system

Abstract

The system according to this embodiment aims to analyze diverse user data and propose and execute the optimal action. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an execution unit. The data collection unit collects user data. The analysis unit analyzes the data collected by the data collection unit to understand the user's current situation and personal preferences. The proposal unit proposes actions based on the analysis results obtained by the analysis unit. The execution unit executes the actions proposed by the proposal 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 character of the chatbot, 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 prior art, the integrated analysis of various data of users and the proposal and execution of optimal actions have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze various data of users and propose and execute optimal actions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an execution unit. The data collection unit collects user data. The analysis unit analyzes the data collected by the data collection unit to understand the user's current situation and personal preferences. The proposal unit proposes actions based on the analysis results obtained by the analysis unit. The execution unit executes the actions proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze diverse user data and propose and execute optimal actions. [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, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system in which, with the user's permission, the AI ​​deeply understands the user's current situation and personal preferences by referring to various data such as calendars, purchase history, and reservation history, and proposes and executes the optimal action based on that understanding. For example, when choosing a gift, this system can look at the recipient's activity data, suggest an appropriate product, and allow the user to purchase it on an e-commerce site. This system goes beyond the framework of existing IT services such as notification delivery and simple recommendations, and can seamlessly perform the actions the user should take. Furthermore, in order to scale the business, it can form alliances with companies and organizations and dramatically transform the lifestyles of people in the world. For example, it collects data such as the user's calendar, purchase history, and reservation history. For example, it collects the history of products the user has purchased in the past and information on restaurants that have been reserved. This data is collected by the collection unit. Next, the collected data is analyzed. The analysis unit analyzes the user's current situation and personal preferences based on the collected data. For example, it analyzes products the user has recently been interested in and places they frequently visit. Based on the analysis results, the proposal unit proposes the optimal action. For example, when a user is choosing a gift, it looks at the recipient's activity data and suggests an appropriate product. Finally, the proposed action is executed. The execution unit carries out the proposed action, such as purchasing a product on an e-commerce site. This system allows users to seamlessly perform the optimal action without complex operations. Furthermore, by forming alliances with companies and organizations, businesses can be scaled and people's lifestyles can be dramatically transformed. In this way, the system can seamlessly execute the optimal action by collecting, analyzing, proposing, and executing user data.

[0029] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects user data. User data includes, but is not limited to, examples such as calendars, purchase history, and reservation history. For example, the collection unit can obtain the user's calendar to understand their schedule. The collection unit can also obtain the user's purchase history to collect information on products purchased in the past. Furthermore, the collection unit can obtain the user's reservation history to collect information on restaurants and hotels that have been booked in the past. For example, the collection unit can obtain the user's calendar via an API to understand their schedule. Purchase history can be obtained via an API of an online shopping site. Reservation history can be obtained via an API of a reservation site. The analysis unit analyzes the data collected by the collection unit to understand the user's current situation and personal preferences. The analysis is performed using, but is not limited to, machine learning algorithms or statistical analysis. For example, the analysis unit can use machine learning algorithms to analyze the user's purchasing patterns. The analysis unit can also use statistical analysis to analyze the user's behavior patterns. Furthermore, the analysis unit can also analyze user text data using natural language processing technology. For example, the analysis unit can cluster user purchasing patterns using machine learning algorithms. Statistical analysis identifies behavioral patterns based on user behavior data. Natural language processing technology analyzes user text data to understand emotions and intentions. The suggestion unit proposes the optimal action based on the analysis results obtained by the analysis unit. Suggestions include, but are not limited to, rule-based suggestions and AI-generated suggestions. For example, the suggestion unit can use rule-based suggestions to propose the most suitable product to the user. The suggestion unit can also use AI to propose the most suitable action to the user. Furthermore, the suggestion unit can provide personalized suggestions based on the user's past behavior data. For example, the suggestion unit can use rule-based suggestions to propose products based on the user's purchase history. AI proposes the optimal action based on the user's behavior data. Personalized suggestions provide suggestions tailored to individual needs based on the user's past behavior data.The execution unit carries out the actions proposed by the proposal unit. Execution includes, but is not limited to, actions performed via APIs or physical actions. For example, the execution unit purchases goods from an e-commerce site via an API. The execution unit can also perform physical actions to complete tasks on behalf of the user. Furthermore, the execution unit can control the user's device and perform specific actions. For example, the execution unit purchases goods from an e-commerce site via an API. Physical actions involve completing tasks using a robot. Device control involves operating smart home devices to perform specific actions. This allows the system according to the embodiment to seamlessly execute optimal actions by collecting, analyzing, proposing, and executing user data.

[0030] The data collection unit can collect data from users' calendars, purchase history, and reservation history. For example, the data collection unit can retrieve a user's calendar to understand their schedule. Calendars include, but are not limited to, Google Calendar or Outlook Calendar. For example, the data collection unit can retrieve a user's schedule through the Google Calendar API. The data collection unit can also retrieve a user's purchase history to collect information on products purchased in the past. Purchase history includes, but is not limited to, online shopping history or in-store purchase history. For example, the data collection unit can retrieve purchase history through the API of an online shopping site. Furthermore, the data collection unit can retrieve a user's reservation history to collect information on restaurants and hotels that have been booked in the past. Reservation history includes, but is not limited to, restaurant reservation history or hotel reservation history. For example, the data collection unit can retrieve reservation history through the API of a reservation site. This allows the data collection unit to collect diverse user data, enabling more accurate analysis.

[0031] The analysis unit can analyze the user's current situation and personal preferences based on the collected data. For example, the analysis unit can analyze the user's purchasing patterns using machine learning algorithms. Machine learning algorithms include, but are not limited to, clustering algorithms and classification algorithms. For example, the analysis unit can group the user's purchasing patterns using clustering algorithms. The analysis unit can also classify the user's purchasing patterns using classification algorithms. Furthermore, the analysis unit can analyze the user's behavior patterns using statistical analysis. Statistical analysis includes, but is not limited to, regression analysis and correlation analysis. For example, the analysis unit can predict the user's behavior patterns using regression analysis. The analysis unit can also identify the relationships between user behavior patterns using correlation analysis. Furthermore, the analysis unit can analyze the user's text data using natural language processing techniques. Natural language processing techniques include, but are not limited to, sentiment analysis and topic modeling. For example, the analysis unit can extract emotions from the user's text data using sentiment analysis. Furthermore, the analysis unit can use topic modeling to extract key topics from the user's text data. This allows the analysis unit to analyze the user's current situation and personal preferences, enabling it to make more appropriate suggestions.

