system

Abstract

The system according to this embodiment aims to provide users with appropriate advice using a generative AI that has learned from data on famous people. [Solution] The system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives questions from the user. The generation unit analyzes the questions received by the reception unit and generates advice using a generation AI that has learned from data on famous people. The provision unit provides the advice generated by the generation unit to the user.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there has been a problem that it is difficult to directly obtain advice from a famous person and the means for obtaining appropriate advice are limited.

[0005] The system according to an embodiment aims to provide appropriate advice to a user by using a generative AI that has learned data of famous people.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives questions from users. The generation unit analyzes the questions received by the reception unit and generates advice using a generation AI that has learned from data on famous people. The provision unit provides the advice generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can provide users with appropriate advice using a generative AI that has learned from data on famous people. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An advice-providing system according to an embodiment of the present invention is a system that uses a generative AI to provide advice from famous people when a person is at a crossroads in their life. In this advice-providing system, the user inputs a question about a life choice, and the generative AI generates advice suitable for the user based on a database in which it has learned famous quotes, anecdotes, and past advice from famous people. This advice is inferred by the generative AI, which has learned the thought patterns and related information of famous people. The generated advice is then provided to the user. For example, the user inputs a question such as, "Should I try a new job?" This question is input to the generative AI. The generative AI analyzes the input question and generates advice suitable for the user based on a database in which it has learned famous quotes, anecdotes, and past advice from famous people. Because the generative AI has learned the thought patterns and related information of famous people, it can provide accurate advice in response to the user's question. For example, in response to the question, "Should I try a new job?", it generates advice based on famous quotes and anecdotes from famous people. The generated advice is then provided to the user. For example, advice such as, "Whether you succeed or fail doesn't matter to me. What's important is that you try it," might be provided. This advice is inferred by a generative AI that has learned the thought patterns and related information of famous people. This mechanism allows users to receive accurate advice regarding life choices. For example, if you want to try something new but are worried about failing, you can make an appropriate decision by referring to the advice provided by the generative AI. Similarly, if you are unsure whether your current job is what you truly want to do, you can find your direction by referring to the advice provided by the generative AI. Furthermore, this invention provides a marketplace for selling official generative AI that has learned from famous people's quotes, experiences, and past advice. This marketplace sells official generative AI with official information obtained with the permission and supervision of the individuals themselves. This allows users to obtain highly reliable advice. For example, when faced with a crossroads in life, you can receive advice from famous people. This allows users to find their direction and make appropriate decisions.Furthermore, the advice provided by the generated AI is highly reliable and beneficial to users because it learns the thought patterns and related information of prominent figures. This allows the advice system to provide user-submitted advice based on data about prominent figures in response to their questions.

[0029] The advice provision system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives questions from users. Questions from users include, but are not limited to, questions about careers, health, and life choices. The reception unit analyzes the questions entered by the user using, for example, natural language processing technology. The reception unit can also identify important parts of the questions using keyword extraction technology. For example, the reception unit analyzes the questions entered by the user and understands the intent of the questions. The generation unit uses a generation AI to analyze the questions received by the reception unit and generates advice based on data of famous people. The generation unit generates advice based on, for example, a database that has learned famous people's quotes, anecdotes, and past advice. The generation unit can also learn famous people's thought patterns and related information. For example, the generation unit generates advice based on famous people's quotes and anecdotes. The generation unit can provide accurate advice to the user's questions using a generation AI. The provision unit provides the advice generated by the generation unit to the user. The service provider, for example, provides the generated advice to the user in text format. The service provider can also provide advice in voice format. For example, the service provider can provide the generated advice in voice format using speech synthesis technology. The service provider has an interface for providing the generated advice to the user. For example, the service provider can provide advice through a web application or a mobile application. This allows the advice provision system according to the embodiment to provide advice based on data of famous people in response to the user's questions. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can input the user's question into a generation AI, and the generation AI can generate advice. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can provide the generated advice to the user using AI.

[0030] The reception desk receives questions from users. These questions may include, but are not limited to, questions about careers, health, or life choices. The reception desk analyzes user-entered questions using natural language processing (NLP) techniques. Specifically, it uses NLP to grammatically analyze user questions, understanding sentence structure and meaning. For example, it performs morphological analysis to identify the part of speech of each word and analyze sentence structure. It also performs semantic analysis to understand the meaning of the sentence. Furthermore, the reception desk can identify important parts of a question using keyword extraction techniques. For example, it uses techniques such as TF-IDF (Term Frequency-Inverse Document Frequency) or Word2Vec to extract important words and phrases from the question. This allows the reception desk to accurately understand the intent of the user's question and lay the foundation for providing appropriate advice. Additionally, the reception desk can refer to the user's past question history and profile information to understand the background and context of the question. For example, it can refer to what questions the user has asked and what advice they have received in the past to deepen its understanding of the current question. This allows the reception department to respond to each user's individual needs and circumstances.

[0031] The generation unit uses a generation AI to analyze questions received by the reception unit and generate advice based on data of famous people. Specifically, the generation AI receives the user's question as input and analyzes the content and intent of the question. For example, the generation AI analyzes the context and keywords of the question and extracts information to generate appropriate advice. The generation unit generates advice based on a database that has learned from famous people's quotes, anecdotes, and past advice. For example, the generation AI searches for famous people's quotes and anecdotes and extracts information related to the user's question. The generation unit can also learn the thinking patterns and related information of famous people. For example, the generation AI learns the statements and behavioral patterns of famous people and generates appropriate advice in response to the user's question. The generation unit can provide accurate advice in response to the user's question using the generation AI. For example, the generation AI generates advice based on famous people's quotes and anecdotes in response to the user's question and provides it to the user. Furthermore, the generation unit can periodically update the generation AI's learning data and provide advice based on the latest information and trends. This allows the generation unit to always provide high-quality advice based on the latest information.

