system

Abstract

The system according to this embodiment aims to provide optimal information by inferring user characteristics and content nature. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user behavior data and content metadata. The analysis unit analyzes the data collected by the collection unit and infers user characteristics and content properties. The generation unit generates optimal prompts based on the analysis results obtained by the analysis unit. The provision unit provides information based on the prompts generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that optimal information provision cannot be made for each user, and individual optimization of the way of presenting and conveying information has not been realized.

[0005] The system according to the embodiment aims to infer the characteristics of the user and the nature of the content and perform optimal information provision.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user behavior data and content metadata. The analysis unit analyzes the data collected by the collection unit and infers user characteristics and content properties. The generation unit generates optimal prompts based on the analysis results obtained by the analysis unit. The provision unit provides information based on the prompts generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can infer user characteristics and content properties to provide optimal information. [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, a labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The content delivery agent according to an embodiment of the present invention is a system that leverages generative AI to establish a new standard for information distribution. This system dynamically optimizes how information is presented and conveyed by collecting user behavior data and content metadata, and having the generative AI analyze them to generate optimal prompts. For example, in commerce, it realizes the optimal presentation according to the characteristics of the product, and in media, it provides information in the optimal format according to the nature of the news. The content delivery agent flexibly responds to a nearly limitless number of search queries and information requests, and systematizes the provision of optimal information for each user and content. This enables enhanced user engagement, improved information quality, maximized information value, strengthened competitiveness, and innovative approaches. For example, if a user searches for "down coat," the generative AI suggests the optimal presentation according to the characteristics of that product. Also, if a user searches for "news," the generative AI provides information in the optimal format according to the nature of the news. This allows users to effectively receive information that meets their needs. As a result, the content delivery agent can solve the challenges of information distribution and fundamentally change how information is presented, thereby significantly improving user engagement and competitiveness.

[0029] The content delivery agent according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user behavior data and content metadata. The collection unit can collect behavior data such as website browsing history, click data, and purchase history. The collection unit can also collect metadata such as content category, tags, and creation date. Some or all of the above-described processes in the collection unit may be performed using AI or not. For example, the collection unit can input user behavior data into an AI model and have the AI ​​perform data collection. The analysis unit analyzes the data collected by the collection unit and infers user characteristics and content properties. The analysis unit can analyze the data using statistical analysis or machine learning algorithms, for example. The analysis unit can also infer user characteristics such as age, gender, and interests. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into an AI model and have the AI ​​perform inferences about user characteristics and content properties. The generation unit generates the optimal prompt based on the analysis results obtained by the analysis unit. The generation unit can generate prompts using, for example, a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit can generate prompts such as questions and suggestions based on user interests. Some or all of the above-described processes in the generation unit may be performed using or without a generation AI. For example, the generation unit can input the analysis results into a generation AI and have the generation AI generate prompts. The provision unit provides information based on the prompts generated by the generation unit. The provision unit can provide information, for example, through a website or a mobile application. The provision unit can also provide information using email or push notifications. Some or all of the above-described processes in the provision unit may be performed using or without an AI.For example, the delivery unit can input the generated prompts into the AI ​​model and have the AI ​​deliver the information. As a result, the content delivery agent according to the embodiment can dynamically optimize how information is presented and conveyed by analyzing user behavior data and content metadata and generating optimal prompts.

[0030] The data collection unit collects user behavior data and content metadata. For example, the data collection unit can collect behavioral data such as website browsing history, click data, and purchase history. Specifically, this includes URLs of pages visited, time spent on pages, links and buttons clicked, and details of purchased items. This data is an important source of information for understanding user interests. The data collection unit can also collect metadata such as content categories, tags, and creation dates. For example, this includes article genres, related keywords, publication dates, and update dates. This allows the data collection unit to combine user behavior data and content metadata for more accurate analysis. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user behavior data into an AI model and have the AI ​​perform data collection. The AI ​​can learn user behavior patterns and collect data efficiently. For example, the AI ​​can detect that users tend to view content of a specific category at certain times and focus data collection during those times. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit and infers user characteristics and content nature. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. Specifically, it infers characteristics such as age, gender, and interests based on user behavior data. For example, a user who frequently views content in a particular category can be inferred to have a strong interest in that category. The analysis unit can also infer the nature of content based on its metadata. For example, content with a specific tag can be inferred to be related to a particular theme or topic. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into an AI model and have the AI ​​perform inferences about user characteristics and content nature. AI can quickly analyze vast amounts of data and perform highly accurate inferences. For example, AI can predict the content that a user is most likely to view next based on user behavior data. This allows the analysis unit to quickly and accurately analyze the collected data and understand user characteristics and content nature. Furthermore, the analytics unit can utilize historical data and statistical information to analyze long-term trends and patterns. This allows the analytics unit to gain a deeper understanding of user behavior and content characteristics, thereby improving the overall system performance.

