system

Abstract

The system according to this embodiment aims to process user behavior data in real time and recommend the most suitable content. [Solution] The system according to the embodiment comprises a data collection unit, a processing unit, and a recommendation unit. The data collection unit collects user behavior data. The processing unit processes the data collected by the data collection unit in real time. The recommendation unit analyzes the user's preferences based on the data processed by the processing unit and recommends the most suitable content.

Description

Technical Field

[0006] , , ,

[0005] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the user's behavior data has not been sufficiently processed in real time to recommend optimal content based on the user's preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to process the user's behavior data in real time and recommend optimal content.

Means for Solving the Problems

[0007] The system according to this embodiment can process user behavior data in real time and recommend optimal content. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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 agent system according to an embodiment of the present invention is a system that generates personalized content in real time based on user behavior and preferences, thereby improving the user experience. This AI agent system collects user behavior data, processes the data in real time, analyzes user preferences, and recommends optimal content, thereby improving user engagement. For example, the AI ​​agent system collects user behavior data using user behavior tracking technology. Next, the AI ​​agent system processes the collected data in real time. Furthermore, the AI ​​agent system analyzes user preferences using machine learning and recommends optimal content to the user. This improves user engagement and increases the time spent on the site due to the increased relevance of the content. In addition, advertising revenue increases due to the provision of personalized content. Thus, the AI ​​agent system can improve the user experience by collecting user behavior data, processing it in real time, and recommending optimal content.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, a processing unit, and a recommendation unit. The data collection unit collects user behavior data. The data collection unit can collect user behavior data such as click data, browsing history, and purchase history. The data collection unit can accurately collect user behavior data using user behavior tracking technology. The processing unit processes the data collected by the data collection unit in real time. The processing unit can process the data in real time using, for example, streaming processing or an in-memory database. The processing unit can minimize data processing latency and provide content based on the latest information. The recommendation unit analyzes user preferences based on the data processed by the processing unit and recommends optimal content. The recommendation unit can accurately recommend content that matches user preferences using machine learning. The recommendation unit can analyze user preferences and recommend optimal content using, for example, machine learning algorithms such as deep learning or support vector machines. As a result, the AI ​​agent system according to this embodiment can improve the user experience by collecting user behavior data, processing it in real time, and recommending optimal content.

[0030] The data collection unit collects user behavior data. For example, it can collect user behavior data such as click data, browsing history, and purchase history. Specifically, it collects data on user clicks and taps on websites and applications, recording detailed behavioral history such as which pages were viewed and which products were purchased. The data collection unit can accurately collect user behavior data using user behavior tracking technologies. For example, it can use cookies and session IDs to consistently track user behavior and understand what kind of content users are interested in. The data collection unit can also collect user device information and location information, enabling a more detailed analysis of user behavior patterns. Furthermore, the data collection unit can collect social media data and user feedback to understand user preferences and opinions. This allows the data collection unit to collect a wide range of user behavior data from diverse data sources and accurately understand user interests and concerns. The collected data is stored in a secure database and managed appropriately to protect privacy. The data collection unit can flexibly adjust the frequency and scope of data collection, and can change data collection settings according to specific campaigns or events. This allows the data collection unit to efficiently and effectively collect user behavior data, thereby improving the overall system performance.

[0031] The processing unit processes the data collected by the collection unit in real time. The processing unit can process data in real time using, for example, streaming processing or an in-memory database. Specifically, it instantly analyzes collected data to understand user behavior patterns and trends. Streaming processing technology allows processing to begin as soon as data is collected, minimizing latency. An in-memory database enables high-speed data access and supports real-time data processing. The processing unit minimizes data processing latency and can provide content based on the latest information. For example, while a user is browsing a website, it can instantly display relevant content and products based on their latest browsing history and click data. The processing unit also performs data cleansing and normalization to maintain data quality. Furthermore, the processing unit uses anomaly detection algorithms to detect abnormal behavior patterns and unauthorized access early, ensuring system security. This allows the processing unit to process collected data quickly and accurately, providing users with the latest and most relevant content. The processing unit is designed with scalability in mind and can be expanded to accommodate increasing data volumes. This allows the processing unit to efficiently process data while maintaining overall system performance.

[0032] The recommendation unit analyzes user preferences based on data processed by the processing unit and recommends the most suitable content. The recommendation unit uses machine learning to accurately recommend content that matches user preferences. Specifically, it uses machine learning algorithms such as deep learning and support vector machines to analyze user behavior data and model user preferences and interests. Deep learning uses multi-layered neural networks to learn complex patterns and achieve highly accurate recommendations. Support vector machines classify user behavior data and identify the most suitable content. The recommendation unit combines these algorithms to analyze user preferences from multiple angles and recommend the most suitable content. For example, it recommends relevant content and products based on content the user has previously viewed or purchased. It can also analyze user behavior patterns and trends to predict content that the user might be interested in in the future. The recommendation unit continuously improves its recommendation results based on real-time updated data, providing the most suitable content tailored to the user's interests. Furthermore, the recommendation unit collects user feedback to improve the accuracy of its recommendation algorithms. For example, it analyzes how users reacted to recommended content and adjusts the recommendation algorithm accordingly. This allows the recommendation department to improve the user experience and increase user satisfaction.

