system

Abstract

The system according to this embodiment aims to enable users to quickly and accurately find the information they need. [Solution] The system according to the embodiment comprises a reception unit, a identification unit, and a provision unit. The reception unit receives a question or input from the user. The identification unit identifies a link to a page or information related to the question or input received by the reception unit. The provision unit provides the link identified by the identification unit.

Description

Technical Field

[0006] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the 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 that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for a user to quickly and accurately find the necessary information.

[0005] The system according to the embodiment aims to enable a user to quickly and accurately find the necessary information.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a specification unit, and a provision unit. The reception unit receives a user's question or input. The specification unit specifies a link to a page or information related to the question or input received by the reception unit. The provision unit provides the link specified by the specification unit.

Effects of the Invention

[0007] The system according to this embodiment allows users to quickly and accurately find the information they need. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The interactive navigation system AI assistant according to an embodiment of the present invention is a system that provides links to relevant pages and information in response to user questions and inputs. This system analyzes user questions and inputs, identifies links to relevant pages and information, and provides them to the user. It also supports site search and filtering via voice and text, and has a function to explain how to use the website and FAQs in real time. For example, when a user inputs a question such as "Please tell me the details of this product" or "Please tell me how to return it," the AI ​​assistant analyzes the question and identifies links to relevant pages and information. The identified links are provided to the user. For example, a link to the product details page is displayed to the user. This allows the user to easily access relevant information. Furthermore, it also has a function to support site search and filtering via voice and text. For example, when a user gives a voice command such as "Narrow down and display products in this category," the AI ​​assistant narrows down and displays products according to that command. It also has a function to explain how to use the website and FAQs in real time. For example, when a user inputs "Tell me how to use this site," the AI ​​assistant explains how to use the site. This allows the user to use the website more effectively. This system allows users to easily access relevant information, making website usage more convenient. For example, if a user is looking for specific information, the AI ​​assistant can provide a link to that information, allowing the user to quickly access the desired information. Furthermore, support for voice and text search and filtering enables users to find information more efficiently. In addition, explanations of how to use the website and FAQs allow users to utilize the website more effectively. This enables the interactive navigation system AI assistant to provide links to relevant pages and information in response to user questions and inputs.

[0029] The interactive navigation system AI assistant according to this embodiment comprises a reception unit, a identification unit, and a provision unit. The reception unit receives user questions or inputs. User questions or inputs include, but are not limited to, text, voice, and images. The reception unit can, for example, convert voice input to text using speech recognition technology. The reception unit can also analyze text input using text analysis technology. For example, if the user inputs "Please tell me the details of this product" by voice, the reception unit converts the voice to text and analyzes it. The identification unit identifies links to pages or information related to the questions or inputs received by the reception unit. The identification unit can, for example, understand the content of the question and identify related pages or information using natural language processing technology. For example, in response to the question "Please tell me the details of this product," the identification unit identifies a link to the product details page. The identification unit can also identify links based on the user's context. For example, the identification unit identifies the most relevant links by considering the user's past actions and current situation. The provision unit provides the user with the links identified by the identification unit. The providing unit can, for example, display links on the user's screen. The providing unit can also provide voice guidance for links. For example, the providing unit can voice guidance such as, "Click here for the product details page." This allows the interactive navigation system AI assistant according to the embodiment to provide links to relevant pages and information in response to user questions and inputs.

[0030] The reception desk receives user questions or inputs. User questions or inputs include, but are not limited to, text, voice, and images. The reception desk can, for example, convert voice input to text using speech recognition technology. Specifically, speech recognition technology analyzes the user's voice in real time and converts the voice waveform into text data. In this process, advanced algorithms are used to remove noise and accurately recognize the voice. The reception desk can also analyze text input using text analysis technology. Text analysis technology uses natural language processing (NLP) to understand the user's input and analyze the context and intent. For example, if a user inputs "Please tell me the details of this product" via voice, the voice is converted to text and analyzed. The analysis includes morphological analysis, grammatical analysis, and semantic analysis, and processes are performed to accurately grasp the user's intent. Furthermore, the reception desk can also analyze image input using image recognition technology. For example, if a user uploads an image of a product, the image is analyzed and information related to the product is extracted. Image recognition technology analyzes information within images using methods such as object detection, image classification, and feature extraction. This allows the reception unit to handle diverse input formats and accurately receive user questions and requests.

[0031] The identification unit identifies links to pages or information related to questions or inputs received by the reception unit. The identification unit can, for example, use natural language processing technology to understand the content of questions and identify relevant pages or information. Specifically, natural language processing technology analyzes the user's question and processes it to understand its intent and purpose. For example, in response to the question, "Please tell me the details of this product," the identification unit identifies a link to the product details page. The identification unit analyzes the keywords and context of the question and generates a query to search for relevant information. This query is sent to a database or search engine to retrieve relevant pages and information. The identification unit can also identify links based on the user's context. For example, it considers the user's past behavior and current situation to identify the most relevant links. It uses the user's past search history, browsing history, and current location information to identify links that best provide information to the user's needs. Furthermore, the identification unit can use machine learning algorithms to learn user preferences and behavioral patterns, enabling more accurate link identification. This allows the identification unit to quickly identify and provide the most appropriate and relevant information in response to user questions and inputs.

[0032] The service provider provides users with links identified by the identification service provider. For example, the service provider can display links on the user's screen. Specifically, the service provider displays identified links on the user's device, making them easily accessible to the user. Links are displayed as text links or buttons, allowing users to access related pages or information by clicking them. The service provider can also provide audio guidance for links. For example, the service provider might say, "Click here for the product details page." Audio guidance is useful when the user cannot visually confirm the information or when they want to operate hands-free. Furthermore, the service provider can send notifications to the user's device. For example, it can notify the user of identified links using smartphone push notifications, email, or SMS. This allows users to quickly access important information without missing anything. The service provider can also collect user feedback and continuously improve the accuracy and effectiveness of the information it provides. For example, it can collect feedback on whether the user clicked on the provided link and whether the information at the linked destination was helpful, and use this feedback to identify and provide links in the future. This allows the service provider to offer users the most relevant information and improve user satisfaction.

