system

Abstract

The system according to this embodiment aims to provide optimal suggestions based on the user's needs and values. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects user profile information. The analysis unit analyzes the information collected by the collection unit. The proposal unit makes optimal suggestions based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, optimal proposals based on the needs and values of users have not been fully made, and there is room for improvement.

[0005] The system according to the embodiment aims to make an optimal proposal based on the needs and values of the user.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects user profile information. The analysis unit analyzes the information collected by the collection unit. The proposal unit makes an optimal proposal based on the analysis result obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can make optimal suggestions based on the user's needs and values. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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). [[ID=I6]]

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI concierge service according to an embodiment of the present invention is a system that makes optimal suggestions based on the user's needs and values. This system collects user profile information and real-time weather and traffic information. Next, the AI ​​analyzes the collected information and makes optimal suggestions based on the user's needs and values. Furthermore, the AI ​​understands the user's needs through conversation and makes appropriate suggestions. Machine learning technology is used to learn from the user's behavior data and improve the accuracy of the suggestions. By integrating multiple data sources and making comprehensive suggestions, the system makes the user's life more comfortable. For example, the AI ​​concierge service collects user profile information. For example, it collects information such as age, gender, hobbies, and past purchase history. Next, the AI ​​concierge service collects real-time weather and traffic information. For example, it collects information such as temperature, precipitation, wind speed, traffic congestion information, and public transportation operating status. Next, the AI ​​concierge service analyzes the collected information. For example, it analyzes using data mining, statistical analysis, and machine learning algorithms. Next, the AI ​​concierge service makes optimal suggestions based on the analysis results. For example, it makes suggestions based on the user's past behavior data and suggestions based on real-time conditions. Next, the AI ​​concierge service understands user needs through conversation and makes appropriate suggestions. For example, it uses voice dialogue, text chat, and natural language processing technology. Next, the AI ​​concierge service learns user behavior data using machine learning technology to improve the accuracy of its suggestions. For example, it uses technologies such as deep learning, support vector machines, and decision trees. Next, the AI ​​concierge service integrates multiple data sources to make comprehensive suggestions. For example, it integrates social media data, sensor data, and user databases. As a result, the AI ​​concierge service can provide plans to make the user's life more comfortable. This allows the AI ​​concierge service to make optimal suggestions based on the user's needs and values.

[0029] The AI ​​concierge service according to this embodiment comprises a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects user profile information. For example, the data collection unit collects information such as age, gender, hobbies, and past purchase history. The data collection unit can collect information, for example, through surveys. The data collection unit can also automatically collect information provided by the user. Furthermore, the data collection unit can also collect user behavior data. For example, the data collection unit can collect website browsing history and purchase history. The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit analyzes the information using data mining techniques. The analysis unit can also analyze the information using statistical analysis techniques. Furthermore, the analysis unit can analyze the information using machine learning algorithms. For example, the analysis unit can analyze the information using deep learning techniques. The suggestion unit makes optimal suggestions based on the analysis results obtained by the analysis unit. For example, the suggestion unit makes suggestions based on the user's past behavior data. The suggestion unit can also make suggestions based on real-time situations. Furthermore, the suggestion unit can also make suggestions based on the user's needs and values. For example, the suggestion function can make suggestions based on the user's hobbies and interests. This allows the AI ​​concierge service according to the embodiment to collect and analyze user profile information and make optimal suggestions.

