system

Abstract

The system according to this embodiment aims to respond quickly and appropriately to user questions and requests. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, a provision unit, and a learning unit. The reception unit receives questions and requests from the user. The analysis unit analyzes the questions and requests received by the reception unit. The generation unit generates answers based on the results analyzed by the analysis unit. The provision unit provides the answers generated by the generation unit. The learning unit learns the user's behavior history.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to respond quickly and appropriately to questions and requests from users.

[0005] The system according to the embodiment aims to respond quickly and appropriately to questions and requests from users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a provision unit, and a learning unit. The reception unit receives questions and requests from users. The analysis unit analyzes the questions and requests received by the reception unit. The generation unit generates answers based on the results analyzed by the analysis unit. The provision unit provides the answers generated by the generation unit. The learning unit learns the user's behavior history. [Effects of the Invention]

[0007] The system according to this embodiment can respond quickly and appropriately to questions and requests from users. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The personal concierge system according to an embodiment of the present invention is a personal concierge service equipped with generative AI integrated within a messaging application. This personal concierge system operates when a user activates the personal concierge service within the messaging application and inputs questions or requests in natural language. For example, a user might input questions such as "Tell me some restaurants nearby" or "What's the weather like tomorrow?". These questions are input to the generative AI, which analyzes the input questions and generates appropriate answers. For example, in response to the question "Tell me some restaurants nearby," the generative AI searches for nearby restaurants based on the user's current location and provides that information. Similarly, in response to the question "What's the weather like tomorrow?", the generative AI analyzes tomorrow's weather based on weather forecast data and provides that information. Furthermore, the generative AI learns the user's past questions and activity history to provide personalized services. For example, based on information about restaurants the user has visited in the past, it suggests restaurants that suit the user's preferences. Also, based on weather information the user has searched for in the past, it prioritizes providing weather information for areas the user is interested in. This mechanism allows users to easily obtain information within the messaging application. For example, if a user types "Tell me about nearby cafes" in a messaging app, the generating AI will search for nearby cafes based on the user's current location and provide that information. Similarly, if a user types "What's the weather like tomorrow?", the generating AI will analyze weather forecast data to predict tomorrow's weather and provide that information. Furthermore, the generating AI learns the user's behavioral history and provides personalized services. For instance, it might suggest cafes that match the user's preferences based on information about cafes the user has visited in the past. It might also prioritize providing weather information for areas the user is interested in, based on weather information the user has previously searched for. In this way, a personal concierge service equipped with generating AI improves user convenience by responding quickly and accurately to user questions and requests and providing personalized services. This enables the personal concierge system to respond quickly and accurately to user questions and requests and provide personalized services.

[0029] The personal concierge system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a provision unit, and a learning unit. The reception unit receives questions and requests from users. The reception unit can, for example, receive questions and requests entered by users in natural language. For example, users can enter questions such as "Tell me about nearby restaurants" or "What's the weather like tomorrow?". The analysis unit analyzes the questions and requests received by the reception unit using a generation AI. The analysis unit can, for example, analyze questions and requests using natural language processing technology. For example, the generation AI analyzes the entered question and generates an appropriate answer. The generation unit generates an answer based on the results analyzed by the analysis unit using the generation AI. For example, the generation unit can use the generation AI to search for nearby restaurants based on the user's current location and provide that information. The generation unit can also use the generation AI to analyze tomorrow's weather based on weather forecast data and provide that information. The provision unit provides the answers generated by the generation unit to the user. For example, the provision unit can display the generated answers to the user. For example, if a user enters "Tell me about nearby cafes," the service provider's generating AI searches for nearby cafes based on the user's current location and provides that information. The learning unit learns the user's behavioral history and provides personalized services. For example, the learning unit can suggest restaurants that suit the user's preferences based on information about restaurants the user has visited in the past. The learning unit can also prioritize providing weather information for areas of interest to the user based on weather information the user has searched for in the past. As a result, the personal concierge system according to this embodiment can respond quickly and accurately to user questions and requests and provide personalized services.

[0030] The reception unit receives questions and requests from users. For example, the reception unit can receive questions and requests entered by users in natural language. Specifically, it provides an interface that accepts text and voice input from users via smartphones or computers. For example, users can input questions such as "Tell me about nearby restaurants" or "What's the weather like tomorrow?" This allows the reception unit to provide flexible input methods to meet diverse user needs. Furthermore, the reception unit can convert voice input into text using speech recognition technology. This allows users to ask questions and make requests by voice, enabling more intuitive operation. After receiving user input, the reception unit sends it to the analysis unit. The reception unit can also perform pre-processing to appropriately classify user input and enable the analysis unit to process it efficiently. For example, it can classify user input by topic, allowing the analysis unit to quickly analyze questions and requests related to specific topics. This enables the reception unit to efficiently process user input and improve the overall system response speed.

[0031] The analysis unit uses generative AI to analyze questions and requests received by the reception unit. The analysis unit can analyze questions and requests using, for example, natural language processing technology. Specifically, the generative AI analyzes the input question and generates an appropriate answer. The generative AI performs contextual and semantic analysis to understand the intent of the question. For example, in response to the question "Tell me about nearby restaurants," the generative AI understands that "nearby" refers to the user's current location and "restaurants" refers to eateries. Furthermore, after understanding the intent of the question, the generative AI retrieves the necessary information from appropriate databases and information sources. For example, it uses a Geographic Information System (GIS) to obtain restaurant information around the user's current location. In addition, the generative AI can generate more personalized answers by considering the user's past behavior history and preferences. This allows the analysis unit to respond quickly and accurately to user questions and requests. Furthermore, the analysis unit uses the generative AI's learning algorithm to learn user input patterns and question trends, enabling more accurate analysis of future questions and requests. This allows the analysis unit to improve the overall response accuracy and user satisfaction of the system.

[0032] The generation unit uses a generation AI to generate answers based on the results analyzed by the analysis unit. For example, the generation unit can use the generation AI to search for nearby restaurants based on the user's current location and provide that information. Specifically, the generation AI obtains the user's current location information and searches a database for restaurant information in the surrounding area. The search results include detailed information such as the restaurant's name, address, business hours, and rating. The generation unit can also use the generation AI to analyze tomorrow's weather based on weather forecast data and provide that information. The generation AI obtains the latest weather forecast information from a meteorological database and analyzes the weather in the user's area of ​​interest. For example, if the user asks, "What's the weather like in Tokyo tomorrow?", the generation AI obtains Tokyo's weather forecast data and provides detailed information such as tomorrow's weather, temperature, and probability of precipitation. Furthermore, the generation unit can generate more personalized answers by considering the user's past behavior history and preferences. For example, it can suggest restaurants that suit the user's preferences based on information about restaurants the user has visited in the past. The generation unit can also collect user feedback and continuously improve the accuracy and quality of answers using the generation AI's learning algorithm. This allows the generation unit to provide highly accurate and personalized answers to user questions and requests.