[0032] The suggestion function can suggest products based on the analysis results, taking into account the recipient's activity data when a user is choosing a gift. For example, the suggestion function can suggest products based on the analysis results, taking into account the recipient's activity data when a user is choosing a gift. Activity data includes, but is not limited to, exercise data and social media activity data. For example, the suggestion function can suggest sports equipment that the recipient has recently become interested in, based on exercise data. The suggestion function can also suggest products that the recipient has recently shown interest in, based on social media activity data. Furthermore, the suggestion function can suggest the most suitable product for the recipient based on the user's past gift-choosing history. For example, the suggestion function can analyze the user's past gift-choosing history and suggest products that the recipient is likely to like. This allows the suggestion function to suggest the most suitable product for the recipient when a user is choosing a gift.

[0033] The execution unit can perform the proposed action and purchase products on an e-commerce site. For example, the execution unit can perform the proposed action and purchase products on an e-commerce site. E-commerce sites include, but are not limited to, Yahoo! Shopping and Amazon. For example, the execution unit can purchase products through the Yahoo! Shopping API. The execution unit can also purchase products through the Amazon API. Furthermore, the execution unit can execute automated processes to purchase products on behalf of the user. For example, the execution unit can add products to the cart and complete the purchase process on behalf of the user. This allows the user to purchase products without any effort by having the execution unit perform the proposed action.

[0034] The suggestion department can make suggestions to users regarding travel plans, restaurant reservations, and overall lifestyle. For example, the suggestion department can suggest travel plans for users. Travel plans include, but are not limited to, selecting destinations and booking accommodations. For example, the suggestion department can suggest optimal destinations based on the user's past travel history. It can also suggest accommodations based on the user's preferences. Furthermore, the suggestion department can also suggest restaurant reservations for users. Restaurant reservations include, but are not limited to, selecting restaurants and making reservations based on the user's preferences. For example, the suggestion department can suggest optimal restaurants based on the user's past restaurant reservation history. It can also make restaurant reservations based on the user's preferences. Furthermore, the suggestion department can make suggestions to users regarding overall lifestyle. Overall lifestyle includes, but are not limited to, suggestions regarding health management and hobbies. For example, the suggestion department can make health management suggestions based on the user's health data. It can also suggest new hobbies based on the user's hobbies. This allows the proposal department to make users' lives more convenient by offering suggestions that encompass all aspects of their lifestyle.

[0035] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can analyze the user's past data collection history. This history includes, but is not limited to, the types and frequency of data collected in the past. The data collection unit can, for example, analyze the types of data collected in the past and select the optimal collection method. The data collection unit can also analyze past collection frequencies and determine the optimal collection frequency. Furthermore, the data collection unit can select a collection method based on the user's past data collection history. For example, it can prioritize data collection methods that the user has frequently used in the past. It can also select the most efficient collection method from the user's past data collection history. Furthermore, it can adjust the collection frequency based on the user's past data collection history. In this way, the data collection unit can select the optimal collection method by analyzing past data collection history.

[0036] The data collection unit can filter data based on the user's lifestyle or areas of interest during data collection. For example, the data collection unit can filter data based on the user's lifestyle. Lifestyle includes, but is not limited to, the home environment or work situation. For example, the data collection unit can collect only the necessary data based on the user's home environment. The data collection unit can also collect only the necessary data based on the user's work situation. Furthermore, the data collection unit can also filter data based on the user's areas of interest. Areas of interest include, but is not limited to, hobbies or topics of interest. For example, the data collection unit can prioritize collecting data related to areas the user is currently interested in. The data collection unit can also filter out unnecessary data based on the user's areas of interest. In this way, the data collection unit can collect only the necessary data by filtering data based on the user's lifestyle and areas of interest.

[0037] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit collects data based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location-based services. For example, the data collection unit can use GPS data to identify the user's current location and prioritize the collection of data related to that area. The data collection unit can also use location-based services to identify the user's location and prioritize the collection of data related to that area. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is in a specific area, the data collection unit will prioritize the collection of data related to that area. Also, if the user is at home, the data collection unit can prioritize the collection of data around the user's home. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information.

[0038] The data collection unit can collect relevant data based on the user's social media activity during data collection. For example, the data collection unit collects data based on the user's social media activity. Social media activity includes, but is not limited to, the content of posts and the number of likes. For example, the data collection unit collects relevant data based on information shared by the user on social media. The data collection unit can also analyze the activity of the user's social media followers and friends and collect relevant data. Furthermore, the data collection unit can collect data related to topics that the user has shown interest in on social media. For example, the data collection unit collects relevant data based on information shared by the user on social media. The data collection unit can also analyze the activity of the user's social media followers and friends and collect relevant data. Furthermore, the data collection unit can also collect data related to topics that the user has shown interest in on social media. In this way, the data collection unit can collect relevant data by analyzing the user's social media activity.

[0039] The analysis unit can adjust the level of detail of the analysis based on the priority of the data during the analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the priority of the data. Data priority includes, but is not limited to, importance scores and urgency. For example, the analysis unit performs a detailed analysis on data with high importance. Conversely, the analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the priority of the data. For example, the analysis unit prioritizes the analysis of data with high importance. Conversely, the analysis unit can postpone the analysis of data with low importance. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the priority of the data.

[0040] The analysis unit can apply different analysis algorithms depending on the type of data during analysis. For example, the analysis unit can apply different analysis algorithms depending on the type of data. Data types include, but are not limited to, text data and numerical data. For example, the analysis unit can apply a natural language processing algorithm to text data. It can also apply a statistical analysis algorithm to numerical data. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the type of data. For example, the analysis unit can apply a sentiment analysis algorithm to text data. It can also apply a regression analysis algorithm to numerical data. This allows the analysis unit to improve the accuracy of the analysis by applying the appropriate analysis algorithm according to the type of data.

[0041] The analysis unit can determine the priority of analysis based on the data acquisition date during analysis. For example, the analysis unit determines the priority of analysis based on the data acquisition date. The data acquisition date includes, but is not limited to, the latest data or historical data. For example, the analysis unit prioritizes the analysis of the latest data. The analysis unit can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the priority of analysis according to the data acquisition date. For example, the analysis unit prioritizes the analysis of the latest data. The analysis unit can also postpone the analysis of older data. In this way, the analysis unit can prioritize the analysis of the latest data by determining the priority of analysis based on the data acquisition date.

