system

Abstract

The system according to this embodiment aims to propose and provide the most suitable AI utilization method for each individual user. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a provision unit, and a feedback collection unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes the optimal AI utilization method based on the analysis results obtained by the analysis unit. The provision unit provides the content proposed by the proposal unit to the user. The feedback collection unit collects the user's reaction to the content provided by the provision 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to propose an optimal AI usage method for each individual user, and there is a problem that the effective utilization of generative AI has not been sufficiently carried out.

[0005] The system according to the embodiment aims to propose and provide an optimal AI usage method for each individual user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a provision unit, and a feedback collection unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes the optimal AI utilization method based on the analysis results obtained by the analysis unit. The provision unit provides the content proposed by the proposal unit to the user. The feedback collection unit collects the user's reaction to the content provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose and provide the most suitable AI utilization method for each individual user. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 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 three or more matters are connected and expressed by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that utilizes generative AI to propose AI usage methods optimized for each individual's personality, attributes, and circumstances. In this system, the user inputs information about their personality, attributes, and circumstances to the generative AI, which analyzes that information and proposes the most suitable AI usage method for the user. The proposal is tailored to the user's needs and is personalized for each individual. For example, the user inputs, "I work in a creative field and want to know how to generate ideas efficiently." This information is input to the generative AI, which then analyzes it. The generative AI proposes the most suitable AI usage method based on the user's personality, attributes, and circumstances. For example, the generative AI might suggest, "You would do well using this AI as an idea generation tool." This proposal is tailored to the user's needs and is personalized for each individual. Furthermore, the generative AI educates the user on specific AI usage methods. For example, the generative AI might provide specific guidance such as, "When using this AI, you should input prompts like this to generate ideas." This allows the user to learn how to effectively utilize the generative AI. This mechanism enables all people who want to utilize generative AI to find an AI usage method that suits them. For example, people in creative fields can utilize generative AI as an idea generation tool, and those seeking to improve business efficiency can use generative AI as a task automation tool. In this way, an AI agent that uses generative AI to propose AI usage methods optimized for each individual's personality, attributes, and circumstances plays a role in connecting generative AI with humans and promoting the effective use of generative AI. As a result, the AI ​​agent system can propose AI usage methods optimized for the user's personality, attributes, and circumstances, and promote the effective use of generative AI.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a provision unit, and a feedback collection unit. The collection unit collects user information. For example, the collection unit can collect information about the user's personality, attributes, and circumstances. For example, the collection unit can conduct a personality diagnostic test and collect the results. The collection unit can also conduct a questionnaire survey and collect user attribute information. Furthermore, the collection unit can collect the user's behavioral history and obtain information about the user's circumstances. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected information using data mining techniques. Furthermore, the analysis unit can perform statistical analysis and analyze based on the user's personality, attributes, and circumstances. Furthermore, the analysis unit can also analyze the collected information using machine learning algorithms. The proposal unit proposes the optimal AI utilization method based on the analysis results obtained by the analysis unit. For example, the proposal unit can select and propose an AI tool according to the user's purpose. Furthermore, the proposal unit can customize and propose an AI tool according to the user's needs. Furthermore, the proposal unit can also propose the optimal AI usage method based on the user's personality, attributes, and circumstances. The delivery unit provides the content proposed by the proposal unit to the user. The delivery unit can provide the proposed content to the user, for example, via email. It can also provide the proposed content to the user via app notifications. Furthermore, it can provide the proposed content to the user via a dashboard display. The feedback collection unit collects user reactions to the content provided by the delivery unit. The feedback collection unit can collect user reactions, for example, through a feedback form. It can also collect user reactions through click-through rates. Furthermore, it can collect user reactions through usage frequency. Thus, the AI ​​agent system according to the embodiment can provide an AI usage method optimized for each individual by collecting and analyzing user information, proposing and providing the optimal AI usage method, and collecting feedback.

[0030] The data collection unit collects user information. For example, the data collection unit can collect information about users' personalities, attributes, and circumstances. Specifically, the data collection unit conducts personality assessment tests and collects the results. Personality assessment tests are conducted in the form of questionnaires that include psychological questions, providing data for detailed analysis of users' personality traits. The data collection unit can also conduct surveys to collect user attribute information. Surveys collect basic attribute information such as age, gender, occupation, hobbies, and interests, providing foundational data for understanding the user's background. Furthermore, the data collection unit can collect users' behavioral history to obtain information about users' circumstances. The collection of behavioral history includes website browsing history, app usage history, and purchase history, and through this data, it is possible to understand users' interests, concerns, and behavioral patterns. The data collection unit integrates the information obtained from these diverse data sources to build a comprehensive dataset for understanding the overall picture of the user. This allows the data collection unit to collect detailed information about users' personalities, attributes, and circumstances and provide it to the analysis unit.

[0031] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit can analyze the collected information using data mining techniques. Data mining techniques are methods for extracting useful patterns and relationships from large amounts of data and are used to gain insights into user behavior and characteristics. The analysis unit can also perform statistical analysis, analyzing information based on user personality, attributes, and circumstances. Statistical analysis quantitatively evaluates user characteristics and behavioral patterns based on the collected data, revealing trends and correlations. Furthermore, the analysis unit can analyze the collected information using machine learning algorithms. Machine learning algorithms are techniques for building models that learn from data and perform predictions and classifications, and are used to predict user characteristics and behavior. For example, future behavior and interests can be predicted based on user personality traits. By combining these techniques, the analysis unit can analyze the collected information from multiple perspectives and gain detailed insights into user characteristics and behavior. This allows the analysis unit to quickly and accurately analyze the collected data and generate useful information to provide to the proposal unit.

[0032] The Proposal Department proposes the optimal AI utilization method based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can select and propose AI tools that suit the user's objectives. Specifically, it selects the most suitable AI tools and applications according to the results and goals the user desires and proposes how to use them. The Proposal Department can also customize and propose AI tools to suit the user's needs. For example, it can adjust the settings and functions of AI tools based on the user's personality and behavioral patterns to enable more effective use. Furthermore, the Proposal Department can propose the optimal AI utilization method based on the user's personality, attributes, and circumstances. For example, if the user is prone to stress, the Proposal Department can propose AI tools that are useful for relaxation and mental health. Through these proposals, the Proposal Department provides support to users in effectively utilizing AI and achieving their goals. The Proposal Department can continuously improve its proposals based on user feedback, enabling it to make more accurate proposals. In this way, the Proposal Department can propose AI utilization methods optimized for the user's needs and characteristics, thereby improving user satisfaction.

