By improving user engagement and satisfaction and maximizing the potential of diverse digital assets, it drives business growth. This enables the AI ​​content recommendation system to effectively compete with global tech giants and establish itself as a digital powerhouse setting new standards for user-centric, AI-driven digital services in Japan and beyond. This enables AI content recommendation systems to efficiently collect, integrate, recommend, update, and optimize user data.

[0029] The AI ​​content recommendation system according to this embodiment comprises a collection unit, an integration unit, a recommendation unit, an update unit, and an optimization unit. The collection unit collects user data. The collection unit can collect, for example, user behavior data, location information, and purchase history. The collection unit can collect, for example, website click data. The collection unit can also collect smartphone location information. Furthermore, the collection unit can also collect purchase history on e-commerce platforms. The integration unit integrates the data collected by the collection unit. The integration unit can, for example, perform data format conversion to unify different data formats. The integration unit can also process duplicate data to create a consistent dataset. Furthermore, the integration unit can seamlessly integrate data from different services. The recommendation unit makes personalized recommendations based on the data integrated by the integration unit. The recommendation unit can, for example, use advanced machine learning algorithms to make recommendations based on user preferences and past behavior. The recommendation unit can also use collaborative filtering to make recommendations based on the behavior data of other users. Furthermore, the recommendation unit can use natural language processing technology to analyze user queries and feedback to improve the accuracy of recommendations. The update unit updates the recommendations made by the recommendation unit in real time. The update unit continuously updates recommendations based, for example, on user behavior and preferences. The update unit can also adjust the recommendations based on user feedback. In addition, the update unit can predict trends and recommend new content and products. The optimization unit optimizes cross-selling based on the recommendations made by the recommendation unit. The optimization unit identifies and suggests related products and services, for example. The optimization unit can also optimize cross-selling opportunities based on the user's purchase history and preferences. Furthermore, the optimization unit can apply optimization algorithms to maximize sales across the entire platform. As a result, the AI ​​content recommendation system according to the embodiment can efficiently collect, integrate, recommend, update, and optimize user data.

[0030] The data collection unit collects user data. For example, it can collect user behavior data, location information, and purchase history. Specifically, when collecting website click data, it collects detailed behavioral data such as which pages users visited, which links they clicked, their time spent on the site, and their scrolling movements. This allows for a detailed understanding of user interests and preferences. When collecting smartphone location information, GPS data is used to obtain the user's current location and travel history, allowing for the identification of the user's living area and frequently visited locations. Furthermore, when collecting purchase history on e-commerce platforms, detailed data such as the type of product purchased, price, purchase date and time, and purchase frequency are collected, allowing for the analysis of user purchasing patterns and preferences. This data is collected in real time and stored in a secure database. The data collection unit can flexibly respond to specific situations and conditions by adjusting the data collection frequency and accuracy. For example, during a specific campaign period, the collection frequency can be increased to obtain detailed data, allowing for real-time evaluation of the campaign's effectiveness. This enables the data collection unit to efficiently collect a wide range of user data from diverse data sources, improving the overall system performance.

[0031] The Integration Unit integrates the data collected by the Collection Unit. For example, the Integration Unit performs data format conversion, unifying different data formats. Specifically, it converts data in different formats, such as website click data, smartphone location data, and e-commerce platform purchase history, into a consistent format. The Integration Unit can also process duplicate data and create consistent datasets. For example, if the same user uses multiple devices, it integrates data collected from each device and eliminates duplicates to create an accurate user profile. Furthermore, the Integration Unit can seamlessly integrate data from different services. For example, it integrates data collected from multiple services, such as social media data, email marketing data, and customer support data, to create a comprehensive user dataset. This allows the Integration Unit to efficiently integrate collected data and ensure data consistency and reliability. Additionally, the Integration Unit can preprocess and clean data to provide high-quality datasets suitable for analysis and recommendations. This allows the Integration Unit to improve the overall data quality of the system and provide a foundation for accurate recommendations.

[0032] The recommendation department provides personalized recommendations based on data integrated by the integration department. For example, it uses advanced machine learning algorithms to make recommendations based on user preferences and past behavior. Specifically, it analyzes users' past purchase and browsing history, and references data from other users with similar preferences to recommend the most suitable content and products. The recommendation department can also use collaborative filtering to make recommendations based on the behavioral data of other users. For example, it analyzes what other products users who purchased the same product have purchased and recommends related products. Furthermore, the recommendation department can use natural language processing techniques to analyze user queries and feedback, improving the accuracy of recommendations. For example, it analyzes keywords searched by users and the content of reviews to understand user intent and sentiment, leading to more appropriate recommendations. This allows the recommendation department to provide personalized recommendations based on user preferences and behavior, improving the user experience. Additionally, the recommendation department can continuously improve its recommendations based on real-time updated data, adapting to the latest trends and user changes. This ensures that the recommendation department always provides optimal recommendations, increasing user satisfaction.

[0033] The update unit updates recommendations made by the recommendation unit in real time. The update unit continuously updates recommendations based on user behavior and preferences, for example. Specifically, it instantly adjusts recommendations based on newly purchased products and viewed content. The update unit can also adjust recommendations based on user feedback. For example, if a user gives a high rating to a recommended product, it will prioritize recommending other related products. Conversely, if a user gives a low rating, similar products will be excluded from recommendations. Furthermore, the update unit can predict trends and recommend new content and products. For example, it can predict trends based on seasons and events and recommend content and products that are suitable for the user. This allows the update unit to always provide the best recommendations based on the user's latest behavior and preferences. In addition, the update unit can continuously improve recommendations based on real-time updated data, responding to the latest trends and changes in user behavior. This allows the update unit to always provide the best recommendations and increase user satisfaction.

[0034] The Optimization Unit optimizes cross-selling based on recommendations made by the Recommendation Unit. For example, the Optimization Unit identifies and suggests relevant products and services. Specifically, it identifies highly relevant products and services based on the user's purchase history and preferences, maximizing cross-selling opportunities. The Optimization Unit can also optimize cross-selling opportunities based on the user's purchase history and preferences. For example, if a user frequently purchases products in a particular category, it will recommend other products related to that category. Furthermore, the Optimization Unit can apply optimization algorithms to maximize sales across the entire platform. For example, it can formulate and implement the most effective cross-selling strategy, taking into account inventory levels and profit margins. This allows the Optimization Unit to effectively recommend highly relevant products and services to users, maximizing overall platform sales. Additionally, the Optimization Unit can continuously improve its cross-selling strategy based on real-time updated data, adapting to the latest trends and user changes. This ensures that the Optimization Unit always provides the optimal cross-selling strategy, increasing user satisfaction.