[0042] The analysis unit can adjust the order of analysis based on the relationships between the data. For example, the analysis unit can adjust the order of analysis based on the relationships between the data. These relationships include, but are not limited to, correlation and causation. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relationships between the data. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relationships between the data.

[0043] The proposal team can adjust the level of detail in their proposals based on the priority of the actions. For example, the proposal team adjusts the level of detail based on the priority of the actions. Action priority includes, but is not limited to, importance scores and urgency. For example, the proposal team can provide detailed proposals for high-importance actions, and concise proposals for low-importance actions. Furthermore, the proposal team can determine the priority of proposals according to the priority of the actions. For example, the proposal team can prioritize proposals for high-importance actions, and postpone proposals for low-importance actions. This allows the proposal team to make efficient proposals by adjusting the level of detail based on the priority of the actions.

[0044] The suggestion unit can apply different suggestion algorithms depending on the type of action when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the type of action. Action types include, but are not limited to, purchase actions and reservation actions. For example, the suggestion unit can apply a purchase suggestion algorithm to purchase actions. It can also apply a reservation suggestion algorithm to reservation actions. Furthermore, the suggestion unit can select the most suitable suggestion algorithm depending on the type of action. For example, the suggestion unit can apply a gift suggestion algorithm to gift selection. It can also apply a travel suggestion algorithm to travel planning. This improves the accuracy of suggestions by allowing the suggestion unit to apply the appropriate suggestion algorithm according to the type of action.

[0045] The proposal department can determine the priority of proposals based on the timing of their implementation. For example, the proposal department can determine the priority of proposals based on the timing of their implementation. The timing of an action includes, but is not limited to, immediate or long-term plans. For example, the proposal department can prioritize proposing actions that need to be done in the immediate future. It can also postpone proposing actions that are far in the future. Furthermore, the proposal department can adjust the priority of proposals according to the timing of their implementation. For example, the proposal department can prioritize proposing actions that need to be done in the immediate future. It can also postpone proposing actions that are far in the future. This allows the proposal department to make efficient proposals by determining the priority of proposals based on the timing of their implementation.

[0046] The proposal team can adjust the order of proposals based on the relationships between actions. For example, the proposal team can adjust the order of proposals based on the relationships between actions. These relationships include, but are not limited to, correlation and causation. For example, the proposal team can prioritize proposing highly relevant actions. It can also postpone proposing less relevant actions. Furthermore, the proposal team can adjust the order of proposals according to the relationships between actions. For example, the proposal team can prioritize proposing highly relevant actions. It can also postpone proposing less relevant actions. This allows the proposal team to make efficient proposals by adjusting the order of proposals based on the relationships between actions.

[0047] The execution unit can analyze the user's past action history at runtime to select the optimal execution method. For example, the execution unit can analyze the user's past action history. This action history includes, but is not limited to, past purchase history and past reservation history. For example, the execution unit can analyze past purchase history and select the optimal execution method. The execution unit can also analyze past reservation history and select the optimal execution method. Furthermore, the execution unit can adjust the execution procedure based on the user's past action history. For example, the execution unit will prioritize using execution methods that the user has successfully used in the past. The execution unit can also select the most efficient execution method from the user's past action history. In this way, the execution unit can select the optimal execution method by analyzing past action history.

[0048] The execution unit can customize the means of execution based on the user's living situation during execution. For example, the execution unit customizes the means of execution based on the user's living situation. Living situation includes, but is not limited to, the home environment and work situation. For example, the execution unit customizes the means of execution based on the user's home environment. The execution unit can also customize the means of execution based on the user's work situation. Furthermore, the execution unit can adjust the means of execution according to the user's living situation. For example, if the user is busy, it can provide and execute a concise procedure. If the user is relaxed, it can provide and execute a detailed procedure. In this way, the execution unit can perform more appropriate execution by customizing the means of execution according to the user's living situation.

[0049] The execution unit can select an execution method based on the user's geographical location information at runtime. For example, the execution unit can select an execution method based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. For example, the execution unit can use GPS data to identify the user's current location and prioritize actions related to that area. Alternatively, the execution unit can use location services to identify the user's location and prioritize actions related to that area. Furthermore, if the user is traveling, the execution unit can prioritize actions related to the travel destination. For example, if the user is in a specific area, the execution unit will prioritize actions related to that area. Also, if the user is at home, the execution unit can prioritize actions around the user's home. In this way, the execution unit can select the optimal execution method by considering the user's geographical location information.

[0050] The execution unit can propose actions based on the user's social media activity at runtime. For example, the execution unit proposes actions based on the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. For example, the execution unit performs relevant actions based on information shared by the user on social media. The execution unit can also analyze the activities of the user's social media followers and friends and perform relevant actions. Furthermore, the execution unit can perform actions related to topics the user has shown interest in on social media. For example, the execution unit performs relevant actions based on information shared by the user on social media. The execution unit can also analyze the activities of the user's social media followers and friends and perform relevant actions. Furthermore, the execution unit can perform actions related to topics the user has shown interest in on social media. In this way, the execution unit can perform relevant actions by analyzing the user's social media activity.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The data collection unit can also collect data about the user's hobbies and interests. For example, it can obtain information about events the user has attended and magazines they subscribe to. The analysis unit analyzes the user's hobbies and interests based on this data, and the suggestion unit can suggest events and products that match the user's interests. For example, if the user is interested in music, it can provide information about concerts being held nearby. Also, if the user enjoys reading, it can suggest new books. In this way, the system can make personalized suggestions based on the user's hobbies and interests.

[0053] The suggestion department can predict future user behavior and make suggestions based on past user behavior data. For example, if a user plans a trip at the same time every year, it can suggest travel destinations that are suitable for that time. It can also provide information related to events if a user participates in a particular event every year. Furthermore, it can analyze user purchasing patterns and suggest products that are likely to be purchased next. This allows the suggestion department to predict future user behavior and make more appropriate suggestions.

[0054] The suggestion department can analyze users' social media activity and make suggestions based on topics that users are interested in. For example, it can analyze topics that users frequently share on social media and suggest products and services related to those topics. It can also analyze the activity of users' followers and friends and make suggestions related to groups with common interests. Furthermore, it can provide information related to events that users have shown interest in on social media. In this way, the suggestion department can make personalized suggestions based on users' social media activity.

[0055] The data collection unit can prioritize collecting data related to a specific region based on the user's geographical location information. For example, if the user is traveling, it will prioritize collecting data related to their travel destination. It can also prioritize collecting data related to a specific region if the user is in that region. Furthermore, if the user is at home, it can prioritize collecting data related to their home area. In this way, the data collection unit can prioritize collecting highly relevant data by considering the user's geographical location information.

