system

Abstract

The system according to this embodiment aims to identify the user's unconscious behavioral patterns and, based on those patterns, suggest what the user should do now. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects information such as the user's purchase history, movement history, posts, and likes. The analysis unit analyzes the information collected by the collection unit and identifies the user's unconscious behavioral patterns. The suggestion unit suggests what the user should do now based on the behavioral patterns identified by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 cannot be said that identifying the unconscious behavior patterns of users and making appropriate proposals based on them have been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to identify the unconscious behavior patterns of users and propose what the user should do now based on them.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects information on the user's purchase history, movement history, and posting history. The analysis unit analyzes the information collected by the collection unit and identifies the user's unconscious behavioral patterns. The suggestion unit suggests what the user should do now based on the behavioral patterns identified by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify the user's unconscious behavioral patterns and, based on those patterns, suggest what the user should do now. [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 applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that suggests what the user should do now by linking information about the user (purchase history, travel history, posting history, etc.) with a generating AI. This AI agent system takes into account that more than 90% of human decision-making is done unconsciously, and supports choices without regret by making the unconscious conscious. For example, the AI ​​agent system collects information such as the user's purchase history, travel history, and posting history. For example, it collects data such as products the user has purchased in the past, places visited, the content of posts, and posts that the user has liked. This allows the system to understand the user's behavior patterns. Next, the AI ​​agent system uses a generating AI to analyze the collected information. The generating AI analyzes the collected data and identifies the user's unconscious behavior patterns. For example, if a user tends to move to a specific place at a specific time, that behavior pattern can be identified. This allows the system to make the user's unconscious behavior conscious. Furthermore, based on the behavior patterns identified by the generating AI, the AI ​​agent system suggests what the user should do now. For example, if a user tends to move to a specific place at a specific time, the system suggests going to that place at that time. Also, if a user tends to purchase a specific product, the system suggests purchasing that product. In this way, the generative AI makes suggestions to help users make choices they won't regret. This allows users to become aware of their unconscious actions and make choices they won't regret. For example, users can take actions suggested by the generative AI to avoid choices they regretted in the past. Also, even if a user doesn't know what they want to do, they can find what they should do by taking actions suggested by the generative AI. This allows users to guide their lives in a better direction. In this way, the AI ​​agent system can help users become aware of their unconscious actions and support them in making choices they won't regret.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects information such as the user's purchase history, travel history, and posting history. For example, the collection unit collects data such as products the user has purchased in the past, places visited, the content of posts, and posts that the user has liked. The collection unit collects detailed data such as what products the user has purchased, where they have traveled, what kind of posts they have made, and which posts they have liked. For example, the collection unit can collect purchase history such as the type of products the user has purchased in the past, the date and time of purchase, and the place of purchase. The collection unit can also collect travel history such as the places the user has traveled, the means of transportation, and the time of travel. Furthermore, the collection unit can also collect posting information such as the content of posts made by the user, the frequency of posts, and the target of posts. The collection unit can also collect like information such as the type of posts the user has liked, the date and time of the like, and the target of the like. The analysis unit analyzes the information collected by the collection unit and identifies the user's unconscious behavioral patterns. The analysis unit analyzes collected data to identify the frequency, consistency, and background of user behavior. If a user tends to move to a specific location at a specific time, the analysis unit can identify this behavioral pattern. If a user tends to purchase a specific product, the analysis unit can also identify this behavioral pattern. The analysis unit can use generative AI to identify user behavioral patterns. The generative AI analyzes collected data to identify the user's unconscious behavioral patterns. For example, the generative AI analyzes the frequency and consistency of user behavior to identify behavioral patterns. The generative AI can also analyze the background of user behavior to identify behavioral patterns. Based on the behavioral patterns identified by the analysis unit, the suggestion unit proposes what the user should do now. For example, if a user tends to move to a specific location at a specific time, the suggestion unit will propose going to that location at that time. If a user tends to purchase a specific product, the suggestion unit will also propose purchasing that product. The suggestion unit can use generative AI to help the user make choices they won't regret.The generating AI suggests what the user should do now, based on the behavioral patterns identified by the analysis unit. For example, if the generating AI tends to go to a specific place at a specific time, it suggests going to that place at that time. The generating AI can also suggest purchasing a specific product if the user tends to buy that product. In this way, the AI ​​agent system according to the embodiment can identify the user's unconscious behavioral patterns and suggest what they should do now, thereby supporting them in making choices they won't regret.