[0035] The data collection unit can collect user behavior data. For example, the data collection unit can collect user click data. For example, the data collection unit can also collect user browsing history. For example, the data collection unit can also collect user purchase history. By collecting user behavior data, more accurate recommendations become possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user behavior data into AI, and the AI ​​can collect the data.

[0036] The integration unit can seamlessly integrate user data from various services. For example, the integration unit can unify different data formats. The integration unit can also process duplicate data. The integration unit can also integrate data from different services. This allows for holistic insights to be gained by integrating data from various services. Some or all of the above-described processes in the integration unit may be performed using AI, or not. For example, the integration unit can input data from different services into an AI, which can then integrate the data.

[0037] The recommendation system can make highly accurate recommendations using advanced machine learning. For example, the recommendation system can use deep learning for recommendations. The recommendation system can also use reinforcement learning for recommendations. The recommendation system can also use collaborative filtering for recommendations. This improves the accuracy of recommendations by utilizing advanced machine learning. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can input user data into an AI, which can then make recommendations.

[0038] The update unit can continuously update recommendations based on user behavior and preferences. For example, the update unit can update recommendations based on user behavior data. The update unit can also update recommendations based on user preference data. The update unit can also update recommendations based on user feedback. This ensures that the latest information is always provided by updating recommendations based on user behavior and preferences. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input user data into AI, and the AI ​​can update the recommendations.

[0039] The optimization unit can identify and suggest relevant products and services across the entire platform. For example, the optimization unit can suggest relevant products based on the user's purchase history. The optimization unit can also suggest relevant services based on the user's preference data. The optimization unit can also apply algorithms to optimize cross-selling opportunities. This allows for the optimization of cross-selling opportunities by identifying and suggesting relevant products and services. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user data into AI, which can then perform the optimization.

[0040] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the data collection unit can identify the time periods when the user frequently accessed the system in the past and collect data during those times. The data collection unit can also select the optimal data collection method based on the devices the user has used in the past. For example, the data collection unit can analyze the user's past behavioral patterns and propose the most effective data collection method. This allows the optimal data collection method to be selected by analyzing the user's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into AI, which can then select the optimal data collection method.

[0041] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, the data collection unit can prioritize collecting relevant data based on the content the user is currently viewing. The data collection unit can also collect only the necessary data based on the user's current activities. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. This allows for the collection of highly relevant data by filtering data based on the user's current activities and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activities and areas of interest into the AI, which can then filter the data.

[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 can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of data around the user's home. This allows for the priority 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, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the AI, which can then prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on content shared by the user on social media. The data collection unit can also analyze the time periods in which a user is active on social media and collect data during those times. The data collection unit can also analyze the user's areas of interest on social media and collect relevant data. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI, and the AI ​​can collect relevant data.

[0044] The integration unit can improve the accuracy of data integration by evaluating the reliability of different data sources during data integration. For example, the integration unit can calculate a reliability score for each data source and prioritize the integration of reliable data. The integration unit can also evaluate the historical performance of data sources and select reliable data. For example, the integration unit can evaluate the reliability of the data source providers and integrate reliable data. This improves the accuracy of integration by evaluating the reliability of different data sources. Some or all of the above processes in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input the reliability of the data sources into the AI, which can then select the reliable data.

[0045] The integration unit can apply different integration algorithms depending on the data category during data integration. For example, the integration unit can apply a natural language processing algorithm to text data and integrate it. For example, the integration unit can also apply an image recognition algorithm to image data and integrate it. For example, the integration unit can also apply a statistical algorithm to numerical data and integrate it. This improves the accuracy of integration by applying different integration algorithms depending on the data category. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the data category into the AI, and the AI ​​can apply an appropriate integration algorithm.

[0046] The integration unit can determine the integration priority based on the data collection timing during data integration. For example, the integration unit may prioritize the integration of the most recent data to provide real-time information. The integration unit may also integrate historical data to understand long-term trends. For example, the integration unit may prioritize the integration of data from a specific period and perform analysis on that period. This allows for the provision of up-to-date information by determining the integration priority based on the data collection timing. Some or all of the above processes in the integration unit may be performed using AI, for example, or not. For example, the integration unit can input the data collection timing into the AI, which can then determine the integration priority.

[0047] The integration unit can adjust the integration order based on the relevance of the data during data integration. For example, the integration unit can prioritize the integration of highly relevant data to perform highly accurate analysis. The integration unit can also, for example, postpone the integration of less relevant data to perform efficient data integration. The integration unit can also, for example, evaluate the relevance of the data and integrate it in the optimal order. This enables efficient data integration by adjusting the integration order based on the relevance of the data. Some or all of the above processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the relevance of the data into the AI, and the AI ​​can adjust the integration order.

[0048] The recommendation system can analyze the user's past behavior data to select the optimal recommendation method. For example, the recommendation system can select the optimal recommendation method based on routes the user has used in the past. For example, the recommendation system can select a recommendation method that avoids congestion based on the user's past behavior history. For example, the recommendation system can analyze the user's past behavior history to select the most efficient recommendation method. In this way, the optimal recommendation method can be selected by analyzing the user's past behavior data. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the user's past behavior data into AI, and the AI ​​can select the optimal recommendation method.

[0049] The recommendation system can customize its recommendation methods based on the user's current activity. For example, it can make relevant recommendations based on the content the user is currently viewing. It can also make optimal recommendations based on the user's current activities. It can also make customized recommendations based on the user's current areas of interest. By customizing the recommendation methods based on the user's current activity, more appropriate recommendations become possible. Some or all of the above processes in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the user's current activity into AI, which can then customize the recommendation methods.

[0050] The recommendation system can select the optimal recommendation method by considering the user's geographical location. For example, if the user is in a specific region, the recommendation system can provide recommendations related to that region. For example, if the user is traveling, the recommendation system can provide recommendations related to the travel destination. For example, if the user is at home, the recommendation system can provide recommendations around the user's home. This allows the system to select the optimal recommendation method by considering the user's geographical location. Some or all of the above processing in the recommendation system may be performed using AI, or not. For example, the recommendation system can input the user's geographical location into the AI, which can then select the optimal recommendation method.