[0056] The proposal department can analyze a user's past proposal history and select the most suitable proposal method. For example, it can analyze a user's past accepted proposal history and use a similar method. It can also analyze a user's past rejected proposal history and try a different method. Furthermore, it can adjust the frequency and timing of proposals based on the user's past proposal history. In this way, the proposal department can select a more effective proposal method by analyzing past proposal history.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects user data. User data includes calendars, purchase history, and reservation history. For example, the data collection unit retrieves the user's calendar via API to understand their schedule. Purchase history is retrieved via the API of online shopping sites, and reservation history is retrieved via the API of reservation sites. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the user's current situation and personal preferences. The analysis is performed using machine learning algorithms, statistical analysis, and natural language processing techniques. For example, the analysis unit uses machine learning algorithms to cluster user purchasing patterns, statistical analysis to identify behavioral patterns, and natural language processing techniques to analyze text data and understand emotions and intentions. Step 3: The proposal department proposes the optimal action based on the analysis results obtained by the analysis department. The proposals include rule-based proposals and AI-based proposals. For example, the proposal department uses rule-based proposals to suggest the best product to the user and AI to suggest the best action to the user. Furthermore, the proposal department makes personalized proposals based on the user's past behavior data. Step 4: The execution unit carries out the actions proposed by the proposal unit. Execution includes actions via APIs and physical actions. For example, the execution unit purchases products on an e-commerce site via an API and completes tasks on behalf of the user by performing physical actions. Furthermore, the execution unit controls the user's device and performs specific actions.

[0059] (Example of form 2) The system according to an embodiment of the present invention is a system in which, with the user's permission, the AI ​​deeply understands the user's current situation and personal preferences by referring to various data such as calendars, purchase history, and reservation history, and proposes and executes the optimal action based on that understanding. For example, when choosing a gift, this system can look at the recipient's activity data, suggest an appropriate product, and allow the user to purchase it on an e-commerce site. This system goes beyond the framework of existing IT services such as notification delivery and simple recommendations, and can seamlessly perform the actions the user should take. Furthermore, in order to scale the business, it can form alliances with companies and organizations and dramatically transform the lifestyles of people in the world. For example, it collects data such as the user's calendar, purchase history, and reservation history. For example, it collects the history of products the user has purchased in the past and information on restaurants that have been reserved. This data is collected by the collection unit. Next, the collected data is analyzed. The analysis unit analyzes the user's current situation and personal preferences based on the collected data. For example, it analyzes products the user has recently been interested in and places they frequently visit. Based on the analysis results, the proposal unit proposes the optimal action. For example, when a user is choosing a gift, it looks at the recipient's activity data and suggests an appropriate product. Finally, the proposed action is executed. The execution unit carries out the proposed action, such as purchasing a product on an e-commerce site. This system allows users to seamlessly perform the optimal action without complex operations. Furthermore, by forming alliances with companies and organizations, businesses can be scaled and people's lifestyles can be dramatically transformed. In this way, the system can seamlessly execute the optimal action by collecting, analyzing, proposing, and executing user data.

[0060] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects user data. User data includes, but is not limited to, examples such as calendars, purchase history, and reservation history. For example, the collection unit can obtain the user's calendar to understand their schedule. The collection unit can also obtain the user's purchase history to collect information on products purchased in the past. Furthermore, the collection unit can obtain the user's reservation history to collect information on restaurants and hotels that have been booked in the past. For example, the collection unit can obtain the user's calendar via an API to understand their schedule. Purchase history can be obtained via an API of an online shopping site. Reservation history can be obtained via an API of a reservation site. The analysis unit analyzes the data collected by the collection unit to understand the user's current situation and personal preferences. The analysis is performed using, but is not limited to, machine learning algorithms or statistical analysis. For example, the analysis unit can use machine learning algorithms to analyze the user's purchasing patterns. The analysis unit can also use statistical analysis to analyze the user's behavior patterns. Furthermore, the analysis unit can also analyze user text data using natural language processing technology. For example, the analysis unit can cluster user purchasing patterns using machine learning algorithms. Statistical analysis identifies behavioral patterns based on user behavior data. Natural language processing technology analyzes user text data to understand emotions and intentions. The suggestion unit proposes the optimal action based on the analysis results obtained by the analysis unit. Suggestions include, but are not limited to, rule-based suggestions and AI-generated suggestions. For example, the suggestion unit can use rule-based suggestions to propose the most suitable product to the user. The suggestion unit can also use AI to propose the most suitable action to the user. Furthermore, the suggestion unit can provide personalized suggestions based on the user's past behavior data. For example, the suggestion unit can use rule-based suggestions to propose products based on the user's purchase history. AI proposes the optimal action based on the user's behavior data. Personalized suggestions provide suggestions tailored to individual needs based on the user's past behavior data.The execution unit carries out the actions proposed by the proposal unit. Execution includes, but is not limited to, actions performed via APIs or physical actions. For example, the execution unit purchases goods from an e-commerce site via an API. The execution unit can also perform physical actions to complete tasks on behalf of the user. Furthermore, the execution unit can control the user's device and perform specific actions. For example, the execution unit purchases goods from an e-commerce site via an API. Physical actions involve completing tasks using a robot. Device control involves operating smart home devices to perform specific actions. This allows the system according to the embodiment to seamlessly execute optimal actions by collecting, analyzing, proposing, and executing user data.

[0061] The data collection unit can collect data from users' calendars, purchase history, and reservation history. For example, the data collection unit can retrieve a user's calendar to understand their schedule. Calendars include, but are not limited to, Google Calendar or Outlook Calendar. For example, the data collection unit can retrieve a user's schedule through the Google Calendar API. The data collection unit can also retrieve a user's purchase history to collect information on products purchased in the past. Purchase history includes, but is not limited to, online shopping history or in-store purchase history. For example, the data collection unit can retrieve purchase history through the API of an online shopping site. Furthermore, the data collection unit can retrieve a user's reservation history to collect information on restaurants and hotels that have been booked in the past. Reservation history includes, but is not limited to, restaurant reservation history or hotel reservation history. For example, the data collection unit can retrieve reservation history through the API of a reservation site. This allows the data collection unit to collect diverse user data, enabling more accurate analysis.