[0032] The generation unit generates optimal prompts based on the analysis results obtained by the analysis unit. The generation unit can generate prompts using, for example, a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. Specifically, the generation unit can generate prompts such as questions and suggestions based on user interests. For example, if a user frequently views content in a particular category, the generation unit can generate prompts that suggest new content related to that category. It can also predict content that the user might be interested in next based on user behavior data and generate prompts that introduce that content. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can input the analysis results into a generation AI and have the generation AI perform prompt generation. The generation AI can learn user interests and behavior patterns and generate optimal prompts. This allows the generation unit to efficiently generate prompts based on user interests and provide users with appropriate information. Furthermore, the generation unit can evaluate the effectiveness of the generated prompts and continuously improve them. For example, it can review the content and format of prompts based on user reactions and feedback to generate more effective prompts. This allows the generation unit to always provide the user with the most optimal information and improve the overall system performance.

[0033] The delivery unit provides information based on prompts generated by the generation unit. The delivery unit can provide information, for example, through websites or mobile applications. Specifically, it can provide information to users through website pop-up notifications, banner ads, or mobile application push notifications. The delivery unit can also provide information using email or push notifications. For example, it can notify users via email about new content or special offers that may be of interest to them. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the generated prompts into an AI model and have the AI ​​perform the information provision. The AI ​​can learn user behavior patterns and interests and provide information at the optimal time. For example, the AI ​​can detect that users tend to visit the website at certain times and provide information accordingly. This allows the delivery unit to provide information to users efficiently and effectively and attract their interest. Furthermore, the delivery unit can collect user reactions and feedback to continuously improve the accuracy and effectiveness of information provision. For example, it can analyze how users reacted to the information provided and reflect this in future information provision. This allows the service provider to consistently deliver optimal information to users and improve the overall system performance.

[0034] The data collection unit can analyze the user's past behavioral data and select the optimal collection method. For example, the data collection unit may prioritize collecting data from devices the user has frequently used in the past. The data collection unit can also analyze the user's past behavioral patterns and select the most effective timing for data collection. The data collection unit can also collect data from applications the user has preferred to use in the past. This allows the optimal collection method to be selected by analyzing the user's past behavioral data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past behavioral data into an AI model and have the AI ​​select the optimal collection method.

[0035] The data collection unit can filter behavioral data based on the user's current areas of interest. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. The data collection unit can also filter and collect relevant data based on the user's current search history. The data collection unit can also collect data based on topics in online communities the user participates in. This allows for the collection of highly relevant data by filtering the data based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's current areas of interest into an AI model and have the AI ​​perform the data filtering.

[0036] 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, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also collect data related to places the user has visited in the past. The data collection unit can also collect data related to places the user plans to visit in the future. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into an AI model and have the AI ​​perform the data collection.

[0037] The data collection unit can analyze a user's social media activity and collect relevant data when collecting behavioral data. For example, the data collection unit can collect data based on content shared by the user on social media. The data collection unit can also collect data based on the activity of accounts that the user follows. The data collection unit can also collect data related to groups and events that the user participates in. This allows for the collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into an AI model and have the AI ​​perform the data collection.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. The analysis unit can also perform a standard analysis on general data. The analysis unit can also perform a simplified analysis on unnecessary data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into an AI model and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a speech recognition algorithm to audio data. By applying different analysis algorithms depending on the data category, more appropriate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into an AI model and have the AI ​​perform the application of the analysis algorithm.

[0040] The analysis unit can adjust the order of analysis based on the data collection timing during the analysis. For example, the analysis unit can prioritize the analysis of the most recent data. The analysis unit can also postpone the analysis of past data. The analysis unit can also prioritize the analysis of data collected during a specific period. This allows for prioritization of the analysis of the most recent data by adjusting the order of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection timing into an AI model and have the AI ​​perform the adjustment of the analysis order.

[0041] The analysis unit can adjust its analysis method based on the relevance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly relevant data. It can also perform a simplified analysis on less relevant data. It can also perform a standard analysis on moderately relevant data. By adjusting the analysis method based on the relevance of the data, it becomes possible to perform a detailed analysis on highly relevant data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into an AI model and have the AI ​​perform the adjustment of the analysis method.