[0033] The delivery department provides users with the content proposed by the proposal department. For example, the delivery department can provide the proposal content to users via email. The email will include detailed explanations of the proposed AI tool, instructions for use, and installation procedures, making it easy for users to understand and implement. The delivery department can also provide the proposal content to users via app notifications. App notifications are sent to users' smartphones and tablets in real time, allowing for the rapid delivery of important information. Furthermore, the delivery department can provide the proposal content to users through a dashboard display. The dashboard is an easy-to-access interface that allows users to see the proposal content and progress at a glance. The delivery department combines these means to effectively provide information to users and support the implementation of the proposal. The delivery department can collect user feedback and continuously improve the delivery methods and content. This allows the delivery department to provide users with information quickly and accurately and support the implementation of the proposal.

[0034] The feedback collection unit collects user responses to the content provided by the provision unit. For example, the feedback collection unit can collect user responses through a feedback form. A feedback form is a format in which users can freely enter their opinions and impressions of the proposed content, and is a means of obtaining detailed feedback. The feedback collection unit can also collect user responses through click-through rates. Click-through rates are an indicator of how interested users are in the proposed content and how much action they have taken, and are used to evaluate the effectiveness of the proposed content. Furthermore, the feedback collection unit can also collect user responses through usage frequency. Usage frequency is an indicator of how often users use the proposed AI tool, and is used to evaluate the practicality of the proposed content. The feedback collection unit can integrate this data and comprehensively evaluate user responses. This allows the feedback collection unit to understand the effectiveness of the proposed content and user satisfaction, and to provide feedback to the proposal unit and provision unit. As a result, the AI ​​agent system according to the embodiment can provide AI utilization methods optimized for each individual by collecting and analyzing user information, proposing and providing the optimal AI utilization method, and collecting feedback.

[0035] The data collection unit can collect information about the user's personality, attributes, and circumstances. For example, the data collection unit can conduct a personality test and collect the results. The data collection unit can also conduct a survey and collect user attribute information. For example, the data collection unit can collect the user's behavioral history and obtain information about the user's circumstances. By collecting information about the user's personality, attributes, and circumstances, the data collection unit can propose an AI application method optimized for each individual. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the results of a personality test into an AI, which can then analyze and collect the information.

[0036] The analysis unit can analyze the collected information and propose the most suitable AI application method to the user. For example, the analysis unit can analyze the collected information using data mining techniques. The analysis unit can also perform statistical analysis, for example, based on the user's personality, attributes, and circumstances. Furthermore, the analysis unit can analyze the collected information using machine learning algorithms. As a result, the analysis unit can propose the most suitable AI application method to the user by analyzing the collected information. Some or all of the above-described processes in the analysis unit are performed using AI. For example, the analysis unit can input the collected information into the AI, which can then analyze the information and propose the most suitable AI application method.

[0037] The proposal department can propose AI utilization methods tailored to user needs. For example, the proposal department can select and propose AI tools according to the user's objectives. The proposal department can also customize and propose AI tools according to user needs. The proposal department can also propose the optimal AI utilization method based on the user's personality, attributes, and circumstances. In this way, the proposal department can provide AI utilization methods optimized for each individual by proposing AI utilization methods tailored to user needs. Some or all of the above processes in the proposal department are performed using AI. For example, the proposal department can select AI tools based on user needs, and the AI ​​can propose the optimal AI utilization method.

[0038] The service provider can provide the proposed AI applications to the user. For example, the service provider can provide the proposed content to the user via email. The service provider can also provide the proposed content to the user via app notifications. The service provider can also provide the proposed content to the user via a dashboard display. In this way, by providing the proposed AI applications to the user, the service provider can enable the user to learn specific ways of using AI. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input the proposed content into the AI ​​and select how the AI ​​will provide it to the user.

[0039] The feedback collection unit can collect user responses to the provided content and provide feedback to the analysis unit. The feedback collection unit can collect user responses, for example, through a feedback form. The feedback collection unit can also collect user responses, for example, through click-through rates. The feedback collection unit can also collect user responses, for example, through usage frequency. In this way, the feedback collection unit can improve the accuracy of the system by collecting user responses to the provided content and providing feedback to the analysis unit. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input user responses into AI, and the AI ​​can analyze the responses and provide feedback.

[0040] The data collection unit can analyze the user's past behavior history and select the optimal information collection method. For example, the data collection unit can prioritize collecting information sources that the user has frequently used in the past. For example, the data collection unit can identify the most efficient time of day for information collection based on the user's behavior patterns. For example, the data collection unit can prioritize collecting highly relevant information based on the user's past behavior history. In this way, the data collection unit can select the optimal information collection method by analyzing the user's past behavior history. 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 behavior history data into AI, which can then select the optimal information collection method.

[0041] The data collection unit can filter information based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to a project the user is currently working on. For example, the data collection unit can filter highly relevant information based on the user's areas of interest. For example, the data collection unit can collect appropriate information depending on the user's living situation (e.g., whether they are at work or on vacation). In this way, the data collection unit can collect highly relevant information by filtering information based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's living situation data into an AI, which can then filter the information.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can collect event information related to the user's current location. For example, the data collection unit can prioritize the collection of nearby news and weather information based on the user's geographical location. For example, the data collection unit can collect region-specific information based on the user's location. In this way, the data collection unit can prioritize the collection of highly relevant information 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 location data into AI, which can then select highly relevant information.

[0043] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. For example, the data collection unit can collect information shared by the user's social media followers and friends. For example, the data collection unit can analyze the content of the user's social media posts and collect relevant information. In this way, the data collection unit can collect relevant information 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 data into AI, which can then select relevant information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a concise analysis on information of low importance. For example, the analysis unit can determine the priority of the analysis according to the importance of the information. This enables efficient analysis by allowing the analysis unit to adjust the level of detail based on the importance of the collected information. 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 information importance data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a natural language processing algorithm to text information. For example, the analysis unit can apply an image recognition algorithm to image information. For example, the analysis unit can apply a speech recognition algorithm to audio information. By applying different analysis algorithms depending on the category of information, the analysis unit can perform more accurate analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input information category data into the AI, and the AI ​​can select an appropriate analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may postpone the analysis of older information. For example, the analysis unit may adjust the priority of analysis according to the timing of information collection. In this way, the analysis unit can prioritize the analysis of the most recent information by determining the priority of analysis based on the timing of information collection. 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 information collection timing data into the AI, and the AI ​​can determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant information. For example, the analysis unit may postpone the analysis of less relevant information. The analysis unit can adjust the order of analysis according to the relevance of the information. This enables efficient analysis by allowing the analysis unit to adjust the order of analysis based on the relevance of the information. 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 information relevance data into the AI, and the AI ​​can determine the order of analysis.