[0033] The monitoring unit can monitor user engagement. For example, the monitoring unit can monitor specific metrics of user engagement such as time spent on a site, click-through rate, and number of comments. The monitoring unit can monitor user engagement in real time and improve the user experience. For example, the monitoring unit can monitor user time spent on a site and provide more personalized content to users who spend more time there. The monitoring unit can monitor user click-through rates and prioritize displaying content with high click-through rates. The monitoring unit can monitor the number of user comments and prioritize displaying content with a large number of comments. In this way, the monitoring unit can improve the user experience by monitoring user engagement.

[0034] The Optimization Unit can optimize advertising revenue. The Optimization Unit can optimize advertising revenue using specific methods such as improving cost per click and optimizing ad impressions. By optimizing advertising revenue, the Optimization Unit can improve profitability. For example, to improve cost per click, the Optimization Unit can improve ad targeting accuracy. To optimize ad impressions, the Optimization Unit can analyze user behavior data and display ads at the optimal time. The Optimization Unit can adjust the frequency of ad display to optimize advertising revenue. In this way, the Optimization Unit can improve profitability by optimizing advertising revenue.

[0035] The data collection unit can collect user behavior data using user behavior tracking technology. For example, the data collection unit can collect user behavior data using cookies. The data collection unit can also collect user behavior data using session IDs. The data collection unit can also collect user behavior data using beacons. This allows the data collection unit to collect accurate behavior data by using user behavior tracking technology.

[0036] The processing unit can process data in real time. For example, the processing unit can process data in real time using streaming processing. The processing unit can also process data in real time using an in-memory database. The processing unit can minimize data processing latency and provide content based on the latest information. This allows the processing unit to provide content based on the latest information by processing data in real time.

[0037] The recommendation system can analyze user preferences using machine learning and recommend the most suitable content. For example, it can use deep learning to analyze user preferences and recommend the most suitable content. It can also use support vector machines to analyze user preferences and recommend the most suitable content. By using machine learning, the recommendation system can accurately recommend content that matches the user's preferences. This allows the recommendation system to improve the user experience by analyzing user preferences using machine learning and recommending the most suitable content.

[0038] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method based on actions the user has frequently performed in the past. The data collection unit can also analyze the user's past behavior patterns and propose an efficient data collection method. The data collection unit can also select the optimal data collection method for a specific time period based on the user's past behavior history. In this way, the data collection unit can select an efficient data collection method by analyzing the user's past behavior history.

[0039] The data collection unit can filter behavioral data based on the user's current activities and areas of interest. For example, the data collection unit can collect only relevant data based on the user's current activities. The data collection unit can also filter the data to be collected based on the user's areas of interest. The data collection unit can also adjust the types of data to be collected, taking into account the user's current activities. This allows the data collection unit to collect highly relevant data by filtering the data based on the user's current activities and areas of interest.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting behavioral data. For example, if the user is in a specific location, the data collection unit can prioritize the collection of data related to that location. The data collection unit can also collect highly relevant data based on the user's geographical location. If the user is on the move, the data collection unit can prioritize the collection of data related to their destination. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location.

[0041] The data collection unit can analyze users' social media activity and collect relevant data when collecting behavioral data. For example, the data collection unit can analyze the content of users' social media posts and collect relevant data. The data collection unit can also analyze the activity of users' social media followers and friends and collect relevant data. The data collection unit can also adjust the data it collects, taking into account the frequency of the user's social media use. This allows the data collection unit to efficiently collect relevant data by analyzing users' social media activity.

[0042] The processing unit can adjust the level of detail in data processing based on the importance of the data. For example, it can process highly important data in detail and less important data simply. The processing unit can also determine processing priorities based on the importance of the data. The processing unit can also adjust the level of detail in processing based on the importance of the data. This allows the processing unit to perform efficient data processing by adjusting the level of detail in processing based on the importance of the data.

[0043] The processing unit can apply different processing algorithms depending on the data category during data processing. For example, the processing unit can apply a natural language processing algorithm to text data. The processing unit can also apply an image recognition algorithm to image data. The processing unit can also apply a speech recognition algorithm to audio data. By applying different processing algorithms depending on the data category, the processing unit can improve the accuracy of data processing.

[0044] The processing unit can determine processing priorities based on when the data was collected during data processing. For example, the processing unit can prioritize processing the most recent data and postpone processing older data. The processing unit can also determine processing priorities based on when the data was collected. The processing unit can also adjust the order of processing, taking into account when the data was collected. This allows the processing unit to prioritize processing the most recent data by determining processing priorities based on when the data was collected.

[0045] The processing unit can adjust the order of processing based on the relevance of the data during data processing. For example, the processing unit can prioritize processing highly relevant data and postpone processing less relevant data. The processing unit can also adjust the order of processing based on the relevance of the data. The processing unit can also determine the processing priority by considering the relevance of the data. This allows the processing unit to prioritize processing highly relevant data by adjusting the order of processing based on the relevance of the data.

[0046] The recommendation team can adjust the level of detail of recommendations based on the importance of the content. For example, it can recommend highly important content in detail and less important content in a simplified manner. The recommendation team can also determine the priority of recommendations based on the importance of the content. The recommendation team can adjust the level of detail of recommendations based on the importance of the content. This allows the recommendation team to make recommendations more efficient by adjusting the level of detail of recommendations based on the importance of the content.