[0033] The identification unit can identify links within a website that a user is browsing. For example, it can analyze the content of the page the user is currently viewing and identify relevant links within that page. For instance, if a user is viewing a product page, the unit can identify links to reviews and FAQ pages related to that product. The unit can also analyze the metadata and keywords of the page the user is viewing and identify relevant links. For example, it can analyze the page title and meta tags and identify links to relevant information. This allows the unit to identify links within a website that a user is browsing.

[0034] The reception desk can accept questions or inputs in both voice and text. The reception desk can, for example, convert voice input to text using speech recognition technology. The reception desk can also analyze text input using text analysis technology. For example, if a user inputs "Please tell me the details of this product" in voice, the reception desk will convert that voice to text and analyze it. The reception desk can also analyze text if a user inputs a question in text. This allows the reception desk to accept questions or inputs in both voice and text. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input voice input into a generating AI and have the generating AI perform the conversion from voice data to text data.

[0035] The explanation section can explain how to use the website the user is viewing. For example, if the user types "Tell me how to use this site," the explanation section will explain the basic operation and settings of that site. The explanation section can also explain FAQs in real time. For example, if the user types "Tell me how to return an item," it will explain the return procedure. In this way, the explanation section can explain how to use the website the user is viewing or provide FAQs. Some or all of the above processing in the explanation section may be performed using AI, for example, or not using AI. For example, the explanation section can input the user's question into a generating AI and have the generating AI produce an appropriate explanation.

[0036] The identification unit can identify links based on the user's context. For example, the identification unit can identify the most relevant links by considering the user's past actions and current situation. For example, the identification unit can identify relevant links based on pages the user has previously viewed and their search history. The identification unit can also identify links by considering the user's current situation and location information. For example, the identification unit can identify links to information related to the user's current location. This allows for link identification based on the user's context. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's context data into a generating AI and have the generating AI perform the identification of relevant links.

[0037] The service provider can provide links at a timing appropriate to the user's context. For example, when a user is viewing a specific page, the service provider can provide links related to that page. The service provider can also provide links related to a specific action the user has performed. For example, when a user adds an item to their cart, the service provider can provide links to related products. This allows the service provider to provide links at a timing appropriate to the user's context. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user context data into a generating AI and have the generating AI execute the timing of link provision.

[0038] The service provider can prioritize providing links of high importance according to the user's attributes. For example, the service provider can provide the most relevant links by considering attributes such as the user's age, gender, and interests. For example, the service provider can provide links to the latest trend information to younger users and links to health information to older users. The service provider can also provide links based on the user's interests. For example, the service provider can provide links related to topics that the user has shown interest in in the past. This allows the service provider to prioritize providing links of high importance according to the user's attributes. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user attribute data into a generating AI and have the generating AI identify high-importance links.

[0039] The identification unit can identify links based on the user's social media activity. For example, the identification unit can analyze the information the user has shared on social media and the content of accounts they follow to identify relevant links. For example, the identification unit can identify links related to topics the user has shown interest in on social media. The identification unit can also identify links by considering the user's social media activity history. For example, the identification unit can identify links related to posts the user has previously "liked". This allows the identification of links based on the user's social media activity. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's social media data into a generating AI and have the generating AI perform the identification of relevant links.

[0040] The reception desk can analyze the user's past question history and select the most suitable reception method. For example, the reception desk can automatically display frequently asked questions as candidates. It can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions to be used during specific time periods based on the user's past question history. This allows the reception desk to analyze the user's past question history and select the most suitable reception method. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's question history data into a generating AI and have the generating AI select the most suitable reception method.

[0041] The reception unit can filter questions or inputs based on the user's current areas of interest. For example, the reception unit can prioritize questions related to the content of pages the user has recently viewed. It can also prioritize questions related to a specific category if the user has shown interest in that category. Furthermore, the reception unit can filter and receive questions based on topics the user has shown interest in in the past. This allows for filtering questions or inputs based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user area of ​​interest data into a generating AI and have the generating AI perform the question filtering.

[0042] The reception desk can prioritize receiving questions that are highly relevant to the user, taking into account the user's geographical location when receiving questions or input. For example, if the user is in a specific region, the reception desk can prioritize receiving questions related to that region. Furthermore, if the user is traveling, the reception desk can prioritize receiving questions related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize receiving questions related to their home area. This allows the reception desk to prioritize receiving questions that are highly relevant to the user, taking into account their geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI identify highly relevant questions.

[0043] The reception unit can analyze the user's social media activity and receive relevant questions when receiving questions or input. For example, the reception unit can prioritize receiving relevant questions based on information the user has shared on social media. It can also receive relevant questions based on the content of accounts the user follows on social media. Furthermore, it can receive relevant questions based on topics the user has shown interest in on social media. This allows the reception unit to analyze the user's social media activity and receive relevant questions. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI identify relevant questions.

[0044] The identification unit can identify the most suitable link by analyzing the user's past browsing history when identifying links. For example, the identification unit can prioritize providing links to pages the user has frequently visited in the past. It can also provide links related to topics the user has shown interest in in the past. Furthermore, the identification unit can provide links related to specific time periods based on the user's past browsing history. This allows the identification unit to identify the most suitable link by analyzing the user's past browsing history. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's browsing history data into a generating AI and have the generating AI perform the task of identifying the most suitable link.

[0045] The identification unit can identify links based on the content of the user's currently viewed page when identifying links. For example, the identification unit can provide links related to the content of the page the user is currently viewing. The identification unit can also provide links related to a specific category if the user is viewing pages in that category. Furthermore, the identification unit can identify links based on keywords of the page the user is currently viewing. This allows the identification of links based on the content of the user's currently viewed page. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's viewed page data into a generating AI and have the generating AI perform the identification of relevant links.

[0046] The identification unit can identify highly relevant links by considering the user's geographical location information when identifying links. For example, if the user is in a specific region, the identification unit can provide links related to that region. Furthermore, if the user is traveling, the identification unit can provide links related to their travel destination. Additionally, if the user is at home, the identification unit can provide links related to their home area. This allows the identification of highly relevant links by considering the user's geographical location information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's geographical location data into a generating AI and have the generating AI perform the identification of highly relevant links.