[0048] The proposal unit can adjust the level of detail of its proposals based on the user's needs. For example, if the user's needs are specific, the proposal unit will provide detailed proposals. For example, if the user's needs are vague, the proposal unit can provide concise proposals. The proposal unit can adjust the level of detail of its proposals according to the user's needs. This allows the proposal unit to provide more appropriate proposals by adjusting the level of detail based on the user's needs. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input user needs data into the AI, and the AI ​​can adjust the level of detail of the proposals.

[0049] The suggestion unit can apply different suggestion algorithms depending on the user's attributes when making suggestions. For example, the suggestion unit might suggest an idea generation tool to a user doing creative work. For example, it might suggest a business automation tool to a user who wants to improve business efficiency. For example, it might suggest an educational tool to a user whose purpose is learning. In this way, the suggestion unit can provide more appropriate suggestions by applying different suggestion algorithms depending on the user's attributes. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user attribute data into AI, and the AI ​​can select an appropriate suggestion algorithm.

[0050] The suggestion unit can determine the priority of suggestions based on the user's past responses when making suggestions. For example, the suggestion unit may prioritize suggestions that the user has previously responded to favorably. For example, the suggestion unit may postpone suggestions that the user has previously responded to negatively. For example, the suggestion unit may adjust the priority of suggestions based on the user's past responses. This allows the suggestion unit to provide more appropriate suggestions by determining the priority of suggestions based on the user's past responses. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past response data into AI, and the AI ​​can determine the priority of suggestions.

[0051] The suggestion unit can adjust the order of suggestions based on the user's relevant information when making suggestions. For example, the suggestion unit may prioritize suggestions related to the user's current project. For example, the suggestion unit may prioritize highly relevant suggestions based on the user's areas of interest. For example, the suggestion unit may adjust the order of suggestions according to the user's attributes. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the order of suggestions based on the user's relevant information. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's relevant information data into AI, and the AI ​​can determine the order of suggestions.

[0052] The delivery unit can analyze the user's past reactions at the time of delivery to select the optimal delivery method. For example, the delivery unit can prioritize delivery methods that the user has reacted favorably to in the past. For example, the delivery unit can avoid delivery methods that the user has reacted negatively to in the past. For example, the delivery unit can select the optimal delivery method based on the user's past reactions. In this way, the delivery unit can select the optimal delivery method by analyzing the user's past reactions. 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 reaction data into AI, and the AI ​​can select the optimal delivery method.

[0053] The information delivery unit can customize the means of delivery based on the user's current situation at the time of delivery. For example, if the user is on the move, the information delivery unit can provide information by voice. For example, if the user is working at a desk, the information delivery unit can provide information by text. For example, if the user is relaxed, the information delivery unit can provide information visually. In this way, the information delivery unit can provide more appropriate information by customizing the means of delivery based on the user's current situation. 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 situation data into the AI, and the AI ​​can select the optimal means of delivery.

[0054] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, the service provider can provide event information related to the user's current location. For example, the service provider can provide local news and weather information based on the user's geographical location. For example, the service provider can provide region-specific information based on the user's location information. In this way, the service provider can select the optimal delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's location data into AI, and the AI ​​can select the optimal delivery method.

[0055] The service provider can analyze the user's social media activity and propose a means of delivery at the time of delivery. For example, the service provider can provide information related to topics the user has shown interest in on social media. For example, the service provider can provide information shared by the user's social media followers and friends. For example, the service provider can analyze the content of the user's social media posts and provide relevant information. In this way, the service provider can propose the optimal means of delivery by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media data into AI, and the AI ​​can select the optimal means of delivery.

[0056] The feedback collection unit can analyze the user's past responses and select the optimal collection method when collecting feedback. For example, the feedback collection unit may prioritize collection methods in which the user has previously shown a positive response. For example, the feedback collection unit may avoid collection methods in which the user has previously shown a negative response. For example, the feedback collection unit can select the optimal collection method based on the user's past responses. In this way, the feedback collection unit can select the optimal collection method by analyzing the user's past responses. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's past response data into AI, and the AI ​​can select the optimal collection method.

[0057] The feedback collection unit can customize the means of collection based on the user's current situation when collecting feedback. For example, if the user is on the move, the feedback collection unit can collect feedback by voice. For example, if the user is working at a desk, the feedback collection unit can collect feedback by text. For example, if the user is relaxed, the feedback collection unit can collect feedback visually. In this way, the feedback collection unit can collect more appropriate feedback by customizing the means of collection based on the user's current situation. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's current situation data into the AI, which can then select the optimal means of collection.

[0058] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, the feedback collection unit can collect feedback related to the user's current location. For example, the feedback collection unit can collect feedback about nearby events and services based on the user's geographical location. For example, the feedback collection unit can collect region-specific feedback based on the user's location information. In this way, the feedback collection unit can select the optimal collection method by taking into account the user's geographical location information. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's location data into AI, and the AI ​​can select the optimal collection method.

[0059] The feedback collection unit can analyze the user's social media activity and suggest methods for collecting feedback. For example, the feedback collection unit can collect feedback related to topics the user has shown interest in on social media. For example, the feedback collection unit can collect feedback based on information shared by the user's social media followers and friends. For example, the feedback collection unit can analyze the content of the user's social media posts and collect relevant feedback. In this way, the feedback collection unit can suggest the optimal collection method by analyzing the user's social media activity. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's social media data into AI, which can then select the optimal collection method.

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

[0061] The analysis unit can evaluate the reliability of the collected information and prioritize the analysis of highly reliable information. For example, it can prioritize the analysis of data collected from highly reliable sources. Data collected from less reliable sources can be analyzed later. By doing so, the analysis unit can provide more accurate analysis results by evaluating the reliability of the collected information. 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 reliability data of the information into an AI, which can evaluate the reliability and determine the priority of the analysis.