[0051] The recommendation unit can analyze a user's social media activity and suggest recommendation methods when making recommendations. For example, the recommendation unit can make relevant recommendations based on content shared by the user on social media. The recommendation unit can also analyze the times of day when a user is active on social media and make recommendations that are best suited to those times. The recommendation unit can also analyze the user's areas of interest on social media and make relevant recommendations. In this way, relevant recommendations can be made by analyzing the user's social media activity. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input the user's social media activity into AI, and the AI ​​can suggest recommendation methods.

[0052] The update unit can optimize the update algorithm by referring to past update data during the update process. For example, the update unit can analyze past update data and select the most effective update algorithm. The update unit can also evaluate user responses from past update data and adjust the algorithm accordingly. For example, the update unit can optimize the timing and content of updates based on past update data. This allows the update algorithm to be optimized by referring to past update data. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input past update data into AI, which can then optimize the update algorithm.

[0053] The update unit can customize the content of updates based on user behavior data during the update process. For example, the update unit can provide individually customized update content based on the user's past behavior data. The update unit can also suggest optimal update content based on the user's current behavior. The update unit can also provide relevant update content based on the user's areas of interest. By customizing the update content based on user behavior data, more appropriate updates become possible. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input user behavior data into AI, which can then customize the update content.

[0054] The update unit can select the optimal update method by considering the user's device information during the update process. For example, if the user is using a smartphone, the update unit can provide an update method optimized for the device. For example, if the user is using a tablet, the update unit can also provide an update method optimized for a larger screen. For example, if the user is using a smartwatch, the update unit can also provide a concise and highly visible update method. This allows the system to select the optimal update method by considering the user's device information. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's device information into the AI, which can then select the optimal update method.

[0055] The update unit can analyze the user's social media activity and suggest update content during the update process. For example, the update unit can suggest relevant update content based on content shared by the user on social media. The update unit can also analyze the user's social media activity times and suggest the most suitable update content for those times. The update unit can also analyze the user's social media interests and suggest relevant update content. In this way, relevant update content can be suggested by analyzing the user's social media activity. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's social media activity into AI, which can then suggest update content.

[0056] The optimization unit can select the optimal optimization method by analyzing the user's past behavior data during optimization. For example, the optimization unit can select the optimal optimization method based on routes previously used by the user. For example, the optimization unit can also select an optimization method that avoids congestion based on the user's past behavior history. For example, the optimization unit can analyze the user's past behavior history and select the most efficient optimization method. In this way, the optimal optimization method can be selected by analyzing the user's past behavior data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the user's past behavior data into AI, and the AI ​​can select the optimal optimization method.

[0057] The optimization unit can customize the optimization methods based on the user's current activity during optimization. For example, the optimization unit performs relevant optimizations based on the content the user is currently viewing. The optimization unit can also perform optimal optimizations based on the user's current activities. The optimization unit can also perform customized optimizations based on the user's current areas of interest. This allows for more appropriate optimization by customizing the optimization methods based on the user's current activity. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the user's current activity into the AI, which can then customize the optimization methods.

[0058] The optimization unit can select the optimal optimization method by considering the user's geographical location information during optimization. For example, if the user is in a specific region, the optimization unit can perform optimizations related to that region. For example, if the user is traveling, the optimization unit can also perform optimizations related to the travel destination. For example, if the user is at home, the optimization unit can also perform optimizations around the user's home. In this way, the optimal optimization method can be selected by considering the user's geographical location information. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the user's geographical location information into the AI, and the AI ​​can select the optimal optimization method.

[0059] The optimization unit can analyze the user's social media activity and propose optimization methods during the optimization process. For example, the optimization unit can perform relevant optimizations based on the content the user shares on social media. The optimization unit can also analyze the time periods in which the user is active on social media and perform optimizations that are optimal for those times. The optimization unit can also analyze the user's areas of interest on social media and perform relevant optimizations. In this way, relevant optimizations can be performed by analyzing the user's social media activity. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the user's social media activity into AI, and the AI ​​can propose optimization methods.

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

[0061] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the data collection unit can identify the time periods when the user frequently accessed the system in the past and collect data during those times. The data collection unit can also select the optimal data collection method based on the devices the user has used in the past. For example, the data collection unit can analyze the user's past behavioral patterns and propose the most effective data collection method. This allows the optimal data collection method to be selected by analyzing the user's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into AI, which can then select the optimal data collection method.

[0062] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, the data collection unit can prioritize collecting relevant data based on the content the user is currently viewing. The data collection unit can also collect only the necessary data based on the user's current activities. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. This allows for the collection of highly relevant data by filtering data based on the user's current activities and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activities and areas of interest into the AI, which can then filter the data.

[0063] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of data around the user's home. This allows for the priority 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, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the AI, which can then prioritize the collection of highly relevant data.

[0064] The integration unit can improve the accuracy of data integration by evaluating the reliability of different data sources during data integration. For example, the integration unit can calculate a reliability score for each data source and prioritize the integration of reliable data. The integration unit can also evaluate the historical performance of data sources and select reliable data. For example, the integration unit can evaluate the reliability of the data source providers and integrate reliable data. This improves the accuracy of integration by evaluating the reliability of different data sources. Some or all of the above processes in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input the reliability of the data sources into the AI, which can then select the reliable data.

[0065] The integration unit can apply different integration algorithms depending on the data category during data integration. For example, the integration unit can apply a natural language processing algorithm to text data and integrate it. For example, the integration unit can also apply an image recognition algorithm to image data and integrate it. For example, the integration unit can also apply a statistical algorithm to numerical data and integrate it. This improves the accuracy of integration by applying different integration algorithms depending on the data category. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the data category into the AI, and the AI ​​can apply an appropriate integration algorithm.

