system

Abstract

The system according to this embodiment aims to provide reliable information quickly when a problem occurs and to support appropriate countermeasures. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and an advice unit. The reception unit receives information input by the user. The analysis unit analyzes the information received by the reception unit. The provision unit provides a solution based on the information analyzed by the analysis unit. The advice unit provides advice based on past cases and the information provided by the provision unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to quickly obtain reliable information when a trouble occurs and it is difficult to take appropriate measures.

[0005] The system according to the embodiment aims to quickly provide reliable information when a trouble occurs and support appropriate measures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and an advice unit. The reception unit receives information entered by the user. The analysis unit analyzes the information received by the reception unit. The provision unit provides a solution based on the information analyzed by the analysis unit. The advice unit provides advice based on past cases and the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly provide reliable information when a problem occurs and support appropriate countermeasures. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​assistant system according to an embodiment of the present invention is a system for resolving various troubles that occur in daily life. This system receives information input by the user, analyzes it, provides solutions, and offers advice based on past cases. For example, the user sequentially shares events and questions with the AI ​​through an easy-to-use input form. For example, the user inputs an event such as "My dog ​​scratched someone else's car" or a question such as "How should I deal with this?" This information is input to the AI. Next, the AI ​​analyzes the input information and provides reliable information such as how to deal with predicted problems, legally required steps, and contact information. For example, the AI ​​indicates the best solution and where to seek advice based on court precedents and legal information. Specifically, it provides advice such as "Contact the police," "Take pictures of the damage," and "Check the coverage details of your car insurance and fire insurance." This mechanism allows users to deal with troubles calmly when faced with them. For example, if a pet dog scratches someone else's car, the user can take appropriate action by following the AI's advice. In addition, because the AI ​​provides reliable information, it can reduce damage caused by incorrect or intentional misinformation. Furthermore, AI can also advise on future actions based on past cases. For example, by providing information such as solutions and compensation amounts for similar problems that have occurred in the past, users can learn more specific ways to deal with the situation. In this way, the AI ​​assistant system supports users so that they can calmly deal with problems and provides a sense of security. It also aims to create a more comfortable society by reducing damage caused by false or intentional misinformation and resolving individual concerns. As a result, the AI ​​assistant system can support users so that they can calmly deal with problems and provide a sense of security.

[0029] The AI ​​assistant system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and an advice unit. The reception unit receives information input by the user. The information input by the user includes, but is not limited to, text information, image information, and audio information. For example, the reception unit receives text information through an input form. The reception unit can also receive image information using a camera or scanner. Furthermore, the reception unit can also receive audio information using a microphone. For example, the reception unit converts the text information input by the user into a format that is easy to analyze. Image information can be converted into text information using OCR technology. Audio information can be converted into text information using speech recognition technology. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the information using data mining technology. Furthermore, the analysis unit can also analyze the information using natural language processing technology. Furthermore, the analysis unit can also analyze the information using machine learning algorithms. For example, the analysis unit derives the optimal solution based on the input information. The provision unit provides a solution based on the information analyzed by the analysis unit. The provision unit provides, for example, procedures, contact information, and first-aid methods. It can also provide instructions on how to contact the police, how to take photos of injuries, and how to check coverage details for car and fire insurance. Furthermore, the provision unit supports users in calmly dealing with problems when they encounter them. The advice unit provides advice based on past cases and the information provided by the provision unit. For example, the advice unit advises on future actions by referring to past cases. The advice unit can also provide advice to reduce damage caused by incorrect or intentional misinformation. Furthermore, the advice unit supports users in calmly dealing with problems when they encounter them. As a result, the AI ​​assistant system according to this embodiment analyzes the information entered by the user and provides appropriate solutions and advice, enabling users to calmly deal with problems.

[0030] The reception unit receives information entered by the user. This information may include, but is not limited to, text, image, and audio information. For example, the reception unit can receive text information through an input form. Specifically, it can receive text information entered by the user via a web browser or mobile application through a dedicated input form. The input form is designed to allow users to easily enter information, and the entered information is immediately transmitted to the system. Furthermore, the reception unit can also receive image information using a camera or scanner. For example, it can receive images taken by the user using a smartphone camera or images of documents scanned with a scanner. This image information is processed appropriately within the system and converted into text information as needed. Additionally, the reception unit can receive audio information using a microphone. For example, it can record the voice spoken by the user through a microphone and transmit that audio data to the system. The audio information is converted into text information using speech recognition technology. This allows the reception unit to efficiently receive diverse forms of information entered by users and prepare them for transmission to the analysis unit.

[0031] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit uses data mining techniques to analyze the information. Data mining techniques are methods for extracting useful patterns and relationships from large amounts of data, and are useful for identifying important elements from user-input information. The analysis unit can also analyze information using natural language processing techniques. Natural language processing techniques are techniques for understanding text data and extracting meaning, and can accurately grasp intentions and requests by analyzing user-input text information. Furthermore, the analysis unit can also analyze information using machine learning algorithms. Machine learning algorithms are methods for making predictions and classifications based on past data, and are useful for deriving optimal solutions from user-input information. For example, the analysis unit derives the optimal solution based on the input information. Specifically, it analyzes user-input text information and proposes appropriate countermeasures based on its content. Image and audio information are similarly analyzed, and necessary information is extracted. This allows the analysis unit to quickly and accurately analyze user-input information and build a foundation for providing appropriate countermeasures.