[0062] The suggestion unit can analyze the user's past behavior history and select the most appropriate suggestion method. For example, it can prioritize suggestion methods that the user has responded to favorably in the past, and avoid suggestion methods that the user has responded to negatively in the past. In this way, the suggestion unit can select a more appropriate suggestion method by analyzing the user's past behavior history. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's past behavior history data into AI, which can then select the most appropriate suggestion method.

[0063] The information delivery unit can customize how it delivers information based on the user's current living situation and areas of interest. For example, it can prioritize providing information related to projects the user is currently working on. It can also provide highly relevant information based on the user's areas of interest. It can provide appropriate information depending on the user's living situation (e.g., whether they are at work or on vacation). In this way, the information delivery unit can provide more relevant information by customizing how it delivers information based on the user's current living situation and 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 living situation data into AI, which can then customize how it delivers information.

[0064] The feedback collection unit can select a method for collecting feedback while considering the user's geographical location. For example, it can collect feedback related to the user's current location. Based on the user's geographical location, it can collect feedback about nearby events and services. Based on the user's location information, it can collect region-specific feedback. In this way, the feedback collection unit can collect more appropriate feedback by considering the user's geographical location. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's location data into AI, which can then select the optimal feedback collection method.

[0065] The data collection unit can analyze a user's social media activity and collect relevant information. For example, it can collect information related to topics the user has shown interest in on social media. It can also collect information shared by the user's social media followers and friends. It can analyze the content of a user's social media posts and collect relevant information. In this way, the data collection unit can collect relevant information 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 data into an AI, which can then select relevant information.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects user information. For example, the data collection unit can collect information about the user's personality, attributes, and circumstances. The data collection unit can conduct personality tests and collect the results. It can also conduct surveys to collect user attribute information. Furthermore, it can collect the user's behavioral history to obtain information about the user's circumstances. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information using data mining techniques. It can also perform statistical analysis and analyze the information based on the user's personality, attributes, and circumstances. Furthermore, it can analyze the collected information using machine learning algorithms. Step 3: The proposal department proposes the optimal AI utilization method based on the analysis results obtained by the analysis department. The proposal department can select and propose AI tools that suit the user's objectives. It can also customize and propose AI tools to meet the user's needs. Furthermore, it can propose the optimal AI utilization method based on the user's personality, attributes, and circumstances. Step 4: The provisioning department provides the user with the content proposed by the proposal department. The provisioning department can provide the user with the proposed content via email. It can also provide the user with the proposed content via app notifications. Furthermore, it can provide the user with the proposed content via a dashboard display. Step 5: The feedback collection unit collects user responses to the content provided by the service provider. The feedback collection unit can collect user responses through feedback forms. It can also collect user responses through click-through rates. Furthermore, it can collect user responses through usage frequency.

[0068] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that utilizes generative AI to propose AI usage methods optimized for each individual's personality, attributes, and circumstances. In this system, the user inputs information about their personality, attributes, and circumstances to the generative AI, which analyzes that information and proposes the most suitable AI usage method for the user. The proposal is tailored to the user's needs and is personalized for each individual. For example, the user inputs, "I work in a creative field and want to know how to generate ideas efficiently." This information is input to the generative AI, which then analyzes it. The generative AI proposes the most suitable AI usage method based on the user's personality, attributes, and circumstances. For example, the generative AI might suggest, "You would do well using this AI as an idea generation tool." This proposal is tailored to the user's needs and is personalized for each individual. Furthermore, the generative AI educates the user on specific AI usage methods. For example, the generative AI might provide specific guidance such as, "When using this AI, you should input prompts like this to generate ideas." This allows the user to learn how to effectively utilize the generative AI. This mechanism enables all people who want to utilize generative AI to find an AI usage method that suits them. For example, people in creative fields can utilize generative AI as an idea generation tool, and those seeking to improve business efficiency can use generative AI as a task automation tool. In this way, an AI agent that uses generative AI to propose AI usage methods optimized for each individual's personality, attributes, and circumstances plays a role in connecting generative AI with humans and promoting the effective use of generative AI. As a result, the AI ​​agent system can propose AI usage methods optimized for the user's personality, attributes, and circumstances, and promote the effective use of generative AI.

[0069] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a provision unit, and a feedback collection unit. The collection unit collects user information. For example, the collection unit can collect information about the user's personality, attributes, and circumstances. For example, the collection unit can conduct a personality diagnostic test and collect the results. The collection unit can also conduct a questionnaire survey and collect user attribute information. Furthermore, the collection unit can collect the user's behavioral history and obtain information about the user's circumstances. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected information using data mining techniques. Furthermore, the analysis unit can perform statistical analysis and analyze based on the user's personality, attributes, and circumstances. Furthermore, the analysis unit can also analyze the collected information using machine learning algorithms. The proposal unit proposes the optimal AI utilization method based on the analysis results obtained by the analysis unit. For example, the proposal unit can select and propose an AI tool according to the user's purpose. Furthermore, the proposal unit can customize and propose an AI tool according to the user's needs. Furthermore, the proposal unit can also propose the optimal AI usage method based on the user's personality, attributes, and circumstances. The delivery unit provides the content proposed by the proposal unit to the user. The delivery unit can provide the proposed content to the user, for example, via email. It can also provide the proposed content to the user via app notifications. Furthermore, it can provide the proposed content to the user via a dashboard display. The feedback collection unit collects user reactions to the content provided by the delivery unit. The feedback collection unit can collect user reactions, for example, through a feedback form. It can also collect user reactions through click-through rates. Furthermore, it can collect user reactions through usage frequency. Thus, the AI ​​agent system according to the embodiment can provide an AI usage method optimized for each individual by collecting and analyzing user information, proposing and providing the optimal AI usage method, and collecting feedback.

[0070] The data collection unit collects user information. For example, the data collection unit can collect information about users' personalities, attributes, and circumstances. Specifically, the data collection unit conducts personality assessment tests and collects the results. Personality assessment tests are conducted in the form of questionnaires that include psychological questions, providing data for detailed analysis of users' personality traits. The data collection unit can also conduct surveys to collect user attribute information. Surveys collect basic attribute information such as age, gender, occupation, hobbies, and interests, providing foundational data for understanding the user's background. Furthermore, the data collection unit can collect users' behavioral history to obtain information about users' circumstances. The collection of behavioral history includes website browsing history, app usage history, and purchase history, and through this data, it is possible to understand users' interests, concerns, and behavioral patterns. The data collection unit integrates the information obtained from these diverse data sources to build a comprehensive dataset for understanding the overall picture of the user. This allows the data collection unit to collect detailed information about users' personalities, attributes, and circumstances and provide it to the analysis unit.