[0032] The service provider delivers the advice generated by the generator to the user. Specifically, the service provider delivers the generated advice to the user in text format. For example, the service provider sends the generated advice to the user as a text message. The service provider can also deliver the advice in audio format. For example, the service provider delivers the generated advice in audio format using speech synthesis technology. By using speech synthesis technology, the user can receive the advice audibly, improving convenience. The service provider has an interface for delivering the generated advice to the user. For example, the service provider delivers the advice through a web application or a mobile application. This allows the user to receive the advice anytime, anywhere using an internet-connected device. Furthermore, the service provider can collect user feedback and continuously improve the quality of the advice. For example, by providing ratings and comments on the advice provided by the user, the service provider can evaluate the quality of the advice and make improvements as needed. This allows the service provider to continue providing high-quality advice to the user.

[0033] The generation unit can generate advice based on a database that has learned from famous people's quotes, personal anecdotes, and past advice. For example, the generation unit can extract famous people's quotes from the database and generate appropriate advice in response to a user's question. The generation unit can also generate advice based on famous people's personal anecdotes. For example, the generation unit can extract famous people's personal anecdotes from the database and generate appropriate advice in response to a user's question. The generation unit can also generate advice based on famous people's past advice. For example, the generation unit can extract famous people's past advice from the database and generate appropriate advice in response to a user's question. In this way, the generation unit can generate advice based on famous people's quotes and personal anecdotes. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input famous people's quotes and personal anecdotes into a generation AI, and the generation AI can generate advice.

[0034] The service provider can provide the generated advice to the user. For example, the service provider can provide the generated advice to the user in text format. The service provider can also provide the generated advice in audio format. For example, the service provider can provide the generated advice in audio format using speech synthesis technology. The service provider has an interface for providing the generated advice to the user. For example, the service provider can provide the advice through a web application or a mobile application. This allows the service provider to provide the generated advice to the user. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can provide the generated advice to the user using AI.

[0035] The reception desk can analyze the user's past question history and select the optimal reception method. For example, the reception desk may prioritize suggesting question formats that the user has frequently used in the past. The reception desk can also accept questions during times that the user has preferred to use in the past. For example, the reception desk may accept questions during times that the user has preferred to use in the past. The reception desk can also analyze the content of the user's past questions and prioritize accepting related questions. For example, the reception desk may analyze the content of the user's past questions and prioritize accepting related questions. This allows the reception desk to select the optimal reception method based on the user's past question history. 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 past question history into AI, and the AI ​​can select the optimal reception method.

[0036] The reception desk can filter questions based on the user's current situation and areas of interest when receiving them. For example, the reception desk can prioritize questions related to topics the user is currently interested in. The reception desk can also filter questions according to the user's current situation (e.g., working, on break). For example, the reception desk can filter questions according to the user's current situation. The reception desk can also filter questions based on the user's current activity (e.g., exercising, reading). For example, the reception desk can filter questions based on the user's current activity. This allows for filtering questions based on the user's current situation and areas of interest. 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 data on the user's current situation and areas of interest into the AI, which can then filter the questions.

[0037] The reception desk can prioritize receiving questions based on the user's geographical location when a question is received. For example, if the user is in a specific region, the reception desk will prioritize receiving questions related to that region. Similarly, if the user is traveling, the reception desk can prioritize receiving questions related to their travel destination. Furthermore, if the user is at home, the reception desk can prioritize receiving questions related to their home. This allows the reception desk to prioritize receiving questions based on the user's geographical location. 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 geographical location into an AI, which can then prioritize receiving questions based on their relevance.

[0038] The reception desk can analyze the user's social media activity when receiving a question and accept relevant questions. For example, the reception desk can prioritize questions related to topics the user has recently shown interest in on social media. The reception desk can also prioritize questions related to celebrities the user follows on social media. For example, the reception desk can prioritize questions related to celebrities the user follows on social media. The reception desk can also analyze the user's social media activity history and prioritize accepting relevant questions. For example, the reception desk can analyze the user's social media activity history and prioritize accepting relevant questions. This allows the reception desk to accept relevant questions based on the user's social media activity. 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 social media activity data into an AI, which can then accept relevant questions.

[0039] The generation unit can adjust the level of detail of the advice based on the importance of the question when generating advice. For example, the generation unit generates detailed advice for high-importance questions. The generation unit can also generate concise advice for low-importance questions. For example, the generation unit generates concise advice for low-importance questions. The generation unit can also adjust the length of the advice according to the importance of the question. For example, the generation unit adjusts the length of the advice according to the importance of the question. This allows the level of detail of the advice to be adjusted according to the importance of the question. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input question importance data into a generation AI, and the generation AI can adjust the level of detail of the advice.

[0040] The generation unit can apply different generation algorithms depending on the category of the question when generating advice. For example, for career-related questions, the generation unit can apply a generation algorithm based on career-related data. Similarly, for relationship-related questions, the generation unit can apply a generation algorithm based on relationship-related data. For example, for relationship-related questions, the generation unit can apply a generation algorithm based on relationship-related data. Similarly, for health-related questions, the generation unit can apply a generation algorithm based on health-related data. For example, for health-related questions, the generation unit can apply a generation algorithm based on health-related data. This allows the generation unit to apply the most suitable generation algorithm depending on the category of the question. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the question category data into a generation AI, which can then apply the most suitable generation algorithm.

[0041] The generation unit can determine the priority of advice based on when the question was submitted. For example, it can prioritize generating advice for recently submitted questions. It can also postpone generating advice for older questions. For example, it can postpone generating advice for older questions. Furthermore, the generation unit can adjust the order in which advice is generated according to the submission date. For example, it can adjust the order in which advice is generated according to the submission date. This allows the priority of advice to be determined according to when the question was submitted. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input question submission date data into a generation AI, and the generation AI can determine the priority of advice.

[0042] The generation unit can adjust the order of advice based on the relevance of the questions when generating advice. For example, the generation unit will prioritize generating advice when the questions are highly relevant. The generation unit can also postpone generating advice when the questions are less relevant. For example, the generation unit will postpone generating advice when the questions are less relevant. The generation unit can also adjust the order of advice generation according to the relevance of the questions. For example, the generation unit will adjust the order of advice generation according to the relevance of the questions. This allows the order of advice to be adjusted according to the relevance of the questions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input question relevance data into a generation AI, and the generation AI can adjust the order of advice.