[0047] The recommendation system can apply different recommendation algorithms depending on the content category during the recommendation process. For example, it can apply a natural language processing algorithm to text content, an image recognition algorithm to image content, and a speech recognition algorithm to audio content. By applying different recommendation algorithms depending on the content category, the recommendation system can improve the accuracy of its recommendations.

[0048] The recommendation team can determine recommendation priorities based on when the content was collected. For example, the recommendation team can prioritize recommending the newest content and postpone older content. The recommendation team can also determine recommendation priorities based on when the content was collected. The recommendation team can also adjust the order of recommendations, taking into account when the content was collected. This allows the recommendation team to prioritize recommending the newest content by determining recommendation priorities based on when the content was collected.

[0049] The recommendation team can adjust the order of recommendations based on the relevance of the content. For example, the recommendation team can prioritize recommending highly relevant content and postpone recommending less relevant content. The recommendation team can also adjust the order of recommendations based on the relevance of the content. The recommendation team can also determine the priority of recommendations by considering the relevance of the content. This allows the recommendation team to prioritize recommending highly relevant content by adjusting the order of recommendations based on the relevance of the content.

[0050] The monitoring unit can optimize its monitoring algorithm by referring to past engagement data during engagement monitoring. For example, the monitoring unit can analyze past engagement data and select the optimal monitoring algorithm. The monitoring unit can also adjust the monitoring algorithm based on the user's past engagement patterns. Furthermore, the monitoring unit can improve monitoring accuracy by referring to past engagement data. This allows the monitoring unit to optimize its monitoring algorithm and improve monitoring accuracy by referring to past engagement data.

[0051] The monitoring unit can select the optimal monitoring method when monitoring engagement, taking into account the user's device information. For example, if the user is using a smartphone, the monitoring unit can select a monitoring method that minimizes battery consumption. If the user is using a tablet, the monitoring unit can select a monitoring method optimized for the large screen. If the user is using a smartwatch, the monitoring unit can select a simple and efficient monitoring method. In this way, the monitoring unit can select the optimal monitoring method and perform efficient monitoring by taking into account the user's device information.

[0052] The optimization unit can optimize its optimization algorithm by referring to past advertising data when optimizing advertising revenue. For example, the optimization unit can analyze past advertising data and select the optimal optimization algorithm. The optimization unit can also adjust the optimization algorithm based on the user's past ad click history. The optimization unit can also improve the accuracy of optimization by referring to past advertising data. As a result, the optimization unit can optimize its optimization algorithm by referring to past advertising data and improve the accuracy of advertising revenue.

[0053] The optimization unit can select the most relevant ads when optimizing ad revenue, taking into account the user's geographical location. For example, if a user is in a specific location, the optimization unit can prioritize displaying ads relevant to that location. The optimization unit can also display highly relevant ads based on the user's geographical location. If a user is on the move, the optimization unit can prioritize displaying ads relevant to their destination. In this way, the optimization unit can prioritize displaying highly relevant ads by taking the user's geographical location into consideration.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The AI ​​agent system comprises a data collection unit that collects user behavior data, a processing unit that processes the collected data in real time, and a recommendation unit that recommends the most suitable content. Furthermore, the AI ​​agent system can consider the type of device the user is using when collecting user behavior data. For example, if the user is using a smartphone, the data collection unit can adjust the frequency of data collection to conserve battery power. If the user is using a tablet, it can select a data collection method optimized for the large screen. If the user is using a smartwatch, it can select a simple and efficient data collection method. In this way, the data collection unit can select the optimal data collection method and perform efficient data collection by considering the user's device information.

[0056] The AI ​​agent system includes a monitoring unit that monitors user engagement. This unit can monitor user engagement in real time and improve the user experience. Furthermore, the monitoring unit can analyze users' social media activity when monitoring user engagement. For example, it can analyze the content of users' social media posts to aid in engagement monitoring. It can also analyze the activity of users' social media followers and friends to collect data relevant to engagement monitoring. The system can also adjust its engagement monitoring methods based on the user's social media usage frequency. This allows the monitoring unit to efficiently monitor engagement by analyzing users' social media activity.

[0057] The AI ​​agent system includes an optimization unit that optimizes advertising revenue. This unit analyzes user behavior data to display the most relevant advertisements. Furthermore, it can consider the user's geographical location when optimizing advertising revenue. For example, if a user is in a specific location, the optimization unit can prioritize displaying advertisements relevant to that location. It can also display highly relevant advertisements based on the user's geographical location. If a user is on the move, it can prioritize displaying advertisements relevant to their destination. In this way, the optimization unit can prioritize displaying highly relevant advertisements by considering the user's geographical location.

[0058] The AI ​​agent system includes a data collection unit that collects user behavior data. When collecting user behavior data, the data collection unit can filter the data based on the user's current activities and areas of interest. For example, the data collection unit can collect only relevant data based on the user's current activities. It can also filter the data to be collected based on the user's areas of interest. Furthermore, it can adjust the types of data collected considering the user's current activities. This allows the data collection unit to collect highly relevant data by filtering it based on the user's current activities and areas of interest.