[0047] The identification unit can analyze the user's social media activity and identify relevant links when identifying links. For example, the identification unit can provide relevant links based on information shared by the user on social media. It can also provide relevant links based on the content of accounts the user follows on social media. Furthermore, it can provide relevant links based on topics the user has shown interest in on social media. This allows the identification unit to analyze the user's social media activity and identify relevant links. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's social media data into a generating AI and have the generating AI perform the identification of relevant links.

[0048] The service provider can analyze the user's past link click history to select the optimal service method when providing links. For example, the service provider can prioritize providing links that the user has frequently clicked in the past. It can also provide links related to topics that the user has shown interest in in the past. Furthermore, the service provider can provide links related to a specific time period based on the user's past link click history. This allows the service provider to analyze the user's past link click history and select the optimal service method. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's link click history data into a generating AI and have the generating AI select the optimal service method.

[0049] The service provider can provide links based on the content of the user's currently viewed page. For example, the service provider can provide links related to the content of the page the user is currently viewing. Furthermore, if the user is viewing pages in a specific category, the service provider can also provide links related to that category. In addition, the service provider can provide links based on keywords in the page the user is currently viewing. This allows the service provider to provide links based on the content of the user's currently viewed page. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's viewed page data into a generating AI and have the generating AI perform the provision of relevant links.

[0050] The service provider can prioritize providing highly relevant links by considering the user's geographical location when providing links. For example, if the user is in a specific region, the service provider can provide links related to that region. Furthermore, if the user is traveling, the service provider can provide links related to their travel destination. Additionally, if the user is at home, the service provider can provide links related to their home area. This allows the service provider to prioritize providing highly relevant links by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant links.

[0051] The service provider can analyze the user's social media activity and provide relevant links when providing links. For example, the service provider can provide relevant links based on information shared by the user on social media. It can also provide relevant links based on the content of accounts followed by the user on social media. Furthermore, it can provide relevant links based on topics the user has shown interest in on social media. This allows the service provider to analyze the user's social media activity and provide relevant links. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant links.

[0052] The explanation unit can analyze the user's past question history to select the most appropriate explanation method during the explanation process. For example, the explanation unit can provide relevant explanations based on questions the user has frequently asked in the past. It can also provide explanations related to topics the user has shown interest in in the past. Furthermore, the explanation unit can provide explanations related to specific time periods based on the user's past question history. This allows the explanation unit to analyze the user's past question history and select the most appropriate explanation method. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the user's question history data into a generating AI and have the generating AI select the most appropriate explanation method.

[0053] The explanation unit can provide explanations based on the content of the user's currently viewed page. For example, the explanation unit can provide explanations related to the content of the page the user is currently viewing. Furthermore, if the user is viewing pages in a specific category, the explanation unit can also provide explanations related to that category. In addition, the explanation unit can provide explanations based on keywords of the page the user is currently viewing. This allows the explanation to be based on the content of the user's currently viewed page. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the user's viewed page data into a generating AI and have the generating AI provide relevant explanations.

[0054] The explanation unit can prioritize highly relevant explanations by considering the user's geographical location. For example, if the user is in a specific region, the explanation unit can provide explanations related to that region. Furthermore, if the user is traveling, the explanation unit can provide explanations related to their travel destination. Additionally, if the user is at home, the explanation unit can provide explanations related to their home area. This allows the explanation unit to prioritize highly relevant explanations by considering the user's geographical location. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the user's geographical location data into a generating AI and have the generating AI provide highly relevant explanations.

[0055] The commentary unit can analyze the user's social media activity and provide relevant commentary during the commentary process. For example, the commentary unit can provide relevant commentary based on information shared by the user on social media. It can also provide relevant commentary based on the content of accounts the user follows on social media. Furthermore, the commentary unit can provide relevant commentary based on topics the user has shown interest in on social media. This allows the commentary unit to analyze the user's social media activity and provide relevant commentary. Some or all of the above processing in the commentary unit may be performed using AI, for example, or without AI. For example, the commentary unit can input the user's social media data into a generating AI and have the generating AI provide relevant commentary.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] An interactive navigation system AI assistant can analyze a user's past behavior history and prioritize providing information related to topics the user has previously shown interest in. For example, if a user has frequently searched for products in a particular category in the past, it can prioritize providing new product information related to that category. It can also provide the latest information related to a particular topic if the user has asked many questions about that topic in the past. Furthermore, it can provide links to relevant pages based on the user's past page visit history. This allows for the provision of more relevant information based on the user's past behavior history. Some or all of the above processes in the system may be performed using AI, for example, or not. For example, the system can input user behavior history data into a generating AI and have the generating AI identify relevant information.

[0058] An interactive navigation system AI assistant can provide relevant information by taking into account the user's current geographical location. For example, if the user is in a specific area, it can provide information about events and shops related to that area. If the user is traveling, it can also provide tourist information and transportation information related to their destination. Furthermore, if the user is at home, it can provide information about services and shops around their home. This allows the system to provide more relevant information based on the user's current geographical location. Some or all of the above processing in the system may be performed using AI, for example, or not using AI. For example, the system can input the user's geographical location data into a generating AI and have the generating AI identify relevant information.

[0059] An interactive navigation system AI assistant can analyze a user's social media activity and provide relevant information. For example, it can provide relevant news and articles based on information the user has shared on social media. It can also provide relevant topic and event information based on the content of accounts the user follows. Furthermore, it can provide information on relevant products and services based on topics the user has shown interest in on social media. This allows for the provision of more relevant information based on the user's social media activity. Some or all of the above processes in the system may be performed using AI, for example, or not. For example, the system can input the user's social media data into a generating AI and have the generating AI identify relevant information.

[0060] An interactive navigation system AI assistant can analyze a user's past question history and prioritize providing information related to questions the user has previously asked. For example, if a user has asked many questions about a particular topic in the past, it can provide the latest information related to that topic. It can also provide relevant FAQs and guides based on the content of questions the user has asked in the past. Furthermore, it can provide links to relevant pages based on the user's history of visited pages. This allows for the provision of more relevant information based on the user's past question history. Some or all of the above processes in the system may be performed using AI, for example, or not. For example, the system can input the user's question history data into a generating AI and have the generating AI identify relevant information.