[0066] The integration unit can determine the integration priority based on the data collection timing during data integration. For example, the integration unit may prioritize the integration of the most recent data to provide real-time information. The integration unit may also integrate historical data to understand long-term trends. For example, the integration unit may prioritize the integration of data from a specific period and perform analysis on that period. This allows for the provision of up-to-date information by determining the integration priority based on the data collection timing. Some or all of the above processes in the integration unit may be performed using AI, for example, or not. For example, the integration unit can input the data collection timing into the AI, which can then determine the integration priority.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects user data. The data collection unit can collect, for example, user behavior data, location information, and purchase history. The data collection unit collects website click data, smartphone location information, and purchase history on e-commerce platforms. Step 2: The Integration Unit integrates the data collected by the Collection Unit. The Integration Unit performs data format conversion and unifies different data formats. It also processes duplicate data to create a consistent dataset. Furthermore, it seamlessly integrates data from different services. Step 3: The recommendation department provides personalized recommendations based on the data integrated by the integration department. The recommendation department uses advanced machine learning algorithms to make recommendations based on user preferences and past behavior. It can also use collaborative filtering to make recommendations based on the behavioral data of other users. Furthermore, it uses natural language processing techniques to analyze user queries and feedback to improve the accuracy of recommendations. Step 4: The update department updates the recommendations made by the recommendation department in real time. The update department continuously updates recommendations based on user behavior and preferences. It also adjusts the recommendations based on user feedback. Furthermore, it predicts trends and recommends new content and products. Step 5: The optimization unit optimizes cross-selling based on recommendations made by the recommendation unit. The optimization unit identifies and suggests relevant products and services. It also optimizes cross-selling opportunities based on the user's purchase history and preferences. Furthermore, it applies optimization algorithms to maximize sales across the entire platform.

[0069] (Example of form 2) An AI content recommendation system according to an embodiment of the present invention is an advanced system that integrates data from media and e-commerce platforms to provide hyper-personalized content and product recommendations. This system leverages cutting-edge AI algorithms and vast amounts of user data to significantly improve user engagement, increase cross-platform sales, and maximize the value of existing digital assets. For example, the AI ​​content recommendation system creates comprehensive user profiles across all platforms. Furthermore, it seamlessly integrates user data from various services to gain holistic insights. The AI ​​content recommendation system utilizes advanced machine learning to provide highly accurate recommendations. Additionally, it continuously updates recommendations based on user behavior and preferences. The AI ​​content recommendation system identifies and suggests relevant products and services across platforms. Through this, the AI ​​content recommendation system aims to transform the digital ecosystem into a highly personalized and interconnected user experience. By improving user engagement and satisfaction and maximizing the potential of diverse digital assets, it drives business growth. This enables the AI ​​content recommendation system to effectively compete with global tech giants and establish itself as a digital powerhouse setting new standards for user-centric, AI-driven digital services in Japan and beyond. This enables AI content recommendation systems to efficiently collect, integrate, recommend, update, and optimize user data.

[0070] The AI ​​content recommendation system according to this embodiment comprises a collection unit, an integration unit, a recommendation unit, an update unit, and an optimization unit. The collection unit collects user data. The collection unit can collect, for example, user behavior data, location information, and purchase history. The collection unit can collect, for example, website click data. The collection unit can also collect smartphone location information. Furthermore, the collection unit can also collect purchase history on e-commerce platforms. The integration unit integrates the data collected by the collection unit. The integration unit can, for example, perform data format conversion to unify different data formats. The integration unit can also process duplicate data to create a consistent dataset. Furthermore, the integration unit can seamlessly integrate data from different services. The recommendation unit makes personalized recommendations based on the data integrated by the integration unit. The recommendation unit can, for example, use advanced machine learning algorithms to make recommendations based on user preferences and past behavior. The recommendation unit can also use collaborative filtering to make recommendations based on the behavior data of other users. Furthermore, the recommendation unit can use natural language processing technology to analyze user queries and feedback to improve the accuracy of recommendations. The update unit updates the recommendations made by the recommendation unit in real time. The update unit continuously updates recommendations based, for example, on user behavior and preferences. The update unit can also adjust the recommendations based on user feedback. In addition, the update unit can predict trends and recommend new content and products. The optimization unit optimizes cross-selling based on the recommendations made by the recommendation unit. The optimization unit identifies and suggests related products and services, for example. The optimization unit can also optimize cross-selling opportunities based on the user's purchase history and preferences. Furthermore, the optimization unit can apply optimization algorithms to maximize sales across the entire platform. As a result, the AI ​​content recommendation system according to the embodiment can efficiently collect, integrate, recommend, update, and optimize user data.

[0071] The data collection unit collects user data. For example, it can collect user behavior data, location information, and purchase history. Specifically, when collecting website click data, it collects detailed behavioral data such as which pages users visited, which links they clicked, their time spent on the site, and their scrolling movements. This allows for a detailed understanding of user interests and preferences. When collecting smartphone location information, GPS data is used to obtain the user's current location and travel history, allowing for the identification of the user's living area and frequently visited locations. Furthermore, when collecting purchase history on e-commerce platforms, detailed data such as the type of product purchased, price, purchase date and time, and purchase frequency are collected, allowing for the analysis of user purchasing patterns and preferences. This data is collected in real time and stored in a secure database. The data collection unit can flexibly respond to specific situations and conditions by adjusting the data collection frequency and accuracy. For example, during a specific campaign period, the collection frequency can be increased to obtain detailed data, allowing for real-time evaluation of the campaign's effectiveness. This enables the data collection unit to efficiently collect a wide range of user data from diverse data sources, improving the overall system performance.

[0072] The Integration Unit integrates the data collected by the Collection Unit. For example, the Integration Unit performs data format conversion, unifying different data formats. Specifically, it converts data in different formats, such as website click data, smartphone location data, and e-commerce platform purchase history, into a consistent format. The Integration Unit can also process duplicate data and create consistent datasets. For example, if the same user uses multiple devices, it integrates data collected from each device and eliminates duplicates to create an accurate user profile. Furthermore, the Integration Unit can seamlessly integrate data from different services. For example, it integrates data collected from multiple services, such as social media data, email marketing data, and customer support data, to create a comprehensive user dataset. This allows the Integration Unit to efficiently integrate collected data and ensure data consistency and reliability. Additionally, the Integration Unit can preprocess and clean data to provide high-quality datasets suitable for analysis and recommendations. This allows the Integration Unit to improve the overall data quality of the system and provide a foundation for accurate recommendations.

[0073] The recommendation department provides personalized recommendations based on data integrated by the integration department. For example, it uses advanced machine learning algorithms to make recommendations based on user preferences and past behavior. Specifically, it analyzes users' past purchase and browsing history, and references data from other users with similar preferences to recommend the most suitable content and products. The recommendation department can also use collaborative filtering to make recommendations based on the behavioral data of other users. For example, it analyzes what other products users who purchased the same product have purchased and recommends related products. Furthermore, the recommendation department can use natural language processing techniques to analyze user queries and feedback, improving the accuracy of recommendations. For example, it analyzes keywords searched by users and the content of reviews to understand user intent and sentiment, leading to more appropriate recommendations. This allows the recommendation department to provide personalized recommendations based on user preferences and behavior, improving the user experience. Additionally, the recommendation department can continuously improve its recommendations based on real-time updated data, adapting to the latest trends and user changes. This ensures that the recommendation department always provides optimal recommendations, increasing user satisfaction.