[0059] The AI ​​agent system includes a data collection unit that collects user behavior data. When collecting user behavior data, the data collection unit can analyze the user's past behavior history and select the optimal collection method. For example, the data collection unit can select the optimal collection method based on actions the user has frequently performed in the past. It can also analyze the user's past behavior patterns and suggest an efficient collection method. Furthermore, it can select the optimal collection method for a specific time period based on the user's past behavior history. In this way, the data collection unit can select an efficient collection method by analyzing the user's past behavior history.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The collection unit collects user behavior data. The collection unit can collect user behavior data such as click data, browsing history, and purchase history. The collection unit can accurately collect user behavior data using user behavior tracking technology. Step 2: The processing unit processes the data collected by the collection unit in real time. The processing unit can process the data in real time, for example, using streaming processing or an in-memory database. The processing unit can minimize data processing latency and provide content based on the latest information. Step 3: The recommendation unit analyzes user preferences based on the data processed by the processing unit and recommends the most suitable content. The recommendation unit can accurately recommend content that matches the user's preferences using machine learning. For example, the recommendation unit can analyze user preferences and recommend the most suitable content using machine learning algorithms such as deep learning and support vector machines.

[0062] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that generates personalized content in real time based on user behavior and preferences, thereby improving the user experience. This AI agent system collects user behavior data, processes the data in real time, analyzes user preferences, and recommends optimal content, thereby improving user engagement. For example, the AI ​​agent system collects user behavior data using user behavior tracking technology. Next, the AI ​​agent system processes the collected data in real time. Furthermore, the AI ​​agent system analyzes user preferences using machine learning and recommends optimal content to the user. This improves user engagement and increases the time spent on the site due to the increased relevance of the content. In addition, advertising revenue increases due to the provision of personalized content. Thus, the AI ​​agent system can improve the user experience by collecting user behavior data, processing it in real time, and recommending optimal content.

[0063] The AI ​​agent system according to this embodiment comprises a data collection unit, a processing unit, and a recommendation unit. The data collection unit collects user behavior data. The data collection unit can collect user behavior data such as click data, browsing history, and purchase history. The data collection unit can accurately collect user behavior data using user behavior tracking technology. The processing unit processes the data collected by the data collection unit in real time. The processing unit can process the data in real time using, for example, streaming processing or an in-memory database. The processing unit can minimize data processing latency and provide content based on the latest information. The recommendation unit analyzes user preferences based on the data processed by the processing unit and recommends optimal content. The recommendation unit can accurately recommend content that matches user preferences using machine learning. The recommendation unit can analyze user preferences and recommend optimal content using, for example, machine learning algorithms such as deep learning or support vector machines. As a result, the AI ​​agent system according to this embodiment can improve the user experience by collecting user behavior data, processing it in real time, and recommending optimal content.

[0064] The data collection unit collects user behavior data. For example, it can collect user behavior data such as click data, browsing history, and purchase history. Specifically, it collects data on user clicks and taps on websites and applications, recording detailed behavioral history such as which pages were viewed and which products were purchased. The data collection unit can accurately collect user behavior data using user behavior tracking technologies. For example, it can use cookies and session IDs to consistently track user behavior and understand what kind of content users are interested in. The data collection unit can also collect user device information and location information, enabling a more detailed analysis of user behavior patterns. Furthermore, the data collection unit can collect social media data and user feedback to understand user preferences and opinions. This allows the data collection unit to collect a wide range of user behavior data from diverse data sources and accurately understand user interests and concerns. The collected data is stored in a secure database and managed appropriately to protect privacy. The data collection unit can flexibly adjust the frequency and scope of data collection, and can change data collection settings according to specific campaigns or events. This allows the data collection unit to efficiently and effectively collect user behavior data, thereby improving the overall system performance.

[0065] The processing unit processes the data collected by the collection unit in real time. The processing unit can process data in real time using, for example, streaming processing or an in-memory database. Specifically, it instantly analyzes collected data to understand user behavior patterns and trends. Streaming processing technology allows processing to begin as soon as data is collected, minimizing latency. An in-memory database enables high-speed data access and supports real-time data processing. The processing unit minimizes data processing latency and can provide content based on the latest information. For example, while a user is browsing a website, it can instantly display relevant content and products based on their latest browsing history and click data. The processing unit also performs data cleansing and normalization to maintain data quality. Furthermore, the processing unit uses anomaly detection algorithms to detect abnormal behavior patterns and unauthorized access early, ensuring system security. This allows the processing unit to process collected data quickly and accurately, providing users with the latest and most relevant content. The processing unit is designed with scalability in mind and can be expanded to accommodate increasing data volumes. This allows the processing unit to efficiently process data while maintaining overall system performance.

[0066] The recommendation unit analyzes user preferences based on data processed by the processing unit and recommends the most suitable content. The recommendation unit uses machine learning to accurately recommend content that matches user preferences. Specifically, it uses machine learning algorithms such as deep learning and support vector machines to analyze user behavior data and model user preferences and interests. Deep learning uses multi-layered neural networks to learn complex patterns and achieve highly accurate recommendations. Support vector machines classify user behavior data and identify the most suitable content. The recommendation unit combines these algorithms to analyze user preferences from multiple angles and recommend the most suitable content. For example, it recommends relevant content and products based on content the user has previously viewed or purchased. It can also analyze user behavior patterns and trends to predict content that the user might be interested in in the future. The recommendation unit continuously improves its recommendation results based on real-time updated data, providing the most suitable content tailored to the user's interests. Furthermore, the recommendation unit collects user feedback to improve the accuracy of its recommendation algorithms. For example, it analyzes how users reacted to recommended content and adjusts the recommendation algorithm accordingly. This allows the recommendation department to improve the user experience and increase user satisfaction.