[0061] An interactive navigation system AI assistant can provide relevant information based on the content of the user's current page. For example, it can provide FAQs or guides related to the content of the page the user is currently viewing. It can also provide the latest information related to a specific category if the user is viewing pages in that category. Furthermore, it can provide relevant articles and news based on keywords in the page the user is currently viewing. This allows for the provision of more relevant information based on the content of the user's current page. Some or all of the above processing in the system may be performed using AI, for example, or not. For example, the system can input the user's page viewing data into a generating AI and have the generating AI identify relevant information.

[0062] An interactive navigation system AI assistant can analyze a user's past link click history and prioritize providing information related to links the user has previously clicked. For example, if a user has frequently clicked product links in a particular category in the past, the system can prioritize providing new product information related to that category. It can also provide relevant articles and news based on the content of links the user has clicked in the past. Furthermore, it can provide links to relevant pages based on the user's past page visit history. This allows the system to provide more relevant information based on the user's past link click history. Some or all of the above processing in the system may be performed using AI, for example, or not using AI. For example, the system can input the user's link click history data into a generating AI and have the generating AI identify relevant information.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives user questions or inputs. User questions or inputs can include text, voice, images, etc. The reception desk can use speech recognition technology to convert voice input into text and text analysis technology to analyze the text input. For example, if a user inputs "Please tell me the details of this product" by voice, the voice will be converted into text and analyzed. Step 2: The identification unit identifies links to pages or information related to the question or input received by the reception unit. The identification unit can understand the content of the question using natural language processing technology and identify relevant pages or information. For example, in response to the question "Please tell me the details of this product," it will identify a link to the product details page. The identification unit can also identify the most relevant links by considering the user's past behavior and current situation. Step 3: The providing unit provides the user with the link identified by the identifying unit. The providing unit can display the link on the user's screen and can also provide audio guidance. For example, it can provide audio guidance such as, "Click here for the product details page."

[0065] (Example of form 2) The interactive navigation system AI assistant according to an embodiment of the present invention is a system that provides links to relevant pages and information in response to user questions and inputs. This system analyzes user questions and inputs, identifies links to relevant pages and information, and provides them to the user. It also supports site search and filtering via voice and text, and has a function to explain how to use the website and FAQs in real time. For example, when a user inputs a question such as "Please tell me the details of this product" or "Please tell me how to return it," the AI ​​assistant analyzes the question and identifies links to relevant pages and information. The identified links are provided to the user. For example, a link to the product details page is displayed to the user. This allows the user to easily access relevant information. Furthermore, it also has a function to support site search and filtering via voice and text. For example, when a user gives a voice command such as "Narrow down and display products in this category," the AI ​​assistant narrows down and displays products according to that command. It also has a function to explain how to use the website and FAQs in real time. For example, when a user inputs "Tell me how to use this site," the AI ​​assistant explains how to use the site. This allows the user to use the website more effectively. This system allows users to easily access relevant information, making website usage more convenient. For example, if a user is looking for specific information, the AI ​​assistant can provide a link to that information, allowing the user to quickly access the desired information. Furthermore, support for voice and text search and filtering enables users to find information more efficiently. In addition, explanations of how to use the website and FAQs allow users to utilize the website more effectively. This enables the interactive navigation system AI assistant to provide links to relevant pages and information in response to user questions and inputs.

[0066] The interactive navigation system AI assistant according to this embodiment comprises a reception unit, a identification unit, and a provision unit. The reception unit receives user questions or inputs. User questions or inputs include, but are not limited to, text, voice, and images. The reception unit can, for example, convert voice input to text using speech recognition technology. The reception unit can also analyze text input using text analysis technology. For example, if the user inputs "Please tell me the details of this product" by voice, the reception unit converts the voice to text and analyzes it. The identification unit identifies links to pages or information related to the questions or inputs received by the reception unit. The identification unit can, for example, understand the content of the question and identify related pages or information using natural language processing technology. For example, in response to the question "Please tell me the details of this product," the identification unit identifies a link to the product details page. The identification unit can also identify links based on the user's context. For example, the identification unit identifies the most relevant links by considering the user's past actions and current situation. The provision unit provides the user with the links identified by the identification unit. The providing unit can, for example, display links on the user's screen. The providing unit can also provide voice guidance for links. For example, the providing unit can voice guidance such as, "Click here for the product details page." This allows the interactive navigation system AI assistant according to the embodiment to provide links to relevant pages and information in response to user questions and inputs.

[0067] The reception desk receives user questions or inputs. User questions or inputs include, but are not limited to, text, voice, and images. The reception desk can, for example, convert voice input to text using speech recognition technology. Specifically, speech recognition technology analyzes the user's voice in real time and converts the voice waveform into text data. In this process, advanced algorithms are used to remove noise and accurately recognize the voice. The reception desk can also analyze text input using text analysis technology. Text analysis technology uses natural language processing (NLP) to understand the user's input and analyze the context and intent. For example, if a user inputs "Please tell me the details of this product" via voice, the voice is converted to text and analyzed. The analysis includes morphological analysis, grammatical analysis, and semantic analysis, and processes are performed to accurately grasp the user's intent. Furthermore, the reception desk can also analyze image input using image recognition technology. For example, if a user uploads an image of a product, the image is analyzed and information related to the product is extracted. Image recognition technology analyzes information within images using methods such as object detection, image classification, and feature extraction. This allows the reception unit to handle diverse input formats and accurately receive user questions and requests.

[0068] The identification unit identifies links to pages or information related to questions or inputs received by the reception unit. The identification unit can, for example, use natural language processing technology to understand the content of questions and identify relevant pages or information. Specifically, natural language processing technology analyzes the user's question and processes it to understand its intent and purpose. For example, in response to the question, "Please tell me the details of this product," the identification unit identifies a link to the product details page. The identification unit analyzes the keywords and context of the question and generates a query to search for relevant information. This query is sent to a database or search engine to retrieve relevant pages and information. The identification unit can also identify links based on the user's context. For example, it considers the user's past behavior and current situation to identify the most relevant links. It uses the user's past search history, browsing history, and current location information to identify links that best provide information to the user's needs. Furthermore, the identification unit can use machine learning algorithms to learn user preferences and behavioral patterns, enabling more accurate link identification. This allows the identification unit to quickly identify and provide the most appropriate and relevant information in response to user questions and inputs.