[0030] The data collection unit collects user profile information. For example, it collects information such as age, gender, hobbies, and past purchase history. Specifically, it collects basic information entered by users when registering for the service, as well as detailed hobby and preference data obtained through surveys. The data collection unit can also automatically collect information provided by users. For example, it can automatically collect behavioral data when users browse websites, their online shopping purchase history, and their social media activity history. This allows for a detailed understanding of users' interests and behavioral patterns. Furthermore, the data collection unit can collect user behavioral data. For example, it can collect website browsing history and purchase history. This allows for an understanding of what products and services users are interested in and when they consider purchasing them. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit uses data mining techniques to analyze the information. Specifically, it extracts useful patterns and trends from the large amount of collected data to predict user behavior and preferences. The analysis unit can also analyze information using statistical analysis techniques. For example, it can identify common characteristics of specific groups based on users' age, gender, and hobbies / preferences, and make personalized suggestions based on these. Furthermore, the analysis unit can analyze information using machine learning algorithms. For example, it can use deep learning techniques to build advanced predictive models from user behavior data to predict future behavior. This allows the analysis unit to analyze collected data quickly and accurately, and to gain a deep understanding of user needs and values. Additionally, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and risk assessment. For example, it can predict purchasing trends during specific seasons or events based on past purchase history and make suggestions at the appropriate time. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Proposal Department makes optimal suggestions based on the analysis results obtained by the Analysis Department. For example, the Proposal Department makes suggestions based on the user's past behavior data. Specifically, it suggests highly relevant products and services based on the products and services the user has purchased in the past. The Proposal Department can also make suggestions based on real-time situations. For example, it suggests optimal products and services based on the content of the web page the user is currently viewing or their current location. Furthermore, the Proposal Department can make suggestions based on the user's needs and values. For example, it suggests events and activities that will interest the user based on their hobbies and interests. This allows the Proposal Department to provide personalized suggestions that meet the individual needs of each user. In addition, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can revise its suggestion algorithm and improve the suggestion content based on feedback from users who have received suggestions. Furthermore, the Proposal Department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the Proposal Department to provide users with quick and reliable optimal suggestions, thereby improving user satisfaction.

[0033] The proposal unit can understand user needs through conversations and make appropriate suggestions. For example, the proposal unit can understand user needs through voice dialogue. The proposal unit can also understand user needs through text chat. Furthermore, the proposal unit can understand user needs using natural language processing technology. For example, the proposal unit can analyze conversations with users and extract their needs. This allows the proposal unit to understand user needs through conversations and make appropriate suggestions. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input conversation data with users into a generating AI and have the generating AI perform the understanding of needs.

[0034] The analysis unit can learn user behavior data using machine learning techniques to improve the accuracy of its suggestions. For example, the analysis unit can learn user behavior data using deep learning techniques. The analysis unit can also learn user behavior data using support vector machines, for example. Furthermore, the analysis unit can learn user behavior data using decision trees. For example, the analysis unit can learn the user's website browsing history and purchase history to improve the accuracy of its suggestions. In this way, the analysis unit improves the accuracy of its suggestions by using machine learning techniques. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user behavior data into a generating AI and have the generating AI perform the task of improving the accuracy of its suggestions.

[0035] The data collection unit can collect real-time weather and traffic information. For example, it can collect weather information such as temperature, precipitation, and wind speed. The data collection unit can also collect traffic information such as congestion information and the operating status of public transportation. Furthermore, the data collection unit can collect information in real time. For example, it can collect information in seconds, minutes, and hours. This allows the data collection unit to make more accurate suggestions by collecting real-time weather and traffic information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather and traffic information into a generating AI and have the generating AI perform the information collection.

[0036] The proposal unit can integrate multiple data sources and make comprehensive proposals. For example, the proposal unit can integrate social media data. The proposal unit can also integrate sensor data. Furthermore, the proposal unit can integrate user databases. For example, the proposal unit can integrate multiple data sources and make comprehensive proposals. This enables the proposal unit to make comprehensive proposals by integrating multiple data sources. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input multiple data sources into a generating AI and have the generating AI perform the data integration.

[0037] The proposal unit can provide plans to make the user's life more comfortable. For example, the proposal unit can provide plans to improve the convenience of daily life. For example, the proposal unit can also provide plans to reduce stress. Furthermore, the proposal unit can also provide plans to save time. For example, the proposal unit can provide plans to make the user's life more comfortable. In this way, the proposal unit can provide plans to make the user's life more comfortable. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's lifestyle data into a generating AI and have the generating AI execute the provision of a comfortable plan.

[0038] The data collection unit can analyze the user's past profile information and select the optimal data collection method. For example, the data collection unit can select the most efficient data collection method based on information the user has provided in the past. For example, the data collection unit can also analyze the user's past behavior patterns and collect information at the appropriate time. Furthermore, the data collection unit can customize the data collection method based on the user's past feedback. In this way, the data collection unit can select the optimal data collection method by analyzing past profile information. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past profile information into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter profile information based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize collecting information related to areas of interest that the user is currently interested in. The data collection unit can also collect appropriate information according to the user's lifestyle (e.g., at work, on vacation). Furthermore, the data collection unit can filter relevant information based on the user's current activity (e.g., exercising, reading). This allows the data collection unit to collect more relevant information by filtering information based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting profile information. For example, the data collection unit can prioritize the collection of event information related to the user's current location. The data collection unit can also collect information on nearby restaurants and tourist attractions based on the user's location. Furthermore, the data collection unit can analyze the user's movement patterns and collect information on places they are likely to visit next. This allows the data collection unit to prioritize the collection of highly relevant information by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the information collection.