[0062] The analysis unit can analyze the user's current situation and personal preferences based on the collected data. For example, the analysis unit can analyze the user's purchasing patterns using machine learning algorithms. Machine learning algorithms include, but are not limited to, clustering algorithms and classification algorithms. For example, the analysis unit can group the user's purchasing patterns using clustering algorithms. The analysis unit can also classify the user's purchasing patterns using classification algorithms. Furthermore, the analysis unit can analyze the user's behavior patterns using statistical analysis. Statistical analysis includes, but is not limited to, regression analysis and correlation analysis. For example, the analysis unit can predict the user's behavior patterns using regression analysis. The analysis unit can also identify the relationships between user behavior patterns using correlation analysis. Furthermore, the analysis unit can analyze the user's text data using natural language processing techniques. Natural language processing techniques include, but are not limited to, sentiment analysis and topic modeling. For example, the analysis unit can extract emotions from the user's text data using sentiment analysis. Furthermore, the analysis unit can use topic modeling to extract key topics from the user's text data. This allows the analysis unit to analyze the user's current situation and personal preferences, enabling it to make more appropriate suggestions.

[0063] The suggestion function can suggest products based on the analysis results, taking into account the recipient's activity data when a user is choosing a gift. For example, the suggestion function can suggest products based on the analysis results, taking into account the recipient's activity data when a user is choosing a gift. Activity data includes, but is not limited to, exercise data and social media activity data. For example, the suggestion function can suggest sports equipment that the recipient has recently become interested in, based on exercise data. The suggestion function can also suggest products that the recipient has recently shown interest in, based on social media activity data. Furthermore, the suggestion function can suggest the most suitable product for the recipient based on the user's past gift-choosing history. For example, the suggestion function can analyze the user's past gift-choosing history and suggest products that the recipient is likely to like. This allows the suggestion function to suggest the most suitable product for the recipient when a user is choosing a gift.

[0064] The execution unit can perform the proposed action and purchase products on an e-commerce site. For example, the execution unit can perform the proposed action and purchase products on an e-commerce site. E-commerce sites include, but are not limited to, Yahoo! Shopping and Amazon. For example, the execution unit can purchase products through the Yahoo! Shopping API. The execution unit can also purchase products through the Amazon API. Furthermore, the execution unit can execute automated processes to purchase products on behalf of the user. For example, the execution unit can add products to the cart and complete the purchase process on behalf of the user. This allows the user to purchase products without any effort by having the execution unit perform the proposed action.

[0065] The suggestion department can make suggestions to users regarding travel plans, restaurant reservations, and overall lifestyle. For example, the suggestion department can suggest travel plans for users. Travel plans include, but are not limited to, selecting destinations and booking accommodations. For example, the suggestion department can suggest optimal destinations based on the user's past travel history. It can also suggest accommodations based on the user's preferences. Furthermore, the suggestion department can also suggest restaurant reservations for users. Restaurant reservations include, but are not limited to, selecting restaurants and making reservations based on the user's preferences. For example, the suggestion department can suggest optimal restaurants based on the user's past restaurant reservation history. It can also make restaurant reservations based on the user's preferences. Furthermore, the suggestion department can make suggestions to users regarding overall lifestyle. Overall lifestyle includes, but are not limited to, suggestions regarding health management and hobbies. For example, the suggestion department can make health management suggestions based on the user's health data. It can also suggest new hobbies based on the user's hobbies. This allows the proposal department to make users' lives more convenient by offering suggestions that encompass all aspects of their lifestyle.

[0066] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit may use facial recognition technology to estimate the user's emotions. Facial recognition technology includes, but is not limited to, methods of analyzing the user's facial expressions using a camera. For example, the data collection unit may capture the user's facial expressions using a camera and estimate the emotions using a facial recognition algorithm. The data collection unit can also estimate the user's emotions using voice analysis technology. Voice analysis technology includes, but is not limited to, methods of analyzing the user's voice using a microphone. For example, the data collection unit may record the user's voice using a microphone and estimate the emotions using a voice analysis algorithm. Furthermore, the data collection unit can adjust the timing of data collection based on the user's emotions. For example, if the user is stressed, data collection may be reduced and resumed when the user is relaxed. Conversely, if the user is relaxed, data collection may be actively performed to obtain detailed information. Furthermore, if the user is busy, data collection may be minimized and detailed collection may be performed later. This allows the data collection unit to adjust the timing of data collection according to the user's emotions, enabling more appropriate data collection.

[0067] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can analyze the user's past data collection history. This history includes, but is not limited to, the types and frequency of data collected in the past. The data collection unit can, for example, analyze the types of data collected in the past and select the optimal collection method. The data collection unit can also analyze past collection frequencies and determine the optimal collection frequency. Furthermore, the data collection unit can select a collection method based on the user's past data collection history. For example, it can prioritize data collection methods that the user has frequently used in the past. It can also select the most efficient collection method from the user's past data collection history. Furthermore, it can adjust the collection frequency based on the user's past data collection history. In this way, the data collection unit can select the optimal collection method by analyzing past data collection history.

[0068] The data collection unit can filter data based on the user's lifestyle or areas of interest during data collection. For example, the data collection unit can filter data based on the user's lifestyle. Lifestyle includes, but is not limited to, the home environment or work situation. For example, the data collection unit can collect only the necessary data based on the user's home environment. The data collection unit can also collect only the necessary data based on the user's work situation. Furthermore, the data collection unit can also filter data based on the user's areas of interest. Areas of interest include, but is not limited to, hobbies or topics of interest. For example, the data collection unit can prioritize collecting data related to areas the user is currently interested in. The data collection unit can also filter out unnecessary data based on the user's areas of interest. In this way, the data collection unit can collect only the necessary data by filtering data based on the user's lifestyle and areas of interest.

[0069] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, the data collection unit may use facial recognition technology to estimate the user's emotions. Facial recognition technology includes, but is not limited to, methods of analyzing the user's facial expressions using a camera. For example, the data collection unit may capture the user's facial expressions using a camera and estimate the emotions using a facial recognition algorithm. The data collection unit can also estimate the user's emotions using voice analysis technology. Voice analysis technology includes, but is not limited to, methods of analyzing the user's voice using a microphone. For example, the data collection unit may record the user's voice using a microphone and estimate the emotions using a voice analysis algorithm. Furthermore, the data collection unit can determine the priority of data to collect based on the user's emotions. For example, if the user is stressed, the collection of less important data may be postponed. Conversely, if the user is relaxed, the collection of highly important data may be prioritized. Furthermore, if the user is busy, only the most important data may be collected. In this way, the data collection unit can prioritize the collection of important data by determining the priority of data to collect according to the user's emotions.