[0074] The update unit updates recommendations made by the recommendation unit in real time. The update unit continuously updates recommendations based on user behavior and preferences, for example. Specifically, it instantly adjusts recommendations based on newly purchased products and viewed content. The update unit can also adjust recommendations based on user feedback. For example, if a user gives a high rating to a recommended product, it will prioritize recommending other related products. Conversely, if a user gives a low rating, similar products will be excluded from recommendations. Furthermore, the update unit can predict trends and recommend new content and products. For example, it can predict trends based on seasons and events and recommend content and products that are suitable for the user. This allows the update unit to always provide the best recommendations based on the user's latest behavior and preferences. In addition, the update unit can continuously improve recommendations based on real-time updated data, responding to the latest trends and changes in user behavior. This allows the update unit to always provide the best recommendations and increase user satisfaction.

[0075] The Optimization Unit optimizes cross-selling based on recommendations made by the Recommendation Unit. For example, the Optimization Unit identifies and suggests relevant products and services. Specifically, it identifies highly relevant products and services based on the user's purchase history and preferences, maximizing cross-selling opportunities. The Optimization Unit can also optimize cross-selling opportunities based on the user's purchase history and preferences. For example, if a user frequently purchases products in a particular category, it will recommend other products related to that category. Furthermore, the Optimization Unit can apply optimization algorithms to maximize sales across the entire platform. For example, it can formulate and implement the most effective cross-selling strategy, taking into account inventory levels and profit margins. This allows the Optimization Unit to effectively recommend highly relevant products and services to users, maximizing overall platform sales. Additionally, the Optimization Unit can continuously improve its cross-selling strategy based on real-time updated data, adapting to the latest trends and user changes. This ensures that the Optimization Unit always provides the optimal cross-selling strategy, increasing user satisfaction.

[0076] The data collection unit can collect user behavior data. For example, the data collection unit can collect user click data. For example, the data collection unit can also collect user browsing history. For example, the data collection unit can also collect user purchase history. By collecting user behavior data, more accurate recommendations become possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user behavior data into AI, and the AI ​​can collect the data.

[0077] The integration unit can seamlessly integrate user data from various services. For example, the integration unit can unify different data formats. The integration unit can also process duplicate data. The integration unit can also integrate data from different services. This allows for holistic insights to be gained by integrating data from various services. Some or all of the above-described processes in the integration unit may be performed using AI, or not. For example, the integration unit can input data from different services into an AI, which can then integrate the data.

[0078] The recommendation system can make highly accurate recommendations using advanced machine learning. For example, the recommendation system can use deep learning for recommendations. The recommendation system can also use reinforcement learning for recommendations. The recommendation system can also use collaborative filtering for recommendations. This improves the accuracy of recommendations by utilizing advanced machine learning. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can input user data into an AI, which can then make recommendations.

[0079] The update unit can continuously update recommendations based on user behavior and preferences. For example, the update unit can update recommendations based on user behavior data. The update unit can also update recommendations based on user preference data. The update unit can also update recommendations based on user feedback. This ensures that the latest information is always provided by updating recommendations based on user behavior and preferences. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input user data into AI, and the AI ​​can update the recommendations.

[0080] The optimization unit can identify and suggest relevant products and services across the entire platform. For example, the optimization unit can suggest relevant products based on the user's purchase history. The optimization unit can also suggest relevant services based on the user's preference data. The optimization unit can also apply algorithms to optimize cross-selling opportunities. This allows for the optimization of cross-selling opportunities by identifying and suggesting relevant products and services. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user data into AI, which can then perform the optimization.

[0081] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can refrain from collecting data and resume collection when the user is relaxed. For example, if the user is excited, the data collection unit can collect data in real time and obtain immediate feedback. For example, if the user is relaxed, the data collection unit can collect detailed data and perform highly accurate analysis. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then adjust the timing of data collection.

[0082] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the data collection unit can identify the time periods when the user frequently accessed the system in the past and collect data during those times. The data collection unit can also select the optimal data collection method based on the devices the user has used in the past. For example, the data collection unit can analyze the user's past behavioral patterns and propose the most effective data collection method. This allows the optimal data collection method to be selected by analyzing the user's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into AI, which can then select the optimal data collection method.

[0083] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, the data collection unit can prioritize collecting relevant data based on the content the user is currently viewing. The data collection unit can also collect only the necessary data based on the user's current activities. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. This allows for the collection of highly relevant data by filtering data based on the user's current activities and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activities and areas of interest into the AI, which can then filter the data.

[0084] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may postpone collecting less important data. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed data. For example, if the user is excited, the data collection unit may prioritize collecting important data in real time. This allows for the priority collection of important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then determine the priority of the data.

[0085] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of data around the user's home. This allows for the priority 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, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the AI, which can then prioritize the collection of highly relevant data.

[0086] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on content shared by the user on social media. The data collection unit can also analyze the time periods in which a user is active on social media and collect data during those times. The data collection unit can also analyze the user's areas of interest on social media and collect relevant data. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI, and the AI ​​can collect relevant data.

[0087] The integration unit can estimate the user's emotions and adjust the data integration method based on the estimated user emotions. For example, if the user is stressed, the integration unit may select a simple data integration method. For example, if the user is relaxed, the integration unit may select a more detailed data integration method. For example, if the user is excited, the integration unit may select a real-time data integration method. This allows for more appropriate data integration by adjusting the data integration method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI or not using AI. For example, the integration unit can input user emotion data into an AI, which can then adjust the data integration method.

[0088] The integration unit can improve the accuracy of data integration by evaluating the reliability of different data sources during data integration. For example, the integration unit can calculate a reliability score for each data source and prioritize the integration of reliable data. The integration unit can also evaluate the historical performance of data sources and select reliable data. For example, the integration unit can evaluate the reliability of the data source providers and integrate reliable data. This improves the accuracy of integration by evaluating the reliability of different data sources. Some or all of the above processes in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input the reliability of the data sources into the AI, which can then select the reliable data.