[0030] The data collection unit collects information such as the user's purchase history, travel history, and posting history. Specifically, it collects data such as products the user has purchased in the past, places they have visited, the content of their posts, and posts they have liked. The data collection unit collects detailed data such as what products the user has purchased, where they have traveled, what kind of posts they have made, and which posts they have liked. For example, it can collect purchase history such as the type of product the user has purchased in the past, the date and time of purchase, and the place of purchase. This includes purchase history from online shopping sites and physical stores, allowing for a detailed understanding of the user's purchasing trends. The data collection unit can also collect travel history such as the places the user has traveled, the means of transportation, and the time of travel. This includes GPS data and transportation usage history, allowing for a detailed understanding of the user's travel patterns. Furthermore, the data collection unit can also collect posting information such as the content of posts the user has made, the frequency of posts, and the target audience of posts. This includes information to understand what topics the user is interested in and what opinions they hold. The data collection unit can also collect "like" information such as the type of posts the user has liked, the date and time of the like, and the target audience of the like. This allows for an understanding of what kind of content the user is interested in. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the data collection unit to identify users' unconscious behavioral patterns. Specifically, it analyzes the collected data to identify the frequency, consistency, and background of user behavior. For example, if a user tends to move to a specific place at a specific time, that behavioral pattern can be identified. The analysis unit can also identify behavioral patterns if a user tends to purchase a specific product. The analysis unit can use generative AI to identify user behavioral patterns. The generative AI analyzes the collected data to identify users' unconscious behavioral patterns. For example, the generative AI analyzes the frequency and consistency of user behavior to identify behavioral patterns. The generative AI can also analyze the background of user behavior to identify behavioral patterns. The generative AI can use natural language processing technology to analyze the content of posts and understand user interests and emotional tendencies. Furthermore, the generative AI can analyze purchase history and travel history to identify user lifestyles and consumption trends. In addition, the generative AI can analyze user behavioral data over time to understand changes and trends in behavior. This allows the analysis unit to quickly and accurately analyze collected data and grasp users' unconscious behavioral patterns in real time. Furthermore, the analysis unit can also utilize historical data and statistical information to analyze long-term behavioral patterns and trends. This enables the analysis unit to gain a detailed understanding of user behavioral patterns and improve the accuracy and reliability of the entire system.

[0032] The suggestion unit proposes what the user should do now based on behavioral patterns identified by the analysis unit. Specifically, if the user tends to go to a specific place at a specific time, the suggestion unit will propose going to that place at that time. The suggestion unit can also propose purchasing a specific product if the user tends to buy that product. The suggestion unit can use generative AI to help the user make choices they won't regret. The generative AI proposes what the user should do now based on behavioral patterns identified by the analysis unit. For example, if the generative AI tends to go to a specific place at a specific time, it will propose going to that place at that time. The generative AI can also propose purchasing a specific product if the user tends to buy that product. Based on the user's past behavioral data, the generative AI can predict future behavior and make optimal suggestions. For example, based on the user's history of participating in a specific event, the generative AI can suggest participation when a similar event is held. Also, based on the user's purchase history, the generative AI can suggest relevant products and services. Furthermore, the generative AI can analyze the user's posts and likes and suggest content and information based on the user's interests. This allows the proposal department to provide optimal suggestions based on user behavior patterns, supporting users in making choices they won't regret. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. As a result, the proposal department can provide users with more appropriate and beneficial suggestions, improving the overall performance of the system.