[0070] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit collects data based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location-based services. For example, the data collection unit can use GPS data to identify the user's current location and prioritize the collection of data related to that area. The data collection unit can also use location-based services to identify the user's location and prioritize the collection of data related to that area. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is in a specific area, the data collection unit will prioritize the collection of data related to that area. Also, if the user is at home, the data collection unit can prioritize the collection of data around the user's home. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information.

[0071] The data collection unit can collect relevant data based on the user's social media activity during data collection. For example, the data collection unit collects data based on the user's social media activity. Social media activity includes, but is not limited to, the content of posts and the number of likes. For example, the data collection unit collects relevant data based on information shared by the user on social media. The data collection unit can also analyze the activity of the user's social media followers and friends and collect relevant data. Furthermore, the data collection unit can collect data related to topics that the user has shown interest in on social media. For example, the data collection unit collects relevant data based on information shared by the user on social media. The data collection unit can also analyze the activity of the user's social media followers and friends and collect relevant data. Furthermore, the data collection unit can also collect data related to topics that the user has shown interest in on social media. In this way, the data collection unit can collect relevant data by analyzing the user's social media activity.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit may use facial recognition technology to estimate the user's emotions. Facial recognition technology includes, but is not limited to, methods of analyzing the user's facial expressions using a camera. For example, the analysis unit may capture the user's facial expressions using a camera and estimate the emotions using a facial recognition algorithm. The analysis unit may also estimate the user's emotions using voice analysis technology. Voice analysis technology includes, but is not limited to, methods of analyzing the user's voice using a microphone. For example, the analysis unit may record the user's voice using a microphone and estimate the emotions using a voice analysis algorithm. Furthermore, the analysis unit can adjust the presentation of the analysis based on the user's emotions. For example, if the user is relaxed, it may provide detailed analysis results. If the user is stressed, it may provide concise analysis results. Furthermore, if the user is excited, it may provide visually appealing analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions.

[0073] The analysis unit can adjust the level of detail of the analysis based on the priority of the data during the analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the priority of the data. Data priority includes, but is not limited to, importance scores and urgency. For example, the analysis unit performs a detailed analysis on data with high importance. Conversely, the analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the priority of the data. For example, the analysis unit prioritizes the analysis of data with high importance. Conversely, the analysis unit can postpone the analysis of data with low importance. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the priority of the data.

[0074] The analysis unit can apply different analysis algorithms depending on the type of data during analysis. For example, the analysis unit can apply different analysis algorithms depending on the type of data. Data types include, but are not limited to, text data and numerical data. For example, the analysis unit can apply a natural language processing algorithm to text data. It can also apply a statistical analysis algorithm to numerical data. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the type of data. For example, the analysis unit can apply a sentiment analysis algorithm to text data. It can also apply a regression analysis algorithm to numerical data. This allows the analysis unit to improve the accuracy of the analysis by applying the appropriate analysis algorithm according to the type of data.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit may use facial recognition technology to estimate the user's emotions. Facial recognition technology includes, but is not limited to, methods of analyzing the user's facial expressions using a camera. For example, the analysis unit may capture the user's facial expressions using a camera and estimate the emotions using a facial recognition algorithm. The analysis unit may also use voice analysis technology to estimate the user's emotions. Voice analysis technology includes, but is not limited to, methods of analyzing the user's voice using a microphone. For example, the analysis unit may record the user's voice using a microphone and estimate the emotions using a voice analysis algorithm. Furthermore, the analysis unit can adjust the length of the analysis based on the user's emotions. For example, if the user is in a hurry, it may provide a short, concise analysis result. If the user is relaxed, it may provide a detailed analysis result. Furthermore, if the user is excited, it may provide a visually appealing analysis result. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis according to the user's emotions.

[0076] The analysis unit can determine the priority of analysis based on the data acquisition date during analysis. For example, the analysis unit determines the priority of analysis based on the data acquisition date. The data acquisition date includes, but is not limited to, the latest data or historical data. For example, the analysis unit prioritizes the analysis of the latest data. The analysis unit can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the priority of analysis according to the data acquisition date. For example, the analysis unit prioritizes the analysis of the latest data. The analysis unit can also postpone the analysis of older data. In this way, the analysis unit can prioritize the analysis of the latest data by determining the priority of analysis based on the data acquisition date.

[0077] The analysis unit can adjust the order of analysis based on the relationships between the data. For example, the analysis unit can adjust the order of analysis based on the relationships between the data. These relationships include, but are not limited to, correlation and causation. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relationships between the data. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relationships between the data.

[0078] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, the suggestion unit can use facial recognition technology to estimate the user's emotions. Facial recognition technology includes, but is not limited to, methods of analyzing the user's facial expressions using a camera. For example, the suggestion unit can capture the user's facial expressions using a camera and estimate their emotions using a facial recognition algorithm. The suggestion unit can also estimate the user's emotions using voice analysis technology. Voice analysis technology includes, but is not limited to, methods of analyzing the user's voice using a microphone. For example, the suggestion unit can record the user's voice using a microphone and estimate their emotions using a voice analysis algorithm. Furthermore, the suggestion unit can adjust the way it presents its suggestions based on the user's emotions. For example, if the user is relaxed, it can provide detailed suggestions. If the user is stressed, it can provide concise suggestions. Furthermore, if the user is excited, it can provide visually appealing suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way it presents its suggestions according to the user's emotions.

[0079] The proposal team can adjust the level of detail in their proposals based on the priority of the actions. For example, the proposal team adjusts the level of detail based on the priority of the actions. Action priority includes, but is not limited to, importance scores and urgency. For example, the proposal team can provide detailed proposals for high-importance actions, and concise proposals for low-importance actions. Furthermore, the proposal team can determine the priority of proposals according to the priority of the actions. For example, the proposal team can prioritize proposals for high-importance actions, and postpone proposals for low-importance actions. This allows the proposal team to make efficient proposals by adjusting the level of detail based on the priority of the actions.

[0080] The suggestion unit can apply different suggestion algorithms depending on the type of action when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the type of action. Action types include, but are not limited to, purchase actions and reservation actions. For example, the suggestion unit can apply a purchase suggestion algorithm to purchase actions. It can also apply a reservation suggestion algorithm to reservation actions. Furthermore, the suggestion unit can select the most suitable suggestion algorithm depending on the type of action. For example, the suggestion unit can apply a gift suggestion algorithm to gift selection. It can also apply a travel suggestion algorithm to travel planning. This improves the accuracy of suggestions by allowing the suggestion unit to apply the appropriate suggestion algorithm according to the type of action.