[0042] The generation unit can adjust the level of detail of prompts based on the importance of the data when generating prompts. For example, the generation unit can generate detailed prompts for important data. The generation unit can also generate standard prompts for general data. The generation unit can also generate simplified prompts for unnecessary data. This allows for the generation of detailed prompts for important data by adjusting the level of detail of prompts based on the importance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the importance of the data into the generation AI and have the generation AI perform the adjustment of the level of detail of prompts.

[0043] The generation unit can apply different generation algorithms depending on the data category when generating prompts. For example, the generation unit can apply a natural language generation algorithm to text data. The generation unit can also apply an image generation algorithm to image data. The generation unit can also apply a speech generation algorithm to speech data. By applying different generation algorithms depending on the data category, more appropriate prompts can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the data category into the generation AI and have the generation AI execute the application of the generation algorithm.

[0044] The generation unit can determine the priority of prompts based on the data collection timing when generating prompts. For example, the generation unit can prioritize generating prompts based on the latest data. The generation unit can also postpone the generation of prompts based on past data. The generation unit can also prioritize generating prompts based on data collected during a specific period. This allows for the priority generation of prompts based on the latest data by determining the priority of prompts based on the data collection timing. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the data collection timing to the generation AI and have the generation AI perform the determination of prompt priority.

[0045] The generation unit can adjust the order of prompts based on the relevance of the data when generating prompts. For example, the generation unit can prioritize generating prompts based on highly relevant data. The generation unit can also postpone the generation of prompts based on less relevant data. The generation unit can also generate prompts in a standard manner based on moderately relevant data. This allows for the priority generation of prompts based on highly relevant data by adjusting the order of prompts based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the relevance of the data into a generation AI and have the generation AI perform the adjustment of the prompt order.

[0046] The information delivery unit can select the optimal delivery method by referring to the user's past behavior history when providing information. For example, the delivery unit may prioritize information delivery methods that the user has preferred in the past. The delivery unit can also analyze the user's past behavior history and provide information at the optimal timing. The delivery unit can also provide information in a way that is optimized for the devices the user has used in the past. This allows the delivery unit to select the optimal information delivery method by referring to the user's past behavior history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's past behavior history into an AI model and have the AI ​​select the optimal delivery method.

[0047] The information provider can customize the content provided based on the user's current areas of interest when providing information. For example, the provider can prioritize providing information related to topics the user is currently interested in. The provider can also customize and provide relevant information based on the user's current search history. The provider can also provide information based on topics in online communities the user participates in. This allows for the provision of highly relevant information by customizing it based on the user's current areas of interest. Some or all of the above processes in the information provider may be performed using AI or not. For example, the provider can input the user's current areas of interest into an AI model and have the AI ​​perform the information customization.

[0048] The information provider can select the optimal method of providing information by considering the user's geographical location. For example, the provider may prioritize providing information related to the user's current location. The provider may also provide information related to places the user has visited in the past. The provider may also provide information related to places the user plans to visit in the future. By considering the user's geographical location, the provider can provide highly relevant information. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider may input the user's geographical location into an AI model and have the AI ​​perform the information provision.

[0049] The information provider can analyze the user's social media activity and adjust the content provided when delivering information. For example, the provider can provide information based on content shared by the user on social media. The provider can also provide information based on the activity of accounts followed by the user. The provider can also provide information related to groups and events the user participates in. This allows for the provision of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's social media activity into an AI model and have the AI ​​deliver the information.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The information delivery unit can analyze the user's past behavioral data and select the most appropriate format for providing information. For example, it can prioritize providing information in formats that the user has preferred in the past. It can also analyze the user's past behavioral patterns and provide information in the most effective format. It can also provide information in a format optimized for the devices the user has used in the past. In this way, the optimal information delivery format can be selected by analyzing the user's past behavioral data. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the user's past behavioral data into an AI model and have the AI ​​select the optimal information delivery format.

[0052] The information delivery unit can determine the priority of information delivery based on the user's current areas of interest. For example, it can prioritize providing information related to topics the user is currently interested in. It can also prioritize providing relevant information based on the user's current search history. It can also provide information based on topics in online communities the user participates in. This allows for the provision of highly relevant information by prioritizing information delivery based on the user's current areas of interest. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the user's current areas of interest into an AI model and have the AI ​​perform the determination of information delivery priorities.

[0053] The information provider can customize the information provided by considering the user's geographical location. For example, it can prioritize providing information related to the user's current location, information related to places the user has visited in the past, or information related to places the user plans to visit in the future. This allows for the provision of highly relevant information by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's geographical location into an AI model and have the AI ​​perform the information provision.