[0071] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit can analyze the collected information using data mining techniques. Data mining techniques are methods for extracting useful patterns and relationships from large amounts of data and are used to gain insights into user behavior and characteristics. The analysis unit can also perform statistical analysis, analyzing information based on user personality, attributes, and circumstances. Statistical analysis quantitatively evaluates user characteristics and behavioral patterns based on the collected data, revealing trends and correlations. Furthermore, the analysis unit can analyze the collected information using machine learning algorithms. Machine learning algorithms are techniques for building models that learn from data and perform predictions and classifications, and are used to predict user characteristics and behavior. For example, future behavior and interests can be predicted based on user personality traits. By combining these techniques, the analysis unit can analyze the collected information from multiple perspectives and gain detailed insights into user characteristics and behavior. This allows the analysis unit to quickly and accurately analyze the collected data and generate useful information to provide to the proposal unit.

[0072] The Proposal Department proposes the optimal AI utilization method based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can select and propose AI tools that suit the user's objectives. Specifically, it selects the most suitable AI tools and applications according to the results and goals the user desires and proposes how to use them. The Proposal Department can also customize and propose AI tools to suit the user's needs. For example, it can adjust the settings and functions of AI tools based on the user's personality and behavioral patterns to enable more effective use. Furthermore, the Proposal Department can propose the optimal AI utilization method based on the user's personality, attributes, and circumstances. For example, if the user is prone to stress, the Proposal Department can propose AI tools that are useful for relaxation and mental health. Through these proposals, the Proposal Department provides support to users in effectively utilizing AI and achieving their goals. The Proposal Department can continuously improve its proposals based on user feedback, enabling it to make more accurate proposals. In this way, the Proposal Department can propose AI utilization methods optimized for the user's needs and characteristics, thereby improving user satisfaction.

[0073] The delivery department provides users with the content proposed by the proposal department. For example, the delivery department can provide the proposal content to users via email. The email will include detailed explanations of the proposed AI tool, instructions for use, and installation procedures, making it easy for users to understand and implement. The delivery department can also provide the proposal content to users via app notifications. App notifications are sent to users' smartphones and tablets in real time, allowing for the rapid delivery of important information. Furthermore, the delivery department can provide the proposal content to users through a dashboard display. The dashboard is an easy-to-access interface that allows users to see the proposal content and progress at a glance. The delivery department combines these means to effectively provide information to users and support the implementation of the proposal. The delivery department can collect user feedback and continuously improve the delivery methods and content. This allows the delivery department to provide users with information quickly and accurately and support the implementation of the proposal.

[0074] The feedback collection unit collects user responses to the content provided by the provision unit. For example, the feedback collection unit can collect user responses through a feedback form. A feedback form is a format in which users can freely enter their opinions and impressions of the proposed content, and is a means of obtaining detailed feedback. The feedback collection unit can also collect user responses through click-through rates. Click-through rates are an indicator of how interested users are in the proposed content and how much action they have taken, and are used to evaluate the effectiveness of the proposed content. Furthermore, the feedback collection unit can also collect user responses through usage frequency. Usage frequency is an indicator of how often users use the proposed AI tool, and is used to evaluate the practicality of the proposed content. The feedback collection unit can integrate this data and comprehensively evaluate user responses. This allows the feedback collection unit to understand the effectiveness of the proposed content and user satisfaction, and to provide feedback to the proposal unit and provision unit. As a result, the AI ​​agent system according to the embodiment can provide AI utilization methods optimized for each individual by collecting and analyzing user information, proposing and providing the optimal AI utilization method, and collecting feedback.

[0075] The data collection unit can collect information about the user's personality, attributes, and circumstances. For example, the data collection unit can conduct a personality test and collect the results. The data collection unit can also conduct a survey and collect user attribute information. For example, the data collection unit can collect the user's behavioral history and obtain information about the user's circumstances. By collecting information about the user's personality, attributes, and circumstances, the data collection unit can propose an AI application method optimized for each individual. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the results of a personality test into an AI, which can then analyze and collect the information.

[0076] The analysis unit can analyze the collected information and propose the most suitable AI application method to the user. For example, the analysis unit can analyze the collected information using data mining techniques. The analysis unit can also perform statistical analysis, for example, based on the user's personality, attributes, and circumstances. Furthermore, the analysis unit can analyze the collected information using machine learning algorithms. As a result, the analysis unit can propose the most suitable AI application method to the user by analyzing the collected information. Some or all of the above-described processes in the analysis unit are performed using AI. For example, the analysis unit can input the collected information into the AI, which can then analyze the information and propose the most suitable AI application method.

[0077] The proposal department can propose AI utilization methods tailored to user needs. For example, the proposal department can select and propose AI tools according to the user's objectives. The proposal department can also customize and propose AI tools according to user needs. The proposal department can also propose the optimal AI utilization method based on the user's personality, attributes, and circumstances. In this way, the proposal department can provide AI utilization methods optimized for each individual by proposing AI utilization methods tailored to user needs. Some or all of the above processes in the proposal department are performed using AI. For example, the proposal department can select AI tools based on user needs, and the AI ​​can propose the optimal AI utilization method.

[0078] The service provider can provide the proposed AI applications to the user. For example, the service provider can provide the proposed content to the user via email. The service provider can also provide the proposed content to the user via app notifications. The service provider can also provide the proposed content to the user via a dashboard display. In this way, by providing the proposed AI applications to the user, the service provider can enable the user to learn specific ways of using AI. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input the proposed content into the AI ​​and select how the AI ​​will provide it to the user.

[0079] The feedback collection unit can collect user responses to the provided content and provide feedback to the analysis unit. The feedback collection unit can collect user responses, for example, through a feedback form. The feedback collection unit can also collect user responses, for example, through click-through rates. The feedback collection unit can also collect user responses, for example, through usage frequency. In this way, the feedback collection unit can improve the accuracy of the system by collecting user responses to the provided content and providing feedback to the analysis unit. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input user responses into AI, and the AI ​​can analyze the responses and provide feedback.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay information collection until the user is relaxed. For example, if the user is focused, the data collection unit can collect information at that time to efficiently acquire data. For example, if the user is excited, the data collection unit can temporarily suspend information collection until the user's emotions calm down. In this way, the data collection unit can collect information at a more appropriate time by adjusting the timing of information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can analyze the emotions and adjust the timing of information collection.