[0033] 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 from data sources that the user has frequently used in the past. The data collection unit can also collect information from applications that the user has preferred to use in the past. The data collection unit can also collect data from information sources that the user has given high ratings to in the past. This enables efficient information collection by selecting the optimal information collection method based on the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior history data into a generating AI and have the generating AI select the optimal information collection method.

[0034] The data collection unit can filter information based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to topics the user is currently interested in. The data collection unit can also filter and collect information related to the user's current lifestyle (e.g., work, hobbies). The data collection unit can also filter and collect information related to the user's current health status. By filtering information based on the user's current lifestyle and areas of interest, it is possible to collect highly relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0035] 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 prioritize the collection of event information related to the user's current location. The data collection unit can also collect information on nearby restaurants and cafes based on the user's current location. The data collection unit can also prioritize the collection of traffic information related to the user's current location. This enables more appropriate information collection by collecting highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant information.

[0036] 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 posts the user has recently liked. The data collection unit can also collect information related to accounts the user has recently followed. The data collection unit can also collect information related to content the user has recently shared. This allows for more appropriate data collection by collecting relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0037] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the collected data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the collected data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0038] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history data. The analysis unit can also apply a movement pattern analysis algorithm to movement history data. The analysis unit can also apply a sentiment analysis algorithm to social media data. By applying different analysis algorithms depending on the data category, more appropriate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0039] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also prioritize the analysis of current data while referring to past data. The analysis unit can also adjust the level of detail of the analysis according to the data collection timing. This allows for more appropriate analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0040] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit may also adjust the level of detail of the analysis according to the relevance of the data. This allows for more appropriate analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0041] The proposal unit can adjust the level of detail of its proposals based on the importance of the behavioral patterns. For example, the proposal unit can provide detailed proposals for high-importance behavioral patterns, and simplified proposals for low-importance behavioral patterns. The proposal unit can also determine the priority of proposals based on the importance of the behavioral patterns. This allows for more appropriate proposals by adjusting the level of detail of proposals based on the importance of the behavioral patterns. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the behavioral patterns into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0042] The suggestion unit can apply different suggestion algorithms depending on the category of the behavioral pattern when making suggestions. For example, the suggestion unit can apply a purchase suggestion algorithm to purchase behavioral patterns. The suggestion unit can also apply a travel suggestion algorithm to travel behavioral patterns. The suggestion unit can also apply a social suggestion algorithm to social media behavioral patterns. By applying different suggestion algorithms depending on the category of the behavioral pattern, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the category of the behavioral pattern into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0043] The proposal unit can determine the priority of proposals based on the timing of behavioral pattern collection. For example, the proposal unit can make proposals based on the most recent behavioral patterns. The proposal unit can also make proposals that prioritize current behavioral patterns while referring to past behavioral patterns. The proposal unit can also adjust the level of detail of proposals according to the timing of behavioral pattern collection. This makes it possible to make more appropriate proposals by determining the priority of proposals based on the timing of behavioral pattern collection. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the timing of behavioral pattern collection into a generating AI and have the generating AI perform the determination of proposal priorities.

[0044] The proposal unit can adjust the order of proposals based on the relevance of behavioral patterns. For example, the proposal unit can make proposals based on highly relevant behavioral patterns. It can also make proposals based on less relevant behavioral patterns. The proposal unit can also adjust the level of detail of the proposals according to the relevance of the behavioral patterns. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of behavioral patterns. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of behavioral patterns into a generating AI and have the generating AI adjust the order of proposals.

[0045] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0046] The data collection unit can analyze the user's past behavior history and select the optimal timing for information collection. For example, it can prioritize collecting information during times when the user frequently collected information in the past. It can also collect information from applications that the user has preferred to use in the past. It can also collect data from information sources that the user has given high ratings to in the past. This enables efficient information collection by selecting the optimal timing for information collection based on the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior history data into a generating AI and have the generating AI select the optimal timing for information collection.