[0054] The information delivery unit can analyze the user's social media activity and customize the content provided when delivering information. For example, it can provide information based on content the user has shared on social media. It can also provide information based on the activity of accounts the user follows. It can also provide information related to groups and events the user participates in. This allows for the provision of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the user's social media activity into an AI model and have the AI ​​deliver the information.

[0055] The information delivery unit can select the optimal delivery method by referring to the user's past behavior history when providing information. For example, it can prioritize information delivery methods that the user has preferred in the past. It can also analyze the user's past behavior history and provide information at the optimal timing. It can also provide information in a way that is optimized for the devices the user has used in the past. This allows the optimal information delivery method to be selected by referring to the user's past behavior history. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the user's past behavior history into an AI model and have the AI ​​select the optimal delivery method.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The collection unit collects user behavior data and content metadata. For example, it can collect behavioral data such as website browsing history, click data, and purchase history, as well as metadata such as content category, tags, and creation date. The processing in the collection unit may or may not be performed using AI. Step 2: The analysis unit analyzes the data collected by the collection unit and infers user characteristics and content nature. For example, statistical analysis or machine learning algorithms can be used to analyze the data and infer user characteristics such as age, gender, and interests. The processing in the analysis unit may be performed using AI or not. Step 3: The generation unit generates the optimal prompt based on the analysis results obtained by the analysis unit. For example, a generation AI can be used to generate prompts such as questions and suggestions based on the user's interests. The processing in the generation unit may be performed using a generation AI or without a generation AI. Step 4: The providing unit provides information based on the prompts generated by the generating unit. For example, information can be provided through a website or mobile application, or via email or push notifications. The processing in the providing unit may or may not be performed using AI.

[0058] (Example of form 2) The content delivery agent according to an embodiment of the present invention is a system that leverages generative AI to establish a new standard for information distribution. This system dynamically optimizes how information is presented and conveyed by collecting user behavior data and content metadata, and having the generative AI analyze them to generate optimal prompts. For example, in commerce, it realizes the optimal presentation according to the characteristics of the product, and in media, it provides information in the optimal format according to the nature of the news. The content delivery agent flexibly responds to a nearly limitless number of search queries and information requests, and systematizes the provision of optimal information for each user and content. This enables enhanced user engagement, improved information quality, maximized information value, strengthened competitiveness, and innovative approaches. For example, if a user searches for "down coat," the generative AI suggests the optimal presentation according to the characteristics of that product. Also, if a user searches for "news," the generative AI provides information in the optimal format according to the nature of the news. This allows users to effectively receive information that meets their needs. As a result, the content delivery agent can solve the challenges of information distribution and fundamentally change how information is presented, thereby significantly improving user engagement and competitiveness.

[0059] The content delivery agent according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user behavior data and content metadata. The collection unit can collect behavior data such as website browsing history, click data, and purchase history. The collection unit can also collect metadata such as content category, tags, and creation date. Some or all of the above-described processes in the collection unit may be performed using AI or not. For example, the collection unit can input user behavior data into an AI model and have the AI ​​perform data collection. The analysis unit analyzes the data collected by the collection unit and infers user characteristics and content properties. The analysis unit can analyze the data using statistical analysis or machine learning algorithms, for example. The analysis unit can also infer user characteristics such as age, gender, and interests. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into an AI model and have the AI ​​perform inferences about user characteristics and content properties. The generation unit generates the optimal prompt based on the analysis results obtained by the analysis unit. The generation unit can generate prompts using, for example, a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit can generate prompts such as questions and suggestions based on user interests. Some or all of the above-described processes in the generation unit may be performed using or without a generation AI. For example, the generation unit can input the analysis results into a generation AI and have the generation AI generate prompts. The provision unit provides information based on the prompts generated by the generation unit. The provision unit can provide information, for example, through a website or a mobile application. The provision unit can also provide information using email or push notifications. Some or all of the above-described processes in the provision unit may be performed using or without an AI.For example, the delivery unit can input the generated prompts into the AI ​​model and have the AI ​​deliver the information. As a result, the content delivery agent according to the embodiment can dynamically optimize how information is presented and conveyed by analyzing user behavior data and content metadata and generating optimal prompts.