[0033] The providing unit delivers the answers generated by the generating unit to the user. For example, the providing unit can display the generated answers to the user. Specifically, if the user enters "Tell me about nearby cafes," the providing unit's generating AI will search for nearby cafes based on the user's current location and provide that information. The providing unit can display the answers on the user's device in various formats, such as text, images, and maps. For example, it can display detailed information such as the cafe's name, address, business hours, and rating on a smartphone screen. The providing unit can also use speech synthesis technology to provide the generated answers to the user in voice. This allows the user to receive information not only visually but also audibly. Furthermore, the providing unit can improve the overall accuracy and quality of the system by collecting user feedback and providing it to the generating and analysis units. For example, if the user enters ratings and comments on the provided information, the providing unit collects that feedback and incorporates it into the generating AI's learning algorithm. This allows the providing unit to provide users with quick and accurate information, thereby improving user satisfaction.

[0034] The learning unit learns from the user's behavior history and provides personalized services. For example, the learning unit can suggest restaurants that suit the user's preferences based on information about restaurants the user has visited in the past. Specifically, the learning unit collects and analyzes the user's behavior history data to understand the user's preferences and tendencies. For example, it can identify the type of cuisine and price range the user prefers based on data such as the type of restaurant the user has visited in the past, their ratings, and the frequency of visits. The learning unit can also prioritize providing weather information for areas of interest to the user based on the weather information the user has searched for in the past. For example, if the user has frequently searched for the weather in a particular area in the past, the weather information for that area will be displayed preferentially. Furthermore, the learning unit can collect user feedback and use learning algorithms to continuously improve the accuracy and quality of the service. For example, when users input ratings and comments on the information provided, the learning unit collects that feedback and reflects it in the learning algorithm of the generating AI. This allows the learning unit to provide personalized services that match the user's preferences and needs, thereby improving user satisfaction.

[0035] The reception desk can accept questions and requests entered by users in natural language. For example, the reception desk can accept questions and requests entered by users in natural language. For example, a user can enter questions such as "Tell me about nearby restaurants" or "What's the weather like tomorrow?" This improves user convenience by accepting questions and requests entered by users in natural language. Natural language processing techniques include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. Some or all of the processing described above in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input questions and requests entered by users in natural language into a generating AI and have the generating AI perform analysis of the questions and requests.

[0036] The analysis unit can analyze questions and requests received by the reception unit using a generation AI. For example, the analysis unit analyzes the questions and requests input by the generation AI. For example, the generation AI can analyze questions and requests using natural language processing technology. This improves the accuracy of question and request analysis by using a generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input questions and requests received by the reception unit into the generation AI and have the generation AI perform the analysis of the questions and requests.

[0037] The generation unit can generate answers based on the results analyzed by the analysis unit using a generation AI. For example, the generation unit can generate answers based on the results analyzed by the generation AI. For example, the generation AI can search for nearby restaurants based on the current location and provide that information. The generation AI can also analyze tomorrow's weather based on weather forecast data and provide that information. As a result, the accuracy of answer generation is improved by using the generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the results analyzed by the analysis unit into the generation AI and have the generation AI perform the generation of answers.

[0038] The providing unit can provide the user with the answers generated by the generating unit. For example, the providing unit can display the generated answers to the user. For example, if the user enters "Tell me about nearby cafes," the providing unit's generating AI will search for nearby cafes based on the user's current location and provide that information. This improves user convenience by providing the user with the generated answers. Specific methods of provision include, but are not limited to, real-time provision and batch provision. Some or all of the processing described above in the providing unit may be performed using, for example, AI, or not using AI. For example, the providing unit can input the answers generated by the generating unit into the generating AI and have the generating AI provide the answers.

[0039] The learning unit can learn from the user's past questions and behavioral history to provide personalized services. For example, the learning unit can suggest restaurants that suit the user's preferences based on information about restaurants the user has visited in the past. It can also prioritize providing weather information for areas of interest to the user based on weather information the user has searched for in the past. In this way, personalized services can be provided by learning from the user's past questions and behavioral history. Personalized services include, but are not limited to, recommendation systems and customized answers. Some or all of the processing described above in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavioral history into a generating AI and have the generating AI perform the provision of personalized services.

[0040] The reception desk can analyze the user's past question history and select the optimal reception method. For example, the reception desk can automatically display as suggestions questions and requests that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions and requests that the user will use at specific times of day based on the user's past question history. In this way, the optimal reception method can be selected by analyzing the past question history. The optimal reception method includes, but is not limited to, the user's past behavior history and current situation. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past question history into a generating AI and have the generating AI select the optimal reception method.

[0041] The reception desk can filter questions and requests based on the user's current situation and areas of interest. For example, if the user is currently located, the reception desk will prioritize questions and requests related to that location. Furthermore, if the user has specific areas of interest, the reception desk can prioritize questions and requests related to those areas. Additionally, if the user tends to engage in specific activities during certain time periods, the reception desk can prioritize questions and requests related to those time periods. This allows for more appropriate questions and requests to be received by filtering based on the user's current situation and areas of interest. Filtering includes, but is not limited to, the user's areas of interest and current situation. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0042] The reception desk can prioritize receiving questions and requests that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving questions and requests related to that region. Furthermore, if the user is traveling, the reception desk can prioritize receiving questions and requests related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize receiving questions and requests related to their home area. This allows for the prioritization of highly relevant questions and requests by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI select highly relevant questions and requests.

[0043] The reception desk can analyze a user's social media activity when receiving questions or requests and accept relevant questions or requests. For example, if a user frequently posts on a particular topic on social media, the reception desk can prioritize questions or requests related to that topic. Similarly, if a user participates in a particular event on social media, the reception desk can prioritize questions or requests related to that event. Furthermore, if a user checks in to a particular location on social media, the reception desk can prioritize questions or requests related to that location. This allows the reception desk to prioritize relevant questions and requests by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the processing described above in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's social media activity into a generating AI and have the generating AI select relevant questions and requests.

[0044] The analysis unit can adjust the level of detail of its analysis based on the importance of the questions and requests. For example, the analysis unit can perform a detailed analysis for high-importance questions and requests. It can also perform a concise analysis for low-importance questions and requests. Furthermore, it can perform an analysis with an appropriate level of detail for questions and requests of moderate importance. By adjusting the level of detail of the analysis based on the importance of the questions and requests, more appropriate analysis results can be provided. Importance includes, but is not limited to, user urgency and business importance. 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 of the questions and requests into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the question or request during analysis. For example, for restaurant searches, the analysis unit can apply an analysis algorithm based on location information and reviews. For weather forecasts, the analysis unit can also apply an analysis algorithm based on meteorological data. Furthermore, for traffic information, the analysis unit can apply an analysis algorithm based on real-time traffic data. This allows for more appropriate analysis results by applying different analysis algorithms depending on the category of the question or request. Categories include, but are not limited to, technical categories and business categories. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the question or request into a generating AI and have the generating AI perform the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on when questions and requests were submitted. For example, the analysis unit may prioritize the analysis of recently submitted questions and requests. It can also postpone the analysis of older questions and requests. Furthermore, if submissions are concentrated in a specific time period, the analysis unit may prioritize the analysis of questions and requests submitted during that time period. By determining the priority of analysis based on the submission date of questions and requests, more appropriate analysis results can be provided. The submission date includes, but is not limited to, the submission date and time, and the frequency of submission. 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 submission dates of questions and requests into a generating AI and have the generating AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of questions and requests during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant questions and requests. It can also postpone the analysis of less relevant questions and requests. Furthermore, it can analyze questions and requests of moderate relevance in an appropriate order. By adjusting the order of analysis based on the relevance of questions and requests, it is possible to provide more appropriate analysis results. Relevance includes, but is not limited to, similarity of content and related topics. 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 of questions and requests into a generating AI and have the generating AI adjust the order of analysis.