[0081] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, the suggestion unit can use facial recognition technology to estimate the user's emotions. Facial recognition technology includes, but is not limited to, methods of analyzing the user's facial expressions using a camera. For example, the suggestion unit can capture the user's facial expressions using a camera and estimate the emotions using a facial recognition algorithm. The suggestion unit can also estimate the user's emotions using voice analysis technology. Voice analysis technology includes, but is not limited to, methods of analyzing the user's voice using a microphone. For example, the suggestion unit can record the user's voice using a microphone and estimate the emotions using a voice analysis algorithm. Furthermore, the suggestion unit can adjust the length of the suggestion based on the user's emotions. For example, if the user is in a hurry, it can provide a short, to-the-point suggestion. If the user is relaxed, it can provide a more detailed suggestion. Furthermore, if the user is excited, it can provide a visually appealing suggestion. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the length of the suggestion according to the user's emotions.

[0082] The proposal department can determine the priority of proposals based on the timing of their implementation. For example, the proposal department can determine the priority of proposals based on the timing of their implementation. The timing of an action includes, but is not limited to, immediate or long-term plans. For example, the proposal department can prioritize proposing actions that need to be done in the immediate future. It can also postpone proposing actions that are far in the future. Furthermore, the proposal department can adjust the priority of proposals according to the timing of their implementation. For example, the proposal department can prioritize proposing actions that need to be done in the immediate future. It can also postpone proposing actions that are far in the future. This allows the proposal department to make efficient proposals by determining the priority of proposals based on the timing of their implementation.

[0083] The proposal team can adjust the order of proposals based on the relationships between actions. For example, the proposal team can adjust the order of proposals based on the relationships between actions. These relationships include, but are not limited to, correlation and causation. For example, the proposal team can prioritize proposing highly relevant actions. It can also postpone proposing less relevant actions. Furthermore, the proposal team can adjust the order of proposals according to the relationships between actions. For example, the proposal team can prioritize proposing highly relevant actions. It can also postpone proposing less relevant actions. This allows the proposal team to make efficient proposals by adjusting the order of proposals based on the relationships between actions.

[0084] The execution unit can estimate the user's emotions and adjust the execution method based on the estimated user emotions. For example, the execution unit may use facial recognition technology to estimate the user's emotions. Facial recognition technology includes, but is not limited to, methods of analyzing the user's facial expressions using a camera. For example, the execution unit may capture the user's facial expressions using a camera and estimate the emotions using a facial recognition algorithm. The execution unit may also estimate the user's emotions using voice analysis technology. Voice analysis technology includes, but is not limited to, methods of analyzing the user's voice using a microphone. For example, the execution unit may record the user's voice using a microphone and estimate the emotions using a voice analysis algorithm. Furthermore, the execution unit can adjust the execution method based on the user's emotions. For example, if the user is relaxed, it may provide and execute detailed instructions. If the user is stressed, it may provide and execute concise instructions. Furthermore, if the user is excited, it may provide and execute visually appealing instructions. This allows the execution unit to perform more appropriate actions by adjusting the execution method according to the user's emotions.

[0085] The execution unit can analyze the user's past action history at runtime to select the optimal execution method. For example, the execution unit can analyze the user's past action history. This action history includes, but is not limited to, past purchase history and past reservation history. For example, the execution unit can analyze past purchase history and select the optimal execution method. The execution unit can also analyze past reservation history and select the optimal execution method. Furthermore, the execution unit can adjust the execution procedure based on the user's past action history. For example, the execution unit will prioritize using execution methods that the user has successfully used in the past. The execution unit can also select the most efficient execution method from the user's past action history. In this way, the execution unit can select the optimal execution method by analyzing past action history.

[0086] The execution unit can customize the means of execution based on the user's living situation during execution. For example, the execution unit customizes the means of execution based on the user's living situation. Living situation includes, but is not limited to, the home environment and work situation. For example, the execution unit customizes the means of execution based on the user's home environment. The execution unit can also customize the means of execution based on the user's work situation. Furthermore, the execution unit can adjust the means of execution according to the user's living situation. For example, if the user is busy, it can provide and execute a concise procedure. If the user is relaxed, it can provide and execute a detailed procedure. In this way, the execution unit can perform more appropriate execution by customizing the means of execution according to the user's living situation.

[0087] The execution unit can estimate the user's emotions and determine the priority of actions based on the estimated emotions. For example, the execution unit may use facial recognition technology to estimate the user's emotions. Facial recognition technology includes, but is not limited to, methods of analyzing the user's facial expressions using a camera. For example, the execution unit may capture the user's facial expressions using a camera and estimate the emotions using a facial recognition algorithm. The execution unit can also estimate the user's emotions using voice analysis technology. Voice analysis technology includes, but is not limited to, methods of analyzing the user's voice using a microphone. For example, the execution unit may record the user's voice using a microphone and estimate the emotions using a voice analysis algorithm. Furthermore, the execution unit can determine the priority of actions based on the user's emotions. For example, if the user is stressed, it may postpone the execution of low-priority actions. Conversely, if the user is relaxed, it may prioritize the execution of high-priority actions. Furthermore, if the user is busy, it may execute only the most important actions. In this way, the execution unit can prioritize important actions by determining the priority of actions according to the user's emotions.

[0088] The execution unit can select an execution method based on the user's geographical location information at runtime. For example, the execution unit can select an execution method based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. For example, the execution unit can use GPS data to identify the user's current location and prioritize actions related to that area. Alternatively, the execution unit can use location services to identify the user's location and prioritize actions related to that area. Furthermore, if the user is traveling, the execution unit can prioritize actions related to the travel destination. For example, if the user is in a specific area, the execution unit will prioritize actions related to that area. Also, if the user is at home, the execution unit can prioritize actions around the user's home. In this way, the execution unit can select the optimal execution method by considering the user's geographical location information.