[0041] The data collection unit can analyze the user's social media activity and collect relevant information when collecting profile information. For example, the data collection unit can collect information based on the user's interests shared on social media. The data collection unit can also analyze the activity of the user's social media followers and friends and collect relevant information. Furthermore, the data collection unit can collect information based on events and groups the user participates in on social media. In this way, the data collection unit can collect relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the information collection.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the profile information during the analysis. For example, the analysis unit can perform a detailed analysis for information of high importance. For example, the analysis unit can perform a concise analysis for information of low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail for information of moderate importance. In this way, the analysis unit can perform a more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the profile information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the profile information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of profile information during analysis. For example, the analysis unit can apply a travel-specific analysis algorithm to travel-related information. For example, the analysis unit can also apply a meal-specific analysis algorithm to meal-related information. Furthermore, the analysis unit can apply an event-specific analysis algorithm to event-related information. This allows the analysis unit to perform more appropriate analysis by applying different analysis algorithms depending on the category of profile information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input profile information category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the submission timing of profile information during the analysis. For example, the analysis unit may prioritize the analysis of recently submitted information. For example, the analysis unit may postpone the analysis of older information. The analysis unit may also analyze information with a moderate level of submission timing with an appropriate priority. This allows the analysis unit to perform more appropriate analysis by determining the priority of analysis based on the submission timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input profile information submission timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the profile information during the analysis. For example, the analysis unit may prioritize the analysis of information with high relevance. For example, the analysis unit may postpone the analysis of information with low relevance. Furthermore, the analysis unit may analyze information with moderate relevance in an appropriate order. In this way, the analysis unit can perform more appropriate analysis by adjusting the order of analysis based on relevance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the profile information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The proposal unit can adjust the level of detail of its proposals based on the importance of the user's needs. For example, the proposal unit can provide detailed proposals for high-priority needs, and concise proposals for low-priority needs. It can also provide proposals with an appropriate level of detail for moderately important needs. By adjusting the level of detail of proposals based on the importance of the needs, the proposal unit can provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user need importance data into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0047] The suggestion unit can apply different suggestion algorithms depending on the user's values ​​when making suggestions. For example, the suggestion unit can make eco-friendly suggestions to users who prioritize environmental protection. For example, the suggestion unit can make healthy suggestions to users who prioritize health. Furthermore, the suggestion unit can make cultural suggestions to users who prioritize cultural experiences. In this way, the suggestion unit can make more appropriate suggestions by applying different suggestion algorithms according to values. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user value data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0048] The proposal department can prioritize proposals based on when the user's needs were submitted. For example, the proposal department might prioritize recently submitted needs. It might also postpone proposing older needs. Furthermore, it might propose needs with a moderate level of priority. This allows the proposal department to make more appropriate proposals by prioritizing proposals based on submission timing. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department could input user needs submission timing data into a generating AI and have the generating AI determine the priority of proposals.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of the user's values. For example, the suggestion unit may prioritize suggestions based on highly relevant values. It may also postpone suggestions based on less relevant values. Furthermore, it may present suggestions based on moderately relevant values ​​in an appropriate order. This allows the suggestion unit to make more appropriate suggestions by adjusting the order of suggestions based on the relevance of values. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user value data into a generating AI and have the generating AI adjust the order of suggestions.

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

[0051] The data collection unit can adjust the scope of data it collects, taking into account the user's geographical location. For example, if the user is in a specific city, it can prioritize collecting information on events and restaurants related to that city. If the user is traveling, it can also collect information on tourist attractions and transportation in their destination. If the user is at home, it can also collect information on nearby services and delivery. In this way, the data collection unit can collect more relevant information by taking geographical location into consideration. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI perform the adjustment of the data scope.