[0067] The monitoring unit can monitor user engagement. For example, the monitoring unit can monitor specific metrics of user engagement such as time spent on a site, click-through rate, and number of comments. The monitoring unit can monitor user engagement in real time and improve the user experience. For example, the monitoring unit can monitor user time spent on a site and provide more personalized content to users who spend more time there. The monitoring unit can monitor user click-through rates and prioritize displaying content with high click-through rates. The monitoring unit can monitor the number of user comments and prioritize displaying content with a large number of comments. In this way, the monitoring unit can improve the user experience by monitoring user engagement.

[0068] The Optimization Unit can optimize advertising revenue. The Optimization Unit can optimize advertising revenue using specific methods such as improving cost per click and optimizing ad impressions. By optimizing advertising revenue, the Optimization Unit can improve profitability. For example, to improve cost per click, the Optimization Unit can improve ad targeting accuracy. To optimize ad impressions, the Optimization Unit can analyze user behavior data and display ads at the optimal time. The Optimization Unit can adjust the frequency of ad display to optimize advertising revenue. In this way, the Optimization Unit can improve profitability by optimizing advertising revenue.

[0069] The data collection unit can collect user behavior data using user behavior tracking technology. For example, the data collection unit can collect user behavior data using cookies. The data collection unit can also collect user behavior data using session IDs. The data collection unit can also collect user behavior data using beacons. This allows the data collection unit to collect accurate behavior data by using user behavior tracking technology.

[0070] The processing unit can process data in real time. For example, the processing unit can process data in real time using streaming processing. The processing unit can also process data in real time using an in-memory database. The processing unit can minimize data processing latency and provide content based on the latest information. This allows the processing unit to provide content based on the latest information by processing data in real time.

[0071] The recommendation system can analyze user preferences using machine learning and recommend the most suitable content. For example, it can use deep learning to analyze user preferences and recommend the most suitable content. It can also use support vector machines to analyze user preferences and recommend the most suitable content. By using machine learning, the recommendation system can accurately recommend content that matches the user's preferences. This allows the recommendation system to improve the user experience by analyzing user preferences using machine learning and recommending the most suitable content.

[0072] 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 reduce the collection timing to lessen the user's burden. If the user is relaxed, the data collection unit can increase the collection timing to collect more detailed data. If the user is excited, the data collection unit can adjust the collection timing to collect appropriate data. In this way, the data collection unit can reduce the user's burden and collect more detailed data by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method based on actions the user has frequently performed in the past. The data collection unit can also analyze the user's past behavior patterns and propose an efficient data collection method. The data collection unit can also select the optimal data collection method for a specific time period based on the user's past behavior history. In this way, the data collection unit can select an efficient data collection method by analyzing the user's past behavior history.

[0074] The data collection unit can filter behavioral data based on the user's current activities and areas of interest. For example, the data collection unit can collect only relevant data based on the user's current activities. The data collection unit can also filter the data to be collected based on the user's areas of interest. The data collection unit can also adjust the types of data to be collected, taking into account the user's current activities. This allows the data collection unit to collect highly relevant data by filtering the data based on the user's current activities and areas of interest.

[0075] The data collection unit can estimate the user's emotions and prioritize the behavioral data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can postpone the collection of less important data. If the user is relaxed, the data collection unit can prioritize the collection of detailed data. If the user is agitated, the data collection unit can prioritize the collection of specific data. In this way, the data collection unit can prioritize the collection of important data by prioritizing the data to collect according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting behavioral data. For example, if the user is in a specific location, the data collection unit can prioritize the collection of data related to that location. The data collection unit can also collect highly relevant data based on the user's geographical location. If the user is on the move, the data collection unit can prioritize the collection of data related to their destination. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location.

[0077] The data collection unit can analyze users' social media activity and collect relevant data when collecting behavioral data. For example, the data collection unit can analyze the content of users' social media posts and collect relevant data. The data collection unit can also analyze the activity of users' social media followers and friends and collect relevant data. The data collection unit can also adjust the data it collects, taking into account the frequency of the user's social media use. This allows the data collection unit to efficiently collect relevant data by analyzing users' social media activity.

[0078] The processing unit can estimate the user's emotions and adjust the priority of data processing based on the estimated emotions. For example, if the user is stressed, the processing unit can postpone processing less important data. If the user is relaxed, the processing unit can also prioritize processing detailed data. If the user is agitated, the processing unit can also prioritize processing specific data. In this way, the processing unit can prioritize processing important data by adjusting the priority of data processing according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The processing unit can adjust the level of detail in data processing based on the importance of the data. For example, it can process highly important data in detail and less important data simply. The processing unit can also determine processing priorities based on the importance of the data. The processing unit can also adjust the level of detail in processing based on the importance of the data. This allows the processing unit to perform efficient data processing by adjusting the level of detail in processing based on the importance of the data.

[0080] The processing unit can apply different processing algorithms depending on the data category during data processing. For example, the processing unit can apply a natural language processing algorithm to text data. The processing unit can also apply an image recognition algorithm to image data. The processing unit can also apply a speech recognition algorithm to audio data. By applying different processing algorithms depending on the data category, the processing unit can improve the accuracy of data processing.