[0081] The data collection unit can analyze the user's past behavior history and select the optimal information collection method. For example, the data collection unit can prioritize collecting information sources that the user has frequently used in the past. For example, the data collection unit can identify the most efficient time of day for information collection based on the user's behavior patterns. For example, the data collection unit can prioritize collecting highly relevant information based on the user's past behavior history. In this way, the data collection unit can select the optimal information collection method by analyzing the user's past behavior history. 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 behavior history data into AI, which can then select the optimal information collection method.

[0082] The data collection unit can filter information based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to a project the user is currently working on. For example, the data collection unit can filter highly relevant information based on the user's areas of interest. For example, the data collection unit can collect appropriate information depending on the user's living situation (e.g., whether they are at work or on vacation). In this way, the data collection unit can collect highly relevant information by filtering information based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's living situation data into an AI, which can then filter the information.

[0083] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting information that helps them relax. For example, if the user is focused, the data collection unit may prioritize collecting work-related information. For example, if the user is excited, the data collection unit may prioritize collecting information that interests them. In this way, the data collection unit can collect more appropriate information by prioritizing the information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can analyze the emotions and determine the priority of information.

[0084] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can collect event information related to the user's current location. For example, the data collection unit can prioritize the collection of nearby news and weather information based on the user's geographical location. For example, the data collection unit can collect region-specific information based on the user's location. In this way, the data collection unit can prioritize the collection of highly relevant information 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 location data into AI, which can then select highly relevant information.

[0085] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. For example, the data collection unit can collect information shared by the user's social media followers and friends. For example, the data collection unit can analyze the content of the user's social media posts and collect relevant information. In this way, the data collection unit can collect relevant information 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 data into AI, which can then select relevant information.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. For example, if the user is excited, the analysis unit can provide analysis results using visually stimulating graphics. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into AI, and the AI ​​can analyze the emotions and adjust the presentation.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a concise analysis on information of low importance. For example, the analysis unit can determine the priority of the analysis according to the importance of the information. This enables efficient analysis by allowing the analysis unit to adjust the level of detail based on the importance of the collected information. 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 information importance data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a natural language processing algorithm to text information. For example, the analysis unit can apply an image recognition algorithm to image information. For example, the analysis unit can apply a speech recognition algorithm to audio information. By applying different analysis algorithms depending on the category of information, the analysis unit can perform more accurate analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input information category data into the AI, and the AI ​​can select an appropriate analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide an analysis result using visually stimulating graphics. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI, which can analyze the emotions and adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may postpone the analysis of older information. For example, the analysis unit may adjust the priority of analysis according to the timing of information collection. In this way, the analysis unit can prioritize the analysis of the most recent information by determining the priority of analysis based on the timing of information collection. 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 information collection timing data into the AI, and the AI ​​can determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant information. For example, the analysis unit may postpone the analysis of less relevant information. The analysis unit can adjust the order of analysis according to the relevance of the information. This enables efficient analysis by allowing the analysis unit to adjust the order of analysis based on the relevance of the information. 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 information relevance data into the AI, and the AI ​​can determine the order of analysis.

[0092] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. If the user is excited, the suggestion unit can provide visually stimulating suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way it presents them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then analyze the emotions and adjust the presentation.

[0093] The proposal unit can adjust the level of detail of its proposals based on the user's needs. For example, if the user's needs are specific, the proposal unit will provide detailed proposals. For example, if the user's needs are vague, the proposal unit can provide concise proposals. The proposal unit can adjust the level of detail of its proposals according to the user's needs. This allows the proposal unit to provide more appropriate proposals by adjusting the level of detail based on the user's needs. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input user needs data into the AI, and the AI ​​can adjust the level of detail of the proposals.

[0094] The suggestion unit can apply different suggestion algorithms depending on the user's attributes when making suggestions. For example, the suggestion unit might suggest an idea generation tool to a user doing creative work. For example, it might suggest a business automation tool to a user who wants to improve business efficiency. For example, it might suggest an educational tool to a user whose purpose is learning. In this way, the suggestion unit can provide more appropriate suggestions by applying different suggestion algorithms depending on the user's attributes. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user attribute data into AI, and the AI ​​can select an appropriate suggestion algorithm.

[0095] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to the point. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is excited, the suggestion unit can provide visually stimulating suggestions. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the length of the suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can analyze the emotions and adjust the length of the suggestions.

[0096] The suggestion unit can determine the priority of suggestions based on the user's past responses when making suggestions. For example, the suggestion unit may prioritize suggestions that the user has previously responded to favorably. For example, the suggestion unit may postpone suggestions that the user has previously responded to negatively. For example, the suggestion unit may adjust the priority of suggestions based on the user's past responses. This allows the suggestion unit to provide more appropriate suggestions by determining the priority of suggestions based on the user's past responses. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past response data into AI, and the AI ​​can determine the priority of suggestions.

[0097] The suggestion unit can adjust the order of suggestions based on the user's relevant information when making suggestions. For example, the suggestion unit may prioritize suggestions related to the user's current project. For example, the suggestion unit may prioritize highly relevant suggestions based on the user's areas of interest. For example, the suggestion unit may adjust the order of suggestions according to the user's attributes. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the order of suggestions based on the user's relevant information. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's relevant information data into AI, and the AI ​​can determine the order of suggestions.

[0098] The service provider can estimate the user's emotions and adjust its delivery method based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed information. If the user is in a hurry, the service provider can provide concise information. If the user is excited, the service provider can provide visually stimulating information. This allows the service provider to provide more appropriate information by adjusting its delivery method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into an AI, which can analyze the emotions and adjust the delivery method.

[0099] The delivery unit can analyze the user's past reactions at the time of delivery to select the optimal delivery method. For example, the delivery unit can prioritize delivery methods that the user has reacted favorably to in the past. For example, the delivery unit can avoid delivery methods that the user has reacted negatively to in the past. For example, the delivery unit can select the optimal delivery method based on the user's past reactions. In this way, the delivery unit can select the optimal delivery method by analyzing the user's past reactions. 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 reaction data into AI, and the AI ​​can select the optimal delivery method.