[0043] The service provider can select the optimal delivery method by referring to the user's past advice history when providing advice. For example, the service provider may prioritize suggesting delivery methods (text, voice, etc.) that the user has preferred in the past. The service provider can also prioritize providing relevant advice from the user's past advice history. For example, the service provider may prioritize providing relevant advice from the user's past advice history. The service provider can also analyze the user's past advice history and select the optimal delivery method. For example, the service provider may analyze the user's past advice history and select the optimal delivery method. This allows the service provider to select the optimal delivery method based on the user's past advice history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past advice history data into AI, and the AI ​​can select the optimal delivery method.

[0044] The service provider can customize the means of providing advice based on the user's current situation. For example, if the user is working, the service provider can provide short, concise advice. If the user is on a break, the service provider can provide detailed advice. For example, if the user is on a break, the service provider can provide detailed advice. If the user is on the move, the service provider can provide advice via voice. For example, if the user is on the move, the service provider can provide advice via voice. This allows the means of providing advice to be customized according to the user's current situation. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's current situation data into the AI, which can then customize the means of providing advice.

[0045] The service provider can select the most appropriate method of providing advice based on the user's geographical location. For example, if the user is in a specific region, the service provider can prioritize providing advice related to that region. Similarly, if the user is traveling, the service provider can prioritize providing advice related to their travel destination. For example, if the user is traveling, the service provider can prioritize providing advice related to their travel destination. Similarly, if the user is at home, the service provider can prioritize providing advice related to their home. This allows the service provider to select the most appropriate method of providing advice based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location into AI, which can then select the most appropriate method of providing advice.

[0046] The service provider can analyze the user's social media activity and propose methods for providing advice. For example, the service provider can prioritize providing advice related to topics the user has recently shown interest in on social media. The service provider can also prioritize providing advice related to celebrities the user follows on social media. For example, the service provider can prioritize providing advice related to celebrities the user follows on social media. The service provider can also analyze the user's social media activity history and prioritize providing relevant advice. For example, the service provider can analyze the user's social media activity history and prioritize providing relevant advice. This allows the service provider to propose the most suitable method for providing advice based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into AI, which can then propose the most suitable method for providing advice.

[0047] The marketplace section can select the optimal display method by referring to the user's past purchase history when displaying marketplace items. For example, the marketplace section can prioritize displaying items related to products the user has previously purchased. The marketplace section can also prioritize displaying products in the user's preferred categories based on their past purchase history. For example, the marketplace section can prioritize displaying products in the user's preferred categories based on their past purchase history. The marketplace section can also analyze the user's past purchase history and recommend related products. For example, the marketplace section can analyze the user's past purchase history and recommend related products. This allows the optimal display method to be selected based on the user's past purchase history. Some or all of the above processing in the marketplace section may be performed using AI or not. For example, the marketplace section can input the user's past purchase history data into AI, which can then select the optimal display method.

[0048] The marketplace section can customize the displayed content based on the user's current areas of interest when displaying the marketplace. For example, the marketplace section can prioritize displaying products related to topics the user is currently interested in. The marketplace section can also customize and display products based on the user's current activities (hobbies, work, etc.). For example, the marketplace section can customize and display products based on the user's current activities. The marketplace section can also analyze the user's current areas of interest and recommend related products. For example, the marketplace section can analyze the user's current areas of interest and recommend related products. This allows the displayed content to be customized based on the user's current areas of interest. Some or all of the above processing in the marketplace section may be performed using AI or not. For example, the marketplace section can input the user's current areas of interest data into AI, which can then customize the displayed content.

[0049] The marketplace section can select the optimal display method based on the user's geographical location when displaying marketplace items. For example, if the user is in a specific region, the marketplace section can prioritize displaying products related to that region. Similarly, if the user is traveling, the marketplace section can prioritize displaying products related to their travel destination. Furthermore, if the user is at home, the marketplace section can prioritize displaying products related to their home. This allows the system to select the optimal display method based on the user's geographical location. Some or all of the above processing in the marketplace section may be performed using AI, or without AI. For example, the marketplace section can input the user's geographical location information into AI, which can then select the optimal display method.

[0050] The marketplace unit can analyze a user's social media activity and suggest content to display when showing marketplace items. For example, the marketplace unit can prioritize displaying products related to topics the user has recently shown interest in on social media. The marketplace unit can also prioritize displaying products related to celebrities the user follows on social media. For example, the marketplace unit can prioritize displaying products related to celebrities the user follows on social media. The marketplace unit can also analyze a user's social media activity history and recommend related products. For example, the marketplace unit can analyze a user's social media activity history and recommend related products. This allows the marketplace unit to suggest optimal content based on the user's social media activity. Some or all of the above processing in the marketplace unit may be performed using AI or not. For example, the marketplace unit can input the user's social media activity data into AI, which can then suggest optimal content.

[0051] The marketplace unit can suggest optimal display content by combining the user's purchase history and social media activity when displaying marketplace items. For example, the marketplace unit can display related products by combining items the user has previously purchased with topics they have shown interest in on social media. The marketplace unit can also display products related to the user's purchase history and the celebrities they follow on social media. For example, the marketplace unit can display products related to the user's purchase history and the celebrities they follow on social media. The marketplace unit can also analyze the user's purchase history and social media activity history and display the most suitable products as recommendations. For example, the marketplace unit can analyze the user's purchase history and social media activity history and display the most suitable products as recommendations. This allows the marketplace unit to suggest optimal display content based on the user's purchase history and social media activity. Some or all of the above processing in the marketplace unit may be performed using AI or not. For example, the marketplace unit can input the user's purchase history and social media activity data into AI, which can then suggest optimal display content.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The advice-providing system can analyze a user's past question history and provide optimal advice. For example, if a user has asked a similar question in the past, it can generate new advice by referring to past advice given to that question. It can also evaluate the effectiveness of advice the user has received in the past and adjust the content of the advice based on that evaluation. Furthermore, based on the user's past question history, it can predict related questions and provide advice on problems the user has not yet realized. In this way, it can provide more accurate advice by utilizing the user's past question history. Some or all of the above processing in the generation unit may be performed using generation AI, or it may be performed without using generation AI.