[0081] The processing unit can estimate the user's emotions and adjust the order of data processing based on the estimated emotions. For example, if the user is stressed, the processing unit can postpone processing less important data. If the user is relaxed, the processing unit can also prioritize processing detailed data. If the user is agitated, the processing unit can also prioritize processing specific data. In this way, the processing unit can prioritize processing important data by adjusting the order of data processing according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The processing unit can determine processing priorities based on when the data was collected during data processing. For example, the processing unit can prioritize processing the most recent data and postpone processing older data. The processing unit can also determine processing priorities based on when the data was collected. The processing unit can also adjust the order of processing, taking into account when the data was collected. This allows the processing unit to prioritize processing the most recent data by determining processing priorities based on when the data was collected.

[0083] The processing unit can adjust the order of processing based on the relevance of the data during data processing. For example, the processing unit can prioritize processing highly relevant data and postpone processing less relevant data. The processing unit can also adjust the order of processing based on the relevance of the data. The processing unit can also determine the processing priority by considering the relevance of the data. This allows the processing unit to prioritize processing highly relevant data by adjusting the order of processing based on the relevance of the data.

[0084] 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 can provide recommendations at a leisurely pace. If the user is in a hurry, the recommendation system can also provide recommendations that emphasize the shortest route. If the user is excited, the recommendation system can also provide recommendations with visually stimulating effects. In this way, the recommendation system can provide recommendations that are appropriate for the user by adjusting the way recommendations are presented according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The recommendation team can adjust the level of detail of recommendations based on the importance of the content. For example, it can recommend highly important content in detail and less important content in a simplified manner. The recommendation team can also determine the priority of recommendations based on the importance of the content. The recommendation team can adjust the level of detail of recommendations based on the importance of the content. This allows the recommendation team to make recommendations more efficient by adjusting the level of detail of recommendations based on the importance of the content.

[0086] The recommendation system can apply different recommendation algorithms depending on the content category during the recommendation process. For example, it can apply a natural language processing algorithm to text content, an image recognition algorithm to image content, and a speech recognition algorithm to audio content. By applying different recommendation algorithms depending on the content category, the recommendation system can improve the accuracy of its recommendations.

[0087] The recommendation system can estimate the user's emotions and adjust the length of recommendations based on those emotions. For example, if the user is in a hurry, the recommendation system can provide short, concise recommendations. If the user is relaxed, it can provide longer recommendations with detailed explanations. If the user is excited, it can provide recommendations with visually stimulating effects. In this way, the recommendation system can provide recommendations that are appropriate for the user by adjusting the length of recommendations according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The recommendation team can determine recommendation priorities based on when the content was collected. For example, the recommendation team can prioritize recommending the newest content and postpone older content. The recommendation team can also determine recommendation priorities based on when the content was collected. The recommendation team can also adjust the order of recommendations, taking into account when the content was collected. This allows the recommendation team to prioritize recommending the newest content by determining recommendation priorities based on when the content was collected.

[0089] The recommendation team can adjust the order of recommendations based on the relevance of the content. For example, the recommendation team can prioritize recommending highly relevant content and postpone recommending less relevant content. The recommendation team can also adjust the order of recommendations based on the relevance of the content. The recommendation team can also determine the priority of recommendations by considering the relevance of the content. This allows the recommendation team to prioritize recommending highly relevant content by adjusting the order of recommendations based on the relevance of the content.

[0090] The monitoring unit can estimate the user's emotions and adjust the engagement monitoring method based on the estimated user emotions. For example, if the user is stressed, the monitoring unit can reduce the frequency of engagement monitoring. If the user is relaxed, the monitoring unit can increase the frequency of engagement monitoring. If the user is excited, the monitoring unit can also adjust the engagement monitoring method. In this way, the monitoring unit can provide user-appropriate monitoring by adjusting the engagement monitoring method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The monitoring unit can optimize its monitoring algorithm by referring to past engagement data during engagement monitoring. For example, the monitoring unit can analyze past engagement data and select the optimal monitoring algorithm. The monitoring unit can also adjust the monitoring algorithm based on the user's past engagement patterns. Furthermore, the monitoring unit can improve monitoring accuracy by referring to past engagement data. This allows the monitoring unit to optimize its monitoring algorithm and improve monitoring accuracy by referring to past engagement data.

[0092] The monitoring unit can estimate the user's emotions and prioritize engagements based on those emotions. For example, if the user is stressed, the monitoring unit can postpone monitoring less important engagements. If the user is relaxed, the monitoring unit can prioritize monitoring detailed engagements. If the user is excited, the monitoring unit can prioritize monitoring specific engagements. This allows the monitoring unit to prioritize important engagements by determining engagement priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The monitoring unit can select the optimal monitoring method when monitoring engagement, taking into account the user's device information. For example, if the user is using a smartphone, the monitoring unit can select a monitoring method that minimizes battery consumption. If the user is using a tablet, the monitoring unit can select a monitoring method optimized for the large screen. If the user is using a smartwatch, the monitoring unit can select a simple and efficient monitoring method. In this way, the monitoring unit can select the optimal monitoring method and perform efficient monitoring by taking into account the user's device information.