[0032] The service provider provides solutions based on the information analyzed by the analysis unit. For example, the service provider provides procedures, contact information, and first aid methods. Specifically, it provides detailed procedures for the problems the user is facing, offering step-by-step guidance for problem-solving. It also provides necessary contact information to ensure the user can quickly access appropriate professionals and services. Furthermore, it provides first aid methods to support the user in responding appropriately in emergencies. The service provider can also provide information on how to contact the police, how to take photos of injuries, and how to check the coverage of auto and fire insurance. For example, if a user is involved in a traffic accident, the service provider provides procedures and necessary information for contacting the police and supports appropriate responses at the accident scene. It also shows how to take photos of injuries to help secure evidence for insurance claims. Furthermore, it provides information on how to check the coverage of auto and fire insurance so that the user understands the scope of their insurance and can take appropriate action. This allows the service provider to support users in calmly dealing with troubles and provide specific information for problem-solving.

[0033] The Advice Department provides advice based on past cases and the information provided by the Service Provider Department. For example, the Advice Department will advise on future actions by referring to past cases. Specifically, it will propose the best course of action based on the cases of users who have faced similar problems in the past. For example, it will refer to past traffic accident cases and provide specific advice on procedures after an accident and how to file an insurance claim. The Advice Department can also provide advice to reduce damage caused by false or intentional misinformation. For example, it will raise awareness about misinformation and scams circulating on the internet and support users in acting based on accurate information. Furthermore, the Advice Department will support users in dealing with troubles calmly. Specifically, it will provide advice to encourage calm judgment so that users do not panic and will show them the mindset needed to take appropriate action. In this way, the Advice Department can support users in dealing with troubles calmly based on past cases and accurate information, minimizing the impact of the trouble.

[0034] The reception desk can receive events and questions entered by the user. For example, the reception desk can receive events entered by the user as text information. The reception desk can also receive questions entered by the user as voice information. For example, the reception desk can also receive events entered by the user as image information using a camera or scanner. This allows the reception desk to appropriately receive events and questions entered by the user. 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 events and questions entered by the user into the AI ​​and convert them into a format that is easy for the AI ​​to analyze.

[0035] The analysis unit can provide optimal solutions and referrals based on court precedents and legal information. For example, the analysis unit can retrieve and analyze court precedents from a database. It can also derive optimal solutions based on legal information. For example, the analysis unit can refer to the most suitable referrals based on information entered by the user. In this way, the analysis unit can provide reliable solutions by offering court precedents and legal information. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input court precedents and legal information into an AI, which can then provide optimal solutions and referrals.

[0036] The service provider can provide instructions on how to contact the police, how to take photos of the damage, and how to check the coverage details of car insurance and fire insurance. For example, the service provider can provide specific procedures and methods for contacting the police. The service provider can also provide specific procedures and methods for taking photos of the damage. For example, the service provider can provide specific procedures and methods for checking the coverage details of car insurance and fire insurance. In this way, the service provider can provide specific solutions so that users can respond appropriately. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input instructions on how to contact the police, how to take photos of the damage, and how to check the coverage details of car insurance and fire insurance into the AI, and the AI ​​can provide the best solution.

[0037] The advisory unit can provide advice on future actions based on past cases. For example, the advisory unit can retrieve and analyze past cases from a database. Furthermore, the advisory unit can also provide advice on future actions based on the results of past cases. For example, the advisory unit can use data from past cases to provide optimal advice. This allows the advisory unit to provide more specific advice by referring to past cases. Some or all of the above processes in the advisory unit may be performed using AI, or not. For example, the advisory unit can input past cases into an AI, which can then provide advice on future actions.

[0038] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions events and questions that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest events and questions that the user will use during a specific time period based on the user's past input history. In this way, the reception desk improves the user's input efficiency by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history into AI, and the AI ​​can suggest the optimal input method.

[0039] The reception desk can automatically complete input content based on the user's current situation and environment during registration. For example, if the user is at home, the reception desk will automatically fill in the address information in the input form. If the user is out, the reception desk can also suggest the most suitable input content based on the user's current location. For example, if the reception desk is participating in a specific event, it will automatically complete information related to that event. In this way, the reception desk reduces the effort required for input by automatically completing input content based on the user's current situation and environment. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's current situation and environment into the AI, which can then automatically complete the input content.

[0040] The reception desk can prioritize receiving input content that is highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize displaying input content related to that region. Similarly, if the user is traveling, the reception desk can prioritize displaying input content related to their travel destination. For example, if the user is at home, the reception desk can prioritize displaying input content related to their home. This allows the reception desk to prioritize receiving input content that is highly relevant by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into the AI, which can then prioritize receiving input content that is highly relevant.

[0041] The reception desk can analyze the user's social media activity and suggest relevant input content upon receiving the user's information. For example, the reception desk can automatically suggest input content related to a problem the user shared on social media. The reception desk can also suggest relevant input content based on information about accounts the user follows on social media. For example, the reception desk can suggest relevant input content based on information about groups the user participates in on social media. In this way, the reception desk can suggest relevant input content by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity into AI, and the AI ​​can suggest relevant input content.

[0042] The analysis unit can evaluate the reliability of the input information during analysis and filter out unreliable information. For example, the analysis unit can verify the source of the input information and exclude unreliable information. The analysis unit can also evaluate the consistency of the input information and filter out inconsistent information. For example, the analysis unit can compare the reliability of the input information with past data and exclude unreliable information. In this way, the analysis unit evaluates the reliability of the input information, excludes unreliable information, and provides accurate analysis results. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the reliability of the input information into the AI, and the AI ​​can filter out unreliable information.

[0043] The analysis unit can improve the accuracy of its analysis by referring to similar past cases during the analysis process. For example, the analysis unit can retrieve similar past cases from a database and perform the analysis. Furthermore, the analysis unit can supplement its analysis results by referring to the results of similar past cases. For example, the analysis unit can adjust its analysis algorithm using data from similar past cases. This allows the analysis unit to improve the accuracy of its analysis by referring to similar past cases. Some or all of the above processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can input similar past cases into the AI, which can then improve the accuracy of the analysis.