[0048] The generation unit can adjust the level of detail in the response based on the importance of the question or request when generating the answer. For example, the generation unit can generate a detailed response for a high-importance question or request. It can also generate a concise response for a low-importance question or request. Furthermore, it can generate a response with an appropriate level of detail for a medium-importance question or request. By adjusting the level of detail in the response based on the importance of the question or request, a more appropriate response can be provided. Importance includes, but is not limited to, user urgency or business importance. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the importance of the question or request into a generation AI and have the generation AI adjust the level of detail in the response.

[0049] The generation unit can apply different generation algorithms depending on the category of the question or request when generating answers. For example, for restaurant searches, the generation unit can apply a generation algorithm based on location information and reviews. For weather forecasts, it can also apply a generation algorithm based on meteorological data. Furthermore, for traffic information, it can apply a generation algorithm based on real-time traffic data. This allows for the provision of more appropriate answers by applying different generation algorithms depending on the category of the question or request. Categories include, but are not limited to, technology categories and business categories. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category of the question or request into a generation AI and have the generation AI apply the generation algorithm.

[0050] The generation unit can determine the priority of answers based on when the questions or requests were submitted. For example, the generation unit will prioritize answers to recently submitted questions or requests. It can also postpone answers to older questions or requests. Furthermore, if submissions are concentrated in a specific time period, the generation unit can prioritize answers to questions or requests submitted during that time period. This allows for the provision of more appropriate answers by prioritizing responses based on when the questions or requests were submitted. Submission timing includes, but is not limited to, the submission date and time, and submission frequency. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not. For example, the generation unit can input the submission timing of questions or requests into a generation AI and have the generation AI determine the priority of answers.

[0051] The generation unit can adjust the order of answers based on the relevance of the questions and requests when generating responses. For example, the generation unit can prioritize answering questions and requests that are highly relevant. It can also postpone answering questions and requests that are less relevant. Furthermore, it can answer questions and requests of moderate relevance in an appropriate order. By adjusting the order of answers based on the relevance of the questions and requests, it is possible to provide more appropriate answers. Relevance includes, but is not limited to, similarity of content and related topics. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the relevance of the questions and requests into a generation AI and have the generation AI perform the adjustment of the order of answers.

[0052] The service provider can select the optimal service method when providing answers by referring to the user's past question history. For example, the service provider may prioritize providing display methods that the user has frequently used in the past. The service provider can also predict and provide display methods to be used during specific time periods based on the user's past question history. Furthermore, the service provider can analyze the user's past question history and provide the most efficient display method. This allows the service provider to select the optimal service method by referring to the user's past question history. The optimal service method may include, but is not limited to, the user's past behavior history and current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past question history into a generating AI and have the generating AI select the optimal service method.

[0053] The service provider can customize the means of providing answers based on the user's current situation. For example, if the user is on the move, the service provider may prioritize providing answers via voice. Alternatively, if the user is at home, the service provider may provide detailed text answers. Furthermore, if the user is in a meeting, the service provider may provide answers in a concise notification format. This allows for the provision of more appropriate answers by customizing the means of providing answers based on the user's current situation. Current situation includes, but is not limited to, the user's location and current activities. Some or all of the processing described above in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's current situation into a generating AI and have the generating AI customize the means of providing answers.

[0054] The information provider can select the optimal method of providing information by considering the user's geographical location when providing responses. For example, if the user is in a specific region, the information provider can prioritize providing information related to that region. Furthermore, if the user is traveling, the information provider can prioritize providing information related to their travel destination. Additionally, if the user is at home, the information provider can prioritize providing information related to their home area. This allows the optimal method of providing information to be selected by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal method of providing information.

[0055] The information provider can analyze the user's social media activity and suggest methods of providing information when providing responses. For example, if the user frequently posts about a particular topic on social media, the information provider can prioritize providing information related to that topic. The information provider can also prioritize providing information related to an event if the user participates in a particular event on social media. Furthermore, if the information provider checks in to a particular location on social media, the information provider can prioritize providing information related to that location. This allows the information provider to suggest the most suitable method of providing information by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the processing described above in the information provider may be performed using AI, for example, or not. For example, the information provider can input the user's social media activity into a generating AI and have the generating AI suggest methods of providing information.

[0056] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also apply algorithms that improve learning efficiency from past learning data. Furthermore, the learning unit can analyze past learning data and apply algorithms that improve learning accuracy. In this way, the learning algorithm can be optimized by referring to past learning data. Learning algorithms include, but are not limited to, supervised learning and unsupervised learning. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0057] The learning unit can customize the learning methods based on the user's behavioral history during the learning process. For example, the learning unit can select the optimal learning method based on the user's behavioral history. The learning unit can also apply methods to improve the efficiency of learning based on the user's behavioral history. Furthermore, the learning unit can analyze the user's behavioral history and apply methods to improve the accuracy of learning. This allows for more appropriate learning by customizing the learning methods based on the user's behavioral history. Behavioral history includes, but is not limited to, website browsing history and purchase history. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavioral history into a generating AI and have the generating AI perform the customization of the learning methods.

[0058] The learning unit can weight the training data based on the submission timing of questions and requests during training. For example, the learning unit can prioritize data from recently submitted questions and requests. It can also lighten the weight of data from older questions and requests. Furthermore, if submissions are concentrated in a specific time period, the learning unit can prioritize data from that time period. This allows for more appropriate training by weighting the training data based on the submission timing of questions and requests. Submission timing includes, but is not limited to, submission date and time, and submission frequency. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the submission timing of questions and requests into a generating AI and have the generating AI perform the weighting of the training data.

[0059] The learning unit can analyze the user's social media activity during learning and suggest learning methods. For example, if the user frequently posts about a particular topic on social media, the learning unit can suggest learning methods related to that topic. It can also suggest learning methods related to an event if the user participates in a particular event on social media. Furthermore, if the user checks in to a specific location on social media, the learning unit can suggest learning methods related to that location. This allows the learning unit to suggest the most suitable learning methods by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the processing described above in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media activity into a generating AI and have the generating AI suggest learning methods.

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

[0061] The reception desk can analyze the user's past question history and select the most appropriate reception method. For example, it can automatically display as suggestions questions and requests that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions and requests that the user will use at specific times of day based on their past question history. In this way, the reception desk can select the most appropriate reception method by analyzing the past question history. The most appropriate reception method includes, but is not limited to, the user's past behavior history and current situation. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past question history into a generating AI and have the generating AI select the most appropriate reception method.