[0052] The analysis unit can apply different analysis methods depending on the category of profile information. For example, health-related information can be analyzed using health data analysis methods. Travel-related information can be analyzed using travel data analysis methods. Shopping-related information can also be analyzed using purchase data analysis methods. This allows the analysis unit to perform more accurate analysis by applying the appropriate analysis method according to the category of profile information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input profile information category data into a generating AI and have the generating AI perform the application of analysis methods.

[0053] The suggestion unit can analyze the user's past behavior data and select the optimal suggestion method. For example, it can make suggestions in a similar format based on the user's past preferred suggestion format. It can also avoid suggestion formats that the user has previously rejected. Furthermore, it can customize the suggestion method based on the user's past feedback. In this way, the suggestion unit can select a more appropriate suggestion method by analyzing past behavior data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past behavior data into a generating AI and have the generating AI select the suggestion method.

[0054] The data collection unit can analyze a user's social media activity and collect relevant information. For example, it can collect information based on the user's interests shared on social media. It can also analyze the activity of a user's followers and friends and collect relevant information. Furthermore, it can collect information based on events and groups the user participates in. In this way, the data collection unit can collect relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the information collection.

[0055] The suggestion unit can apply different suggestion algorithms depending on the user's values. For example, it can offer eco-friendly suggestions to users who prioritize environmental protection, healthy suggestions to users who prioritize health, and cultural suggestions to users who value cultural experiences. By applying different suggestion algorithms according to values, the suggestion unit can make more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user value data into a generating AI and have the generating AI apply the suggestion algorithm.

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

[0057] Step 1: The data collection unit collects user profile information. For example, the data collection unit collects information such as age, gender, hobbies, and past purchase history. The data collection unit can collect information through surveys. The data collection unit can also automatically collect information provided by the user. Furthermore, the data collection unit can also collect user behavior data. For example, the data collection unit can collect website browsing history and purchase history. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using data mining techniques, statistical analysis techniques, machine learning algorithms, and deep learning techniques. Step 3: The proposal department makes optimal suggestions based on the analysis results obtained by the analysis department. The proposal department can make suggestions based on the user's past behavior data, real-time situation, user needs and values, hobbies and interests.

[0058] (Example of form 2) An AI concierge service according to an embodiment of the present invention is a system that makes optimal suggestions based on the user's needs and values. This system collects user profile information and real-time weather and traffic information. Next, the AI ​​analyzes the collected information and makes optimal suggestions based on the user's needs and values. Furthermore, the AI ​​understands the user's needs through conversation and makes appropriate suggestions. Machine learning technology is used to learn from the user's behavior data and improve the accuracy of the suggestions. By integrating multiple data sources and making comprehensive suggestions, the system makes the user's life more comfortable. For example, the AI ​​concierge service collects user profile information. For example, it collects information such as age, gender, hobbies, and past purchase history. Next, the AI ​​concierge service collects real-time weather and traffic information. For example, it collects information such as temperature, precipitation, wind speed, traffic congestion information, and public transportation operating status. Next, the AI ​​concierge service analyzes the collected information. For example, it analyzes using data mining, statistical analysis, and machine learning algorithms. Next, the AI ​​concierge service makes optimal suggestions based on the analysis results. For example, it makes suggestions based on the user's past behavior data and suggestions based on real-time conditions. Next, the AI ​​concierge service understands user needs through conversation and makes appropriate suggestions. For example, it uses voice dialogue, text chat, and natural language processing technology. Next, the AI ​​concierge service learns user behavior data using machine learning technology to improve the accuracy of its suggestions. For example, it uses technologies such as deep learning, support vector machines, and decision trees. Next, the AI ​​concierge service integrates multiple data sources to make comprehensive suggestions. For example, it integrates social media data, sensor data, and user databases. As a result, the AI ​​concierge service can provide plans to make the user's life more comfortable. This allows the AI ​​concierge service to make optimal suggestions based on the user's needs and values.