[0047] The data collection unit can filter information based on the user's current lifestyle and areas of interest during data collection. For example, it can prioritize collecting information related to topics the user is currently interested in. It can also filter and collect information related to the user's current lifestyle (e.g., work, hobbies). It can also filter and collect information related to the user's current health status. By filtering information based on the user's current lifestyle and areas of interest, it is possible to collect highly relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0048] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during data collection. For example, it can prioritize collecting event information related to the user's current location. It can also collect information on nearby restaurants and cafes based on the user's current location. It can also prioritize collecting traffic information related to the user's current location. This enables more appropriate information collection by collecting highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant information.

[0049] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, it can collect information related to posts the user has recently liked. It can also collect information related to accounts the user has recently followed. It can also collect information related to content the user has recently shared. This allows for more appropriate data collection by collecting relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant information.

[0050] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during the analysis. For example, it can perform a detailed analysis on data with high importance and a simplified analysis on data with low importance. It can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the collected data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the collected data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0051] The following briefly describes the processing flow for example form 1.

[0052] Step 1: The data collection unit collects information such as the user's purchase history, travel history, posts, and likes. Specifically, it collects data such as products the user has purchased in the past, places they have visited, the content of their posts, and posts they have liked. For example, the data collection unit can collect purchase history such as the type of product the user has purchased in the past, the date and time of purchase, and the place of purchase. It can also collect travel history such as places the user has traveled, the means of transportation, and the time of travel, as well as post information such as the content of posts, frequency, and target audience, and like information such as the type of post they have liked, the date and time, and the target audience. Step 2: The analysis unit analyzes the information collected by the collection unit to identify the user's unconscious behavioral patterns. Specifically, it analyzes the collected data to identify the frequency, consistency, and background of the user's behavior. For example, if a user tends to move to a specific place at a specific time of day or tends to purchase a specific product, the analysis unit will identify that behavioral pattern. The analysis unit can use generative AI to analyze the collected data and identify the user's unconscious behavioral patterns. Step 3: The suggestion unit proposes what the user should do now, based on the behavioral patterns identified by the analysis unit. Specifically, if the user tends to go to a specific place at a specific time, the suggestion unit will propose going to that place at that time. It can also propose purchasing a specific product if the user tends to buy that product. The suggestion unit uses generative AI to propose what the user should do now, based on the behavioral patterns identified by the analysis unit. This helps the user make choices they won't regret.

[0053] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that suggests what the user should do now by linking information about the user (purchase history, travel history, posting history, etc.) with a generating AI. This AI agent system takes into account that more than 90% of human decision-making is done unconsciously, and supports choices without regret by making the unconscious conscious. For example, the AI ​​agent system collects information such as the user's purchase history, travel history, posts, and likes. For example, it collects data such as products the user has purchased in the past, places visited, the content of posts, and posts that the user has liked. This makes it possible to understand the user's behavior patterns. Next, the AI ​​agent system has a generating AI analyze the collected information. The generating AI analyzes the collected data and identifies the user's unconscious behavior patterns. For example, if the user tends to move to a specific place at a specific time, that behavior pattern can be identified. This makes the user's unconscious behavior conscious. Furthermore, based on the behavior patterns identified by the generating AI, the AI ​​agent system suggests what the user should do now. For example, if the user tends to move to a specific place at a specific time, it suggests going to that place at that time. Also, if the user tends to purchase a specific product, it suggests purchasing that product. In this way, the generative AI makes suggestions to help users make choices they won't regret. This allows users to become aware of their unconscious actions and make choices they won't regret. For example, users can take actions suggested by the generative AI to avoid choices they regretted in the past. Also, even if a user doesn't know what they want to do, they can find what they should do by taking actions suggested by the generative AI. This allows users to guide their lives in a better direction. In this way, the AI ​​agent system can help users become aware of their unconscious actions and support them in making choices they won't regret.