[0089] The integration unit can apply different integration algorithms depending on the data category during data integration. For example, the integration unit can apply a natural language processing algorithm to text data and integrate it. For example, the integration unit can also apply an image recognition algorithm to image data and integrate it. For example, the integration unit can also apply a statistical algorithm to numerical data and integrate it. This improves the accuracy of integration by applying different integration algorithms depending on the data category. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the data category into the AI, and the AI ​​can apply an appropriate integration algorithm.

[0090] The integration unit can estimate the user's emotions and adjust the display method of the integrated data based on the estimated user emotions. For example, if the user is tense, the integration unit can provide a simple and highly visible display method. For example, if the user is relaxed, the integration unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the integration unit can also provide a display method that gets straight to the point. This allows for more appropriate data display by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input user emotion data into AI, and the AI ​​can adjust the display method.

[0091] The integration unit can determine the integration priority based on the data collection timing during data integration. For example, the integration unit may prioritize the integration of the most recent data to provide real-time information. The integration unit may also integrate historical data to understand long-term trends. For example, the integration unit may prioritize the integration of data from a specific period and perform analysis on that period. This allows for the provision of up-to-date information by determining the integration priority based on the data collection timing. Some or all of the above processes in the integration unit may be performed using AI, for example, or not. For example, the integration unit can input the data collection timing into the AI, which can then determine the integration priority.

[0092] The integration unit can adjust the integration order based on the relevance of the data during data integration. For example, the integration unit can prioritize the integration of highly relevant data to perform highly accurate analysis. The integration unit can also, for example, postpone the integration of less relevant data to perform efficient data integration. The integration unit can also, for example, evaluate the relevance of the data and integrate it in the optimal order. This enables efficient data integration by adjusting the integration order based on the relevance of the data. Some or all of the above processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the relevance of the data into the AI, and the AI ​​can adjust the integration order.

[0093] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, the recommendation system will provide recommendations that proceed at a relaxed pace. If the user is in a hurry, the recommendation system may also provide recommendations that emphasize the shortest route. If the user is excited, the recommendation system may also provide recommendations with visually stimulating effects. By adjusting the way recommendations are presented based on the user's emotions, more appropriate recommendations can be made. 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 recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into an AI, which can then adjust the way recommendations are presented.

[0094] The recommendation system can analyze the user's past behavior data to select the optimal recommendation method. For example, the recommendation system can select the optimal recommendation method based on routes the user has used in the past. For example, the recommendation system can select a recommendation method that avoids congestion based on the user's past behavior history. For example, the recommendation system can analyze the user's past behavior history to select the most efficient recommendation method. In this way, the optimal recommendation method can be selected by analyzing the user's past behavior data. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the user's past behavior data into AI, and the AI ​​can select the optimal recommendation method.

[0095] The recommendation system can customize its recommendation methods based on the user's current activity. For example, it can make relevant recommendations based on the content the user is currently viewing. It can also make optimal recommendations based on the user's current activities. It can also make customized recommendations based on the user's current areas of interest. By customizing the recommendation methods based on the user's current activity, more appropriate recommendations become possible. Some or all of the above processes in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the user's current activity into AI, which can then customize the recommendation methods.

[0096] The recommendation system can estimate the user's emotions and prioritize recommendations based on those emotions. For example, if the user is stressed, the recommendation system might postpone less important recommendations. If the user is relaxed, the recommendation system might prioritize more detailed recommendations. If the user is excited, the recommendation system might prioritize important recommendations in real time. This allows for prioritizing important recommendations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into an AI, which can then determine the recommendation priority.

[0097] The recommendation system can select the optimal recommendation method by considering the user's geographical location. For example, if the user is in a specific region, the recommendation system can provide recommendations related to that region. For example, if the user is traveling, the recommendation system can provide recommendations related to the travel destination. For example, if the user is at home, the recommendation system can provide recommendations around the user's home. This allows the system to select the optimal recommendation method by considering the user's geographical location. Some or all of the above processing in the recommendation system may be performed using AI, or not. For example, the recommendation system can input the user's geographical location into the AI, which can then select the optimal recommendation method.

[0098] The recommendation unit can analyze a user's social media activity and suggest recommendation methods when making recommendations. For example, the recommendation unit can make relevant recommendations based on content shared by the user on social media. The recommendation unit can also analyze the times of day when a user is active on social media and make recommendations that are best suited to those times. The recommendation unit can also analyze the user's areas of interest on social media and make relevant recommendations. In this way, relevant recommendations can be made by analyzing the user's social media activity. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input the user's social media activity into AI, and the AI ​​can suggest recommendation methods.

[0099] The update unit can estimate the user's emotions and adjust the update frequency based on the estimated emotions. For example, if the user is stressed, the update unit can reduce the update frequency and update when the user is relaxed. For example, if the user is relaxed, the update unit can update frequently to provide the latest information. For example, if the user is excited, the update unit can update in real time to provide immediate feedback. This allows for updates to be performed at a more appropriate time by adjusting the update frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not using AI. For example, the update unit can input user emotion data into AI, and the AI ​​can adjust the update frequency.

[0100] The update unit can optimize the update algorithm by referring to past update data during the update process. For example, the update unit can analyze past update data and select the most effective update algorithm. The update unit can also evaluate user responses from past update data and adjust the algorithm accordingly. For example, the update unit can optimize the timing and content of updates based on past update data. This allows the update algorithm to be optimized by referring to past update data. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input past update data into AI, which can then optimize the update algorithm.

[0101] The update unit can customize the content of updates based on user behavior data during the update process. For example, the update unit can provide individually customized update content based on the user's past behavior data. The update unit can also suggest optimal update content based on the user's current behavior. The update unit can also provide relevant update content based on the user's areas of interest. By customizing the update content based on user behavior data, more appropriate updates become possible. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input user behavior data into AI, which can then customize the update content.

[0102] The update unit can estimate the user's emotions and determine the priority of updates based on the estimated emotions. For example, if the user is stressed, the update unit may postpone less important updates. For example, if the user is relaxed, the update unit may prioritize detailed updates. For example, if the user is excited, the update unit may prioritize important updates in real time. This allows important updates to be prioritized by determining the priority of updates based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not using AI. For example, the update unit can input user emotion data into an AI, which can then determine the priority of updates.

[0103] The update unit can select the optimal update method by considering the user's device information during the update process. For example, if the user is using a smartphone, the update unit can provide an update method optimized for the device. For example, if the user is using a tablet, the update unit can also provide an update method optimized for a larger screen. For example, if the user is using a smartwatch, the update unit can also provide a concise and highly visible update method. This allows the system to select the optimal update method by considering the user's device information. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's device information into the AI, which can then select the optimal update method.