[0044] The analysis unit can customize the analysis results by taking into account the user's geographical location information during analysis. For example, if the user is in a specific region, the analysis unit will prioritize displaying analysis results related to that region. Similarly, if the user is traveling, the analysis unit can prioritize displaying analysis results related to their travel destination. For example, if the user is at home, the analysis unit will prioritize displaying analysis results related to their home. This allows the analysis unit to provide highly relevant analysis results by considering the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can input the user's geographical location information into the AI, which can then customize the analysis results.

[0045] The analysis unit can analyze the user's social media activity during analysis and reflect relevant information in the analysis. For example, the analysis unit can supplement the analysis results based on information shared by the user on social media. The analysis unit can also supplement the analysis results based on information about accounts that the user follows on social media. For example, the analysis unit can supplement the analysis results based on information about groups that the user participates in on social media. In this way, the analysis unit can reflect relevant information in the analysis by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's social media activity into AI, and the AI ​​can reflect relevant information in the analysis.

[0046] The information provider can adjust the level of detail in the information provided based on its importance. For example, it may prioritize providing highly important information and include detailed explanations. Alternatively, it may provide less important information concisely and provide links to more detailed information as needed. For example, it may provide moderately important information with an appropriate level of detail. In this way, the information provider appropriately provides users with the information they need by adjusting the level of detail in the information provided based on its importance. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the importance of the information into the AI, and the AI ​​can adjust the level of detail in the provided content.

[0047] The information provider can apply different information provision algorithms depending on the category of information at the time of provision. For example, when providing information on legal issues, the provider can use an algorithm with legal expertise. Similarly, when providing information on health issues, the provider can use an algorithm with medical expertise. For example, when providing information on everyday troubles, the provider can use an algorithm with general knowledge. In this way, the provider provides appropriate information to the user by applying the most suitable information provision algorithm according to the category of information. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the category of information into the AI, and the AI ​​can apply different information provision algorithms.

[0048] The information provider can prioritize providing highly relevant information by considering the user's geographical location at the time of delivery. For example, if the user is in a specific region, the information provider can prioritize providing information related to that region. Furthermore, if the user is traveling, the information provider can prioritize providing information related to the travel destination. For example, if the user is at home, the information provider can prioritize providing information related to home. In this way, the information provider can prioritize providing highly relevant information by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, or not. For example, the information provider can input the user's geographical location into AI, which can then prioritize providing highly relevant information.

[0049] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can provide relevant information based on information shared by the user on social media. The service provider can also provide relevant information based on information about accounts followed by the user on social media. For example, the service provider can provide relevant information based on information about groups the user participates in on social media. In this way, the service provider can provide relevant information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity into AI, and the AI ​​can provide relevant information.

[0050] The advice unit can improve the accuracy of its advice based on past cases. For example, it can provide optimal advice based on similar past cases. It can also supplement its advice by referring to the results of past cases. For example, it can adjust its advice algorithm using data from past cases. This allows the advice unit to provide more specific advice by improving its accuracy based on past cases. Some or all of the above processes in the advice unit may be performed using AI or not. For example, the advice unit can input past cases into an AI, which can then improve the accuracy of its advice.

[0051] The advice unit can customize the advice it provides based on the user's current situation and environment. For example, if the user is at home, the advice unit will provide advice related to home. It can also provide advice related to the user's location if the user is out. For example, if the user is participating in a specific event, the advice unit will provide advice related to that event. In this way, the advice unit provides appropriate advice by customizing it based on the user's current situation and environment. Some or all of the above processing in the advice unit may be performed using AI, or not. For example, the advice unit can input the user's current situation and environment into the AI, which can then customize the advice.

[0052] The advice unit can provide optimal advice by considering the user's geographical location. For example, if the user is in a specific region, the advice unit will provide advice relevant to that region. It can also provide advice relevant to the user's travel destination if the user is traveling. For example, if the user is at home, the advice unit will provide advice relevant to home. This allows the advice unit to provide highly relevant advice by considering the user's geographical location. Some or all of the above processing in the advice unit may be performed using AI, or not. For example, the advice unit can input the user's geographical location into the AI, which can then provide optimal advice.

[0053] The advice unit can analyze the user's social media activity and provide relevant advice when providing advice. For example, the advice unit can provide relevant advice based on information the user has shared on social media. It can also provide relevant advice based on information about accounts the user follows on social media. For example, the advice unit can provide relevant advice based on information about groups the user participates in on social media. In this way, the advice unit can provide relevant advice by analyzing the user's social media activity. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input the user's social media activity into AI, and the AI ​​can provide relevant advice.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception desk can translate user input in real time, enabling multilingual support. For example, it can translate user input in Japanese into English, allowing them to consult with an English-speaking expert. It can also translate user input in English into Japanese, allowing them to consult with a Japanese-speaking expert. Furthermore, the reception desk can simultaneously translate user input into multiple languages, gathering opinions from multiple experts. This allows the reception desk to support users in smoothly conducting multilingual consultations.

[0056] The analysis unit can automatically generate templates for relevant legal documents and contracts based on user input. For example, if a user enters details of a dispute, the analysis unit will generate an appropriate contract template based on that information. The analysis unit can also automatically fill in the necessary fields of legal documents based on the information entered by the user. Furthermore, the analysis unit can provide the generated documents to the user, allowing for modifications and additions as needed. This enables the analysis unit to support users in quickly creating legal documents.

[0057] The advisory unit can analyze users' past behavioral patterns, predict future problems, and provide advice in advance. For example, it can analyze problems users have frequently encountered in the past and issue warnings if similar problems are likely to occur again. The advisory unit can also suggest preventative measures based on users' behavioral patterns. Furthermore, the advisory unit can provide advice about potential problems that users might cause before they take a particular action. In this way, the advisory unit can support users in preventing problems before they occur.