[0100] The information delivery unit can customize the means of delivery based on the user's current situation at the time of delivery. For example, if the user is on the move, the information delivery unit can provide information by voice. For example, if the user is working at a desk, the information delivery unit can provide information by text. For example, if the user is relaxed, the information delivery unit can provide information visually. In this way, the information delivery unit can provide more appropriate information by customizing the means of delivery based on the user's current situation. 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 situation data into the AI, and the AI ​​can select the optimal means of delivery.

[0101] The service provider can estimate the user's emotions and determine the priority of its offerings based on those emotions. For example, if the user is stressed, the service provider can prioritize providing information that helps them relax. For example, if the user is focused, the service provider can prioritize providing work-related information. For example, if the user is excited, the service provider can prioritize providing information that interests them. In this way, the service provider can provide more appropriate information by determining the priority of its offerings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into an AI, which can analyze the emotions and determine the priority of its offerings.

[0102] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, the service provider can provide event information related to the user's current location. For example, the service provider can provide local news and weather information based on the user's geographical location. For example, the service provider can provide region-specific information based on the user's location information. In this way, the service provider can select the optimal delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's location data into AI, and the AI ​​can select the optimal delivery method.

[0103] The service provider can analyze the user's social media activity and propose a means of delivery at the time of delivery. For example, the service provider can provide information related to topics the user has shown interest in on social media. For example, the service provider can provide information shared by the user's social media followers and friends. For example, the service provider can analyze the content of the user's social media posts and provide relevant information. In this way, the service provider can propose the optimal means of delivery by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media data into AI, and the AI ​​can select the optimal means of delivery.

[0104] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, the feedback collection unit may request detailed feedback. For example, if the user is in a hurry, the feedback collection unit may request concise feedback. For example, if the user is excited, the feedback collection unit may request visually stimulating feedback. In this way, the feedback collection unit can collect more appropriate feedback by adjusting the feedback collection method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input user emotion data into an AI, which can analyze the emotions and adjust the feedback collection method.

[0105] The feedback collection unit can analyze the user's past responses and select the optimal collection method when collecting feedback. For example, the feedback collection unit may prioritize collection methods in which the user has previously shown a positive response. For example, the feedback collection unit may avoid collection methods in which the user has previously shown a negative response. For example, the feedback collection unit can select the optimal collection method based on the user's past responses. In this way, the feedback collection unit can select the optimal collection method by analyzing the user's past responses. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's past response data into AI, and the AI ​​can select the optimal collection method.

[0106] The feedback collection unit can customize the means of collection based on the user's current situation when collecting feedback. For example, if the user is on the move, the feedback collection unit can collect feedback by voice. For example, if the user is working at a desk, the feedback collection unit can collect feedback by text. For example, if the user is relaxed, the feedback collection unit can collect feedback visually. In this way, the feedback collection unit can collect more appropriate feedback by customizing the means of collection based on the user's current situation. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's current situation data into the AI, which can then select the optimal means of collection.

[0107] The feedback collection unit can estimate the user's emotions and determine the priority of feedback collection based on the estimated emotions. For example, if the user is stressed, the feedback collection unit can prioritize collecting feedback that helps them relax. For example, if the user is focused, the feedback collection unit can prioritize collecting work-related feedback. For example, if the user is excited, the feedback collection unit can prioritize collecting interesting feedback. In this way, the feedback collection unit can collect more appropriate feedback by determining the priority of feedback collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input user emotion data into an AI, which can analyze the emotions and determine the priority of feedback collection.

[0108] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, the feedback collection unit can collect feedback related to the user's current location. For example, the feedback collection unit can collect feedback about nearby events and services based on the user's geographical location. For example, the feedback collection unit can collect region-specific feedback based on the user's location information. In this way, the feedback collection unit can select the optimal collection method by taking into account the user's geographical location information. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's location data into AI, and the AI ​​can select the optimal collection method.

[0109] The feedback collection unit can analyze the user's social media activity and suggest methods for collecting feedback. For example, the feedback collection unit can collect feedback related to topics the user has shown interest in on social media. For example, the feedback collection unit can collect feedback based on information shared by the user's social media followers and friends. For example, the feedback collection unit can analyze the content of the user's social media posts and collect relevant feedback. In this way, the feedback collection unit can suggest the optimal collection method by analyzing the user's social media activity. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's social media data into AI, which can then select the optimal collection method.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] 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 stressed, it can prioritize analyzing information that helps them relax. If the user is focused, it can prioritize analyzing information related to work. If the user is excited, it can prioritize analyzing information that interests them. In this way, the analysis unit can provide more appropriate information by determining the priority of analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI, which can analyze the emotions and determine the priority of analysis.

[0112] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is relaxed, it can offer detailed suggestions. If the user is in a hurry, it can offer concise suggestions. If the user is excited, it can offer visually stimulating suggestions. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the content of its suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then analyze the emotions and adjust the content of its suggestions.

[0113] 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 stressed, the information delivery can be delayed until the user is relaxed. If the user is focused, the information can be delivered at that time to efficiently provide data. If the user is excited, the information delivery can be temporarily suspended until the user's emotions calm down. In this way, the information delivery unit can deliver information at a more appropriate time by adjusting the timing of delivery based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input user emotion data into an AI, which can analyze the emotions and adjust the timing of delivery.

[0114] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, it may request detailed feedback. If the user is in a hurry, it may request concise feedback. If the user is excited, it may request visually stimulating feedback. In this way, the feedback collection unit can collect more appropriate feedback by adjusting the feedback collection method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input user emotion data into an AI, which can analyze the emotions and adjust the feedback collection method.

[0115] The data collection unit can estimate the user's emotions and prioritize the information to collect based on the estimated emotions. For example, if the user is stressed, it can prioritize collecting information that helps them relax. If the user is focused, it can prioritize collecting work-related information. If the user is excited, it can prioritize collecting information that interests them. In this way, the data collection unit can collect more appropriate information by prioritizing the information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can analyze the emotions and determine the priority of the information.

[0116] The analysis unit can evaluate the reliability of the collected information and prioritize the analysis of highly reliable information. For example, it can prioritize the analysis of data collected from highly reliable sources. Data collected from less reliable sources can be analyzed later. By doing so, the analysis unit can provide more accurate analysis results by evaluating the reliability of the collected information. 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 reliability data of the information into an AI, which can evaluate the reliability and determine the priority of the analysis.