[0054] The advice-providing system can customize the content of the advice based on the user's current situation. For example, if the user is at work, the generation unit can generate short, concise advice. If the user is on a break, the generation unit can generate detailed advice. Furthermore, if the user is on the move, the generation unit can generate advice delivered via voice. This allows the system to provide appropriate advice tailored to the user's current situation. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI.

[0055] The advice provision system can customize the content of the advice based on the user's geographical location information. For example, if the user is in a specific region, it can generate advice related to that region. If the user is traveling, it can also generate advice related to their travel destination. Furthermore, if the user is at home, it can generate advice related to their home. This allows the system to provide appropriate advice based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0056] The advice-providing system can analyze a user's social media activity and provide relevant advice. For example, it can generate advice related to topics the user has recently shown interest in on social media. It can also generate advice related to prominent figures the user follows on social media. Furthermore, it can analyze the user's social media activity history and provide relevant advice. This allows for the provision of appropriate advice based on the user's social media activity. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI.

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

[0058] Step 1: The reception desk receives questions from users. These questions may include questions about careers, health, and life choices. The reception desk can analyze the questions entered by users using natural language processing technology and identify key parts of the questions using keyword extraction technology. This allows the reception desk to understand the intent of the questions. Step 2: The generation unit uses a generation AI to analyze the questions received by the reception unit and generate advice based on data about famous people. The generation unit generates advice based on a database that has learned from famous people's quotes, experiences, and past advice, and can also learn the thinking patterns and related information of famous people. This allows it to provide accurate advice in response to the user's questions. Step 3: The provider unit provides the user with the advice generated by the generator unit. The provider unit can provide the generated advice to the user in text or audio format. For example, the provider unit can provide advice generated using speech synthesis technology in audio format. The provider unit also has an interface for providing advice through web applications and mobile applications.

[0059] (Example of form 2)An advice-providing system according to an embodiment of the present invention is a system that uses a generative AI to provide advice from famous people when a person is at a crossroads in their life. In this advice-providing system, the user inputs a question about a life choice, and the generative AI generates advice suitable for the user based on a database in which it has learned famous quotes, anecdotes, and past advice from famous people. This advice is inferred by the generative AI, which has learned the thought patterns and related information of famous people. The generated advice is then provided to the user. For example, the user inputs a question such as, "Should I try a new job?" This question is input to the generative AI. The generative AI analyzes the input question and generates advice suitable for the user based on a database in which it has learned famous quotes, anecdotes, and past advice from famous people. Because the generative AI has learned the thought patterns and related information of famous people, it can provide accurate advice in response to the user's question. For example, in response to the question, "Should I try a new job?", it generates advice based on famous quotes and anecdotes from famous people. The generated advice is then provided to the user. For example, advice such as, "Whether you succeed or fail doesn't matter to me. What's important is that you try it," might be provided. This advice is inferred by a generative AI that has learned the thought patterns and related information of famous people. This mechanism allows users to receive accurate advice regarding life choices. For example, if you want to try something new but are worried about failing, you can make an appropriate decision by referring to the advice provided by the generative AI. Similarly, if you are unsure whether your current job is what you truly want to do, you can find your direction by referring to the advice provided by the generative AI. Furthermore, this invention provides a marketplace for selling official generative AI that has learned from famous people's quotes, experiences, and past advice. This marketplace sells official generative AI with official information obtained with the permission and supervision of the individuals themselves. This allows users to obtain highly reliable advice. For example, when faced with a crossroads in life, you can receive advice from famous people. This allows users to find their direction and make appropriate decisions.Furthermore, the advice provided by the generated AI is highly reliable and beneficial to users because it learns the thought patterns and related information of prominent figures. This allows the advice system to provide user-submitted advice based on data about prominent figures in response to their questions.

[0060] The advice provision system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives questions from users. Questions from users include, but are not limited to, questions about careers, health, and life choices. The reception unit analyzes the questions entered by the user using, for example, natural language processing technology. The reception unit can also identify important parts of the questions using keyword extraction technology. For example, the reception unit analyzes the questions entered by the user and understands the intent of the questions. The generation unit uses a generation AI to analyze the questions received by the reception unit and generates advice based on data of famous people. The generation unit generates advice based on, for example, a database that has learned famous people's quotes, anecdotes, and past advice. The generation unit can also learn famous people's thought patterns and related information. For example, the generation unit generates advice based on famous people's quotes and anecdotes. The generation unit can provide accurate advice to the user's questions using a generation AI. The provision unit provides the advice generated by the generation unit to the user. The service provider, for example, provides the generated advice to the user in text format. The service provider can also provide advice in voice format. For example, the service provider can provide the generated advice in voice format using speech synthesis technology. The service provider has an interface for providing the generated advice to the user. For example, the service provider can provide advice through a web application or a mobile application. This allows the advice provision system according to the embodiment to provide advice based on data of famous people in response to the user's questions. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can input the user's question into a generation AI, and the generation AI can generate advice. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can provide the generated advice to the user using AI.

[0061] The reception desk receives questions from users. These questions may include, but are not limited to, questions about careers, health, or life choices. The reception desk analyzes user-entered questions using natural language processing (NLP) techniques. Specifically, it uses NLP to grammatically analyze user questions, understanding sentence structure and meaning. For example, it performs morphological analysis to identify the part of speech of each word and analyze sentence structure. It also performs semantic analysis to understand the meaning of the sentence. Furthermore, the reception desk can identify important parts of a question using keyword extraction techniques. For example, it uses techniques such as TF-IDF (Term Frequency-Inverse Document Frequency) or Word2Vec to extract important words and phrases from the question. This allows the reception desk to accurately understand the intent of the user's question and lay the foundation for providing appropriate advice. Additionally, the reception desk can refer to the user's past question history and profile information to understand the background and context of the question. For example, it can refer to what questions the user has asked and what advice they have received in the past to deepen its understanding of the current question. This allows the reception department to respond to each user's individual needs and circumstances.