[0062] The generation unit can adjust the level of detail in the response based on the importance of the question or request when generating the answer. For example, it can generate a detailed response for high-importance questions or requests, and a concise response for low-importance questions or requests. Furthermore, it can generate a response with an appropriate level of detail for moderately important questions or requests. By adjusting the level of detail in the response based on the importance of the question or request, it is possible to provide a more appropriate answer. Importance includes, but is not limited to, user urgency or business importance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the question or request into the generation AI and have the generation AI adjust the level of detail in the response.

[0063] The reception desk can filter questions and requests based on the user's current situation and areas of interest. For example, if the user is currently located, it can prioritize questions and requests related to that location. Similarly, if the user has a specific area of ​​interest, it can prioritize questions and requests related to that area. Furthermore, if the user tends to take certain actions during certain time periods, it can prioritize questions and requests related to those time periods. This filtering based on the user's current situation and areas of interest enables the reception of more appropriate questions and requests. Filtering includes, but is not limited to, the user's areas of interest and current situation. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0064] The analysis unit can adjust the level of detail of the analysis based on the importance of the question or request. For example, it can perform a detailed analysis for high-importance questions or requests, a concise analysis for low-importance questions or requests, and an analysis of appropriate detail for moderately important questions or requests. By adjusting the level of detail of the analysis based on the importance of the question or request, it is possible to provide more appropriate analysis results. Importance includes, but is not limited to, user urgency or business importance. 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 of the question or request into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0065] The generation unit can apply different generation algorithms depending on the category of the question or request when generating answers. For example, for restaurant searches, it can apply a generation algorithm based on location information and reviews. The generation unit can also apply a generation algorithm based on meteorological data for weather forecasts. Furthermore, it can apply a generation algorithm based on real-time traffic data for traffic information. By applying different generation algorithms depending on the category of the question or request, it is possible to provide more appropriate answers. Categories include, but are not limited to, technology categories and business categories. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category of the question or request into a generation AI and have the generation AI perform the application of the generation algorithm.

[0066] The service provider can select the optimal service method when providing answers by referring to the user's past question history. For example, it may prioritize providing display methods that the user has frequently used in the past. The service provider can also predict and provide display methods to be used during specific time periods based on the user's past question history. Furthermore, the service provider can analyze the user's past question history and provide the most efficient display method. This allows the service provider to select the optimal service method by referring to the user's past question history. The optimal service method may include, but is not limited to, the user's past behavior history and current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past question history into a generating AI and have the generating AI select the optimal service method.

[0067] The learning unit can customize the learning methods based on the user's behavioral history during the learning process. For example, it can select the optimal learning method based on the user's behavioral history. The learning unit can also apply methods to improve the efficiency of learning based on the user's behavioral history. Furthermore, the learning unit can analyze the user's behavioral history and apply methods to improve the accuracy of learning. This allows for more appropriate learning by customizing the learning methods based on the user's behavioral history. Behavioral history includes, but is not limited to, website browsing history and purchase history. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavioral history into a generating AI and have the generating AI perform the customization of the learning methods.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk receives questions and requests from users. For example, it can accept questions and requests entered by users in natural language. Specifically, users can enter questions such as "Can you tell me about nearby restaurants?" or "What's the weather like tomorrow?" Step 2: The analysis unit uses a generation AI to analyze the questions and requests received by the reception unit. For example, natural language processing technology can be used to analyze the questions and requests. Specifically, the generation AI analyzes the input question and generates an appropriate answer. Step 3: The generation unit uses the generation AI to generate answers based on the results analyzed by the analysis unit. For example, the generation AI can search for nearby restaurants based on the user's current location and provide that information. Alternatively, the generation AI can analyze tomorrow's weather based on weather forecast data and provide that information. Step 4: The providing unit provides the user with the answer generated by the generating unit. For example, the generated answer can be displayed to the user. Specifically, if the user enters "Tell me about nearby cafes," the providing unit's generating AI searches for nearby cafes based on the user's current location and provides that information. Step 5: The learning unit learns the user's behavior history and provides personalized services. For example, it can suggest restaurants that suit the user's preferences based on information about restaurants the user has visited in the past. It can also prioritize providing weather information for areas the user is interested in based on weather information the user has searched for in the past.

[0070] (Example of form 2) The personal concierge system according to an embodiment of the present invention is a personal concierge service equipped with generative AI integrated within a messaging application. This personal concierge system operates when a user activates the personal concierge service within the messaging application and inputs questions or requests in natural language. For example, a user might input questions such as "Tell me some restaurants nearby" or "What's the weather like tomorrow?". These questions are input to the generative AI, which analyzes the input questions and generates appropriate answers. For example, in response to the question "Tell me some restaurants nearby," the generative AI searches for nearby restaurants based on the user's current location and provides that information. Similarly, in response to the question "What's the weather like tomorrow?", the generative AI analyzes tomorrow's weather based on weather forecast data and provides that information. Furthermore, the generative AI learns the user's past questions and activity history to provide personalized services. For example, based on information about restaurants the user has visited in the past, it suggests restaurants that suit the user's preferences. Also, based on weather information the user has searched for in the past, it prioritizes providing weather information for areas the user is interested in. This mechanism allows users to easily obtain information within the messaging application. For example, if a user types "Tell me about nearby cafes" in a messaging app, the generating AI will search for nearby cafes based on the user's current location and provide that information. Similarly, if a user types "What's the weather like tomorrow?", the generating AI will analyze weather forecast data to predict tomorrow's weather and provide that information. Furthermore, the generating AI learns the user's behavioral history and provides personalized services. For instance, it might suggest cafes that match the user's preferences based on information about cafes the user has visited in the past. It might also prioritize providing weather information for areas the user is interested in, based on weather information the user has previously searched for. In this way, a personal concierge service equipped with generating AI improves user convenience by responding quickly and accurately to user questions and requests and providing personalized services. This enables the personal concierge system to respond quickly and accurately to user questions and requests and provide personalized services.

[0071] The personal concierge system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a provision unit, and a learning unit. The reception unit receives questions and requests from users. The reception unit can, for example, receive questions and requests entered by users in natural language. For example, users can enter questions such as "Tell me about nearby restaurants" or "What's the weather like tomorrow?". The analysis unit analyzes the questions and requests received by the reception unit using a generation AI. The analysis unit can, for example, analyze questions and requests using natural language processing technology. For example, the generation AI analyzes the entered question and generates an appropriate answer. The generation unit generates an answer based on the results analyzed by the analysis unit using the generation AI. For example, the generation unit can use the generation AI to search for nearby restaurants based on the user's current location and provide that information. The generation unit can also use the generation AI to analyze tomorrow's weather based on weather forecast data and provide that information. The provision unit provides the answers generated by the generation unit to the user. For example, the provision unit can display the generated answers to the user. For example, if a user enters "Tell me about nearby cafes," the service provider's generating AI searches for nearby cafes based on the user's current location and provides that information. The learning unit learns the user's behavioral history and provides personalized services. For example, the learning unit can suggest restaurants that suit the user's preferences based on information about restaurants the user has visited in the past. The learning unit can also prioritize providing weather information for areas of interest to the user based on weather information the user has searched for in the past. As a result, the personal concierge system according to this embodiment can respond quickly and accurately to user questions and requests and provide personalized services.