[0060] The data collection unit collects user behavior data and content metadata. For example, the data collection unit can collect behavioral data such as website browsing history, click data, and purchase history. Specifically, this includes URLs of pages visited, time spent on pages, links and buttons clicked, and details of purchased items. This data is an important source of information for understanding user interests. The data collection unit can also collect metadata such as content categories, tags, and creation dates. For example, this includes article genres, related keywords, publication dates, and update dates. This allows the data collection unit to combine user behavior data and content metadata for more accurate analysis. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user behavior data into an AI model and have the AI ​​perform data collection. The AI ​​can learn user behavior patterns and collect data efficiently. For example, the AI ​​can detect that users tend to view content of a specific category at certain times and focus data collection during those times. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0061] The analysis unit analyzes the data collected by the collection unit and infers user characteristics and content nature. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. Specifically, it infers characteristics such as age, gender, and interests based on user behavior data. For example, a user who frequently views content in a particular category can be inferred to have a strong interest in that category. The analysis unit can also infer the nature of content based on its metadata. For example, content with a specific tag can be inferred to be related to a particular theme or topic. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into an AI model and have the AI ​​perform inferences about user characteristics and content nature. AI can quickly analyze vast amounts of data and perform highly accurate inferences. For example, AI can predict the content that a user is most likely to view next based on user behavior data. This allows the analysis unit to quickly and accurately analyze the collected data and understand user characteristics and content nature. Furthermore, the analytics unit can utilize historical data and statistical information to analyze long-term trends and patterns. This allows the analytics unit to gain a deeper understanding of user behavior and content characteristics, thereby improving the overall system performance.

[0062] The generation unit generates optimal prompts based on the analysis results obtained by the analysis unit. The generation unit can generate prompts using, for example, a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. Specifically, the generation unit can generate prompts such as questions and suggestions based on user interests. For example, if a user frequently views content in a particular category, the generation unit can generate prompts that suggest new content related to that category. It can also predict content that the user might be interested in next based on user behavior data and generate prompts that introduce that content. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can input the analysis results into a generation AI and have the generation AI perform prompt generation. The generation AI can learn user interests and behavior patterns and generate optimal prompts. This allows the generation unit to efficiently generate prompts based on user interests and provide users with appropriate information. Furthermore, the generation unit can evaluate the effectiveness of the generated prompts and continuously improve them. For example, it can review the content and format of prompts based on user reactions and feedback to generate more effective prompts. This allows the generation unit to always provide the user with the most optimal information and improve the overall system performance.

[0063] The delivery unit provides information based on prompts generated by the generation unit. The delivery unit can provide information, for example, through websites or mobile applications. Specifically, it can provide information to users through website pop-up notifications, banner ads, or mobile application push notifications. The delivery unit can also provide information using email or push notifications. For example, it can notify users via email about new content or special offers that may be of interest to them. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the generated prompts into an AI model and have the AI ​​perform the information provision. The AI ​​can learn user behavior patterns and interests and provide information at the optimal time. For example, the AI ​​can detect that users tend to visit the website at certain times and provide information accordingly. This allows the delivery unit to provide information to users efficiently and effectively and attract their interest. Furthermore, the delivery unit can collect user reactions and feedback to continuously improve the accuracy and effectiveness of information provision. For example, it can analyze how users reacted to the information provided and reflect this in future information provision. This allows the service provider to consistently deliver optimal information to users and improve the overall system performance.

[0064] The data collection unit can estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to collect data when the user is relaxed. If the user is excited, the data collection unit can also collect data immediately to obtain real-time behavioral data. If the user is tired, the data collection unit can adjust the collection timing to collect data after the user has rested. This allows for more appropriate data collection 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0065] The data collection unit can analyze the user's past behavioral data and select the optimal collection method. For example, the data collection unit may prioritize collecting data from devices the user has frequently used in the past. The data collection unit can also analyze the user's past behavioral patterns and select the most effective timing for data collection. The data collection unit can also collect data from applications the user has preferred to use in the past. This allows the optimal collection method to be selected by analyzing the user's past behavioral data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past behavioral data into an AI model and have the AI ​​select the optimal collection method.

[0066] The data collection unit can filter behavioral data based on the user's current areas of interest. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. The data collection unit can also filter and collect relevant data based on the user's current search history. The data collection unit can also collect data based on topics in online communities the user participates in. This allows for the collection of highly relevant data by filtering the data based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's current areas of interest into an AI model and have the AI ​​perform the data filtering.

[0067] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed data. If the user is in a hurry, the data collection unit may also prioritize collecting important data. If the user is excited, the data collection unit may also prioritize collecting real-time data. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0068] 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, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also collect data related to places the user has visited in the past. The data collection unit can also collect data related to places the user plans to visit in the future. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into an AI model and have the AI ​​perform the data collection.