[0058] The analysis unit can automatically collect opinions from relevant experts based on user input and incorporate them into the analysis results. For example, if a user inputs a legal issue, the analysis unit can collect opinions from legal experts and incorporate them into the analysis results. Similarly, if a user inputs a medical issue, it can collect opinions from medical experts and incorporate them into the analysis results. Furthermore, if a user inputs a technical issue, it can collect opinions from technical experts and incorporate them into the analysis results. By incorporating expert opinions, the analysis unit can provide more reliable analysis results.

[0059] The advice section can provide information on relevant communities and support groups based on user input. For example, if a user seeks advice on a specific problem, the advice section can provide information on online communities and support groups related to that problem. Similarly, if a user seeks advice on a specific illness, it can provide information on patient support groups related to that illness. Furthermore, if a user seeks advice on a legal issue, it can provide information on forums where legal professionals participate. This allows the advice section to enable users to receive additional support from relevant communities and support groups.

[0060] The service provider can suggest relevant educational resources and training programs based on user input. For example, if a user seeks advice on acquiring a specific skill, the service provider can suggest online courses or training programs related to that skill. If a user wants to learn how to solve a specific problem, the service provider can also provide educational resources related to that problem. Furthermore, if a user seeks advice on career advancement, the service provider can provide information on relevant seminars and workshops. In this way, the service provider can support users in acquiring the necessary knowledge and skills.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives information entered by the user. This information includes text, image, and audio information. The reception desk receives text information through an input form, image information using a camera or scanner, and audio information using a microphone. Furthermore, the reception desk converts the text information entered by the user into a format that is easy to analyze, converts image information into text using OCR technology, and converts audio information into text using speech recognition technology. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses data mining technology, natural language processing technology, and machine learning algorithms to analyze the information and derive the optimal solution based on the input information. Step 3: The service provider provides solutions based on the information analyzed by the analysis provider. The service provider provides instructions, contact information, first aid methods, how to contact the police, how to take photos of the damage, and how to check the coverage of car insurance and fire insurance, to support users in calmly dealing with problems when they face them. Step 4: The Advice Department provides advice based on past cases and the information provided by the Service Provider. The Advice Department refers to past cases to advise on future actions, provides advice to reduce damage caused by incorrect or intentional misinformation, and supports users so that they can calmly deal with problems when they face them.

[0063] (Example of form 2) The AI ​​assistant system according to an embodiment of the present invention is a system for resolving various troubles that occur in daily life. This system receives information input by the user, analyzes it, provides solutions, and offers advice based on past cases. For example, the user sequentially shares events and questions with the AI ​​through an easy-to-use input form. For example, the user inputs an event such as "My dog ​​scratched someone else's car" or a question such as "How should I deal with this?" This information is input to the AI. Next, the AI ​​analyzes the input information and provides reliable information such as how to deal with predicted problems, legally required steps, and contact information. For example, the AI ​​indicates the best solution and where to seek advice based on court precedents and legal information. Specifically, it provides advice such as "Contact the police," "Take pictures of the damage," and "Check the coverage details of your car insurance and fire insurance." This mechanism allows users to deal with troubles calmly when faced with them. For example, if a pet dog scratches someone else's car, the user can take appropriate action by following the AI's advice. In addition, because the AI ​​provides reliable information, it can reduce damage caused by incorrect or intentional misinformation. Furthermore, AI can also advise on future actions based on past cases. For example, by providing information such as solutions and compensation amounts for similar problems that have occurred in the past, users can learn more specific ways to deal with the situation. In this way, the AI ​​assistant system supports users so that they can calmly deal with problems and provides a sense of security. It also aims to create a more comfortable society by reducing damage caused by false or intentional misinformation and resolving individual concerns. As a result, the AI ​​assistant system can support users so that they can calmly deal with problems and provide a sense of security.

[0064] The AI ​​assistant system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and an advice unit. The reception unit receives information input by the user. The information input by the user includes, but is not limited to, text information, image information, and audio information. For example, the reception unit receives text information through an input form. The reception unit can also receive image information using a camera or scanner. Furthermore, the reception unit can also receive audio information using a microphone. For example, the reception unit converts the text information input by the user into a format that is easy to analyze. Image information can be converted into text information using OCR technology. Audio information can be converted into text information using speech recognition technology. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the information using data mining technology. Furthermore, the analysis unit can also analyze the information using natural language processing technology. Furthermore, the analysis unit can also analyze the information using machine learning algorithms. For example, the analysis unit derives the optimal solution based on the input information. The provision unit provides a solution based on the information analyzed by the analysis unit. The provision unit provides, for example, procedures, contact information, and first-aid methods. It can also provide instructions on how to contact the police, how to take photos of injuries, and how to check coverage details for car and fire insurance. Furthermore, the provision unit supports users in calmly dealing with problems when they encounter them. The advice unit provides advice based on past cases and the information provided by the provision unit. For example, the advice unit advises on future actions by referring to past cases. The advice unit can also provide advice to reduce damage caused by incorrect or intentional misinformation. Furthermore, the advice unit supports users in calmly dealing with problems when they encounter them. As a result, the AI ​​assistant system according to this embodiment analyzes the information entered by the user and provides appropriate solutions and advice, enabling users to calmly deal with problems.