[0094] The optimization unit can estimate the user's emotions and adjust the advertising revenue optimization method based on the estimated user emotions. For example, if the user is stressed, the optimization unit can reduce the frequency of ad display. If the user is relaxed, the optimization unit can increase the frequency of ad display. If the user is excited, the optimization unit can prioritize displaying specific ads. In this way, the optimization unit can improve revenue by adjusting the advertising revenue optimization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The optimization unit can optimize its optimization algorithm by referring to past advertising data when optimizing advertising revenue. For example, the optimization unit can analyze past advertising data and select the optimal optimization algorithm. The optimization unit can also adjust the optimization algorithm based on the user's past ad click history. The optimization unit can also improve the accuracy of optimization by referring to past advertising data. As a result, the optimization unit can optimize its optimization algorithm by referring to past advertising data and improve the accuracy of advertising revenue.

[0096] The optimization unit can estimate the user's emotions and determine the priority of ad revenue based on those emotions. For example, if the user is stressed, the optimization unit can postpone the display of less important ads. If the user is relaxed, the optimization unit can also prioritize the display of detailed ads. If the user is excited, the optimization unit can also prioritize the display of specific ads. In this way, the optimization unit can prioritize the display of important ads by determining the priority of ad revenue according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The optimization unit can select the most relevant ads when optimizing ad revenue, taking into account the user's geographical location. For example, if a user is in a specific location, the optimization unit can prioritize displaying ads relevant to that location. The optimization unit can also display highly relevant ads based on the user's geographical location. If a user is on the move, the optimization unit can prioritize displaying ads relevant to their destination. In this way, the optimization unit can prioritize displaying highly relevant ads by taking the user's geographical location into consideration.

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

[0099] The AI ​​agent system comprises a data collection unit that collects user behavior data, a processing unit that processes the collected data in real time, and a recommendation unit that recommends the most suitable content. Furthermore, the AI ​​agent system can consider the type of device the user is using when collecting user behavior data. For example, if the user is using a smartphone, the data collection unit can adjust the frequency of data collection to conserve battery power. If the user is using a tablet, it can select a data collection method optimized for the large screen. If the user is using a smartwatch, it can select a simple and efficient data collection method. In this way, the data collection unit can select the optimal data collection method and perform efficient data collection by considering the user's device information.

[0100] The AI ​​agent system includes a monitoring unit that monitors user engagement. This unit can monitor user engagement in real time and improve the user experience. Furthermore, the monitoring unit can analyze users' social media activity when monitoring user engagement. For example, it can analyze the content of users' social media posts to aid in engagement monitoring. It can also analyze the activity of users' social media followers and friends to collect data relevant to engagement monitoring. The system can also adjust its engagement monitoring methods based on the user's social media usage frequency. This allows the monitoring unit to efficiently monitor engagement by analyzing users' social media activity.

[0101] The AI ​​agent system includes an optimization unit that optimizes advertising revenue. This unit analyzes user behavior data to display the most relevant advertisements. Furthermore, it can consider the user's geographical location when optimizing advertising revenue. For example, if a user is in a specific location, the optimization unit can prioritize displaying advertisements relevant to that location. It can also display highly relevant advertisements based on the user's geographical location. If a user is on the move, it can prioritize displaying advertisements relevant to their destination. In this way, the optimization unit can prioritize displaying highly relevant advertisements by considering the user's geographical location.

[0102] The AI ​​agent system includes a data collection unit that collects user behavior data. When collecting user behavior data, the data collection unit can filter the data based on the user's current activities and areas of interest. For example, the data collection unit can collect only relevant data based on the user's current activities. It can also filter the data to be collected based on the user's areas of interest. Furthermore, it can adjust the types of data collected considering the user's current activities. This allows the data collection unit to collect highly relevant data by filtering it based on the user's current activities and areas of interest.

[0103] The AI ​​agent system includes a data collection unit that collects user behavior data. When collecting user behavior data, the data collection unit can analyze the user's past behavior history and select the optimal collection method. For example, the data collection unit can select the optimal collection method based on actions the user has frequently performed in the past. It can also analyze the user's past behavior patterns and suggest an efficient collection method. Furthermore, it can select the optimal collection method for a specific time period based on the user's past behavior history. In this way, the data collection unit can select an efficient collection method by analyzing the user's past behavior history.

[0104] The AI ​​agent system can estimate the user's emotions and adjust the timing of behavioral data collection based on those emotions. For example, if the user is stressed, the data collection unit can reduce the collection timing to lessen the user's burden. If the user is relaxed, it can increase the collection timing to collect more detailed data. If the user is excited, it can adjust the collection timing to collect appropriate data. In this way, the data collection unit can reduce the user's burden and collect more detailed data by adjusting the collection timing according to the user's emotions.

[0105] The AI ​​agent system can estimate the user's emotions and prioritize the behavioral data to collect based on those emotions. For example, if the user is stressed, the data collection unit can postpone the collection of less important data. If the user is relaxed, it can prioritize the collection of detailed data. If the user is excited, it can prioritize the collection of specific data. In this way, the data collection unit can prioritize the collection of important data by determining the priority of data to collect according to the user's emotions.

[0106] The AI ​​agent system can estimate the user's emotions and adjust the priority of data processing based on those emotions. For example, if the user is stressed, the processing unit can postpone processing less important data. If the user is relaxed, it can prioritize processing detailed data. If the user is agitated, it can prioritize processing specific data. In this way, the processing unit can prioritize important data by adjusting the priority of data processing according to the user's emotions.