[0069] The data collection unit can analyze a user's social media activity and collect relevant data when collecting behavioral data. For example, the data collection unit can collect data based on content shared by the user on social media. The data collection unit can also collect data based on the activity of accounts that the user follows. The data collection unit can also collect data related to groups and events that the user participates in. This allows for the collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into an AI model and have the AI ​​perform the data collection.

[0070] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. If the user is in a hurry, the analysis unit can also perform a rapid analysis. If the user is excited, the analysis unit can also perform a real-time analysis. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. The analysis unit can also perform a standard analysis on general data. The analysis unit can also perform a simplified analysis on unnecessary data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into an AI model and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a speech recognition algorithm to audio data. By applying different analysis algorithms depending on the data category, more appropriate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into an AI model and have the AI ​​perform the application of the analysis algorithm.

[0073] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit may prioritize detailed analysis. If the user is in a hurry, the analysis unit may also prioritize rapid analysis. If the user is excited, the analysis unit may also prioritize real-time analysis. This allows important analyses to be prioritized by determining the priority of analysis 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The analysis unit can adjust the order of analysis based on the data collection timing during the analysis. For example, the analysis unit can prioritize the analysis of the most recent data. The analysis unit can also postpone the analysis of past data. The analysis unit can also prioritize the analysis of data collected during a specific period. This allows for prioritization of the analysis of the most recent data by adjusting the order of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection timing into an AI model and have the AI ​​perform the adjustment of the analysis order.

[0075] The analysis unit can adjust its analysis method based on the relevance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly relevant data. It can also perform a simplified analysis on less relevant data. It can also perform a standard analysis on moderately relevant data. By adjusting the analysis method based on the relevance of the data, it becomes possible to perform a detailed analysis on highly relevant data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into an AI model and have the AI ​​perform the adjustment of the analysis method.

[0076] The generation unit can estimate the user's emotions and adjust the way it expresses the prompts it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit may use a calm expression. If the user is in a hurry, the generation unit may use a concise expression. If the user is excited, the generation unit may use a lively expression. By adjusting the expression of prompts according to the user's emotions, more appropriate prompts can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0077] The generation unit can adjust the level of detail of prompts based on the importance of the data when generating prompts. For example, the generation unit can generate detailed prompts for important data. The generation unit can also generate standard prompts for general data. The generation unit can also generate simplified prompts for unnecessary data. This allows for the generation of detailed prompts for important data by adjusting the level of detail of prompts based on the importance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the importance of the data into the generation AI and have the generation AI perform the adjustment of the level of detail of prompts.

[0078] The generation unit can apply different generation algorithms depending on the data category when generating prompts. For example, the generation unit can apply a natural language generation algorithm to text data. The generation unit can also apply an image generation algorithm to image data. The generation unit can also apply a speech generation algorithm to speech data. By applying different generation algorithms depending on the data category, more appropriate prompts can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the data category into the generation AI and have the generation AI execute the application of the generation algorithm.

[0079] The generation unit can estimate the user's emotions and adjust the length of the prompts it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate longer prompts. If the user is in a hurry, it can also generate shorter prompts. If the user is excited, it can also generate prompts of standard length. This allows for the generation of more appropriate prompts by adjusting the length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The generation unit can determine the priority of prompts based on the data collection timing when generating prompts. For example, the generation unit can prioritize generating prompts based on the latest data. The generation unit can also postpone the generation of prompts based on past data. The generation unit can also prioritize generating prompts based on data collected during a specific period. This allows for the priority generation of prompts based on the latest data by determining the priority of prompts based on the data collection timing. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the data collection timing to the generation AI and have the generation AI perform the determination of prompt priority.

[0081] The generation unit can adjust the order of prompts based on the relevance of the data when generating prompts. For example, the generation unit can prioritize generating prompts based on highly relevant data. The generation unit can also postpone the generation of prompts based on less relevant data. The generation unit can also generate prompts in a standard manner based on moderately relevant data. This allows for the priority generation of prompts based on highly relevant data by adjusting the order of prompts based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the relevance of the data into a generation AI and have the generation AI perform the adjustment of the prompt order.

[0082] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is relaxed, the information provider can deliver information in a calm tone. If the user is in a hurry, the information provider can deliver information in a concise tone. If the user is excited, the information provider can deliver information in a lively tone. This allows for more appropriate information delivery by adjusting the method of information delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The information delivery unit can select the optimal delivery method by referring to the user's past behavior history when providing information. For example, the delivery unit may prioritize information delivery methods that the user has preferred in the past. The delivery unit can also analyze the user's past behavior history and provide information at the optimal timing. The delivery unit can also provide information in a way that is optimized for the devices the user has used in the past. This allows the delivery unit to select the optimal information delivery method by referring to the user's past behavior history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's past behavior history into an AI model and have the AI ​​select the optimal delivery method.