[0072] The reception unit receives questions and requests from users. For example, the reception unit can receive questions and requests entered by users in natural language. Specifically, it provides an interface that accepts text and voice input from users via smartphones or computers. For example, users can input questions such as "Tell me about nearby restaurants" or "What's the weather like tomorrow?" This allows the reception unit to provide flexible input methods to meet diverse user needs. Furthermore, the reception unit can convert voice input into text using speech recognition technology. This allows users to ask questions and make requests by voice, enabling more intuitive operation. After receiving user input, the reception unit sends it to the analysis unit. The reception unit can also perform pre-processing to appropriately classify user input and enable the analysis unit to process it efficiently. For example, it can classify user input by topic, allowing the analysis unit to quickly analyze questions and requests related to specific topics. This enables the reception unit to efficiently process user input and improve the overall system response speed.

[0073] The analysis unit uses generative AI to analyze questions and requests received by the reception unit. The analysis unit can analyze questions and requests using, for example, natural language processing technology. Specifically, the generative AI analyzes the input question and generates an appropriate answer. The generative AI performs contextual and semantic analysis to understand the intent of the question. For example, in response to the question "Tell me about nearby restaurants," the generative AI understands that "nearby" refers to the user's current location and "restaurants" refers to eateries. Furthermore, after understanding the intent of the question, the generative AI retrieves the necessary information from appropriate databases and information sources. For example, it uses a Geographic Information System (GIS) to obtain restaurant information around the user's current location. In addition, the generative AI can generate more personalized answers by considering the user's past behavior history and preferences. This allows the analysis unit to respond quickly and accurately to user questions and requests. Furthermore, the analysis unit uses the generative AI's learning algorithm to learn user input patterns and question trends, enabling more accurate analysis of future questions and requests. This allows the analysis unit to improve the overall response accuracy and user satisfaction of the system.

[0074] The generation unit uses a generation AI to generate answers based on the results analyzed by the analysis unit. For example, the generation unit can use the generation AI to search for nearby restaurants based on the user's current location and provide that information. Specifically, the generation AI obtains the user's current location information and searches a database for restaurant information in the surrounding area. The search results include detailed information such as the restaurant's name, address, business hours, and rating. The generation unit can also use the generation AI to analyze tomorrow's weather based on weather forecast data and provide that information. The generation AI obtains the latest weather forecast information from a meteorological database and analyzes the weather in the user's area of ​​interest. For example, if the user asks, "What's the weather like in Tokyo tomorrow?", the generation AI obtains Tokyo's weather forecast data and provides detailed information such as tomorrow's weather, temperature, and probability of precipitation. Furthermore, the generation unit can generate more personalized answers by considering the user's past behavior history and preferences. For example, it can suggest restaurants that suit the user's preferences based on information about restaurants the user has visited in the past. The generation unit can also collect user feedback and continuously improve the accuracy and quality of answers using the generation AI's learning algorithm. This allows the generation unit to provide highly accurate and personalized answers to user questions and requests.

[0075] The providing unit delivers the answers generated by the generating unit to the user. For example, the providing unit can display the generated answers to the user. Specifically, if the user enters "Tell me about nearby cafes," the providing unit's generating AI will search for nearby cafes based on the user's current location and provide that information. The providing unit can display the answers on the user's device in various formats, such as text, images, and maps. For example, it can display detailed information such as the cafe's name, address, business hours, and rating on a smartphone screen. The providing unit can also use speech synthesis technology to provide the generated answers to the user in voice. This allows the user to receive information not only visually but also audibly. Furthermore, the providing unit can improve the overall accuracy and quality of the system by collecting user feedback and providing it to the generating and analysis units. For example, if the user enters ratings and comments on the provided information, the providing unit collects that feedback and incorporates it into the generating AI's learning algorithm. This allows the providing unit to provide users with quick and accurate information, thereby improving user satisfaction.

[0076] The learning unit learns from the user's behavior history and provides personalized services. For example, the learning unit can suggest restaurants that suit the user's preferences based on information about restaurants the user has visited in the past. Specifically, the learning unit collects and analyzes the user's behavior history data to understand the user's preferences and tendencies. For example, it can identify the type of cuisine and price range the user prefers based on data such as the type of restaurant the user has visited in the past, their ratings, and the frequency of visits. The learning unit can also prioritize providing weather information for areas of interest to the user based on the weather information the user has searched for in the past. For example, if the user has frequently searched for the weather in a particular area in the past, the weather information for that area will be displayed preferentially. Furthermore, the learning unit can collect user feedback and use learning algorithms to continuously improve the accuracy and quality of the service. For example, when users input ratings and comments on the information provided, the learning unit collects that feedback and reflects it in the learning algorithm of the generating AI. This allows the learning unit to provide personalized services that match the user's preferences and needs, thereby improving user satisfaction.

[0077] The reception desk can accept questions and requests entered by users in natural language. For example, the reception desk can accept questions and requests entered by users in natural language. For example, a user can enter questions such as "Tell me about nearby restaurants" or "What's the weather like tomorrow?" This improves user convenience by accepting questions and requests entered by users in natural language. Natural language processing techniques include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. Some or all of the processing described above in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input questions and requests entered by users in natural language into a generating AI and have the generating AI perform analysis of the questions and requests.

[0078] The analysis unit can analyze questions and requests received by the reception unit using a generation AI. For example, the analysis unit analyzes the questions and requests input by the generation AI. For example, the generation AI can analyze questions and requests using natural language processing technology. This improves the accuracy of question and request analysis by using a generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input questions and requests received by the reception unit into the generation AI and have the generation AI perform the analysis of the questions and requests.

[0079] The generation unit can generate answers based on the results analyzed by the analysis unit using a generation AI. For example, the generation unit can generate answers based on the results analyzed by the generation AI. For example, the generation AI can search for nearby restaurants based on the current location and provide that information. The generation AI can also analyze tomorrow's weather based on weather forecast data and provide that information. As a result, the accuracy of answer generation is improved by using the generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the results analyzed by the analysis unit into the generation AI and have the generation AI perform the generation of answers.

[0080] The providing unit can provide the user with the answers generated by the generating unit. For example, the providing unit can display the generated answers to the user. For example, if the user enters "Tell me about nearby cafes," the providing unit's generating AI will search for nearby cafes based on the user's current location and provide that information. This improves user convenience by providing the user with the generated answers. Specific methods of provision include, but are not limited to, real-time provision and batch provision. Some or all of the processing described above in the providing unit may be performed using, for example, AI, or not using AI. For example, the providing unit can input the answers generated by the generating unit into the generating AI and have the generating AI provide the answers.