[0104] The update unit can analyze the user's social media activity and suggest update content during the update process. For example, the update unit can suggest relevant update content based on content shared by the user on social media. The update unit can also analyze the user's social media activity times and suggest the most suitable update content for those times. The update unit can also analyze the user's social media interests and suggest relevant update content. In this way, relevant update content can be suggested by analyzing the user's social media activity. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's social media activity into AI, which can then suggest update content.

[0105] The optimization unit can estimate the user's emotions and adjust the optimization method based on the estimated emotions. For example, if the user is relaxed, the optimization unit will perform optimization at a relaxed pace. If the user is in a hurry, the optimization unit may also perform optimization that emphasizes the shortest route. If the user is excited, the optimization unit may also perform optimization that adds visually stimulating effects. By adjusting the optimization method based on the user's emotions, more appropriate optimization becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into AI, and the AI ​​can adjust the optimization method.

[0106] The optimization unit can select the optimal optimization method by analyzing the user's past behavior data during optimization. For example, the optimization unit can select the optimal optimization method based on routes previously used by the user. For example, the optimization unit can also select an optimization method that avoids congestion based on the user's past behavior history. For example, the optimization unit can analyze the user's past behavior history and select the most efficient optimization method. In this way, the optimal optimization method can be selected by analyzing the user's past behavior data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the user's past behavior data into AI, and the AI ​​can select the optimal optimization method.

[0107] The optimization unit can customize the optimization methods based on the user's current activity during optimization. For example, the optimization unit performs relevant optimizations based on the content the user is currently viewing. The optimization unit can also perform optimal optimizations based on the user's current activities. The optimization unit can also perform customized optimizations based on the user's current areas of interest. This allows for more appropriate optimization by customizing the optimization methods based on the user's current activity. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the user's current activity into the AI, which can then customize the optimization methods.

[0108] The optimization unit can estimate the user's emotions and determine optimization priorities based on the estimated emotions. For example, if the user is stressed, the optimization unit may postpone less important optimizations. For example, if the user is relaxed, the optimization unit may prioritize detailed optimizations. For example, if the user is excited, the optimization unit may prioritize important optimizations in real time. This allows for prioritizing important optimizations by determining optimization priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not using AI. For example, the optimization unit can input user emotion data into an AI, which can then determine optimization priorities.

[0109] The optimization unit can select the optimal optimization method by considering the user's geographical location information during optimization. For example, if the user is in a specific region, the optimization unit can perform optimizations related to that region. For example, if the user is traveling, the optimization unit can also perform optimizations related to the travel destination. For example, if the user is at home, the optimization unit can also perform optimizations around the user's home. In this way, the optimal optimization method can be selected by considering the user's geographical location information. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the user's geographical location information into the AI, and the AI ​​can select the optimal optimization method.

[0110] The optimization unit can analyze the user's social media activity and propose optimization methods during the optimization process. For example, the optimization unit can perform relevant optimizations based on the content the user shares on social media. The optimization unit can also analyze the time periods in which the user is active on social media and perform optimizations that are optimal for those times. The optimization unit can also analyze the user's areas of interest on social media and perform relevant optimizations. In this way, relevant optimizations can be performed by analyzing the user's social media activity. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the user's social media activity into AI, and the AI ​​can propose optimization methods.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can refrain from collecting data and resume collection when the user is relaxed. For example, if the user is excited, the data collection unit can collect data in real time and obtain immediate feedback. For example, if the user is relaxed, the data collection unit can collect detailed data and perform highly accurate analysis. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then adjust the timing of data collection.

[0113] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the data collection unit can identify the time periods when the user frequently accessed the system in the past and collect data during those times. The data collection unit can also select the optimal data collection method based on the devices the user has used in the past. For example, the data collection unit can analyze the user's past behavioral patterns and propose the most effective data collection method. This allows the optimal data collection method to be selected by analyzing the user's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into AI, which can then select the optimal data collection method.

[0114] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, the data collection unit can prioritize collecting relevant data based on the content the user is currently viewing. The data collection unit can also collect only the necessary data based on the user's current activities. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. This allows for the collection of highly relevant data by filtering data based on the user's current activities and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activities and areas of interest into the AI, which can then filter the data.

[0115] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may postpone collecting less important data. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed data. For example, if the user is excited, the data collection unit may prioritize collecting important data in real time. This allows for the priority collection of important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then determine the priority of the data.

[0116] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of data around the user's home. This allows for the priority 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, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the AI, which can then prioritize the collection of highly relevant data.

[0117] The integration unit can estimate the user's emotions and adjust the data integration method based on the estimated user emotions. For example, if the user is stressed, the integration unit may select a simple data integration method. For example, if the user is relaxed, the integration unit may select a more detailed data integration method. For example, if the user is excited, the integration unit may select a real-time data integration method. This allows for more appropriate data integration by adjusting the data integration method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI or not using AI. For example, the integration unit can input user emotion data into an AI, which can then adjust the data integration method.

[0118] The integration unit can improve the accuracy of data integration by evaluating the reliability of different data sources during data integration. For example, the integration unit can calculate a reliability score for each data source and prioritize the integration of reliable data. The integration unit can also evaluate the historical performance of data sources and select reliable data. For example, the integration unit can evaluate the reliability of the data source providers and integrate reliable data. This improves the accuracy of integration by evaluating the reliability of different data sources. Some or all of the above processes in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input the reliability of the data sources into the AI, which can then select the reliable data.

[0119] The integration unit can apply different integration algorithms depending on the data category during data integration. For example, the integration unit can apply a natural language processing algorithm to text data and integrate it. For example, the integration unit can also apply an image recognition algorithm to image data and integrate it. For example, the integration unit can also apply a statistical algorithm to numerical data and integrate it. This improves the accuracy of integration by applying different integration algorithms depending on the data category. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the data category into the AI, and the AI ​​can apply an appropriate integration algorithm.

[0120] The integration unit can estimate the user's emotions and adjust the display method of the integrated data based on the estimated user emotions. For example, if the user is tense, the integration unit can provide a simple and highly visible display method. For example, if the user is relaxed, the integration unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the integration unit can also provide a display method that gets straight to the point. This allows for more appropriate data display by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input user emotion data into AI, and the AI ​​can adjust the display method.