[0054] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects information such as the user's purchase history, travel history, posts, and likes. For example, the collection unit collects data such as products the user has purchased in the past, places visited, the content of posts, and posts that the user has liked. The collection unit collects detailed data such as what products the user has purchased, where they have traveled, what kind of posts they have made, and which posts they have liked. For example, the collection unit can collect purchase history such as the type of products the user has purchased in the past, the date and time of purchase, and the place of purchase. The collection unit can also collect travel history such as the places the user has traveled, the means of transportation, and the time of travel. Furthermore, the collection unit can also collect post information such as the content of posts made by the user, the frequency of posts, and the target of posts. The collection unit can also collect like information such as the type of posts the user has liked, the date and time of the like, and the target of the like. The analysis unit analyzes the information collected by the collection unit and identifies the user's unconscious behavioral patterns. The analysis unit analyzes collected data to identify the frequency, consistency, and background of user behavior. If a user tends to move to a specific location at a specific time, the analysis unit can identify this behavioral pattern. If a user tends to purchase a specific product, the analysis unit can also identify this behavioral pattern. The analysis unit can use generative AI to identify user behavioral patterns. The generative AI analyzes collected data to identify the user's unconscious behavioral patterns. For example, the generative AI analyzes the frequency and consistency of user behavior to identify behavioral patterns. The generative AI can also analyze the background of user behavior to identify behavioral patterns. Based on the behavioral patterns identified by the analysis unit, the suggestion unit proposes what the user should do now. For example, if a user tends to move to a specific location at a specific time, the suggestion unit will propose going to that location at that time. If a user tends to purchase a specific product, the suggestion unit will also propose purchasing that product. The suggestion unit can use generative AI to help the user make choices they won't regret.The generating AI suggests what the user should do now, based on the behavioral patterns identified by the analysis unit. For example, if the generating AI tends to go to a specific place at a specific time, it suggests going to that place at that time. The generating AI can also suggest purchasing a specific product if the user tends to buy that product. In this way, the AI ​​agent system according to the embodiment can identify the user's unconscious behavioral patterns and suggest what they should do now, thereby supporting them in making choices they won't regret.

[0055] The data collection unit collects information such as the user's purchase history, travel history, and posting history. Specifically, it collects data such as products the user has purchased in the past, places they have visited, the content of their posts, and posts they have liked. The data collection unit collects detailed data such as what products the user has purchased, where they have traveled, what kind of posts they have made, and which posts they have liked. For example, it can collect purchase history such as the type of product the user has purchased in the past, the date and time of purchase, and the place of purchase. This includes purchase history from online shopping sites and physical stores, allowing for a detailed understanding of the user's purchasing trends. The data collection unit can also collect travel history such as the places the user has traveled, the means of transportation, and the time of travel. This includes GPS data and transportation usage history, allowing for a detailed understanding of the user's travel patterns. Furthermore, the data collection unit can also collect posting information such as the content of posts the user has made, the frequency of posts, and the target audience of posts. This includes information to understand what topics the user is interested in and what opinions they hold. The data collection unit can also collect "like" information such as the type of posts the user has liked, the date and time of the like, and the target audience of the like. This allows for an understanding of what kind of content the user is interested in. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0056] The analysis unit analyzes the information collected by the data collection unit to identify users' unconscious behavioral patterns. Specifically, it analyzes the collected data to identify the frequency, consistency, and background of user behavior. For example, if a user tends to move to a specific place at a specific time, that behavioral pattern can be identified. The analysis unit can also identify behavioral patterns if a user tends to purchase a specific product. The analysis unit can use generative AI to identify user behavioral patterns. The generative AI analyzes the collected data to identify users' unconscious behavioral patterns. For example, the generative AI analyzes the frequency and consistency of user behavior to identify behavioral patterns. The generative AI can also analyze the background of user behavior to identify behavioral patterns. The generative AI can use natural language processing technology to analyze the content of posts and understand user interests and emotional tendencies. Furthermore, the generative AI can analyze purchase history and travel history to identify user lifestyles and consumption trends. In addition, the generative AI can analyze user behavioral data over time to understand changes and trends in behavior. This allows the analysis unit to quickly and accurately analyze collected data and grasp users' unconscious behavioral patterns in real time. Furthermore, the analysis unit can also utilize historical data and statistical information to analyze long-term behavioral patterns and trends. This enables the analysis unit to gain a detailed understanding of user behavioral patterns and improve the accuracy and reliability of the entire system.