[0081] The learning unit can learn from the user's past questions and behavioral history to provide personalized services. For example, the learning unit can suggest restaurants that suit the user's preferences based on information about restaurants the user has visited in the past. It can also prioritize providing weather information for areas of interest to the user based on weather information the user has searched for in the past. In this way, personalized services can be provided by learning from the user's past questions and behavioral history. Personalized services include, but are not limited to, recommendation systems and customized answers. Some or all of the processing described above in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavioral history into a generating AI and have the generating AI perform the provision of personalized services.

[0082] The reception desk can estimate the user's emotions and adjust how questions and requests are handled based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of questions and requests. This allows for more appropriate question and request handling by adjusting the reception method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The reception desk can analyze the user's past question history and select the optimal reception method. For example, the reception desk can automatically display as suggestions questions and requests that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions and requests that the user will use at specific times of day based on the user's past question history. In this way, the optimal reception method can be selected by analyzing the past question history. The optimal reception method includes, but is not limited to, the user's past behavior history and current situation. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past question history into a generating AI and have the generating AI select the optimal reception method.

[0084] The reception desk can filter questions and requests based on the user's current situation and areas of interest. For example, if the user is currently located, the reception desk will prioritize questions and requests related to that location. Furthermore, if the user has specific areas of interest, the reception desk can prioritize questions and requests related to those areas. Additionally, if the user tends to engage in specific activities during certain time periods, the reception desk can prioritize questions and requests related to those time periods. This allows for more appropriate questions and requests to be received by filtering based on the user's current situation and areas of interest. Filtering includes, but is not limited to, the user's areas of interest and current situation. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0085] The reception desk can estimate the user's emotions and determine the priority of questions and requests based on the estimated emotions. For example, if the user is nervous, the reception desk will prioritize high-priority questions and requests. Conversely, if the user is relaxed, the reception desk can also accept less important questions and requests. Furthermore, if the user is in a hurry, the reception desk can prioritize questions and requests that require a quick response. This allows for more appropriate responses by prioritizing questions and requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The reception desk can prioritize receiving questions and requests that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving questions and requests related to that region. Furthermore, if the user is traveling, the reception desk can prioritize receiving questions and requests related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize receiving questions and requests related to their home area. This allows for the prioritization of highly relevant questions and requests by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI select highly relevant questions and requests.

[0087] The reception desk can analyze a user's social media activity when receiving questions or requests and accept relevant questions or requests. For example, if a user frequently posts on a particular topic on social media, the reception desk can prioritize questions or requests related to that topic. Similarly, if a user participates in a particular event on social media, the reception desk can prioritize questions or requests related to that event. Furthermore, if a user checks in to a particular location on social media, the reception desk can prioritize questions or requests related to that location. This allows the reception desk to prioritize relevant questions and requests by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the processing described above in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's social media activity into a generating AI and have the generating AI select relevant questions and requests.

[0088] 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 tense, the analysis unit can provide a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that gets straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 perform emotion estimation.

[0089] The analysis unit can adjust the level of detail of its analysis based on the importance of the questions and requests. For example, the analysis unit can perform a detailed analysis for high-importance questions and requests. It can also perform a concise analysis for low-importance questions and requests. Furthermore, it can perform an analysis with an appropriate level of detail for questions and requests of moderate importance. By adjusting the level of detail of the analysis based on the importance of the questions and requests, more appropriate analysis results can be provided. Importance includes, but is not limited to, user urgency and business importance. 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 of the questions and requests into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the category of the question or request during analysis. For example, for restaurant searches, the analysis unit can apply an analysis algorithm based on location information and reviews. For weather forecasts, the analysis unit can also apply an analysis algorithm based on meteorological data. Furthermore, for traffic information, the analysis unit can apply an analysis algorithm based on real-time traffic data. This allows for more appropriate analysis results by applying different analysis algorithms depending on the category of the question or request. Categories include, but are not limited to, technical categories and business categories. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the question or request into a generating AI and have the generating AI perform the application of the analysis algorithm.

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

[0092] The analysis unit can determine the priority of analysis based on when questions and requests were submitted. For example, the analysis unit may prioritize the analysis of recently submitted questions and requests. It can also postpone the analysis of older questions and requests. Furthermore, if submissions are concentrated in a specific time period, the analysis unit may prioritize the analysis of questions and requests submitted during that time period. By determining the priority of analysis based on the submission date of questions and requests, more appropriate analysis results can be provided. The submission date includes, but is not limited to, the submission date and time, and the frequency of submission. 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 submission dates of questions and requests into a generating AI and have the generating AI determine the priority of analysis.

[0093] The analysis unit can adjust the order of analysis based on the relevance of questions and requests during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant questions and requests. It can also postpone the analysis of less relevant questions and requests. Furthermore, it can analyze questions and requests of moderate relevance in an appropriate order. By adjusting the order of analysis based on the relevance of questions and requests, it is possible to provide more appropriate analysis results. Relevance includes, but is not limited to, similarity of content and related topics. 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 of questions and requests into a generating AI and have the generating AI adjust the order of analysis.

[0094] The generation unit can estimate the user's emotions and adjust the response generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate responses that proceed at a leisurely pace. If the user is in a hurry, the generation unit can also generate responses that emphasize the shortest route. Furthermore, if the user is excited, the generation unit can generate responses with visually stimulating effects. This allows for more appropriate responses to be provided by adjusting the response generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0095] The generation unit can adjust the level of detail in the response based on the importance of the question or request when generating the answer. For example, the generation unit can generate a detailed response for a high-importance question or request. It can also generate a concise response for a low-importance question or request. Furthermore, it can generate a response with an appropriate level of detail for a medium-importance question or request. By adjusting the level of detail in the response based on the importance of the question or request, a more appropriate response can be provided. Importance includes, but is not limited to, user urgency or business importance. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the importance of the question or request into a generation AI and have the generation AI adjust the level of detail in the response.

[0096] The generation unit can apply different generation algorithms depending on the category of the question or request when generating answers. For example, for restaurant searches, the generation unit can apply a generation algorithm based on location information and reviews. For weather forecasts, it can also apply a generation algorithm based on meteorological data. Furthermore, for traffic information, it can apply a generation algorithm based on real-time traffic data. This allows for the provision of more appropriate answers by applying different generation algorithms depending on the category of the question or request. Categories include, but are not limited to, technology categories and business categories. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category of the question or request into a generation AI and have the generation AI apply the generation algorithm.

[0097] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise response. If the user is relaxed, the generation unit can also generate a longer response with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a response with visually stimulating effects. By adjusting the length of the response according to the user's emotions, a more appropriate response can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0098] The generation unit can determine the priority of answers based on when the questions or requests were submitted. For example, the generation unit will prioritize answers to recently submitted questions or requests. It can also postpone answers to older questions or requests. Furthermore, if submissions are concentrated in a specific time period, the generation unit can prioritize answers to questions or requests submitted during that time period. This allows for the provision of more appropriate answers by prioritizing responses based on when the questions or requests were submitted. Submission timing includes, but is not limited to, the submission date and time, and submission frequency. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not. For example, the generation unit can input the submission timing of questions or requests into a generation AI and have the generation AI determine the priority of answers.