[0062] The generation unit uses a generation AI to analyze questions received by the reception unit and generate advice based on data of famous people. Specifically, the generation AI receives the user's question as input and analyzes the content and intent of the question. For example, the generation AI analyzes the context and keywords of the question and extracts information to generate appropriate advice. The generation unit generates advice based on a database that has learned from famous people's quotes, anecdotes, and past advice. For example, the generation AI searches for famous people's quotes and anecdotes and extracts information related to the user's question. The generation unit can also learn the thinking patterns and related information of famous people. For example, the generation AI learns the statements and behavioral patterns of famous people and generates appropriate advice in response to the user's question. The generation unit can provide accurate advice in response to the user's question using the generation AI. For example, the generation AI generates advice based on famous people's quotes and anecdotes in response to the user's question and provides it to the user. Furthermore, the generation unit can periodically update the generation AI's learning data and provide advice based on the latest information and trends. This allows the generation unit to always provide high-quality advice based on the latest information.

[0063] The service provider delivers the advice generated by the generator to the user. Specifically, the service provider delivers the generated advice to the user in text format. For example, the service provider sends the generated advice to the user as a text message. The service provider can also deliver the advice in audio format. For example, the service provider delivers the generated advice in audio format using speech synthesis technology. By using speech synthesis technology, the user can receive the advice audibly, improving convenience. The service provider has an interface for delivering the generated advice to the user. For example, the service provider delivers the advice through a web application or a mobile application. This allows the user to receive the advice anytime, anywhere using an internet-connected device. Furthermore, the service provider can collect user feedback and continuously improve the quality of the advice. For example, by providing ratings and comments on the advice provided by the user, the service provider can evaluate the quality of the advice and make improvements as needed. This allows the service provider to continue providing high-quality advice to the user.

[0064] The generation unit can generate advice based on a database that has learned from famous people's quotes, personal anecdotes, and past advice. For example, the generation unit can extract famous people's quotes from the database and generate appropriate advice in response to a user's question. The generation unit can also generate advice based on famous people's personal anecdotes. For example, the generation unit can extract famous people's personal anecdotes from the database and generate appropriate advice in response to a user's question. The generation unit can also generate advice based on famous people's past advice. For example, the generation unit can extract famous people's past advice from the database and generate appropriate advice in response to a user's question. In this way, the generation unit can generate advice based on famous people's quotes and personal anecdotes. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input famous people's quotes and personal anecdotes into a generation AI, and the generation AI can generate advice.

[0065] The service provider can provide the generated advice to the user. For example, the service provider can provide the generated advice to the user in text format. The service provider can also provide the generated advice in audio format. For example, the service provider can provide the generated advice in audio format using speech synthesis technology. The service provider has an interface for providing the generated advice to the user. For example, the service provider can provide the advice through a web application or a mobile application. This allows the service provider to provide the generated advice to the user. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can provide the generated advice to the user using AI.

[0066] The reception desk can estimate the user's emotions and adjust the timing of question acceptance based on the estimated emotions. For example, if the user is stressed, the reception desk will accept questions at a time when the user can relax. Alternatively, if the user is focused, the reception desk can accept questions immediately. For example, the reception desk will accept questions when the user is focused. Furthermore, if the user is tired, the reception desk can accept questions after a break. For example, the reception desk will accept questions after the user has taken a break. This allows the timing of question acceptance to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the 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, which can then estimate the emotions.

[0067] The reception desk can analyze the user's past question history and select the optimal reception method. For example, the reception desk may prioritize suggesting question formats that the user has frequently used in the past. The reception desk can also accept questions during times that the user has preferred to use in the past. For example, the reception desk may accept questions during times that the user has preferred to use in the past. The reception desk can also analyze the content of the user's past questions and prioritize accepting related questions. For example, the reception desk may analyze the content of the user's past questions and prioritize accepting related questions. This allows the reception desk to select the optimal reception method based on the user's past question history. 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 past question history into AI, and the AI ​​can select the optimal reception method.

[0068] The reception desk can filter questions based on the user's current situation and areas of interest when receiving them. For example, the reception desk can prioritize questions related to topics the user is currently interested in. The reception desk can also filter questions according to the user's current situation (e.g., working, on break). For example, the reception desk can filter questions according to the user's current situation. The reception desk can also filter questions based on the user's current activity (e.g., exercising, reading). For example, the reception desk can filter questions based on the user's current activity. This allows for filtering questions based on the user's current situation and areas of interest. 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 data on the user's current situation and areas of interest into the AI, which can then filter the questions.

[0069] The reception desk can estimate the user's emotions and determine the priority of questions to accept based on the estimated emotions. For example, if the user is feeling anxious, the reception desk may prioritize urgent questions. Similarly, if the user is relaxed, the reception desk may prioritize general questions. For example, if the user is relaxed, the reception desk may prioritize general questions. Similarly, if the user is excited, the reception desk may prioritize interesting questions. For example, if the user is excited, the reception desk may prioritize interesting questions. This allows the system to prioritize questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the 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, which can then estimate the emotions.

[0070] The reception desk can prioritize receiving questions based on the user's geographical location when a question is received. For example, if the user is in a specific region, the reception desk will prioritize receiving questions related to that region. Similarly, if the user is traveling, the reception desk can prioritize receiving questions related to their travel destination. Furthermore, if the user is at home, the reception desk can prioritize receiving questions related to their home. This allows the reception desk to prioritize receiving questions based on the user's geographical location. 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 geographical location into an AI, which can then prioritize receiving questions based on their relevance.

[0071] The reception desk can analyze the user's social media activity when receiving a question and accept relevant questions. For example, the reception desk can prioritize questions related to topics the user has recently shown interest in on social media. The reception desk can also prioritize questions related to celebrities the user follows on social media. For example, the reception desk can prioritize questions related to celebrities the user follows on social media. The reception desk can also analyze the user's social media activity history and prioritize accepting relevant questions. For example, the reception desk can analyze the user's social media activity history and prioritize accepting relevant questions. This allows the reception desk to accept relevant questions based on the user's social media activity. 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 social media activity data into an AI, which can then accept relevant questions.