[0059] The AI ​​concierge service according to this embodiment comprises a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects user profile information. For example, the data collection unit collects information such as age, gender, hobbies, and past purchase history. The data collection unit can collect information, for example, through surveys. The data collection unit can also automatically collect information provided by the user. Furthermore, the data collection unit can also collect user behavior data. For example, the data collection unit can collect website browsing history and purchase history. The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit analyzes the information using data mining techniques. The analysis unit can also analyze the information using statistical analysis techniques. Furthermore, the analysis unit can analyze the information using machine learning algorithms. For example, the analysis unit can analyze the information using deep learning techniques. The suggestion unit makes optimal suggestions based on the analysis results obtained by the analysis unit. For example, the suggestion unit makes suggestions based on the user's past behavior data. The suggestion unit can also make suggestions based on real-time situations. Furthermore, the suggestion unit can also make suggestions based on the user's needs and values. For example, the suggestion function can make suggestions based on the user's hobbies and interests. This allows the AI ​​concierge service according to the embodiment to collect and analyze user profile information and make optimal suggestions.

[0060] The data collection unit collects user profile information. For example, it collects information such as age, gender, hobbies, and past purchase history. Specifically, it collects basic information entered by users when registering for the service, as well as detailed hobby and preference data obtained through surveys. The data collection unit can also automatically collect information provided by users. For example, it can automatically collect behavioral data when users browse websites, their online shopping purchase history, and their social media activity history. This allows for a detailed understanding of users' interests and behavioral patterns. Furthermore, the data collection unit can collect user behavioral data. For example, it can collect website browsing history and purchase history. This allows for an understanding of what products and services users are interested in and when they consider purchasing them. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0061] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit uses data mining techniques to analyze the information. Specifically, it extracts useful patterns and trends from the large amount of collected data to predict user behavior and preferences. The analysis unit can also analyze information using statistical analysis techniques. For example, it can identify common characteristics of specific groups based on users' age, gender, and hobbies / preferences, and make personalized suggestions based on these. Furthermore, the analysis unit can analyze information using machine learning algorithms. For example, it can use deep learning techniques to build advanced predictive models from user behavior data to predict future behavior. This allows the analysis unit to analyze collected data quickly and accurately, and to gain a deep understanding of user needs and values. Additionally, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and risk assessment. For example, it can predict purchasing trends during specific seasons or events based on past purchase history and make suggestions at the appropriate time. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0062] The Proposal Department makes optimal suggestions based on the analysis results obtained by the Analysis Department. For example, the Proposal Department makes suggestions based on the user's past behavior data. Specifically, it suggests highly relevant products and services based on the products and services the user has purchased in the past. The Proposal Department can also make suggestions based on real-time situations. For example, it suggests optimal products and services based on the content of the web page the user is currently viewing or their current location. Furthermore, the Proposal Department can make suggestions based on the user's needs and values. For example, it suggests events and activities that will interest the user based on their hobbies and interests. This allows the Proposal Department to provide personalized suggestions that meet the individual needs of each user. In addition, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can revise its suggestion algorithm and improve the suggestion content based on feedback from users who have received suggestions. Furthermore, the Proposal Department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the Proposal Department to provide users with quick and reliable optimal suggestions, thereby improving user satisfaction.

[0063] The proposal unit can understand user needs through conversations and make appropriate suggestions. For example, the proposal unit can understand user needs through voice dialogue. The proposal unit can also understand user needs through text chat. Furthermore, the proposal unit can understand user needs using natural language processing technology. For example, the proposal unit can analyze conversations with users and extract their needs. This allows the proposal unit to understand user needs through conversations and make appropriate suggestions. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input conversation data with users into a generating AI and have the generating AI perform the understanding of needs.

[0064] The analysis unit can learn user behavior data using machine learning techniques to improve the accuracy of its suggestions. For example, the analysis unit can learn user behavior data using deep learning techniques. The analysis unit can also learn user behavior data using support vector machines, for example. Furthermore, the analysis unit can learn user behavior data using decision trees. For example, the analysis unit can learn the user's website browsing history and purchase history to improve the accuracy of its suggestions. In this way, the analysis unit improves the accuracy of its suggestions by using machine learning techniques. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user behavior data into a generating AI and have the generating AI perform the task of improving the accuracy of its suggestions.

[0065] The data collection unit can collect real-time weather and traffic information. For example, it can collect weather information such as temperature, precipitation, and wind speed. The data collection unit can also collect traffic information such as congestion information and the operating status of public transportation. Furthermore, the data collection unit can collect information in real time. For example, it can collect information in seconds, minutes, and hours. This allows the data collection unit to make more accurate suggestions by collecting real-time weather and traffic information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather and traffic information into a generating AI and have the generating AI perform the information collection.