[0099] The generation unit can adjust the order of answers based on the relevance of the questions and requests when generating responses. For example, the generation unit can prioritize answering questions and requests that are highly relevant. It can also postpone answering questions and requests that are less relevant. Furthermore, it can answer questions and requests of moderate relevance in an appropriate order. By adjusting the order of answers based on the relevance of the questions and requests, it is possible to provide more appropriate answers. Relevance includes, but is not limited to, similarity of content and related topics. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the relevance of the questions and requests into a generation AI and have the generation AI perform the adjustment of the order of answers.

[0100] The service provider can estimate the user's emotions and adjust the way it provides answers based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display method. By adjusting the way it provides answers according to the user's emotions, it is possible to provide more appropriate answers. 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 service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0101] The service provider can select the optimal service method when providing answers by referring to the user's past question history. For example, the service provider may prioritize providing display methods that the user has frequently used in the past. The service provider can also predict and provide display methods to be used during specific time periods based on the user's past question history. Furthermore, the service provider can analyze the user's past question history and provide the most efficient display method. This allows the service provider to select the optimal service method by referring to the user's past question history. The optimal service method may include, but is not limited to, the user's past behavior history and current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past question history into a generating AI and have the generating AI select the optimal service method.

[0102] The service provider can customize the means of providing answers based on the user's current situation. For example, if the user is on the move, the service provider may prioritize providing answers via voice. Alternatively, if the user is at home, the service provider may provide detailed text answers. Furthermore, if the user is in a meeting, the service provider may provide answers in a concise notification format. This allows for the provision of more appropriate answers by customizing the means of providing answers based on the user's current situation. Current situation includes, but is not limited to, the user's location and current activities. Some or all of the processing described above in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's current situation into a generating AI and have the generating AI customize the means of providing answers.

[0103] The service provider can estimate the user's emotions and determine the order in which to provide answers based on the estimated emotions. For example, if the user is nervous, the service provider will prioritize providing high-importance answers. Conversely, if the user is relaxed, the service provider can also provide less important answers. Furthermore, if the user is in a hurry, the service provider can prioritize providing answers that require a quick response. This allows for the provision of more appropriate answers by determining the order in which to provide answers according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0104] The information provider can select the optimal method of providing information by considering the user's geographical location when providing responses. For example, if the user is in a specific region, the information provider can prioritize providing information related to that region. Furthermore, if the user is traveling, the information provider can prioritize providing information related to their travel destination. Additionally, if the user is at home, the information provider can prioritize providing information related to their home area. This allows the optimal method of providing information to be selected by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal method of providing information.

[0105] The information provider can analyze the user's social media activity and suggest methods of providing information when providing responses. For example, if the user frequently posts about a particular topic on social media, the information provider can prioritize providing information related to that topic. The information provider can also prioritize providing information related to an event if the user participates in a particular event on social media. Furthermore, if the information provider checks in to a particular location on social media, the information provider can prioritize providing information related to that location. This allows the information provider to suggest the most suitable method of providing information by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the processing described above in the information provider may be performed using AI, for example, or not. For example, the information provider can input the user's social media activity into a generating AI and have the generating AI suggest methods of providing information.

[0106] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit can select detailed training data. If the user is in a hurry, the learning unit can also select concise training data. Furthermore, if the user is excited, the learning unit can select visually stimulating training data. This allows for more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 learning unit may be performed using AI, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0107] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also apply algorithms that improve learning efficiency from past learning data. Furthermore, the learning unit can analyze past learning data and apply algorithms that improve learning accuracy. In this way, the learning algorithm can be optimized by referring to past learning data. Learning algorithms include, but are not limited to, supervised learning and unsupervised learning. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0108] The learning unit can customize the learning methods based on the user's behavioral history during the learning process. For example, the learning unit can select the optimal learning method based on the user's behavioral history. The learning unit can also apply methods to improve the efficiency of learning based on the user's behavioral history. Furthermore, the learning unit can analyze the user's behavioral history and apply methods to improve the accuracy of learning. This allows for more appropriate learning by customizing the learning methods based on the user's behavioral history. Behavioral history includes, but is not limited to, website browsing history and purchase history. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavioral history into a generating AI and have the generating AI perform the customization of the learning methods.

[0109] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can increase the learning frequency when the user is relaxed. It can also decrease the learning frequency when the user is in a hurry. Furthermore, it can appropriately adjust the learning frequency when the user is excited. This allows for more appropriate learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 learning unit may be performed using AI, or not using AI. For example, the learning unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0110] The learning unit can weight the training data based on the submission timing of questions and requests during training. For example, the learning unit can prioritize data from recently submitted questions and requests. It can also lighten the weight of data from older questions and requests. Furthermore, if submissions are concentrated in a specific time period, the learning unit can prioritize data from that time period. This allows for more appropriate training by weighting the training data based on the submission timing of questions and requests. Submission timing includes, but is not limited to, submission date and time, and submission frequency. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the submission timing of questions and requests into a generating AI and have the generating AI perform the weighting of the training data.

[0111] The learning unit can analyze the user's social media activity during learning and suggest learning methods. For example, if the user frequently posts about a particular topic on social media, the learning unit can suggest learning methods related to that topic. It can also suggest learning methods related to an event if the user participates in a particular event on social media. Furthermore, if the user checks in to a specific location on social media, the learning unit can suggest learning methods related to that location. This allows the learning unit to suggest the most suitable learning methods by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the processing described above in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media activity into a generating AI and have the generating AI suggest learning methods.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The reception desk can analyze the user's past question history and select the most appropriate reception method. For example, it can automatically display as suggestions questions and requests that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions and requests that the user will use at specific times of day based on their past question history. In this way, the reception desk can select the most appropriate reception method by analyzing the past question history. The most appropriate reception method includes, but is not limited to, the user's past behavior history and current situation. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past question history into a generating AI and have the generating AI select the most appropriate reception method.

[0114] 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 nervous, it can provide a simple and easy-to-understand analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, it can provide a concise analysis result that gets straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 perform emotion estimation.

[0115] The generation unit can adjust the level of detail in the response based on the importance of the question or request when generating the answer. For example, it can generate a detailed response for high-importance questions or requests, and a concise response for low-importance questions or requests. Furthermore, it can generate a response with an appropriate level of detail for moderately important questions or requests. By adjusting the level of detail in the response based on the importance of the question or request, it is possible to provide a more appropriate answer. Importance includes, but is not limited to, user urgency or business importance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the question or request into the generation AI and have the generation AI adjust the level of detail in the response.