[0069] The service provider provides users with links identified by the identification service provider. For example, the service provider can display links on the user's screen. Specifically, the service provider displays identified links on the user's device, making them easily accessible to the user. Links are displayed as text links or buttons, allowing users to access related pages or information by clicking them. The service provider can also provide audio guidance for links. For example, the service provider might say, "Click here for the product details page." Audio guidance is useful when the user cannot visually confirm the information or when they want to operate hands-free. Furthermore, the service provider can send notifications to the user's device. For example, it can notify the user of identified links using smartphone push notifications, email, or SMS. This allows users to quickly access important information without missing anything. The service provider can also collect user feedback and continuously improve the accuracy and effectiveness of the information it provides. For example, it can collect feedback on whether the user clicked on the provided link and whether the information at the linked destination was helpful, and use this feedback to identify and provide links in the future. This allows the service provider to offer users the most relevant information and improve user satisfaction.

[0070] The identification unit can identify links within a website that a user is browsing. For example, it can analyze the content of the page the user is currently viewing and identify relevant links within that page. For instance, if a user is viewing a product page, the unit can identify links to reviews and FAQ pages related to that product. The unit can also analyze the metadata and keywords of the page the user is viewing and identify relevant links. For example, it can analyze the page title and meta tags and identify links to relevant information. This allows the unit to identify links within a website that a user is browsing.

[0071] The reception desk can accept questions or inputs in both voice and text. The reception desk can, for example, convert voice input to text using speech recognition technology. The reception desk can also analyze text input using text analysis technology. For example, if a user inputs "Please tell me the details of this product" in voice, the reception desk will convert that voice to text and analyze it. The reception desk can also analyze text if a user inputs a question in text. This allows the reception desk to accept questions or inputs in both voice and text. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input voice input into a generating AI and have the generating AI perform the conversion from voice data to text data.

[0072] The explanation section can explain how to use the website the user is viewing. For example, if the user types "Tell me how to use this site," the explanation section will explain the basic operation and settings of that site. The explanation section can also explain FAQs in real time. For example, if the user types "Tell me how to return an item," it will explain the return procedure. In this way, the explanation section can explain how to use the website the user is viewing or provide FAQs. Some or all of the above processing in the explanation section may be performed using AI, for example, or not using AI. For example, the explanation section can input the user's question into a generating AI and have the generating AI produce an appropriate explanation.

[0073] The identification unit can identify links based on the user's context. For example, the identification unit can identify the most relevant links by considering the user's past actions and current situation. For example, the identification unit can identify relevant links based on pages the user has previously viewed and their search history. The identification unit can also identify links by considering the user's current situation and location information. For example, the identification unit can identify links to information related to the user's current location. This allows for link identification based on the user's context. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's context data into a generating AI and have the generating AI perform the identification of relevant links.

[0074] The service provider can provide links at a timing appropriate to the user's context. For example, when a user is viewing a specific page, the service provider can provide links related to that page. The service provider can also provide links related to a specific action the user has performed. For example, when a user adds an item to their cart, the service provider can provide links to related products. This allows the service provider to provide links at a timing appropriate to the user's context. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user context data into a generating AI and have the generating AI execute the timing of link provision.

[0075] The service provider can prioritize providing links of high importance according to the user's attributes. For example, the service provider can provide the most relevant links by considering attributes such as the user's age, gender, and interests. For example, the service provider can provide links to the latest trend information to younger users and links to health information to older users. The service provider can also provide links based on the user's interests. For example, the service provider can provide links related to topics that the user has shown interest in in the past. This allows the service provider to prioritize providing links of high importance according to the user's attributes. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user attribute data into a generating AI and have the generating AI identify high-importance links.

[0076] The identification unit can identify links based on the user's social media activity. For example, the identification unit can analyze the information the user has shared on social media and the content of accounts they follow to identify relevant links. For example, the identification unit can identify links related to topics the user has shown interest in on social media. The identification unit can also identify links by considering the user's social media activity history. For example, the identification unit can identify links related to posts the user has previously "liked". This allows the identification of links based on the user's social media activity. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's social media data into a generating AI and have the generating AI perform the identification of relevant links.

[0077] The reception desk can estimate the user's emotions and adjust the way questions or inputs are received based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input and quickly receive questions or inputs. This allows the reception desk to adjust the way questions or inputs are received based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0078] The reception desk can analyze the user's past question history and select the most suitable reception method. For example, the reception desk can automatically display frequently asked questions as candidates. It can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions to be used during specific time periods based on the user's past question history. This allows the reception desk to analyze the user's past question history and select the most suitable reception method. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's question history data into a generating AI and have the generating AI select the most suitable reception method.

[0079] The reception unit can filter questions or inputs based on the user's current areas of interest. For example, the reception unit can prioritize questions related to the content of pages the user has recently viewed. It can also prioritize questions related to a specific category if the user has shown interest in that category. Furthermore, the reception unit can filter and receive questions based on topics the user has shown interest in in the past. This allows for filtering questions or inputs based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user area of ​​interest data into a generating AI and have the generating AI perform the question filtering.

[0080] The reception desk can estimate the user's emotions and determine the priority of questions or inputs to be received based on the estimated emotions. For example, if the user is nervous, the reception desk may prioritize high-priority questions. If the user is relaxed, the reception desk may also prioritize detailed questions. Furthermore, if the user is in a hurry, the reception desk may prioritize questions requiring a quick response. This allows for the prioritization of questions or inputs based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0081] The reception desk can prioritize receiving questions that are highly relevant to the user, taking into account the user's geographical location when receiving questions or input. For example, if the user is in a specific region, the reception desk can prioritize receiving questions related to that region. Furthermore, if the user is traveling, the reception desk can prioritize receiving questions related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize receiving questions related to their home area. This allows the reception desk to prioritize receiving questions that are highly relevant to the user, taking into account their geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI identify highly relevant questions.

[0082] The reception unit can analyze the user's social media activity and receive relevant questions when receiving questions or input. For example, the reception unit can prioritize receiving relevant questions based on information the user has shared on social media. It can also receive relevant questions based on the content of accounts the user follows on social media. Furthermore, it can receive relevant questions based on topics the user has shown interest in on social media. This allows the reception unit to analyze the user's social media activity and receive relevant questions. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI identify relevant questions.