[0065] The reception unit receives information entered by the user. This information may include, but is not limited to, text, image, and audio information. For example, the reception unit can receive text information through an input form. Specifically, it can receive text information entered by the user via a web browser or mobile application through a dedicated input form. The input form is designed to allow users to easily enter information, and the entered information is immediately transmitted to the system. Furthermore, the reception unit can also receive image information using a camera or scanner. For example, it can receive images taken by the user using a smartphone camera or images of documents scanned with a scanner. This image information is processed appropriately within the system and converted into text information as needed. Additionally, the reception unit can receive audio information using a microphone. For example, it can record the voice spoken by the user through a microphone and transmit that audio data to the system. The audio information is converted into text information using speech recognition technology. This allows the reception unit to efficiently receive diverse forms of information entered by users and prepare them for transmission to the analysis unit.

[0066] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit uses data mining techniques to analyze the information. Data mining techniques are methods for extracting useful patterns and relationships from large amounts of data, and are useful for identifying important elements from user-input information. The analysis unit can also analyze information using natural language processing techniques. Natural language processing techniques are techniques for understanding text data and extracting meaning, and can accurately grasp intentions and requests by analyzing user-input text information. Furthermore, the analysis unit can also analyze information using machine learning algorithms. Machine learning algorithms are methods for making predictions and classifications based on past data, and are useful for deriving optimal solutions from user-input information. For example, the analysis unit derives the optimal solution based on the input information. Specifically, it analyzes user-input text information and proposes appropriate countermeasures based on its content. Image and audio information are similarly analyzed, and necessary information is extracted. This allows the analysis unit to quickly and accurately analyze user-input information and build a foundation for providing appropriate countermeasures.

[0067] The service provider provides solutions based on the information analyzed by the analysis unit. For example, the service provider provides procedures, contact information, and first aid methods. Specifically, it provides detailed procedures for the problems the user is facing, offering step-by-step guidance for problem-solving. It also provides necessary contact information to ensure the user can quickly access appropriate professionals and services. Furthermore, it provides first aid methods to support the user in responding appropriately in emergencies. The service provider can also provide information on how to contact the police, how to take photos of injuries, and how to check the coverage of auto and fire insurance. For example, if a user is involved in a traffic accident, the service provider provides procedures and necessary information for contacting the police and supports appropriate responses at the accident scene. It also shows how to take photos of injuries to help secure evidence for insurance claims. Furthermore, it provides information on how to check the coverage of auto and fire insurance so that the user understands the scope of their insurance and can take appropriate action. This allows the service provider to support users in calmly dealing with troubles and provide specific information for problem-solving.

[0068] The Advice Department provides advice based on past cases and the information provided by the Service Provider Department. For example, the Advice Department will advise on future actions by referring to past cases. Specifically, it will propose the best course of action based on the cases of users who have faced similar problems in the past. For example, it will refer to past traffic accident cases and provide specific advice on procedures after an accident and how to file an insurance claim. The Advice Department can also provide advice to reduce damage caused by false or intentional misinformation. For example, it will raise awareness about misinformation and scams circulating on the internet and support users in acting based on accurate information. Furthermore, the Advice Department will support users in dealing with troubles calmly. Specifically, it will provide advice to encourage calm judgment so that users do not panic and will show them the mindset needed to take appropriate action. In this way, the Advice Department can support users in dealing with troubles calmly based on past cases and accurate information, minimizing the impact of the trouble.

[0069] The reception desk can receive events and questions entered by the user. For example, the reception desk can receive events entered by the user as text information. The reception desk can also receive questions entered by the user as voice information. For example, the reception desk can also receive events entered by the user as image information using a camera or scanner. This allows the reception desk to appropriately receive events and questions entered by the user. 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 events and questions entered by the user into the AI ​​and convert them into a format that is easy for the AI ​​to analyze.

[0070] The analysis unit can provide optimal solutions and referrals based on court precedents and legal information. For example, the analysis unit can retrieve and analyze court precedents from a database. It can also derive optimal solutions based on legal information. For example, the analysis unit can refer to the most suitable referrals based on information entered by the user. In this way, the analysis unit can provide reliable solutions by offering court precedents and legal information. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input court precedents and legal information into an AI, which can then provide optimal solutions and referrals.

[0071] The service provider can provide instructions on how to contact the police, how to take photos of the damage, and how to check the coverage details of car insurance and fire insurance. For example, the service provider can provide specific procedures and methods for contacting the police. The service provider can also provide specific procedures and methods for taking photos of the damage. For example, the service provider can provide specific procedures and methods for checking the coverage details of car insurance and fire insurance. In this way, the service provider can provide specific solutions so that users can respond appropriately. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input instructions on how to contact the police, how to take photos of the damage, and how to check the coverage details of car insurance and fire insurance into the AI, and the AI ​​can provide the best solution.

[0072] The advisory unit can provide advice on future actions based on past cases. For example, the advisory unit can retrieve and analyze past cases from a database. Furthermore, the advisory unit can also provide advice on future actions based on the results of past cases. For example, the advisory unit can use data from past cases to provide optimal advice. This allows the advisory unit to provide more specific advice by referring to past cases. Some or all of the above processes in the advisory unit may be performed using AI, or not. For example, the advisory unit can input past cases into an AI, which can then provide advice on future actions.

[0073] The reception desk can estimate the user's emotions and dynamically change the input form interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive 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. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of events and questions. In this way, the reception desk reduces user stress and makes input smoother by providing an interface that responds to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines 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 reception desk may be performed using AI or not. For example, the reception desk can input the user's emotions into the AI, which can then dynamically change the input form interface.

[0074] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions events and questions that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest events and questions that the user will use during a specific time period based on the user's past input history. In this way, the reception desk improves the user's input efficiency by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history into AI, and the AI ​​can suggest the optimal input method.