[0084] The information provider can customize the content provided based on the user's current areas of interest when providing information. For example, the provider can prioritize providing information related to topics the user is currently interested in. The provider can also customize and provide relevant information based on the user's current search history. The provider can also provide information based on topics in online communities the user participates in. This allows for the provision of highly relevant information by customizing it based on the user's current areas of interest. Some or all of the above processes in the information provider may be performed using AI or not. For example, the provider can input the user's current areas of interest into an AI model and have the AI ​​perform the information customization.

[0085] The information provider can estimate the user's emotions and determine the priority of information delivery based on the estimated emotions. For example, if the user is relaxed, the information provider may prioritize providing detailed information. If the user is in a hurry, the information provider may also prioritize providing important information. If the user is excited, the information provider may also prioritize providing real-time information. This allows for the prioritization of important information by determining the priority of information delivery 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The information provider can select the optimal method of providing information by considering the user's geographical location. For example, the provider may prioritize providing information related to the user's current location. The provider may also provide information related to places the user has visited in the past. The provider may also provide information related to places the user plans to visit in the future. By considering the user's geographical location, the provider can provide highly relevant information. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider may input the user's geographical location into an AI model and have the AI ​​perform the information provision.

[0087] The information provider can analyze the user's social media activity and adjust the content provided when delivering information. For example, the provider can provide information based on content shared by the user on social media. The provider can also provide information based on the activity of accounts followed by the user. The provider can also provide information related to groups and events the user participates in. This allows for the provision of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's social media activity into an AI model and have the AI ​​deliver the information.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The information delivery unit can estimate the user's emotions and adjust the timing of information delivery based on the estimated emotions. For example, if the user is relaxed, the timing of information delivery can be delayed to make it easier for the user to receive the information. If the user is in a hurry, information can be delivered immediately to enable a quick response. If the user is excited, information can be delivered in real time to maintain the user's excitement. By adjusting the timing of information delivery according to the user's emotions, more effective information delivery becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI or not using AI. For example, the information delivery unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The information delivery unit can analyze the user's past behavioral data and select the most appropriate format for providing information. For example, it can prioritize providing information in formats that the user has preferred in the past. It can also analyze the user's past behavioral patterns and provide information in the most effective format. It can also provide information in a format optimized for the devices the user has used in the past. In this way, the optimal information delivery format can be selected by analyzing the user's past behavioral data. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the user's past behavioral data into an AI model and have the AI ​​select the optimal information delivery format.

[0091] The information provider can estimate the user's emotions and adjust the content of the information provided based on the estimated emotions. For example, if the user is relaxed, detailed information can be provided. If the user is in a hurry, concise information can be provided. If the user is excited, lively information can be provided. By adjusting the content of the information provided according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The information delivery unit can determine the priority of information delivery based on the user's current areas of interest. For example, it can prioritize providing information related to topics the user is currently interested in. It can also prioritize providing relevant information based on the user's current search history. It can also provide information based on topics in online communities the user participates in. This allows for the provision of highly relevant information by prioritizing information delivery based on the user's current areas of interest. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the user's current areas of interest into an AI model and have the AI ​​perform the determination of information delivery priorities.

[0093] The information provider can estimate the user's emotions and adjust the frequency of information delivery based on the estimated emotions. For example, if the user is relaxed, the frequency of information delivery can be reduced. If the user is in a hurry, the frequency of information delivery can be increased. If the user is excited, information can be delivered in real time. By adjusting the frequency of information delivery according to the user's emotions, more effective information delivery becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The information provider can customize the information provided by considering the user's geographical location. For example, it can prioritize providing information related to the user's current location, information related to places the user has visited in the past, or information related to places the user plans to visit in the future. This allows for the provision of highly relevant information by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's geographical location into an AI model and have the AI ​​perform the information provision.

[0095] The information provider can estimate the user's emotions and adjust the format of information delivery based on the estimated emotions. For example, if the user is relaxed, the information can be delivered in a calm format. If the user is in a hurry, the information can be delivered in a concise format. If the user is excited, the information can be delivered in a lively format. By adjusting the format of information delivery according to the user's emotions, more appropriate information can be delivered. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The information delivery unit can analyze the user's social media activity and customize the content provided when delivering information. For example, it can provide information based on content the user has shared on social media. It can also provide information based on the activity of accounts the user follows. It can also provide information related to groups and events the user participates in. This allows for the provision of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the user's social media activity into an AI model and have the AI ​​deliver the information.