[0083] The identification unit can estimate the user's emotions and adjust the link identification method based on the estimated user emotions. For example, if the user is relaxed, the identification unit can provide detailed links. It can also prioritize providing the most relevant links if the user is in a hurry. Furthermore, if the user is excited, it can provide visually appealing links. This allows the link identification method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the identification unit may be performed using AI or not. For example, the identification unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0084] The identification unit can identify the most suitable link by analyzing the user's past browsing history when identifying links. For example, the identification unit can prioritize providing links to pages the user has frequently visited in the past. It can also provide links related to topics the user has shown interest in in the past. Furthermore, the identification unit can provide links related to specific time periods based on the user's past browsing history. This allows the identification unit to identify the most suitable link by analyzing the user's past browsing history. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's browsing history data into a generating AI and have the generating AI perform the task of identifying the most suitable link.

[0085] The identification unit can identify links based on the content of the user's currently viewed page when identifying links. For example, the identification unit can provide links related to the content of the page the user is currently viewing. The identification unit can also provide links related to a specific category if the user is viewing pages in that category. Furthermore, the identification unit can identify links based on keywords of the page the user is currently viewing. This allows the identification of links based on the content of the user's currently viewed page. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's viewed page data into a generating AI and have the generating AI perform the identification of relevant links.

[0086] The identification unit can estimate the user's emotions and determine the priority of the links to identify based on the estimated emotions. For example, if the user is stressed, the identification unit may prioritize providing high-importance links. It may also prioritize providing detailed links if the user is relaxed. Furthermore, if the user is in a hurry, it may prioritize providing links that can be accessed quickly. This allows the identification unit to determine the priority of the links to identify based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the identification unit may be performed using AI or not. For example, the identification unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0087] The identification unit can identify highly relevant links by considering the user's geographical location information when identifying links. For example, if the user is in a specific region, the identification unit can provide links related to that region. Furthermore, if the user is traveling, the identification unit can provide links related to their travel destination. Additionally, if the user is at home, the identification unit can provide links related to their home area. This allows the identification of highly relevant links by considering the user's geographical location information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's geographical location data into a generating AI and have the generating AI perform the identification of highly relevant links.

[0088] The identification unit can analyze the user's social media activity and identify relevant links when identifying links. For example, the identification unit can provide relevant links based on information shared by the user on social media. It can also provide relevant links based on the content of accounts the user follows on social media. Furthermore, it can provide relevant links based on topics the user has shown interest in on social media. This allows the identification unit to analyze the user's social media activity and identify relevant links. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's social media data into a generating AI and have the generating AI perform the identification of relevant links.

[0089] The service provider can estimate the user's emotions and adjust how links are provided based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed links. If the user is in a hurry, the service provider can also prioritize providing the most relevant links. Furthermore, if the user is excited, the service provider can provide visually appealing links. This allows the service provider to adjust how links are provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user voice data into a generative AI and have the generative AI perform emotion estimation.

[0090] The service provider can analyze the user's past link click history to select the optimal service method when providing links. For example, the service provider can prioritize providing links that the user has frequently clicked in the past. It can also provide links related to topics that the user has shown interest in in the past. Furthermore, the service provider can provide links related to a specific time period based on the user's past link click history. This allows the service provider to analyze the user's past link click history and select the optimal service method. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's link click history data into a generating AI and have the generating AI select the optimal service method.

[0091] The service provider can provide links based on the content of the user's currently viewed page. For example, the service provider can provide links related to the content of the page the user is currently viewing. Furthermore, if the user is viewing pages in a specific category, the service provider can also provide links related to that category. In addition, the service provider can provide links based on keywords in the page the user is currently viewing. This allows the service provider to provide links based on the content of the user's currently viewed page. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's viewed page data into a generating AI and have the generating AI perform the provision of relevant links.

[0092] The service provider can estimate the user's emotions and prioritize the links to be provided based on those emotions. For example, if the user is stressed, the service provider may prioritize providing high-priority links. Similarly, if the user is relaxed, the service provider may prioritize providing more detailed links. Furthermore, if the user is in a hurry, the service provider may prioritize providing links that can be accessed quickly. This allows the service provider to prioritize links based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 voice data into a generative AI and have the generative AI perform emotion estimation.

[0093] The service provider can prioritize providing highly relevant links by considering the user's geographical location when providing links. For example, if the user is in a specific region, the service provider can provide links related to that region. Furthermore, if the user is traveling, the service provider can provide links related to their travel destination. Additionally, if the user is at home, the service provider can provide links related to their home area. This allows the service provider to prioritize providing highly relevant links by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant links.

[0094] The service provider can analyze the user's social media activity and provide relevant links when providing links. For example, the service provider can provide relevant links based on information shared by the user on social media. It can also provide relevant links based on the content of accounts followed by the user on social media. Furthermore, it can provide relevant links based on topics the user has shown interest in on social media. This allows the service provider to analyze the user's social media activity and provide relevant links. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant links.

[0095] The commentary unit can estimate the user's emotions and adjust its commentary method based on the estimated emotions. For example, if the user is relaxed, the commentary unit can provide a detailed commentary. If the user is in a hurry, it can provide a concise commentary that gets straight to the point. Furthermore, if the user is excited, it can provide a visually appealing commentary. This allows the commentary method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the commentary unit may be performed using AI, for example, or without AI. For example, the commentary unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0096] The explanation unit can analyze the user's past question history to select the most appropriate explanation method during the explanation process. For example, the explanation unit can provide relevant explanations based on questions the user has frequently asked in the past. It can also provide explanations related to topics the user has shown interest in in the past. Furthermore, the explanation unit can provide explanations related to specific time periods based on the user's past question history. This allows the explanation unit to analyze the user's past question history and select the most appropriate explanation method. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the user's question history data into a generating AI and have the generating AI select the most appropriate explanation method.

[0097] The explanation unit can provide explanations based on the content of the user's currently viewed page. For example, the explanation unit can provide explanations related to the content of the page the user is currently viewing. Furthermore, if the user is viewing pages in a specific category, the explanation unit can also provide explanations related to that category. In addition, the explanation unit can provide explanations based on keywords of the page the user is currently viewing. This allows the explanation to be based on the content of the user's currently viewed page. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the user's viewed page data into a generating AI and have the generating AI provide relevant explanations.