[0072] The generation unit can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is depressed, the generation unit will generate advice that includes many words of encouragement. The generation unit can also generate calm advice if the user is excited. For example, if the user is excited, the generation unit will generate calm advice. The generation unit can also generate detailed advice if the user is relaxed. For example, if the user is relaxed, the generation unit will generate detailed advice. This allows the way advice is expressed to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI, and the generation AI can adjust the way advice is expressed.

[0073] The generation unit can adjust the level of detail of the advice based on the importance of the question when generating advice. For example, the generation unit generates detailed advice for high-importance questions. The generation unit can also generate concise advice for low-importance questions. For example, the generation unit generates concise advice for low-importance questions. The generation unit can also adjust the length of the advice according to the importance of the question. For example, the generation unit adjusts the length of the advice according to the importance of the question. This allows the level of detail of the advice to be adjusted according to the importance of the question. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input question importance data into a generation AI, and the generation AI can adjust the level of detail of the advice.

[0074] The generation unit can apply different generation algorithms depending on the category of the question when generating advice. For example, for career-related questions, the generation unit can apply a generation algorithm based on career-related data. Similarly, for relationship-related questions, the generation unit can apply a generation algorithm based on relationship-related data. For example, for relationship-related questions, the generation unit can apply a generation algorithm based on relationship-related data. Similarly, for health-related questions, the generation unit can apply a generation algorithm based on health-related data. For example, for health-related questions, the generation unit can apply a generation algorithm based on health-related data. This allows the generation unit to apply the most suitable generation algorithm depending on the category of the question. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the question category data into a generation AI, which can then apply the most suitable generation algorithm.

[0075] The generation unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise advice. If the user is relaxed, the generation unit can also generate longer advice with detailed explanations. If the user is excited, the generation unit can also generate advice with visually stimulating effects. This allows the length of the advice to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the length of the advice.

[0076] The generation unit can determine the priority of advice based on when the question was submitted. For example, it can prioritize generating advice for recently submitted questions. It can also postpone generating advice for older questions. For example, it can postpone generating advice for older questions. Furthermore, the generation unit can adjust the order in which advice is generated according to the submission date. For example, it can adjust the order in which advice is generated according to the submission date. This allows the priority of advice to be determined according to when the question was submitted. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input question submission date data into a generation AI, and the generation AI can determine the priority of advice.

[0077] The generation unit can adjust the order of advice based on the relevance of the questions when generating advice. For example, the generation unit will prioritize generating advice when the questions are highly relevant. The generation unit can also postpone generating advice when the questions are less relevant. For example, the generation unit will postpone generating advice when the questions are less relevant. The generation unit can also adjust the order of advice generation according to the relevance of the questions. For example, the generation unit will adjust the order of advice generation according to the relevance of the questions. This allows the order of advice to be adjusted according to the relevance of the questions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input question relevance data into a generation AI, and the generation AI can adjust the order of advice.

[0078] The service provider can estimate the user's emotions and adjust the way advice is delivered based on the estimated emotions. For example, if the user is depressed, the service provider will provide advice that includes many words of encouragement. If the user is excited, the service provider can also provide calm advice. For example, if the user is excited, the service provider will provide calm advice. If the user is relaxed, the service provider can also provide detailed advice. For example, if the user is relaxed, the service provider will provide detailed advice. This allows the service provider to adjust the way advice is delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's emotion data into a generative AI, which can then adjust the way advice is delivered.

[0079] The service provider can select the optimal delivery method by referring to the user's past advice history when providing advice. For example, the service provider may prioritize suggesting delivery methods (text, voice, etc.) that the user has preferred in the past. The service provider can also prioritize providing relevant advice from the user's past advice history. For example, the service provider may prioritize providing relevant advice from the user's past advice history. The service provider can also analyze the user's past advice history and select the optimal delivery method. For example, the service provider may analyze the user's past advice history and select the optimal delivery method. This allows the service provider to select the optimal delivery method based on the user's past advice history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past advice history data into AI, and the AI ​​can select the optimal delivery method.

[0080] The service provider can customize the means of providing advice based on the user's current situation. For example, if the user is working, the service provider can provide short, concise advice. If the user is on a break, the service provider can provide detailed advice. For example, if the user is on a break, the service provider can provide detailed advice. If the user is on the move, the service provider can provide advice via voice. For example, if the user is on the move, the service provider can provide advice via voice. This allows the means of providing advice to be customized according to the user's current situation. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's current situation data into the AI, which can then customize the means of providing advice.

[0081] The service provider can estimate the user's emotions and determine the order in which advice is provided based on the estimated emotions. For example, if the user is feeling anxious, the service provider may prioritize providing urgent advice. Similarly, if the user is relaxed, the service provider may prioritize providing general advice. Furthermore, if the user is excited, the service provider may prioritize providing interesting advice. This allows the service provider to determine the order in which advice is provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI, which can then determine the order in which advice is provided.

[0082] The service provider can select the most appropriate method of providing advice based on the user's geographical location. For example, if the user is in a specific region, the service provider can prioritize providing advice related to that region. Similarly, if the user is traveling, the service provider can prioritize providing advice related to their travel destination. For example, if the user is traveling, the service provider can prioritize providing advice related to their travel destination. Similarly, if the user is at home, the service provider can prioritize providing advice related to their home. This allows the service provider to select the most appropriate method of providing advice based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location into AI, which can then select the most appropriate method of providing advice.

[0083] The service provider can analyze the user's social media activity and propose methods for providing advice. For example, the service provider can prioritize providing advice related to topics the user has recently shown interest in on social media. The service provider can also prioritize providing advice related to celebrities the user follows on social media. For example, the service provider can prioritize providing advice related to celebrities the user follows on social media. The service provider can also analyze the user's social media activity history and prioritize providing relevant advice. For example, the service provider can analyze the user's social media activity history and prioritize providing relevant advice. This allows the service provider to propose the most suitable method for providing advice based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into AI, which can then propose the most suitable method for providing advice.