[0121] The integration unit can determine the integration priority based on the data collection timing during data integration. For example, the integration unit may prioritize the integration of the most recent data to provide real-time information. The integration unit may also integrate historical data to understand long-term trends. For example, the integration unit may prioritize the integration of data from a specific period and perform analysis on that period. This allows for the provision of up-to-date information by determining the integration priority based on the data collection timing. Some or all of the above processes in the integration unit may be performed using AI, for example, or not. For example, the integration unit can input the data collection timing into the AI, which can then determine the integration priority.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The data collection unit collects user data. The data collection unit can collect, for example, user behavior data, location information, and purchase history. The data collection unit collects website click data, smartphone location information, and purchase history on e-commerce platforms. Step 2: The Integration Unit integrates the data collected by the Collection Unit. The Integration Unit performs data format conversion and unifies different data formats. It also processes duplicate data to create a consistent dataset. Furthermore, it seamlessly integrates data from different services. Step 3: The recommendation department provides personalized recommendations based on the data integrated by the integration department. The recommendation department uses advanced machine learning algorithms to make recommendations based on user preferences and past behavior. It can also use collaborative filtering to make recommendations based on the behavioral data of other users. Furthermore, it uses natural language processing techniques to analyze user queries and feedback to improve the accuracy of recommendations. Step 4: The update department updates the recommendations made by the recommendation department in real time. The update department continuously updates recommendations based on user behavior and preferences. It also adjusts the recommendations based on user feedback. Furthermore, it predicts trends and recommends new content and products. Step 5: The optimization unit optimizes cross-selling based on recommendations made by the recommendation unit. The optimization unit identifies and suggests relevant products and services. It also optimizes cross-selling opportunities based on the user's purchase history and preferences. Furthermore, it applies optimization algorithms to maximize sales across the entire platform.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the data collection unit, integration unit, recommendation unit, update unit, and optimization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user data using the camera 42 and microphone 38B of the smart device 14 and processes it with the control unit 46A. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the collected data. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes personalized recommendations based on the integrated data. The update unit is implemented by the control unit 46A of the smart device 14 and updates recommendations in real time based on the user's behavior and preferences. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes cross-selling opportunities. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the data collection unit, integration unit, recommendation unit, update unit, and optimization unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user data using the camera 42 and microphone 238 of the smart glasses 214 and processes it with the control unit 46A. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the collected data. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and makes personalized recommendations based on the integrated data. The update unit is implemented, for example, by the control unit 46A of the smart glasses 214 and updates recommendations in real time based on the user's behavior and preferences. The optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and optimizes cross-selling opportunities. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the data collection unit, integration unit, recommendation unit, update unit, and optimization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user data using the camera 42 and microphone 238 of the headset terminal 314 and processes it with the control unit 46A. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the collected data. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes personalized recommendations based on the integrated data. The update unit is implemented by the control unit 46A of the headset terminal 314 and updates recommendations in real time based on user behavior and preferences. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes cross-selling opportunities. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the data collection unit, integration unit, recommendation unit, update unit, and optimization unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user data using the camera 42 and microphone 238 of the robot 414 and processes it with the control unit 46A. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the collected data. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and makes personalized recommendations based on the integrated data. The update unit is implemented, for example, by the control unit 46A of the robot 414 and updates recommendations in real time based on the user's behavior and preferences. The optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and optimizes cross-selling opportunities. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A collection unit that collects user data, An integration unit that integrates the data collected by the aforementioned collection unit, A recommendation unit that makes personalized recommendations based on the data integrated by the aforementioned integration unit, The update unit updates the recommendations made by the aforementioned recommendation unit in real time, The system includes an optimization unit that optimizes cross-selling based on recommendations made by the recommendation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect user behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned integration unit is Seamlessly integrate user data from various services. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned recommendation department, Using advanced machine learning to make highly accurate recommendations The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned update unit is Recommendations are continuously updated based on user behavior and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 6) The optimization unit, Identify and suggest relevant products and services across the entire platform. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze users' past behavioral data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned integration unit is We estimate user sentiment and adjust the data integration method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned integration unit is When integrating data, evaluate the reliability of different data sources to improve the accuracy of the integration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned integration unit is During data integration, different integration algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned integration unit is It estimates the user's emotions and adjusts how the integrated data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned integration unit is When integrating data, prioritize integration based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned integration unit is When integrating data, adjust the integration order based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recommendation department, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recommendation department, During the recommendation process, the system analyzes the user's past behavioral data to select the most suitable recommendation method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recommendation department, When making recommendations, customize the recommendation method based on the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recommendation department, It estimates the user's emotions and determines the priority of recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned recommendation department, When making recommendations, the system selects the optimal recommendation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned recommendation department, When making recommendations, we analyze the user's social media activity and suggest methods for making recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned update unit is It estimates user sentiment and adjusts the update frequency based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned update unit is During updates, the update algorithm is optimized by referring to past update data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned update unit is During updates, customize the content of the update based on user behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned update unit is It estimates user sentiment and determines update priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned update unit is During updates, the system selects the optimal update method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned update unit is When updating, we analyze users' social media activity and suggest content for the update. The system described in Appendix 1, characterized by the features described herein. (Note 31) The optimization unit, It estimates the user's emotions and adjusts the optimization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The optimization unit, During optimization, the system analyzes past user behavior data to select the most suitable optimization method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The optimization unit, During optimization, the optimization methods are customized based on the user's current activity. The system described in Appendix 1, characterized by the features described herein. (Note 34) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The optimization unit, During optimization, the optimal optimization method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The optimization unit, During optimization, we analyze users' social media activity and propose optimization methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0196] 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 collection unit that collects user data, An integration unit that integrates the data collected by the aforementioned collection unit, A recommendation unit that makes personalized recommendations based on the data integrated by the aforementioned integration unit, The update unit updates the recommendations made by the aforementioned recommendation unit in real time, The system includes an optimization unit that optimizes cross-selling based on recommendations made by the recommendation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect user behavior data. The system according to feature 1.

3. The aforementioned integration unit is Seamlessly integrate user data from various services. The system according to feature 1.

4. The aforementioned recommendation department, Using advanced machine learning to make highly accurate recommendations The system according to feature 1.

5. The aforementioned update unit is, Recommendations are continuously updated based on user behavior and preferences. The system according to feature 1.

6. The optimization unit, Identify and suggest relevant products and services across the entire platform. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze users' past behavioral data to select the optimal data collection method. The system according to feature 1.

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