[0057] The suggestion unit proposes what the user should do now based on behavioral patterns identified by the analysis unit. Specifically, if the user tends to go to a specific place at a specific time, the suggestion unit will propose going to that place at that time. The suggestion unit can also propose purchasing a specific product if the user tends to buy that product. The suggestion unit can use generative AI to help the user make choices they won't regret. The generative AI proposes what the user should do now based on behavioral patterns identified by the analysis unit. For example, if the generative AI tends to go to a specific place at a specific time, it will propose going to that place at that time. The generative AI can also propose purchasing a specific product if the user tends to buy that product. Based on the user's past behavioral data, the generative AI can predict future behavior and make optimal suggestions. For example, based on the user's history of participating in a specific event, the generative AI can suggest participation when a similar event is held. Also, based on the user's purchase history, the generative AI can suggest relevant products and services. Furthermore, the generative AI can analyze the user's posts and likes and suggest content and information based on the user's interests. This allows the proposal department to provide optimal suggestions based on user behavior patterns, supporting users in making choices they won't regret. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. As a result, the proposal department can provide users with more appropriate and beneficial suggestions, improving the overall performance of the system.

[0058] 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 collect information during times when the user is relaxed. If the user is excited, the data collection unit can also collect information after the user has calmed down. If the user is tired, the data collection unit can also collect information after the user has rested. By adjusting the timing of information collection according to the user's emotions, more appropriate information collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0059] 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 from data sources that the user has frequently used in the past. The data collection unit can also collect information from applications that the user has preferred to use in the past. The data collection unit can also collect data from information sources that the user has given high ratings to in the past. This enables efficient information collection by selecting the optimal information collection method based on the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior history data into a generating AI and have the generating AI select the optimal information collection method.

[0060] The data collection unit can filter information based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to topics the user is currently interested in. The data collection unit can also filter and collect information related to the user's current lifestyle (e.g., work, hobbies). The data collection unit can also filter and collect information related to the user's current health status. By filtering information based on the user's current lifestyle and areas of interest, it is possible to collect highly relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0061] 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. If the user is excited, the data collection unit may also prioritize collecting information that helps them calm down. If the user is tired, the data collection unit may also prioritize collecting information that helps them rest. This allows for more appropriate information collection by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0062] 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 prioritize the collection of event information related to the user's current location. The data collection unit can also collect information on nearby restaurants and cafes based on the user's current location. The data collection unit can also prioritize the collection of traffic information related to the user's current location. This enables more appropriate information collection by collecting highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant information.

[0063] 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 posts the user has recently liked. The data collection unit can also collect information related to accounts the user has recently followed. The data collection unit can also collect information related to content the user has recently shared. This allows for more appropriate data collection by collecting relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0064] 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. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. If the user is excited, the analysis unit can also provide visually stimulating analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0065] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the collected data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the collected data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0066] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history data. The analysis unit can also apply a movement pattern analysis algorithm to movement history data. The analysis unit can also apply a sentiment analysis algorithm to social media data. By applying different analysis algorithms depending on the data category, more appropriate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0067] 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, the analysis unit will prioritize analyzing information that helps reduce stress. If the user is relaxed, the analysis unit can also perform a detailed analysis. If the user is in a hurry, the analysis unit can also perform a rapid analysis. This allows for more appropriate analysis by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0068] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also prioritize the analysis of current data while referring to past data. The analysis unit can also adjust the level of detail of the analysis according to the data collection timing. This allows for more appropriate analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0069] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit may also adjust the level of detail of the analysis according to the relevance of the data. This allows for more appropriate analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0070] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented 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. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the 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 a generative AI and have the generative AI adjust the way suggestions are presented.