[0098] The commentary unit can estimate the user's emotions and prioritize commentary based on those emotions. For example, if the user is nervous, the commentary unit may prioritize providing high-importance commentary. It may also prioritize providing detailed commentary if the user is relaxed. Furthermore, if the user is in a hurry, it may prioritize providing commentary that can be quickly understood. This allows the commentary unit to prioritize commentary based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the commentary unit may be performed using AI, or not. For example, the commentary unit can input user voice data into a generative AI and have the generative AI perform emotion estimation.

[0099] The explanation unit can prioritize highly relevant explanations by considering the user's geographical location. For example, if the user is in a specific region, the explanation unit can provide explanations related to that region. Furthermore, if the user is traveling, the explanation unit can provide explanations related to their travel destination. Additionally, if the user is at home, the explanation unit can provide explanations related to their home area. This allows the explanation unit to prioritize highly relevant explanations by considering the user's geographical location. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the user's geographical location data into a generating AI and have the generating AI provide highly relevant explanations.

[0100] The commentary unit can analyze the user's social media activity and provide relevant commentary during the commentary process. For example, the commentary unit can provide relevant commentary based on information shared by the user on social media. It can also provide relevant commentary based on the content of accounts the user follows on social media. Furthermore, the commentary unit can provide relevant commentary based on topics the user has shown interest in on social media. This allows the commentary unit to analyze the user's social media activity and provide relevant commentary. Some or all of the above processing in the commentary unit may be performed using AI, for example, or without AI. For example, the commentary unit can input the user's social media data into a generating AI and have the generating AI provide relevant commentary.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] An interactive navigation system AI assistant can estimate the user's emotions and adjust the tone of its responses to the user's questions based on those emotions. For example, if the user is stressed, the system can provide responses in a gentle tone; if the user is relaxed, it can provide responses in a more casual tone. If the user is excited, the system can provide responses in an energetic tone. This ensures that responses are provided in an appropriate tone according to the user's emotions, improving the user experience. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input user voice data into a generative AI and have the generative AI perform emotion estimation.

[0103] An interactive navigation system AI assistant can analyze a user's past behavior history and prioritize providing information related to topics the user has previously shown interest in. For example, if a user has frequently searched for products in a particular category in the past, it can prioritize providing new product information related to that category. It can also provide the latest information related to a particular topic if the user has asked many questions about that topic in the past. Furthermore, it can provide links to relevant pages based on the user's past page visit history. This allows for the provision of more relevant information based on the user's past behavior history. Some or all of the above processes in the system may be performed using AI, for example, or not. For example, the system can input user behavior history data into a generating AI and have the generating AI identify relevant information.

[0104] An interactive navigation system AI assistant can provide relevant information by taking into account the user's current geographical location. For example, if the user is in a specific area, it can provide information about events and shops related to that area. If the user is traveling, it can also provide tourist information and transportation information related to their destination. Furthermore, if the user is at home, it can provide information about services and shops around their home. This allows the system to provide more relevant information based on the user's current geographical location. Some or all of the above processing in the system may be performed using AI, for example, or not using AI. For example, the system can input the user's geographical location data into a generating AI and have the generating AI identify relevant information.

[0105] An interactive navigation system AI assistant can analyze a user's social media activity and provide relevant information. For example, it can provide relevant news and articles based on information the user has shared on social media. It can also provide relevant topic and event information based on the content of accounts the user follows. Furthermore, it can provide information on relevant products and services based on topics the user has shown interest in on social media. This allows for the provision of more relevant information based on the user's social media activity. Some or all of the above processes in the system may be performed using AI, for example, or not. For example, the system can input the user's social media data into a generating AI and have the generating AI identify relevant information.

[0106] An interactive navigation system AI assistant can estimate the user's emotions and adjust the format of the information provided to the user based on the estimated emotions. For example, if the user is stressed, the system can provide information in a concise and easy-to-understand format; if the user is relaxed, it can provide detailed information. Furthermore, if the user is excited, it can provide information in a visually appealing format. This ensures that information is provided in an appropriate format according to the user's emotions, improving the user experience. 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 system may be performed using AI or not. For example, the system can input user voice data into a generative AI and have the generative AI perform emotion estimation.

[0107] An interactive navigation system AI assistant can analyze a user's past question history and prioritize providing information related to questions the user has previously asked. For example, if a user has asked many questions about a particular topic in the past, it can provide the latest information related to that topic. It can also provide relevant FAQs and guides based on the content of questions the user has asked in the past. Furthermore, it can provide links to relevant pages based on the user's history of visited pages. This allows for the provision of more relevant information based on the user's past question history. Some or all of the above processes in the system may be performed using AI, for example, or not. For example, the system can input the user's question history data into a generating AI and have the generating AI identify relevant information.

[0108] An interactive navigation system AI assistant can estimate a user's emotions and prioritize the information provided to the user based on those emotions. For example, if the user is stressed, it can prioritize providing high-priority information; if the user is relaxed, it can prioritize providing detailed information. If the user is in a hurry, it can prioritize providing information that can be accessed quickly. This ensures that information is provided with appropriate priorities according to the user's emotions, improving the user experience. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input user voice data into a generative AI and have the generative AI perform emotion estimation.

[0109] An interactive navigation system AI assistant can provide relevant information based on the content of the user's current page. For example, it can provide FAQs or guides related to the content of the page the user is currently viewing. It can also provide the latest information related to a specific category if the user is viewing pages in that category. Furthermore, it can provide relevant articles and news based on keywords in the page the user is currently viewing. This allows for the provision of more relevant information based on the content of the user's current page. Some or all of the above processing in the system may be performed using AI, for example, or not. For example, the system can input the user's page viewing data into a generating AI and have the generating AI identify relevant information.

[0110] An interactive navigation system AI assistant can estimate the user's emotions and adjust the level of detail of the information provided to the user based on the estimated emotions. For example, if the user is stressed, the system can provide concise and to-the-point information; if the user is relaxed, it can provide detailed information. Furthermore, if the user is excited, it can provide detailed information in a visually appealing format. This ensures that information is provided at an appropriate level of detail according to the user's emotions, improving the user experience. 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 system may be performed using AI or not. For example, the system can input user voice data into a generative AI and have the generative AI perform emotion estimation.