[0084] The marketplace unit can estimate the user's emotions and adjust how the marketplace is displayed based on the estimated emotions. For example, if the user is relaxed, the marketplace unit can provide an interface with bright colors. If the user is tense, the marketplace unit can also provide an interface with calm colors. For example, if the user is excited, the marketplace unit can provide an interface with visually stimulating effects. For example, if the user is excited, the marketplace unit can provide an interface with visually stimulating effects. This allows the marketplace to be displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the marketplace unit may be performed using AI or not. For example, the marketplace unit can input user emotion data into a generative AI, which can then adjust how the marketplace is displayed.

[0085] The marketplace section can select the optimal display method by referring to the user's past purchase history when displaying marketplace items. For example, the marketplace section can prioritize displaying items related to products the user has previously purchased. The marketplace section can also prioritize displaying products in the user's preferred categories based on their past purchase history. For example, the marketplace section can prioritize displaying products in the user's preferred categories based on their past purchase history. The marketplace section can also analyze the user's past purchase history and recommend related products. For example, the marketplace section can analyze the user's past purchase history and recommend related products. This allows the optimal display method to be selected based on the user's past purchase history. Some or all of the above processing in the marketplace section may be performed using AI or not. For example, the marketplace section can input the user's past purchase history data into AI, which can then select the optimal display method.

[0086] The marketplace section can customize the displayed content based on the user's current areas of interest when displaying the marketplace. For example, the marketplace section can prioritize displaying products related to topics the user is currently interested in. The marketplace section can also customize and display products based on the user's current activities (hobbies, work, etc.). For example, the marketplace section can customize and display products based on the user's current activities. The marketplace section can also analyze the user's current areas of interest and recommend related products. For example, the marketplace section can analyze the user's current areas of interest and recommend related products. This allows the displayed content to be customized based on the user's current areas of interest. Some or all of the above processing in the marketplace section may be performed using AI or not. For example, the marketplace section can input the user's current areas of interest data into AI, which can then customize the displayed content.

[0087] The marketplace unit can estimate the user's emotions and determine the display order of marketplace items based on the estimated emotions. For example, if the user is relaxed, the marketplace unit may prioritize displaying products with detailed information. Similarly, if the user is in a hurry, the marketplace unit may prioritize displaying products with concise information. For example, if the user is excited, the marketplace unit may prioritize displaying visually stimulating products. This allows the display order of marketplace items to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the marketplace unit may be performed using AI or not. For example, the marketplace unit can input user emotion data into a generative AI, which can then determine the display order of marketplace items.

[0088] The marketplace section can select the optimal display method based on the user's geographical location when displaying marketplace items. For example, if the user is in a specific region, the marketplace section can prioritize displaying products related to that region. Similarly, if the user is traveling, the marketplace section can prioritize displaying products related to their travel destination. Furthermore, if the user is at home, the marketplace section can prioritize displaying products related to their home. This allows the system to select the optimal display method based on the user's geographical location. Some or all of the above processing in the marketplace section may be performed using AI, or without AI. For example, the marketplace section can input the user's geographical location information into AI, which can then select the optimal display method.

[0089] The marketplace unit can analyze a user's social media activity and suggest content to display when showing marketplace items. For example, the marketplace unit can prioritize displaying products related to topics the user has recently shown interest in on social media. The marketplace unit can also prioritize displaying products related to celebrities the user follows on social media. For example, the marketplace unit can prioritize displaying products related to celebrities the user follows on social media. The marketplace unit can also analyze a user's social media activity history and recommend related products. For example, the marketplace unit can analyze a user's social media activity history and recommend related products. This allows the marketplace unit to suggest optimal content based on the user's social media activity. Some or all of the above processing in the marketplace unit may be performed using AI or not. For example, the marketplace unit can input the user's social media activity data into AI, which can then suggest optimal content.

[0090] The marketplace unit can suggest optimal display content by combining the user's purchase history and social media activity when displaying marketplace items. For example, the marketplace unit can display related products by combining items the user has previously purchased with topics they have shown interest in on social media. The marketplace unit can also display products related to the user's purchase history and the celebrities they follow on social media. For example, the marketplace unit can display products related to the user's purchase history and the celebrities they follow on social media. The marketplace unit can also analyze the user's purchase history and social media activity history and display the most suitable products as recommendations. For example, the marketplace unit can analyze the user's purchase history and social media activity history and display the most suitable products as recommendations. This allows the marketplace unit to suggest optimal display content based on the user's purchase history and social media activity. Some or all of the above processing in the marketplace unit may be performed using AI or not. For example, the marketplace unit can input the user's purchase history and social media activity data into AI, which can then suggest optimal display content.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The advice-providing system can estimate the user's emotions and customize the content of the advice based on those emotions. For example, if the user is feeling anxious, the generation unit can generate advice that provides reassurance. If the user is excited, the generation unit can generate advice that encourages calmness. Furthermore, if the user is depressed, the generation unit can generate advice that includes many words of encouragement. This allows the system to provide appropriate advice that matches the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. Some or all of the above-described processing in the generation unit may be performed using a generative AI, or it may be performed without a generative AI.

[0093] The advice-providing system can analyze a user's past question history and provide optimal advice. For example, if a user has asked a similar question in the past, it can generate new advice by referring to past advice given to that question. It can also evaluate the effectiveness of advice the user has received in the past and adjust the content of the advice based on that evaluation. Furthermore, based on the user's past question history, it can predict related questions and provide advice on problems the user has not yet realized. In this way, it can provide more accurate advice by utilizing the user's past question history. Some or all of the above processing in the generation unit may be performed using generation AI, or it may be performed without using generation AI.

[0094] The advice-providing system can customize the content of the advice based on the user's current situation. For example, if the user is at work, the generation unit can generate short, concise advice. If the user is on a break, the generation unit can generate detailed advice. Furthermore, if the user is on the move, the generation unit can generate advice delivered via voice. This allows the system to provide appropriate advice tailored to the user's current situation. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI.