[0071] The proposal unit can adjust the level of detail of its proposals based on the importance of the behavioral patterns. For example, the proposal unit can provide detailed proposals for high-importance behavioral patterns, and simplified proposals for low-importance behavioral patterns. The proposal unit can also determine the priority of proposals based on the importance of the behavioral patterns. This allows for more appropriate proposals by adjusting the level of detail of proposals based on the importance of the behavioral patterns. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the behavioral patterns into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0072] The suggestion unit can apply different suggestion algorithms depending on the category of the behavioral pattern when making suggestions. For example, the suggestion unit can apply a purchase suggestion algorithm to purchase behavioral patterns. The suggestion unit can also apply a travel suggestion algorithm to travel behavioral patterns. The suggestion unit can also apply a social suggestion algorithm to social media behavioral patterns. By applying different suggestion algorithms depending on the category of the behavioral pattern, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the category of the behavioral pattern into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0073] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize suggestions that help reduce stress. If the user is relaxed, the suggestion unit can also provide more detailed suggestions. If the user is in a hurry, the suggestion unit can also prioritize suggestions that can be implemented quickly. This allows for more appropriate suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 a generative AI and have the generative AI determine the priority of suggestions.

[0074] The proposal unit can determine the priority of proposals based on the timing of behavioral pattern collection. For example, the proposal unit can make proposals based on the most recent behavioral patterns. The proposal unit can also make proposals that prioritize current behavioral patterns while referring to past behavioral patterns. The proposal unit can also adjust the level of detail of proposals according to the timing of behavioral pattern collection. This makes it possible to make more appropriate proposals by determining the priority of proposals based on the timing of behavioral pattern collection. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the timing of behavioral pattern collection into a generating AI and have the generating AI perform the determination of proposal priorities.

[0075] The proposal unit can adjust the order of proposals based on the relevance of behavioral patterns. For example, the proposal unit can make proposals based on highly relevant behavioral patterns. It can also make proposals based on less relevant behavioral patterns. The proposal unit can also adjust the level of detail of the proposals according to the relevance of the behavioral patterns. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of behavioral patterns. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of behavioral patterns into a generating AI and have the generating AI adjust the order of proposals.

[0076] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0077] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, suggestions can be made during a time when they are relaxed. If the user is excited, suggestions can be made after they have calmed down. If the user is tired, suggestions can be made after they have rested. By adjusting the timing of suggestions according to the user's emotions, more appropriate suggestions can be made. 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 using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the timing of suggestions.

[0078] The data collection unit can estimate the user's emotions and adjust the information collection method 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 excited, it can prioritize collecting information that helps them calm down. If the user is tired, it can prioritize collecting information that helps them rest. By adjusting the information collection method according to the user's emotions, more appropriate information can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI adjust the information collection method.

[0079] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis can be performed during a time when they are relaxed. If the user is excited, the analysis can be performed after they have calmed down. If the user is tired, the analysis can be performed after they have rested. By adjusting the timing of the analysis according to the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the timing of the analysis.

[0080] 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 stressed, it can offer suggestions to help them relax. If the user is excited, it can offer suggestions to help them calm down. If the user is tired, it can offer suggestions related to rest. By adjusting the content of suggestions according to the user's emotions, more appropriate suggestions can be made. 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 processing described above in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the content of its suggestions.

[0081] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, it can use an analysis method that helps them relax. If the user is excited, it can also use an analysis method that helps them calm down. If the user is tired, it can also use an analysis method that helps them rest. By adjusting the analysis method according to the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the analysis method.

[0082] The data collection unit can analyze the user's past behavior history and select the optimal timing for information collection. For example, it can prioritize collecting information during times when the user frequently collected information in the past. It can also collect information from applications that the user has preferred to use in the past. It can also collect data from information sources that the user has given high ratings to in the past. This enables efficient information collection by selecting the optimal timing for information collection based on the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior history data into a generating AI and have the generating AI select the optimal timing for information collection.