[0107] The AI ​​agent 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 can provide recommendations at a leisurely pace. If the user is in a hurry, it can provide recommendations that emphasize the shortest route. If the user is excited, it can provide recommendations with visually stimulating effects. In this way, the recommendation system can provide recommendations that are appropriate for the user by adjusting the way recommendations are presented according to the user's emotions.

[0108] The AI ​​agent system can estimate a user's emotions and adjust the advertising revenue optimization method based on those emotions. For example, the optimization unit can reduce the frequency of ad displays if the user is stressed, increase the frequency if the user is relaxed, and prioritize certain ads if the user is excited. In this way, the optimization unit can improve revenue by adjusting the advertising revenue optimization method according to the user's emotions.

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

[0110] Step 1: The collection unit collects user behavior data. The collection unit can collect user behavior data such as click data, browsing history, and purchase history. The collection unit can accurately collect user behavior data using user behavior tracking technology. Step 2: The processing unit processes the data collected by the collection unit in real time. The processing unit can process the data in real time, for example, using streaming processing or an in-memory database. The processing unit can minimize data processing latency and provide content based on the latest information. Step 3: The recommendation unit analyzes user preferences based on the data processed by the processing unit and recommends the most suitable content. The recommendation unit can accurately recommend content that matches the user's preferences using machine learning. For example, the recommendation unit can analyze user preferences and recommend the most suitable content using machine learning algorithms such as deep learning and support vector machines.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the data collection unit, processing unit, recommendation unit, monitoring 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 is implemented by the control unit 46A of the smart device 14 and collects user behavior data. The processing unit is implemented by the specific processing unit 290 of the data processing unit 12 and processes the collected data in real time. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends optimal content by analyzing user preferences. The monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors user engagement. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes advertising revenue. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the data collection unit, processing unit, recommendation unit, monitoring unit, and optimization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects user behavior data. The processing unit is implemented by the specific processing unit 290 of the data processing unit 12 and processes the collected data in real time. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends optimal content by analyzing user preferences. The monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors user engagement. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes advertising revenue. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the data collection unit, processing unit, recommendation unit, monitoring 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 is implemented by the control unit 46A of the headset terminal 314 and collects user behavior data. The processing unit is implemented by the specific processing unit 290 of the data processing unit 12 and processes the collected data in real time. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends optimal content by analyzing user preferences. The monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors user engagement. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes advertising revenue. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the collection unit, processing unit, recommendation unit, monitoring unit, and optimization unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects user behavior data. The processing unit is implemented by the specific processing unit 290 of the data processing unit 12 and processes the collected data in real time. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends optimal content by analyzing user preferences. The monitoring unit is implemented by the control unit 46A of the robot 414 and monitors user engagement. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes advertising revenue. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A data collection unit that collects user behavior data, A processing unit that processes the data collected by the aforementioned collection unit in real time, The system includes a recommendation unit that analyzes user preferences based on data processed by the aforementioned processing unit and recommends the most suitable content. A system characterized by the following features. (Note 2) It includes a monitoring unit that monitors user engagement. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features an optimization unit to optimize advertising revenue. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We collect user behavior data using user behavior tracking technology. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned processing unit, Processing data in real time The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned recommendation department, We use machine learning to analyze user preferences and recommend the most suitable content. 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 behavioral data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past behavior history and 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 behavioral 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 determines the priority of behavioral data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting behavioral data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting behavioral data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned processing unit, It estimates the user's emotions and adjusts the priority of data processing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned processing unit, During data processing, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned processing unit, When processing data, different processing algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned processing unit, It estimates the user's emotions and adjusts the order of data processing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned processing unit, During data processing, the processing priority is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned processing unit, During data processing, adjust the order of processing based on the relationships between 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, When making recommendations, adjust the level of detail based on the importance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the content category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recommendation department, It estimates the user's sentiment and adjusts the length of recommendations based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned recommendation department, When making recommendations, we prioritize recommendations based on when the content was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned recommendation department, When making recommendations, the order of recommendations is adjusted based on the relevance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned monitoring unit, We estimate user sentiment and adjust engagement monitoring methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned monitoring unit, When monitoring engagement, the monitoring algorithm is optimized by referring to past engagement data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned monitoring unit, It estimates user emotions and determines engagement priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned monitoring unit, When monitoring engagement, the optimal monitoring method is selected by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The optimization unit, We estimate user sentiment and adjust how we optimize ad revenue based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The optimization unit, When optimizing ad revenue, the optimization algorithm is optimized by referring to past ad data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The optimization unit, It estimates user sentiment and prioritizes advertising revenue based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The optimization unit, When optimizing ad revenue, the system selects the most suitable ads by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user behavior data, A processing unit that processes the data collected by the aforementioned collection unit in real time, The system includes a recommendation unit that analyzes user preferences based on data processed by the aforementioned processing unit and recommends the most suitable content. A system characterized by the following features.

2. It includes a monitoring unit that monitors user engagement. The system according to feature 1.

3. It features an optimization unit to optimize advertising revenue. The system according to feature 1.

4. The aforementioned collection unit is We collect user behavior data using user behavior tracking technology. The system according to feature 1.

5. The aforementioned processing unit, Processing data in real time The system according to feature 1.

6. The aforementioned recommendation department, We use machine learning to analyze user preferences and recommend the most suitable content. The system according to feature 1.

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

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

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

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

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