[0075] The reception desk can automatically complete input content based on the user's current situation and environment during registration. For example, if the user is at home, the reception desk will automatically fill in the address information in the input form. If the user is out, the reception desk can also suggest the most suitable input content based on the user's current location. For example, if the reception desk is participating in a specific event, it will automatically complete information related to that event. In this way, the reception desk reduces the effort required for input by automatically completing input content based on the user's current situation and environment. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's current situation and environment into the AI, which can then automatically complete the input content.

[0076] The reception desk can estimate the user's emotions and prioritize input content based on the estimated emotions. For example, if the user is nervous, the reception desk may prioritize displaying important input fields to allow for quick input. If the user is relaxed, the reception desk may also provide detailed input fields and suggest customizable input methods. For example, if the user is in a hurry, the reception desk may display the most important input fields first to allow for quick input. This allows the reception desk to quickly input important information by prioritizing input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's emotions into the AI, which can then determine the priority of input content.

[0077] The reception desk can prioritize receiving input content that is highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize displaying input content related to that region. Similarly, if the user is traveling, the reception desk can prioritize displaying input content related to their travel destination. For example, if the user is at home, the reception desk can prioritize displaying input content related to their home. This allows the reception desk to prioritize receiving input content that is highly relevant by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into the AI, which can then prioritize receiving input content that is highly relevant.

[0078] The reception desk can analyze the user's social media activity and suggest relevant input content upon receiving the user's information. For example, the reception desk can automatically suggest input content related to a problem the user shared on social media. The reception desk can also suggest relevant input content based on information about accounts the user follows on social media. For example, the reception desk can suggest relevant input content based on information about groups the user participates in on social media. In this way, the reception desk can suggest relevant input content by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity into AI, and the AI ​​can suggest relevant input content.

[0079] The analysis unit can estimate the user's emotions and dynamically adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit may use an algorithm that provides quick analysis results. Alternatively, if the user is relaxed, the analysis unit may use an algorithm that provides detailed analysis results. For example, if the user is in a hurry, the analysis unit may use an algorithm that provides concise analysis results. In this way, the analysis unit provides appropriate analysis results by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 analysis unit may be performed using AI or not. For example, the analysis unit can input the user's emotions into the AI, which can dynamically adjust the analysis algorithm.

[0080] The analysis unit can evaluate the reliability of the input information during analysis and filter out unreliable information. For example, the analysis unit can verify the source of the input information and exclude unreliable information. The analysis unit can also evaluate the consistency of the input information and filter out inconsistent information. For example, the analysis unit can compare the reliability of the input information with past data and exclude unreliable information. In this way, the analysis unit evaluates the reliability of the input information, excludes unreliable information, and provides accurate analysis results. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the reliability of the input information into the AI, and the AI ​​can filter out unreliable information.

[0081] The analysis unit can improve the accuracy of its analysis by referring to similar past cases during the analysis process. For example, the analysis unit can retrieve similar past cases from a database and perform the analysis. Furthermore, the analysis unit can supplement its analysis results by referring to the results of similar past cases. For example, the analysis unit can adjust its analysis algorithm using data from similar past cases. This allows the analysis unit to improve the accuracy of its analysis by referring to similar past cases. Some or all of the above processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can input similar past cases into the AI, which can then improve the accuracy of the analysis.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is in a hurry, the analysis unit can provide a concise display method. This allows the analysis unit to provide a user-friendly display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's emotions into the AI, which can then adjust the display method of the analysis results.

[0083] The analysis unit can customize the analysis results by taking into account the user's geographical location information during analysis. For example, if the user is in a specific region, the analysis unit will prioritize displaying analysis results related to that region. Similarly, if the user is traveling, the analysis unit can prioritize displaying analysis results related to their travel destination. For example, if the user is at home, the analysis unit will prioritize displaying analysis results related to their home. This allows the analysis unit to provide highly relevant analysis results by considering the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can input the user's geographical location information into the AI, which can then customize the analysis results.

[0084] The analysis unit can analyze the user's social media activity during analysis and reflect relevant information in the analysis. For example, the analysis unit can supplement the analysis results based on information shared by the user on social media. The analysis unit can also supplement the analysis results based on information about accounts that the user follows on social media. For example, the analysis unit can supplement the analysis results based on information about groups that the user participates in on social media. In this way, the analysis unit can reflect relevant information in the analysis by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's social media activity into AI, and the AI ​​can reflect relevant information in the analysis.

[0085] The information provider can estimate the user's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the user is nervous, the provider can provide simple and easily visible information. If the user is relaxed, the provider can also provide detailed information. For example, if the user is in a hurry, the provider can provide concise information. In this way, the provider provides information that is easy for the user to understand by adjusting the way the information is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's emotions into the AI, and the AI ​​can adjust the way the information is presented.

[0086] The information provider can adjust the level of detail in the information provided based on its importance. For example, it may prioritize providing highly important information and include detailed explanations. Alternatively, it may provide less important information concisely and provide links to more detailed information as needed. For example, it may provide moderately important information with an appropriate level of detail. In this way, the information provider appropriately provides users with the information they need by adjusting the level of detail in the information provided based on its importance. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the importance of the information into the AI, and the AI ​​can adjust the level of detail in the provided content.

[0087] The information provider can apply different information provision algorithms depending on the category of information at the time of provision. For example, when providing information on legal issues, the provider can use an algorithm with legal expertise. Similarly, when providing information on health issues, the provider can use an algorithm with medical expertise. For example, when providing information on everyday troubles, the provider can use an algorithm with general knowledge. In this way, the provider provides appropriate information to the user by applying the most suitable information provision algorithm according to the category of information. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the category of information into the AI, and the AI ​​can apply different information provision algorithms.

[0088] The service provider can estimate the user's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing important information. Conversely, if the user is relaxed, the service provider may provide more detailed information. For example, if the user is in a hurry, the service provider will prioritize providing concise information. In this way, the service provider can quickly provide important information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's emotions into an AI, which can then determine the priority of the information.