[0066] The proposal unit can integrate multiple data sources and make comprehensive proposals. For example, the proposal unit can integrate social media data. The proposal unit can also integrate sensor data. Furthermore, the proposal unit can integrate user databases. For example, the proposal unit can integrate multiple data sources and make comprehensive proposals. This enables the proposal unit to make comprehensive proposals by integrating multiple data sources. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input multiple data sources into a generating AI and have the generating AI perform the data integration.

[0067] The proposal unit can provide plans to make the user's life more comfortable. For example, the proposal unit can provide plans to improve the convenience of daily life. For example, the proposal unit can also provide plans to reduce stress. Furthermore, the proposal unit can also provide plans to save time. For example, the proposal unit can provide plans to make the user's life more comfortable. In this way, the proposal unit can provide plans to make the user's life more comfortable. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's lifestyle data into a generating AI and have the generating AI execute the provision of a comfortable plan.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of profile information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect information when the user is relaxed. If the user is relaxed, the data collection unit can also collect profile information immediately and make quick suggestions. If the user is in a hurry, the data collection unit can shorten the collection timing and quickly collect only the minimum necessary information. In this way, the data collection unit can collect information at a more appropriate time by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0069] The data collection unit can analyze the user's past profile information and select the optimal data collection method. For example, the data collection unit can select the most efficient data collection method based on information the user has provided in the past. For example, the data collection unit can also analyze the user's past behavior patterns and collect information at the appropriate time. Furthermore, the data collection unit can customize the data collection method based on the user's past feedback. In this way, the data collection unit can select the optimal data collection method by analyzing past profile information. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past profile information into a generating AI and have the generating AI select the optimal data collection method.

[0070] The data collection unit can filter profile information based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize collecting information related to areas of interest that the user is currently interested in. The data collection unit can also collect appropriate information according to the user's lifestyle (e.g., at work, on vacation). Furthermore, the data collection unit can filter relevant information based on the user's current activity (e.g., exercising, reading). This allows the data collection unit to collect more relevant information by filtering information based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0071] The data collection unit can estimate the user's emotions and determine the priority of profile information to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit may prioritize collecting information related to relaxation. For example, if the user is excited, the data collection unit may prioritize collecting information related to entertainment. Also, if the user is tired, the data collection unit may prioritize collecting information related to rest. In this way, the data collection unit can collect more appropriate information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0072] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting profile information. For example, the data collection unit can prioritize the collection of event information related to the user's current location. The data collection unit can also collect information on nearby restaurants and tourist attractions based on the user's location. Furthermore, the data collection unit can analyze the user's movement patterns and collect information on places they are likely to visit next. This allows the data collection unit to prioritize the collection of highly relevant information by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the information collection.

[0073] The data collection unit can analyze the user's social media activity and collect relevant information when collecting profile information. For example, the data collection unit can collect information based on the user's interests shared on social media. The data collection unit can also analyze the activity of the user's social media followers and friends and collect relevant information. Furthermore, the data collection unit can collect information based on events and groups the user participates in on social media. In this way, the data collection unit can collect relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the information collection.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the profile information during the analysis. For example, the analysis unit can perform a detailed analysis for information of high importance. For example, the analysis unit can perform a concise analysis for information of low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail for information of moderate importance. In this way, the analysis unit can perform a more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the profile information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the profile information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the category of profile information during analysis. For example, the analysis unit can apply a travel-specific analysis algorithm to travel-related information. For example, the analysis unit can also apply a meal-specific analysis algorithm to meal-related information. Furthermore, the analysis unit can apply an event-specific analysis algorithm to event-related information. This allows the analysis unit to perform more appropriate analysis by applying different analysis algorithms depending on the category of profile information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input profile information category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. If the user is excited, the analysis unit can also perform a visually engaging analysis. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0078] The analysis unit can determine the priority of analysis based on the submission timing of profile information during the analysis. For example, the analysis unit may prioritize the analysis of recently submitted information. For example, the analysis unit may postpone the analysis of older information. The analysis unit may also analyze information with a moderate level of submission timing with an appropriate priority. This allows the analysis unit to perform more appropriate analysis by determining the priority of analysis based on the submission timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input profile information submission timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the profile information during the analysis. For example, the analysis unit may prioritize the analysis of information with high relevance. For example, the analysis unit may postpone the analysis of information with low relevance. Furthermore, the analysis unit may analyze information with moderate relevance in an appropriate order. In this way, the analysis unit can perform more appropriate analysis by adjusting the order of analysis based on relevance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the profile information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0080] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. If the user is excited, the suggestion unit can also provide visually appealing suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way it presents its suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way it presents its suggestions.