[0111] An interactive navigation system AI assistant can analyze a user's past link click history and prioritize providing information related to links the user has previously clicked. For example, if a user has frequently clicked product links in a particular category in the past, the system can prioritize providing new product information related to that category. It can also provide relevant articles and news based on the content of links the user has clicked in the past. Furthermore, it can provide links to relevant pages based on the user's past page visit history. This allows the system to provide more relevant information based on the user's past link click history. Some or all of the above processing in the system may be performed using AI, for example, or not using AI. For example, the system can input the user's link click history data into a generating AI and have the generating AI identify relevant information.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception desk receives user questions or inputs. User questions or inputs can include text, voice, images, etc. The reception desk can use speech recognition technology to convert voice input into text and text analysis technology to analyze the text input. For example, if a user inputs "Please tell me the details of this product" by voice, the voice will be converted into text and analyzed. Step 2: The identification unit identifies links to pages or information related to the question or input received by the reception unit. The identification unit can understand the content of the question using natural language processing technology and identify relevant pages or information. For example, in response to the question "Please tell me the details of this product," it will identify a link to the product details page. The identification unit can also identify the most relevant links by considering the user's past behavior and current situation. Step 3: The providing unit provides the user with the link identified by the identifying unit. The providing unit can display the link on the user's screen and can also provide audio guidance. For example, it can provide audio guidance such as, "Click here for the product details page."

[0114] 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.

[0115] 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.

[0116] 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.

[0117] For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives voice and text input from the user. For example, voice input is received using the microphone 38B of the smart device 14 and converted to text using speech recognition technology. The identification unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the user's questions and inputs using natural language processing technology to identify links to relevant pages and information. The provision unit is implemented by the output device 40 of the smart device 14 and provides the identified links to the user. For example, the links are displayed on the display 40A or announced by voice through the speaker 40B. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives voice input from the user. For example, the voice input is received using the microphone 238 of the smart glasses 214 and converted into text by speech recognition technology. The identification unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the user's questions and inputs using natural language processing technology to identify links to relevant pages and information. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the identified links to the user. For example, the information is provided by voice through the speaker 240. 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.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives voice input from the user. For example, voice input is received using the microphone 238 of the headset terminal 314 and converted into text by speech recognition technology. The identification unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the user's questions and inputs using natural language processing technology to identify links to relevant pages and information. The provision unit is implemented by the display 343 of the headset terminal 314 and provides the identified links to the user. For example, the links are displayed on the display 343 or announced by voice through the speaker 240. 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.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives voice input from the user. For example, the voice input is received using the microphone 238 of the robot 414 and converted into text using speech recognition technology. The identification unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the user's questions and inputs using natural language processing technology to identify links to relevant pages and information. The provision unit is implemented by the speaker 240 of the robot 414 and provides the identified links to the user. For example, the information is provided by voice through the speaker 240. 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A reception area that accepts user questions or inputs, A unit that identifies a link to a page or information related to a question or input received by the reception unit, The system comprises a providing unit that provides the link identified by the specifying unit. A system characterized by the following features. (Note 2) The specified part is, Identify the link within the website that the user is viewing. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is We accept questions or input via voice and text. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a section that explains how to use the website the user is browsing. The system described in Appendix 1, characterized by the features described herein. (Note 5) The specified part is, Identify the link based on the user's context. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, The link is provided at a timing appropriate to the user's context. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Prioritize providing links of high importance based on the user's attributes. The system described in Appendix 1, characterized by the features described herein. (Note 8) The specified part is, Identify the link based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and adjusts how questions or inputs are received based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Analyze the user's past question history and select the appropriate method of handling inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a question or input, filter it based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is It estimates the user's emotions and determines the priority of questions or inputs to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving questions or input, the system prioritizes receiving relevant questions by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When a question or input is received, 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 15) The specified part is, We estimate the user's emotions and adjust how links are identified based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The specified part is, When identifying a link, the system analyzes the user's past browsing history to determine the appropriate link. The system described in Appendix 1, characterized by the features described herein. (Note 17) The specified part is, When identifying a link, the system identifies the link based on the content of the user's current page. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, It estimates the user's emotions and prioritizes the links to identify based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, When identifying links, the system considers the user's geographical location to determine the most relevant links. The system described in Appendix 1, characterized by the features described herein. (Note 20) The specified part is, When identifying links, the system analyzes the user's social media activity to identify relevant links. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts how links are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing links, the system analyzes the user's past link click history to select the appropriate method of provision. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing links, provide links based on the content of the user's currently viewed page. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the links it provides based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing links, we prioritize providing relevant links by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing links, we analyze the user's social media activity and provide relevant links. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned explanatory section is, It estimates the user's emotions and adjusts the explanation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned explanatory section is, During the explanation, the system analyzes the user's past question history to select the appropriate explanation method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned explanatory section is, During the explanation, the explanation will be based on the content of the user's current page. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned explanatory section is, It estimates the user's emotions and determines the priority of explanations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned explanatory section is, When providing explanations, we prioritize relevant explanations that take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned explanatory section is, During the explanation, we analyze the user's social media activity and provide relevant commentary. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0186] 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 area that accepts user questions or inputs, A unit that identifies a link to a page or information related to a question or input received by the reception unit, The system comprises a providing unit that provides the link identified by the specifying unit. A system characterized by the following features.

2. The specified part is, Identify the link within the website that the user is viewing. The system according to feature 1.

3. The aforementioned reception unit is We accept questions or input via voice and text. The system according to feature 1.

4. The system includes an explanatory section that explains how to use the website the user is currently viewing. The system according to feature 1.

5. The specified part is, Identify the link based on the user's context. The system according to feature 1.

6. The aforementioned supply unit is, The link is provided at a timing appropriate to the user's context. The system according to feature 1.

7. The aforementioned supply unit is, Prioritize providing links of high importance based on the user's attributes. The system according to feature 1.

8. The specified part is, Identify the link based on the user's social media activity. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and adjusts the method of receiving questions or inputs based on the estimated user emotions. The system according to feature 1.

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