[0089] The information provider can prioritize providing highly relevant information by considering the user's geographical location at the time of delivery. For example, if the user is in a specific region, the information provider can prioritize providing information related to that region. Furthermore, if the user is traveling, the information provider can prioritize providing information related to the travel destination. For example, if the user is at home, the information provider can prioritize providing information related to home. In this way, the information provider can prioritize providing highly relevant information by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, or not. For example, the information provider can input the user's geographical location into AI, which can then prioritize providing highly relevant information.

[0090] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can provide relevant information based on information shared by the user on social media. The service provider can also provide relevant information based on information about accounts followed by the user on social media. For example, the service provider can provide relevant information based on information about groups the user participates in on social media. In this way, the service provider can provide relevant information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity into AI, and the AI ​​can provide relevant information.

[0091] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is nervous, the advice unit will provide advice in a calm tone. Conversely, if the user is relaxed, it can provide detailed advice. For example, if the user is in a hurry, the advice unit will provide concise and to-the-point advice. In this way, the advice unit provides advice that is easy for the user to understand by adjusting the way it expresses advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input the user's emotions into the AI, which can then adjust the way it expresses the advice.

[0092] The advice unit can improve the accuracy of its advice based on past cases. For example, it can provide optimal advice based on similar past cases. It can also supplement its advice by referring to the results of past cases. For example, it can adjust its advice algorithm using data from past cases. This allows the advice unit to provide more specific advice by improving its accuracy based on past cases. Some or all of the above processes in the advice unit may be performed using AI or not. For example, the advice unit can input past cases into an AI, which can then improve the accuracy of its advice.

[0093] The advice unit can customize the advice it provides based on the user's current situation and environment. For example, if the user is at home, the advice unit will provide advice related to home. It can also provide advice related to the user's location if the user is out. For example, if the user is participating in a specific event, the advice unit will provide advice related to that event. In this way, the advice unit provides appropriate advice by customizing it based on the user's current situation and environment. Some or all of the above processing in the advice unit may be performed using AI, or not. For example, the advice unit can input the user's current situation and environment into the AI, which can then customize the advice.

[0094] The advice unit can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is stressed, the advice unit will prioritize important advice. If the user is relaxed, the advice unit can also provide detailed advice. For example, if the user is in a hurry, the advice unit will prioritize concise advice. In this way, the advice unit provides important advice quickly by prioritizing advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input the user's emotions into an AI, which can then determine the priority of advice.

[0095] The advice unit can provide optimal advice by considering the user's geographical location. For example, if the user is in a specific region, the advice unit will provide advice relevant to that region. It can also provide advice relevant to the user's travel destination if the user is traveling. For example, if the user is at home, the advice unit will provide advice relevant to home. This allows the advice unit to provide highly relevant advice by considering the user's geographical location. Some or all of the above processing in the advice unit may be performed using AI, or not. For example, the advice unit can input the user's geographical location into the AI, which can then provide optimal advice.

[0096] The advice unit can analyze the user's social media activity and provide relevant advice when providing advice. For example, the advice unit can provide relevant advice based on information the user has shared on social media. It can also provide relevant advice based on information about accounts the user follows on social media. For example, the advice unit can provide relevant advice based on information about groups the user participates in on social media. In this way, the advice unit can provide relevant advice by analyzing the user's social media activity. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input the user's social media activity into AI, and the AI ​​can provide relevant advice.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The reception desk can translate user input in real time, enabling multilingual support. For example, it can translate user input in Japanese into English, allowing them to consult with an English-speaking expert. It can also translate user input in English into Japanese, allowing them to consult with a Japanese-speaking expert. Furthermore, the reception desk can simultaneously translate user input into multiple languages, gathering opinions from multiple experts. This allows the reception desk to support users in smoothly conducting multilingual consultations.

[0099] The analysis unit can automatically generate templates for relevant legal documents and contracts based on user input. For example, if a user enters details of a dispute, the analysis unit will generate an appropriate contract template based on that information. The analysis unit can also automatically fill in the necessary fields of legal documents based on the information entered by the user. Furthermore, the analysis unit can provide the generated documents to the user, allowing for modifications and additions as needed. This enables the analysis unit to support users in quickly creating legal documents.

[0100] The service provider can estimate the user's emotions and adjust the tone of the information provided based on those emotions. For example, if the user is feeling anxious, the service provider can provide information in a gentle tone to reassure the user. If the user is feeling angry, the service provider can provide information in a calm and objective tone to soothe the user's emotions. Furthermore, if the user is relaxed, the service provider can provide information in a casual tone to create a friendly atmosphere. In this way, the service provider can provide information in an appropriate tone according to the user's emotions.

[0101] The advisory unit can analyze users' past behavioral patterns, predict future problems, and provide advice in advance. For example, it can analyze problems users have frequently encountered in the past and issue warnings if similar problems are likely to occur again. The advisory unit can also suggest preventative measures based on users' behavioral patterns. Furthermore, the advisory unit can provide advice about potential problems that users might cause before they take a particular action. In this way, the advisory unit can support users in preventing problems before they occur.

[0102] The reception desk can estimate the user's emotions and dynamically adjust the input verification process based on those emotions. For example, if the user is stressed, the reception desk can simplify the verification process to allow for quicker verification. If the user is relaxed, it can provide a more detailed verification process to allow for careful review. Furthermore, if the user is in a hurry, it can prioritize displaying only the most important verification items to enable quick verification. In this way, the reception desk can provide an appropriate verification process tailored to the user's emotions.