[0117] The suggestion unit can analyze the user's past behavior history and select the most appropriate suggestion method. For example, it can prioritize suggestion methods that the user has responded to favorably in the past, and avoid suggestion methods that the user has responded to negatively in the past. In this way, the suggestion unit can select a more appropriate suggestion method by analyzing the user's past behavior history. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's past behavior history data into AI, which can then select the most appropriate suggestion method.

[0118] The information delivery unit can customize how it delivers information based on the user's current living situation and areas of interest. For example, it can prioritize providing information related to projects the user is currently working on. It can also provide highly relevant information based on the user's areas of interest. It can provide appropriate information depending on the user's living situation (e.g., whether they are at work or on vacation). In this way, the information delivery unit can provide more relevant information by customizing how it delivers information based on the user's current living situation and 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 living situation data into AI, which can then customize how it delivers information.

[0119] The feedback collection unit can select a method for collecting feedback while considering the user's geographical location. For example, it can collect feedback related to the user's current location. Based on the user's geographical location, it can collect feedback about nearby events and services. Based on the user's location information, it can collect region-specific feedback. In this way, the feedback collection unit can collect more appropriate feedback by considering the user's geographical location. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's location data into AI, which can then select the optimal feedback collection method.

[0120] The data collection unit can analyze a user's social media activity and collect relevant information. For example, it can collect information related to topics the user has shown interest in on social media. It can also collect information shared by the user's social media followers and friends. It can analyze the content of a user's social media posts and collect relevant information. In this way, the data collection unit can collect relevant information 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 data into an AI, which can then select relevant information.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects user information. For example, the data collection unit can collect information about the user's personality, attributes, and circumstances. The data collection unit can conduct personality tests and collect the results. It can also conduct surveys to collect user attribute information. Furthermore, it can collect the user's behavioral history to obtain information about the user's circumstances. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information using data mining techniques. It can also perform statistical analysis and analyze the information based on the user's personality, attributes, and circumstances. Furthermore, it can analyze the collected information using machine learning algorithms. Step 3: The proposal department proposes the optimal AI utilization method based on the analysis results obtained by the analysis department. The proposal department can select and propose AI tools that suit the user's objectives. It can also customize and propose AI tools to meet the user's needs. Furthermore, it can propose the optimal AI utilization method based on the user's personality, attributes, and circumstances. Step 4: The provisioning department provides the user with the content proposed by the proposal department. The provisioning department can provide the user with the proposed content via email. It can also provide the user with the proposed content via app notifications. Furthermore, it can provide the user with the proposed content via a dashboard display. Step 5: The feedback collection unit collects user responses to the content provided by the service provider. The feedback collection unit can collect user responses through feedback forms. It can also collect user responses through click-through rates. Furthermore, it can collect user responses through usage frequency.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, provision unit, and feedback collection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user information using the camera 42 and microphone 38B of the smart device 14 and conducts personality diagnostic tests and questionnaire surveys using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information using data mining techniques and machine learning algorithms. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal way to utilize AI based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the smart device 14 and provides the proposed content to the user through email, app notifications, and dashboard displays. The feedback collection unit is implemented in the specific processing unit 46A of the smart device 14 and collects user responses through feedback forms, click-through rates, and usage frequency. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, provision unit, and feedback collection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user information using the camera 42 and microphone 238 of the smart glasses 214 and conducts personality diagnostic tests and questionnaire surveys using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information using data mining techniques and machine learning algorithms. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal way to utilize AI based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the smart glasses 214 and provides the proposed content to the user through email, app notifications, and dashboard displays. The feedback collection unit is implemented in the specific processing unit 46A of the smart glasses 214 and collects user responses through feedback forms, click-through rates, and usage frequency. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, provision unit, and feedback collection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user information using the camera 42 and microphone 238 of the headset terminal 314 and conducts personality diagnostic tests and questionnaire surveys using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information using data mining techniques and machine learning algorithms. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal AI utilization method based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides the proposed content to the user through email, app notifications, and dashboard displays. The feedback collection unit is implemented in the specific processing unit 46A of the headset terminal 314 and collects user responses through feedback forms, click-through rates, and usage frequency. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, provision unit, and feedback collection unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the robot 414 and conducts personality diagnostic tests and questionnaire surveys using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information using data mining techniques and machine learning algorithms. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal way to utilize AI based on the analysis results. The provision unit is implemented in the control unit 46A of the robot 414 and provides the proposed content to the user through email, app notifications, and dashboard displays. The feedback collection unit is implemented in the control unit 46A of the robot 414 and collects user responses through feedback forms, click-through rates, and usage frequency. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A collection unit that collects user information, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal method for utilizing AI, A provisioning unit that provides the content proposed by the proposal unit to the user, The system includes a feedback collection unit that collects user reactions to the content provided by the aforementioned provisioning unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information about the user's personality, attributes, and circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the collected information and propose the most suitable AI application method for the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose AI utilization methods tailored to user needs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Providing users with proposed AI applications. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback collection unit is Collect user feedback on the provided content and feed it back to the analysis department. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the user's needs. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's attributes. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on the user's past responses. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the user's relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, we analyze past user feedback to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the delivery method will be customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of offerings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback collection unit is We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback collection unit is When collecting feedback, we analyze users' past responses to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback collection unit is When collecting feedback, customize the collection method based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned feedback collection unit is It estimates the user's emotions and prioritizes feedback collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned feedback collection unit is When collecting feedback, the optimal collection method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned feedback collection unit is When collecting feedback, we analyze users' social media activity and propose methods for collecting it. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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 information, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal method for utilizing AI, A provisioning unit that provides the content proposed by the proposal unit to the user, The system includes a feedback collection unit that collects user reactions to the content provided by the aforementioned provisioning unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect information about the user's personality, attributes, and circumstances. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed, and the optimal way to utilize AI is proposed to the user. The system according to feature 1.

4. The aforementioned proposal section is, We propose AI utilization methods tailored to user needs. The system according to feature 1.

5. The aforementioned supply unit is, Providing users with proposed AI application methods. The system according to feature 1.

6. The aforementioned feedback collection unit is The system collects user feedback on the provided content and provides it back to the analysis unit. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

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

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