[0116] The service provider can estimate the user's emotions and adjust the way it provides answers based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. By adjusting the way it provides answers according to the user's emotions, it can provide more appropriate answers. 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 service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0117] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, detailed training data can be selected. If the user is in a hurry, concise training data can be selected. Furthermore, if the user is excited, visually stimulating training data can be selected. This allows for more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0118] The reception desk can filter questions and requests based on the user's current situation and areas of interest. For example, if the user is currently located, it can prioritize questions and requests related to that location. Similarly, if the user has a specific area of ​​interest, it can prioritize questions and requests related to that area. Furthermore, if the user tends to take certain actions during certain time periods, it can prioritize questions and requests related to those time periods. This filtering based on the user's current situation and areas of interest enables the reception of more appropriate questions and requests. Filtering includes, but is not limited to, the user's areas of interest and current situation. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0119] The analysis unit can adjust the level of detail of the analysis based on the importance of the question or request. For example, it can perform a detailed analysis for high-importance questions or requests, a concise analysis for low-importance questions or requests, and an analysis of appropriate detail for moderately important questions or requests. By adjusting the level of detail of the analysis based on the importance of the question or request, it is possible to provide more appropriate analysis results. Importance includes, but is not limited to, user urgency or business importance. 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 of the question or request into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0120] The generation unit can apply different generation algorithms depending on the category of the question or request when generating answers. For example, for restaurant searches, it can apply a generation algorithm based on location information and reviews. The generation unit can also apply a generation algorithm based on meteorological data for weather forecasts. Furthermore, it can apply a generation algorithm based on real-time traffic data for traffic information. By applying different generation algorithms depending on the category of the question or request, it is possible to provide more appropriate answers. Categories include, but are not limited to, technology categories and business categories. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category of the question or request into a generation AI and have the generation AI perform the application of the generation algorithm.

[0121] The service provider can select the optimal service method when providing answers by referring to the user's past question history. For example, it may prioritize providing display methods that the user has frequently used in the past. The service provider can also predict and provide display methods to be used during specific time periods based on the user's past question history. Furthermore, the service provider can analyze the user's past question history and provide the most efficient display method. This allows the service provider to select the optimal service method by referring to the user's past question history. The optimal service method may include, but is not limited to, the user's past behavior history and current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past question history into a generating AI and have the generating AI select the optimal service method.

[0122] The learning unit can customize the learning methods based on the user's behavioral history during the learning process. For example, it can select the optimal learning method based on the user's behavioral history. The learning unit can also apply methods to improve the efficiency of learning based on the user's behavioral history. Furthermore, the learning unit can analyze the user's behavioral history and apply methods to improve the accuracy of learning. This allows for more appropriate learning by customizing the learning methods based on the user's behavioral history. Behavioral history includes, but is not limited to, website browsing history and purchase history. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavioral history into a generating AI and have the generating AI perform the customization of the learning methods.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The reception desk receives questions and requests from users. For example, it can accept questions and requests entered by users in natural language. Specifically, users can enter questions such as "Can you tell me about nearby restaurants?" or "What's the weather like tomorrow?" Step 2: The analysis unit uses a generation AI to analyze the questions and requests received by the reception unit. For example, natural language processing technology can be used to analyze the questions and requests. Specifically, the generation AI analyzes the input question and generates an appropriate answer. Step 3: The generation unit uses the generation AI to generate answers based on the results analyzed by the analysis unit. For example, the generation AI can search for nearby restaurants based on the user's current location and provide that information. Alternatively, the generation AI can analyze tomorrow's weather based on weather forecast data and provide that information. Step 4: The providing unit provides the user with the answer generated by the generating unit. For example, the generated answer can be displayed to the user. Specifically, if the user enters "Tell me about nearby cafes," the providing unit's generating AI searches for nearby cafes based on the user's current location and provides that information. Step 5: The learning unit learns the user's behavior history and provides personalized services. For example, it can suggest restaurants that suit the user's preferences based on information about restaurants the user has visited in the past. It can also prioritize providing weather information for areas the user is interested in based on weather information the user has searched for in the past.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, provision unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives questions and requests entered by the user in natural language. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the questions and requests using generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates answers based on the analyzed results. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated answers to the user. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's behavior history and provides personalized services. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, provision unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives questions and requests entered by the user in natural language. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the questions and requests using generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates answers based on the analyzed results. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the generated answers to the user. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's behavior history and provides personalized services. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, provision unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives questions and requests entered by the user in natural language. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the questions and requests using generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates answers based on the analyzed results. The provision unit is implemented by the speaker 240 of the headset terminal 314 and provides the generated answers to the user. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's behavior history and provides personalized services. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, provision unit, and learning unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives questions and requests entered by the user in natural language. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the questions and requests using generation AI. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates answers based on the analyzed results. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated answers to the user. The learning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and learns the user's behavior history and provides personalized services. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A reception desk that handles questions and requests from users, An analysis unit analyzes questions and requests received by the reception unit, A generation unit that generates an answer based on the results of the analysis performed by the analysis unit, A providing unit that provides the answer generated by the generation unit, It comprises a learning unit that learns the user's behavior history, A system characterized by the following features. (Note 2) The aforementioned reception unit is It accepts questions and requests entered by users in natural language. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The generation AI analyzes the questions and requests received by the reception department. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The generation AI generates the answer based on the results analyzed by the analysis unit. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The user is provided with the answer generated by the generation unit. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, We learn from the user's past questions and behavioral history to provide personalized services. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how questions and requests are received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of handling inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving questions or requests, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of questions and requests to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving questions or requests, the system prioritizes those that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving questions or requests, the system analyzes the user's social media activity and accepts relevant questions and requests. 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 questions and requests. 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 the question or request. 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 the analysis, the priority of the analysis will be determined based on when the questions and requests were 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 questions and requests. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the response generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating answers, adjust the level of detail in the answers based on the importance of the question or request. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating answers, different generation algorithms are applied depending on the category of the question or request. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating responses, the priority of the responses is determined based on when the questions or requests were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating answers, the order of answers is adjusted based on the relevance of the questions and requests. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate the user's emotions and adjust how we provide responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing an answer, the system will refer to the user's past question history to select the most appropriate method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing responses, customize the method of delivery based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and determines the order in which responses are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing responses, the optimal method of delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing responses, we analyze the user's social media activity and suggest methods for providing the responses. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned learning unit, During learning, the learning method is customized based on the user's behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned learning unit, During training, the training data is weighted based on when questions and requests were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned learning unit, During the learning process, the system analyzes the user's social media activity and suggests learning methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 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 reception desk that handles questions and requests from users, An analysis unit analyzes questions and requests received by the reception unit, A generation unit that generates an answer based on the results of the analysis performed by the analysis unit, A providing unit that provides the answer generated by the generation unit, It comprises a learning unit that learns the user's behavior history, A system characterized by the following features.

2. The aforementioned reception unit is It accepts questions and requests entered by users in natural language. The system according to feature 1.

3. The aforementioned analysis unit, The generation AI analyzes the questions and requests received by the reception unit. The system according to feature 1.

4. The generating unit is The generation AI generates an answer based on the results analyzed by the analysis unit. The system according to feature 1.

5. The aforementioned supply unit is, The user is provided with the answer generated by the generation unit. The system according to feature 1.

6. The aforementioned learning unit, We learn from the user's past questions and behavioral history to provide personalized services. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how questions and requests are received based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of handling inquiries. The system according to feature 1.

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