[0103] The analysis unit can automatically collect opinions from relevant experts based on user input and incorporate them into the analysis results. For example, if a user inputs a legal issue, the analysis unit can collect opinions from legal experts and incorporate them into the analysis results. Similarly, if a user inputs a medical issue, it can collect opinions from medical experts and incorporate them into the analysis results. Furthermore, if a user inputs a technical issue, it can collect opinions from technical experts and incorporate them into the analysis results. By incorporating expert opinions, the analysis unit can provide more reliable analysis results.

[0104] The service provider can estimate the user's emotions and adjust the format of the information provided based on those emotions. For example, if the user is stressed, the service provider can provide information using visually easy-to-understand graphs and diagrams. If the user is relaxed, it can provide detailed text information, allowing the user to read it carefully. Furthermore, if the user is in a hurry, it can provide information summarized in bullet points for quick comprehension. In this way, the service provider can deliver information in an appropriate format according to the user's emotions.

[0105] The advice section can provide information on relevant communities and support groups based on user input. For example, if a user seeks advice on a specific problem, the advice section can provide information on online communities and support groups related to that problem. Similarly, if a user seeks advice on a specific illness, it can provide information on patient support groups related to that illness. Furthermore, if a user seeks advice on a legal issue, it can provide information on forums where legal professionals participate. This allows the advice section to enable users to receive additional support from relevant communities and support groups.

[0106] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide notifications in a simple and easy-to-understand format so that the user can understand them immediately. If the user is relaxed, it can provide notifications that include detailed analysis results so that the user can carefully review the content. Furthermore, if the user is in a hurry, it can provide notifications that summarize the key points concisely so that the user can understand them quickly. In this way, the analysis unit can provide appropriate notification methods according to the user's emotions.

[0107] The service provider can suggest relevant educational resources and training programs based on user input. For example, if a user seeks advice on acquiring a specific skill, the service provider can suggest online courses or training programs related to that skill. If a user wants to learn how to solve a specific problem, the service provider can also provide educational resources related to that problem. Furthermore, if a user seeks advice on career advancement, the service provider can provide information on relevant seminars and workshops. In this way, the service provider can support users in acquiring the necessary knowledge and skills.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk receives information entered by the user. This information includes text, image, and audio information. The reception desk receives text information through an input form, image information using a camera or scanner, and audio information using a microphone. Furthermore, the reception desk converts the text information entered by the user into a format that is easy to analyze, converts image information into text using OCR technology, and converts audio information into text using speech recognition technology. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses data mining technology, natural language processing technology, and machine learning algorithms to analyze the information and derive the optimal solution based on the input information. Step 3: The service provider provides solutions based on the information analyzed by the analysis provider. The service provider provides instructions, contact information, first aid methods, how to contact the police, how to take photos of the damage, and how to check the coverage of car insurance and fire insurance, to support users in calmly dealing with problems when they face them. Step 4: The Advice Department provides advice based on past cases and the information provided by the Service Provider. The Advice Department refers to past cases to advise on future actions, provides advice to reduce damage caused by incorrect or intentional misinformation, and supports users so that they can calmly deal with problems when they face them.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and advice unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives user text information, image information, and voice information using the reception device 38 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the information using data mining technology and natural language processing technology. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides a course of action based on the analyzed information. The advice unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides advice based on past cases. 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.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and advice unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives the user's voice information using the microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the information using data mining technology and natural language processing technology. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides a course of action based on the analyzed information. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides advice based on past cases. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and advice unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives user voice information using the microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the information using data mining technology and natural language processing technology. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides a course of action based on the analyzed information. The advice unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides advice based on past cases. 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.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and advice unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives user voice information using the microphone 238 of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the information using data mining technology and natural language processing technology. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides a course of action based on the analyzed information. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides advice based on past cases. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A reception desk that receives information entered by the user, An analysis unit that analyzes the information received by the reception unit, A providing unit that provides a method of dealing with the situation based on the information analyzed by the aforementioned analysis unit, The system includes an advice unit that provides advice based on past cases, using the information provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It accepts user input of events and questions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on court precedents and legal information, we will provide you with the best solutions and resources for consultation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, This service provides information on how to contact the police, how to take photos of the damage, and how to check the coverage details of your car insurance and fire insurance. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned advice section, We will advise you on future actions based on past cases. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and dynamically changes the input form interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is During registration, the system automatically completes the input based on the user's current situation and environment. 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 prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is During registration, the system prioritizes accepting input that is highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During registration, the system analyzes the user's social media activity and suggests relevant input content. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and dynamically adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the reliability of the input information is evaluated, and information with low reliability is filtered out. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referring to similar past cases. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the analysis results are customized by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the system analyzes users' social media activity and incorporates relevant information into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. 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 prioritizes the information 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 information, we prioritize providing highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, When giving advice, we improve the accuracy of the advice based on past cases. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advice section, When providing advice, customize the advice based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, When providing advice, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned advice section, When providing advice, we analyze the user's social media activity and offer relevant advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives information entered by the user, An analysis unit that analyzes the information received by the reception unit, A providing unit that provides a method of dealing with the situation based on the information analyzed by the aforementioned analysis unit, The system includes an advice unit that provides advice based on past cases, using the information provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned reception unit is It accepts user input of events and questions. The system according to feature 1.

3. The aforementioned analysis unit, Based on court precedents and legal information, we will provide you with the best solutions and resources for consultation. The system according to feature 1.

4. The aforementioned supply unit is, This service provides information on how to contact the police, how to take photos of the damage, and how to check the coverage details of your car insurance and fire insurance. The system according to feature 1.

5. The aforementioned advice section, We will advise you on future actions based on past cases. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and dynamically changes the input form interface based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is During registration, the system automatically completes the input based on the user's current situation and environment. The system according to feature 1.

Images