[0097] The information provider can estimate the user's emotions and determine the priority of information delivery based on the estimated emotions. For example, if the user is relaxed, detailed information may be prioritized. If the user is in a hurry, important information may be prioritized. If the user is excited, real-time information may be prioritized. In this way, by determining the priority of information delivery according to the user's emotions, important information can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The information delivery unit can select the optimal delivery method by referring to the user's past behavior history when providing information. For example, it can prioritize information delivery methods that the user has preferred in the past. It can also analyze the user's past behavior history and provide information at the optimal timing. It can also provide information in a way that is optimized for the devices the user has used in the past. This allows the optimal information delivery method to be selected by referring to the user's past behavior history. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the user's past behavior history into an AI model and have the AI ​​select the optimal delivery method.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The collection unit collects user behavior data and content metadata. For example, it can collect behavioral data such as website browsing history, click data, and purchase history, as well as metadata such as content category, tags, and creation date. The processing in the collection unit may or may not be performed using AI. Step 2: The analysis unit analyzes the data collected by the collection unit and infers user characteristics and content nature. For example, statistical analysis or machine learning algorithms can be used to analyze the data and infer user characteristics such as age, gender, and interests. The processing in the analysis unit may be performed using AI or not. Step 3: The generation unit generates the optimal prompt based on the analysis results obtained by the analysis unit. For example, a generation AI can be used to generate prompts such as questions and suggestions based on the user's interests. The processing in the generation unit may be performed using a generation AI or without a generation AI. Step 4: The providing unit provides information based on the prompts generated by the generating unit. For example, information can be provided through a website or mobile application, or via email or push notifications. The processing in the providing unit may or may not be performed using AI.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user behavior data using the control unit 46A of the smart device 14 and collects content metadata using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the collected data using, for example, the identification processing unit 290 of the data processing unit 12 to infer user characteristics and content properties. The generation unit generates an optimal prompt using, for example, the identification processing unit 290 of the data processing unit 12. The provision unit provides information based on the prompt generated by, for example, the control unit 46A of the smart device 14. 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.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user behavior data by the control unit 46A of the smart glasses 214 and collects content metadata by the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and infers the user's characteristics and the nature of the content. The generation unit generates an optimal prompt by the identification processing unit 290 of the data processing unit 12. The provision unit provides information based on the prompt generated by the control unit 46A of the smart glasses 214. 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.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user behavior data using the control unit 46A of the headset terminal 314 and collects content metadata using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and infers user characteristics and content properties. The generation unit generates an optimal prompt using the identification processing unit 290 of the data processing unit 12. The provision unit provides information based on the prompt generated by the control unit 46A of the headset terminal 314. 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.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user behavior data by the control unit 46A of the robot 414 and collects content metadata by the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and infers the user's characteristics and the nature of the content. The generation unit generates an optimal prompt by the identification processing unit 290 of the data processing unit 12. The provision unit provides information based on the prompt generated by the control unit 46A of the robot 414. 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) A collection unit that collects user behavior data and content metadata, An analysis unit analyzes the data collected by the aforementioned collection unit and infers user characteristics and content nature, A generation unit that generates an optimal prompt based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides information based on prompts generated by the generation unit. A system characterized by the following features. (Note 2) 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 3) The aforementioned collection unit is Analyze users' past behavioral data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting behavioral data, filter it based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) 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 7) 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 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, 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 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the data collection period. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the analysis method is adjusted based on the relationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts how prompts are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating prompts, adjust the level of detail of the prompts based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating prompts, different generation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts the length of the prompts generated based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating prompts, the priority of prompts is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating prompts, adjust the order of prompts based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, the system selects the optimal method of delivery by referring to the user's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing information, customize the content based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, the optimal method of delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing information, we analyze the user's social media activity and adjust the content accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user behavior data and content metadata, An analysis unit analyzes the data collected by the aforementioned collection unit and infers user characteristics and content nature, A generation unit that generates an optimal prompt based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides information based on prompts generated by the generation unit. A system characterized by the following features.

2. 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.

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

4. The aforementioned collection unit is When collecting behavioral data, filter it based on the user's current areas of interest. The system according to feature 1.

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

6. 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 according to feature 1.

7. The aforementioned collection unit is When collecting behavioral data, analyze users' social media activity and collect relevant data. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system according to feature 1.

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