[0081] The proposal unit can adjust the level of detail of its proposals based on the importance of the user's needs. For example, the proposal unit can provide detailed proposals for high-priority needs, and concise proposals for low-priority needs. It can also provide proposals with an appropriate level of detail for moderately important needs. By adjusting the level of detail of proposals based on the importance of the needs, the proposal unit can provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user need importance data into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0082] The suggestion unit can apply different suggestion algorithms depending on the user's values ​​when making suggestions. For example, the suggestion unit can make eco-friendly suggestions to users who prioritize environmental protection. For example, the suggestion unit can make healthy suggestions to users who prioritize health. Furthermore, the suggestion unit can make cultural suggestions to users who prioritize cultural experiences. In this way, the suggestion unit can make more appropriate suggestions by applying different suggestion algorithms according to values. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user value data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0083] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will make short, concise suggestions. If the user is relaxed, the suggestion unit may make detailed suggestions. If the user is excited, the suggestion unit may make visually appealing suggestions. This allows the suggestion unit to make more appropriate suggestions by adjusting the length of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0084] The proposal department can prioritize proposals based on when the user's needs were submitted. For example, the proposal department might prioritize recently submitted needs. It might also postpone proposing older needs. Furthermore, it might propose needs with a moderate level of priority. This allows the proposal department to make more appropriate proposals by prioritizing proposals based on submission timing. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department could input user needs submission timing data into a generating AI and have the generating AI determine the priority of proposals.

[0085] The suggestion unit can adjust the order of suggestions based on the relevance of the user's values. For example, the suggestion unit may prioritize suggestions based on highly relevant values. It may also postpone suggestions based on less relevant values. Furthermore, it may present suggestions based on moderately relevant values ​​in an appropriate order. This allows the suggestion unit to make more appropriate suggestions by adjusting the order of suggestions based on the relevance of values. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user value data into a generating AI and have the generating AI adjust the order of suggestions.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is stressed, it can suggest relaxing activities or methods of relaxation. If the user is excited, it can also suggest entertainment or active activities. If the user is sad, it can provide positive content or support to lift their spirits. This allows the suggestion unit to make more appropriate suggestions by adjusting the content of its suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the content of its suggestions.

[0088] The data collection unit can estimate the user's emotions and adjust the types of data collected based on the estimated emotions. For example, if the user is relaxed, it can prioritize collecting data related to hobbies and entertainment. If the user is stressed, it can also collect data related to stress reduction. If the user is excited, it can also collect data related to activities and events. This allows the data collection unit to collect more appropriate data by adjusting the types of data collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the types of data.

[0089] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the user is relaxed, it can select a time to perform a detailed analysis. If the user is in a hurry, it can perform a concise analysis quickly. If the user is excited, it can select a time to provide visually appealing analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the timing of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the timing of the analysis.

[0090] The suggestion unit can estimate the user's emotions and adjust the format of the suggestions based on those emotions. For example, if the user is relaxed, it can provide detailed, text-based suggestions. If the user is in a hurry, it can provide concise, bullet-point suggestions. If the user is excited, it can provide visually appealing, graphic suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the format based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the format of the suggestions.

[0091] The analysis unit can estimate the user's emotions and adjust the visualization method of the analysis based on the estimated user emotions. For example, if the user is relaxed, it can use detailed graphs and charts for visualization. If the user is in a hurry, it can use simple icons and symbols for visualization. If the user is excited, it can use animations and interactive visualizations. In this way, the analysis unit can provide more appropriate analysis results by adjusting the visualization method of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the visualization method of the analysis.