[0083] The data collection unit can filter information based on the user's current lifestyle and areas of interest during data collection. For example, it can prioritize collecting information related to topics the user is currently interested in. It can also filter and collect information related to the user's current lifestyle (e.g., work, hobbies). It can also filter and collect information related to the user's current health status. By filtering information based on the user's current lifestyle and areas of interest, it is possible to collect highly relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0084] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during data collection. For example, it can prioritize collecting event information related to the user's current location. It can also collect information on nearby restaurants and cafes based on the user's current location. It can also prioritize collecting traffic information related to the user's current location. This enables more appropriate information collection by collecting highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of 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, it can collect information related to posts the user has recently liked. It can also collect information related to accounts the user has recently followed. It can also collect information related to content the user has recently shared. This allows for more appropriate data collection by collecting relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant information.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during the analysis. For example, it can perform a detailed analysis on data with high importance and a simplified analysis on data with low importance. It can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the collected data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the collected data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0087] The following briefly describes the processing flow for example form 2.

[0088] Step 1: The data collection unit collects information such as the user's purchase history, travel history, posts, and likes. Specifically, it collects data such as products the user has purchased in the past, places they have visited, the content of their posts, and posts they have liked. For example, the data collection unit can collect purchase history such as the type of product the user has purchased in the past, the date and time of purchase, and the place of purchase. It can also collect travel history such as places the user has traveled, the means of transportation, and the time of travel, as well as post information such as the content of posts, frequency, and target audience, and like information such as the type of post they have liked, the date and time, and the target audience. Step 2: The analysis unit analyzes the information collected by the collection unit to identify the user's unconscious behavioral patterns. Specifically, it analyzes the collected data to identify the frequency, consistency, and background of the user's behavior. For example, if a user tends to move to a specific place at a specific time of day or tends to purchase a specific product, the analysis unit will identify that behavioral pattern. The analysis unit can use generative AI to analyze the collected data and identify the user's unconscious behavioral patterns. Step 3: The suggestion unit proposes what the user should do now, based on the behavioral patterns identified by the analysis unit. Specifically, if the user tends to go to a specific place at a specific time, the suggestion unit will propose going to that place at that time. It can also propose purchasing a specific product if the user tends to buy that product. The suggestion unit uses generative AI to propose what the user should do now, based on the behavioral patterns identified by the analysis unit. This helps the user make choices they won't regret.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] Each of the multiple elements described above, including the collection unit, analysis unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects information such as the user's purchase history, travel history, posts, and likes. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to identify the user's unconscious behavioral patterns. The suggestion unit is implemented by the control unit 46A of the smart device 14 and suggests what the user should do now based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0093] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] Each of the multiple elements described above, including the collection unit, analysis unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and collects information such as the user's purchase history, travel history, posts, and likes. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to identify the user's unconscious behavioral patterns. The suggestion unit is implemented by the control unit 46A of the smart glasses 214 and suggests what the user should do now based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0109] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the collection unit, analysis unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects information such as the user's purchase history, travel history, posts, and likes. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to identify the user's unconscious behavioral patterns. The suggestion unit is implemented by the control unit 46A of the headset terminal 314 and suggests what the user should do now based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the collection unit, analysis unit, and suggestion unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects information such as the user's purchase history, movement history, posts, and likes. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to identify the user's unconscious behavioral patterns. The suggestion unit is implemented by the control unit 46A of the robot 414 and suggests what the user should do now based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] (Note 1) A collection unit that collects user purchase history, movement history, and posting history information, An analysis unit analyzes the information collected by the aforementioned collection unit to identify the user's unconscious behavioral patterns, The system includes a suggestion unit that proposes what the user should do now based on the behavioral patterns identified by the analysis unit. A system characterized by the following features. (Note 2) 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 3) 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 4) 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 5) 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 6) 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 7) 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 8) 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 9) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the behavioral pattern. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making proposals, prioritize them based on when behavioral patterns were collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of behavioral patterns. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0161] 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 purchase history, movement history, and posting history information, An analysis unit analyzes the information collected by the aforementioned collection unit to identify the user's unconscious behavioral patterns, The system includes a suggestion unit that proposes what the user should do now based on the behavioral patterns identified by the analysis unit. A system characterized by the following features.

2. 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.

3. 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.

4. The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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

6. The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system according to feature 1.

8. 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 according to feature 1.

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