[0089] The execution unit can propose actions based on the user's social media activity at runtime. For example, the execution unit proposes actions based on the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. For example, the execution unit performs relevant actions based on information shared by the user on social media. The execution unit can also analyze the activities of the user's social media followers and friends and perform relevant actions. Furthermore, the execution unit can perform actions related to topics the user has shown interest in on social media. For example, the execution unit performs relevant actions based on information shared by the user on social media. The execution unit can also analyze the activities of the user's social media followers and friends and perform relevant actions. Furthermore, the execution unit can perform actions related to topics the user has shown interest in on social media. In this way, the execution unit can perform relevant actions by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented in, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal action based on the analysis results. The execution unit is implemented, for example, by the control unit 46A of the smart device 14 and executes the proposed action. === Hard Collateral 1-2 === Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and execution unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal action based on the analysis results. The execution unit is implemented, for example, by the control unit 46A of the smart glasses 214 and executes the proposed action. === Hard Collateral 1-3 === Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and execution unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal action based on the analysis results. The execution unit is implemented, for example, by the control unit 46A of the headset terminal 314 and executes the proposed action. === Hard Collateral 1-4 === Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and execution unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal action based on the analysis results. The execution unit is implemented, for example, by the control unit 46A of the robot 414 and executes the proposed action.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The system can also collect and analyze user health data. For example, the data collection unit can acquire heart rate and sleep data from the user's wearable device. The analysis unit can analyze the user's health status based on this data, and the suggestion unit can propose actions tailored to the user's health status. For example, if the user is feeling stressed, it can suggest activities to help them relax. It can also provide advice on how to get better sleep based on the user's sleep data. In this way, the system can support the user's health management.

[0092] The data collection unit can also collect data about the user's hobbies and interests. For example, it can obtain information about events the user has attended and magazines they subscribe to. The analysis unit analyzes the user's hobbies and interests based on this data, and the suggestion unit can suggest events and products that match the user's interests. For example, if the user is interested in music, it can provide information about concerts being held nearby. Also, if the user enjoys reading, it can suggest new books. In this way, the system can make personalized suggestions based on the user's hobbies and interests.

[0093] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on those emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is stressed, it can provide concise analysis results. Furthermore, if the user is excited, it can provide visually appealing analysis results. In this way, the analysis unit can provide more appropriate information by adjusting how the analysis results are displayed according to the user's emotions.

[0094] The suggestion department can predict future user behavior and make suggestions based on past user behavior data. For example, if a user plans a trip at the same time every year, it can suggest travel destinations that are suitable for that time. It can also provide information related to events if a user participates in a particular event every year. Furthermore, it can analyze user purchasing patterns and suggest products that are likely to be purchased next. This allows the suggestion department to predict future user behavior and make more appropriate suggestions.

[0095] The execution unit can estimate the user's emotions and adjust the timing of execution based on those emotions. For example, if the user is stressed, it may refrain from execution and resume it when the user is relaxed. Conversely, if the user is relaxed, it may actively execute the task and complete it. Furthermore, if the user is busy, it may minimize execution and perform more detailed execution later. In this way, the execution unit can perform more appropriate tasks by adjusting the timing of execution according to the user's emotions.

[0096] The suggestion department can analyze users' social media activity and make suggestions based on topics that users are interested in. For example, it can analyze topics that users frequently share on social media and suggest products and services related to those topics. It can also analyze the activity of users' followers and friends and make suggestions related to groups with common interests. Furthermore, it can provide information related to events that users have shown interest in on social media. In this way, the suggestion department can make personalized suggestions based on users' social media activity.

[0097] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on those estimates. For example, if the user is stressed, the frequency of data collection can be reduced and resumed when the user is relaxed. Conversely, if the user is relaxed, the frequency of data collection can be increased to obtain more detailed information. Furthermore, if the user is busy, data collection can be minimized and more detailed data can be collected later. In this way, the data collection unit can adjust the frequency of data collection according to the user's emotions, enabling more appropriate data collection.

[0098] The data collection unit can prioritize collecting data related to a specific region based on the user's geographical location information. For example, if the user is traveling, it will prioritize collecting data related to their travel destination. It can also prioritize collecting data related to a specific region if the user is in that region. Furthermore, if the user is at home, it can prioritize collecting data related to their home area. In this way, the data collection unit can prioritize collecting highly relevant data by considering the user's geographical location information.

[0099] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is stressed, it can postpone the analysis of less important data. Conversely, if the user is relaxed, it can prioritize the analysis of highly important data. Furthermore, if the user is busy, it can prioritize the analysis of only the most important data. In this way, the analysis unit can prioritize the analysis of important data by determining the priority of analysis according to the user's emotions.

[0100] The proposal department can analyze a user's past proposal history and select the most suitable proposal method. For example, it can analyze a user's past accepted proposal history and use a similar method. It can also analyze a user's past rejected proposal history and try a different method. Furthermore, it can adjust the frequency and timing of proposals based on the user's past proposal history. In this way, the proposal department can select a more effective proposal method by analyzing past proposal history.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The data collection unit collects user data. User data includes calendars, purchase history, and reservation history. For example, the data collection unit retrieves the user's calendar via API to understand their schedule. Purchase history is retrieved via the API of online shopping sites, and reservation history is retrieved via the API of reservation sites. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the user's current situation and personal preferences. The analysis is performed using machine learning algorithms, statistical analysis, and natural language processing techniques. For example, the analysis unit uses machine learning algorithms to cluster user purchasing patterns, statistical analysis to identify behavioral patterns, and natural language processing techniques to analyze text data and understand emotions and intentions. Step 3: The proposal department proposes the optimal action based on the analysis results obtained by the analysis department. The proposals include rule-based proposals and AI-based proposals. For example, the proposal department uses rule-based proposals to suggest the best product to the user and AI to suggest the best action to the user. Furthermore, the proposal department makes personalized proposals based on the user's past behavior data. Step 4: The execution unit carries out the actions proposed by the proposal unit. Execution includes actions via APIs and physical actions. For example, the execution unit purchases products on an e-commerce site via an API and completes tasks on behalf of the user by performing physical actions. Furthermore, the execution unit controls the user's device and performs specific actions.

[0103] 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.

[0104] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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. 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.

[0121] 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.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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. 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.

[0137] 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.

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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. 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.

[0154] 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.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user data, The analysis unit analyzes the data collected by the aforementioned collection unit to understand the user's current situation and personal preferences, A proposal unit proposes an action based on the analysis results obtained by the aforementioned analysis unit, An execution unit that executes the action proposed by the proposal unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Collect user calendar, purchase history, and reservation history data. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, the system analyzes the user's current situation and personal preferences. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, when a user chooses a gift, we suggest products based on the recipient's activity data. The system according to feature 1.

5. The execution unit is, I will take the suggested action and purchase the product on the e-commerce site. The system according to feature 1.

6. The aforementioned proposal section is, It provides users with travel planning, restaurant reservations, and suggestions on all aspects of their lifestyle. The system according to feature 1.

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

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

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