[0092] The data collection unit can adjust the scope of data it collects, taking into account the user's geographical location. For example, if the user is in a specific city, it can prioritize collecting information on events and restaurants related to that city. If the user is traveling, it can also collect information on tourist attractions and transportation in their destination. If the user is at home, it can also collect information on nearby services and delivery. In this way, the data collection unit can collect more relevant information by taking geographical location into consideration. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI perform the adjustment of the data scope.

[0093] The analysis unit can apply different analysis methods depending on the category of profile information. For example, health-related information can be analyzed using health data analysis methods. Travel-related information can be analyzed using travel data analysis methods. Shopping-related information can also be analyzed using purchase data analysis methods. This allows the analysis unit to perform more accurate analysis by applying the appropriate analysis method according to the category of profile information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input profile information category data into a generating AI and have the generating AI perform the application of analysis methods.

[0094] The suggestion unit can analyze the user's past behavior data and select the optimal suggestion method. For example, it can make suggestions in a similar format based on the user's past preferred suggestion format. It can also avoid suggestion formats that the user has previously rejected. Furthermore, it can customize the suggestion method based on the user's past feedback. In this way, the suggestion unit can select a more appropriate suggestion method by analyzing past behavior data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past behavior data into a generating AI and have the generating AI select the suggestion method.

[0095] The data collection unit can analyze a user's social media activity and collect relevant information. For example, it can collect information based on the user's interests shared on social media. It can also analyze the activity of a user's followers and friends and collect relevant information. Furthermore, it can collect information based on events and groups the user participates in. In this way, the data collection unit can collect relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the information collection.

[0096] The suggestion unit can apply different suggestion algorithms depending on the user's values. For example, it can offer eco-friendly suggestions to users who prioritize environmental protection, healthy suggestions to users who prioritize health, and cultural suggestions to users who value cultural experiences. By applying different suggestion algorithms according to values, the suggestion unit can make more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user value data into a generating AI and have the generating AI apply the suggestion algorithm.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects user profile information. For example, the data collection unit collects information such as age, gender, hobbies, and past purchase history. The data collection unit can collect information through surveys. The data collection unit can also automatically collect information provided by the user. Furthermore, the data collection unit can also collect user behavior data. For example, the data collection unit can collect website browsing history and purchase history. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information using data mining techniques, statistical analysis techniques, machine learning algorithms, and deep learning techniques. Step 3: The proposal department makes optimal suggestions based on the analysis results obtained by the analysis department. The proposal department can make suggestions based on the user's past behavior data, real-time situation, user needs and values, hobbies and interests.

[0099] 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.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] 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.

[0102] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user profile information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes optimal suggestions based on the analysis results. The proposal unit may be implemented in the control unit 46A of the smart device 14, for example. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] 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.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user profile information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes optimal suggestions based on the analysis results. The proposal unit may be implemented in the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user profile information using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes optimal suggestions based on the analysis results. The proposal unit may be implemented in the control unit 46A of the headset terminal 314, for example. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user profile information using the camera 42 and microphone 238 of the robot 414 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and makes optimal suggestions based on the analysis results. The proposal unit may be implemented in, for example, the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A collection unit that collects user profile information, An analysis unit analyzes the information collected by the aforementioned collection unit, The system includes a proposal unit that makes optimal suggestions based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We understand user needs through conversations and make appropriate suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We use machine learning techniques to learn from user behavior data and improve the accuracy of our suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect real-time weather and traffic information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Integrate multiple data sources to provide comprehensive recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We provide plans to make users' lives more comfortable. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of profile information collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past profile information and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting profile information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of profile information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting profile information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting profile information, we analyze the user's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the profile information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of profile information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the profile information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the profile information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the user's needs. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's values. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the user's needs were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the user's values. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user profile information, An analysis unit analyzes the information collected by the aforementioned collection unit, The system includes a proposal unit that makes optimal suggestions based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned proposal section is, We understand user needs through conversations and make appropriate suggestions. The system according to feature 1.

3. The aforementioned analysis unit, We use machine learning techniques to learn from user behavior data and improve the accuracy of our suggestions. The system according to feature 1.

4. The aforementioned collection unit is Collect real-time weather and traffic information. The system according to feature 1.

5. The aforementioned proposal section is, Integrate multiple data sources to provide comprehensive recommendations. The system according to feature 1.

6. The aforementioned proposal section is, We provide plans to make users' lives more comfortable. The system according to feature 1.

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

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

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

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

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