[0095] The advice provision system can customize the content of the advice based on the user's geographical location information. For example, if the user is in a specific region, it can generate advice related to that region. If the user is traveling, it can also generate advice related to their travel destination. Furthermore, if the user is at home, it can generate advice related to their home. This allows the system to provide appropriate advice based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0096] The advice-providing system can analyze a user's social media activity and provide relevant advice. For example, it can generate advice related to topics the user has recently shown interest in on social media. It can also generate advice related to prominent figures the user follows on social media. Furthermore, it can analyze the user's social media activity history and provide relevant advice. This allows for the provision of appropriate advice based on the user's social media activity. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI.

[0097] The advice-providing system can estimate the user's emotions and adjust the way the advice is expressed based on those emotions. For example, if the user is depressed, the generation unit can generate advice that includes many words of encouragement. If the user is excited, the generation unit can generate calm advice. Furthermore, if the user is relaxed, the generation unit can generate detailed advice. This allows the system to provide appropriate advice that matches the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. Some or all of the processing described above in the generation unit may be performed using a generative AI, or it may be performed without a generative AI.

[0098] The advice-providing system can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the generator can produce short, concise advice. If the user is relaxed, the generator can produce longer advice that includes detailed explanations. Furthermore, if the user is excited, the generator can produce advice with visually stimulating effects. This allows the system to provide appropriate advice tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. Some or all of the processing described above in the generator may be performed using a generative AI or not.

[0099] The advice-providing system can estimate the user's emotions and adjust the way advice is delivered based on those estimated emotions. For example, if the user is depressed, the system can provide advice that includes many words of encouragement. If the user is excited, the system can provide calm advice. Furthermore, if the user is relaxed, the system can provide detailed advice. This allows the system to provide appropriate advice that matches the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the processing described above in the advice-providing unit may be performed using AI or not.

[0100] The advice-providing system can estimate the user's emotions and determine the order in which advice is provided based on those emotions. For example, if the user is feeling anxious, the system can prioritize providing urgent advice. If the user is relaxed, the system can prioritize providing general advice. Furthermore, if the user is excited, the system can prioritize providing interesting advice. This allows the system to provide appropriate advice tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the processing described above in the advice-providing unit may be performed using AI or not.

[0101] The advice-providing system can estimate the user's emotions and adjust the display method of the marketplace based on the estimated emotions. For example, if the user is relaxed, the marketplace can provide an interface with bright colors. If the user is stressed, the marketplace can provide an interface with calm colors. Furthermore, if the user is excited, the marketplace can provide an interface with visually stimulating effects. This allows for the provision of an appropriate marketplace display method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Some or all of the above processing in the marketplace may be performed using AI or not.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The reception desk receives questions from users. These questions may include questions about careers, health, and life choices. The reception desk can analyze the questions entered by users using natural language processing technology and identify key parts of the questions using keyword extraction technology. This allows the reception desk to understand the intent of the questions. Step 2: The generation unit uses a generation AI to analyze the questions received by the reception unit and generate advice based on data about famous people. The generation unit generates advice based on a database that has learned from famous people's quotes, experiences, and past advice, and can also learn the thinking patterns and related information of famous people. This allows it to provide accurate advice in response to the user's questions. Step 3: The provider unit provides the user with the advice generated by the generator unit. The provider unit can provide the generated advice to the user in text or audio format. For example, the provider unit can provide advice generated using speech synthesis technology in audio format. The provider unit also has an interface for providing advice through web applications and mobile applications.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] Each of the multiple elements described above, including the reception unit, generation unit, and provision 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 from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates advice based on data of famous people. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated advice to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the reception unit, generation unit, and provision 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 from the user. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates advice based on data of famous people. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the generated advice to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the reception unit, generation unit, and provision 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 from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates advice based on data of famous people. The provision unit is implemented by the speaker 240 of the headset terminal 314 and provides the generated advice to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the reception unit, generation unit, and provision 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 from the user. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and generates advice based on data of famous people. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated advice to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] (Note 1) A reception desk that handles questions from users, A generation unit analyzes the questions received by the reception unit and generates advice using a generation AI that has learned from data on famous people. The system includes a providing unit that provides advice generated by the generation unit to the user. A system characterized by the following features. (Note 2) The generating unit is This system generates advice based on a database that has learned from famous quotes, personal anecdotes, and past advice. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Provide the generated advice to the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of question submissions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) 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 6) The aforementioned reception unit is When receiving a question, 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 7) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the questions to be asked based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving questions, the system prioritizes questions that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving a question, the system analyzes the user's social media activity and selects relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating advice, adjust the level of detail in the advice based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating advice, different generation algorithms are applied depending on the question category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating advice, we prioritize the advice based on when the question was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating advice, the order of advice is adjusted based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, It estimates the user's emotions and adjusts how advice is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing advice, the system will refer to the user's past advice history to select the most appropriate delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing advice, customize the method of providing advice based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates the user's emotions and determines the order in which advice is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, the optimal delivery method is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing advice, we analyze the user's social media activity and propose methods for delivering advice. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned marketplace section is: It estimates user sentiment and adjusts how the marketplace is displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned marketplace section is: When displaying items in the marketplace, the system selects the optimal display method by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned marketplace section is: When displaying the marketplace, customize the displayed content based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned marketplace section is: It estimates user sentiment and determines the display order of marketplace items based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned marketplace section is: When displaying marketplace listings, the system selects the optimal display method based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned marketplace section is: When displaying marketplace listings, the system analyzes the user's social media activity to suggest content to display. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned marketplace section is: When displaying marketplace listings, the system suggests optimal content by combining the user's purchase history and social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0176] 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 from users, A generation unit analyzes the questions received by the reception unit and generates advice using a generation AI that has learned from data on famous people. The system includes a providing unit that provides advice generated by the generation unit to the user. A system characterized by the following features.

2. The generating unit is This system generates advice based on a database that has learned from famous quotes, personal anecdotes, and past advice. The system according to feature 1.

3. The aforementioned supply unit is, Provide the generated advice to the user. The system according to feature 1.

4. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of question submissions based on those estimated emotions. The system according to feature 1.

5. 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.

6. The aforementioned reception unit is When receiving a question, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and determines the priority of questions to ask based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When receiving questions, the system prioritizes questions that are highly relevant based on the user's geographical location. The system according to feature 1.

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