system

Abstract

The system according to this embodiment aims to enable users to easily create application forms. [Solution] The system according to the embodiment comprises a reception unit, an acquisition unit, a generation unit, and a provision unit. The reception unit receives information input from the user. The acquisition unit acquires the information received by the reception unit from a database. The generation unit creates an application form based on the information acquired by the acquisition unit. The provision unit provides the application form created by the generation unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 prior art, there were problems that preparing an application form and filling in forms were laborious and it was unclear what to describe.

[0005] The system according to the embodiment aims to enable a user to easily create an application form.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an acquisition unit, a generation unit, and a provision unit. The reception unit receives information input from a user. The acquisition unit acquires the information received by the reception unit from a database. The generation unit creates an application form based on the information acquired by the acquisition unit. The provision unit provides the application form created by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to easily create application forms. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The application form creation platform according to an embodiment of the present invention is a system for reducing the effort required to create application forms and fill out forms. This system solves the problems of current handwritten application forms and users not knowing what information to include by providing a mechanism that allows application forms to be automatically created through chat communication. Specifically, it consists of the following steps: First, each company registers the content to be entered in the application form and each item in advance. Next, the user enters the necessary information through chat. This information is automatically retrieved from a database (DB) where the user's information is registered and from the chat communication. Finally, the application form is automatically created based on the retrieved information. For example, if a user enters their address in response to a chat message such as "Please enter your address," that information is automatically reflected in the application form. In addition, necessary information is automatically supplemented from past records, so users can complete the application form without much effort. This mechanism significantly reduces the effort required to create application forms, allowing users to easily create them. Furthermore, each company can provide a highly convenient service to users by placing this platform on their own page. As a result, the application form creation platform significantly reduces the effort required for users to create application forms and enables them to create them efficiently.

[0029] The application form creation platform according to this embodiment comprises a reception unit, an acquisition unit, a generation unit, and a provision unit. The reception unit receives information input from the user. The reception unit can receive information by methods such as text input, voice input, and image input. The reception unit can also register the information entered by the user in a database. The acquisition unit retrieves the information received by the reception unit from the database. The acquisition unit can retrieve information from databases of various types and structures, such as relational databases and NoSQL databases. The acquisition unit may also include a supplementation unit that supplements necessary information by referring to past records. The generation unit creates an application form based on the information acquired by the acquisition unit. The generation unit can automatically create an application form from chat exchanges, for example. The generation unit creates the application form using a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. The provision unit provides the application form created by the generation unit to the user. The provision unit can provide the application form by methods such as email, an online portal, or postal mail. As a result, the application form creation platform according to this embodiment can automate everything from user information input to the creation and submission of the application form, thereby reducing the effort involved.

[0030] The reception desk receives information input from users. The reception desk can accept information through methods such as text input, voice input, and image input. Specifically, for text input, it provides an interface for users to input necessary information using a keyboard. For voice input, it uses speech recognition technology to convert the user's speech into text data and accepts it as input information. For image input, users upload photos they have taken or scanned documents, and image recognition technology extracts the necessary information. These input methods enhance user convenience and provide diverse information input options, allowing the system to cater to a wide range of users. Furthermore, the reception desk can also register the information entered by users into a database. Database registration is important for maintaining consistency and integrity of input information and enables efficient data access in subsequent processing. For example, information entered by users can be saved to the database in real time, allowing other departments to access it immediately. This allows the reception desk to efficiently and accurately receive information input from users and play a role in ensuring the smooth operation of the entire system.

[0031] The acquisition unit retrieves information received by the reception unit from the database. The acquisition unit can retrieve information from databases of various types and structures, such as relational databases and NoSQL databases. In relational databases, necessary information can be efficiently searched and retrieved using SQL queries. On the other hand, NoSQL databases allow for flexible and scalable data retrieval by utilizing document-oriented and key-value data structures. The acquisition unit can also be equipped with a supplementary unit that supplements necessary information by referring to past records. The supplementary unit has the function of automatically supplementing the information necessary for creating the current application by referring to past application forms and related data. For example, it can refer to information of users who have made similar applications in the past, saving the effort of re-entering data. As a result, the acquisition unit can quickly and accurately retrieve necessary information from the database, significantly improving the efficiency of application form creation. Furthermore, the acquisition unit also plays a role in appropriately managing database updates and synchronization to maintain data integrity and consistency. As a result, the acquisition unit can streamline data management throughout the system and provide highly reliable information.

[0032] The generation unit creates application forms based on information acquired by the acquisition unit. For example, the generation unit can automatically create application forms from chat conversations. The generation unit uses generation AI to create application forms. Generation AI includes text generation AI (e.g., LLM) and multimodal generation AI. Specifically, text generation AI automatically generates each item of the application form using natural language processing technology based on information entered by the user and data obtained from the acquisition unit. For example, it can create an application form with appropriate wording and format based on basic information provided by the user and past application content. Multimodal generation AI can generate application forms by integrating various data such as images and audio, not just text. This allows the generation unit to make the most of user input information and create application forms accurately and efficiently. Furthermore, the generation unit can also have a function to automatically check the content of the generated application form to confirm that there are no errors or deficiencies. This allows the generation unit to support users in creating high-quality application forms without effort, and improve the efficiency and accuracy of the entire application process.

[0033] The provisioning department provides the application forms created by the generation department to the users. The provisioning department can provide the application forms by methods such as email, online portal, or postal mail. Specifically, it can attach a PDF file of the application form to an email and send it to the user. An online portal provides an interface where users can log in and download the application form. In the case of postal mail, a service can be provided to print the generated application form and send it to the specified address. This allows the provisioning department to implement flexible delivery methods that meet user needs and ensure smooth receipt of application forms. Furthermore, the provisioning department can also have functions to confirm receipt of the provided application forms and collect feedback. For example, it can confirm that users have received the application forms by tracking whether they have been opened via email or downloaded from the online portal. It can also collect feedback from users and use it to improve the content and delivery method of the application forms. This allows the provisioning department to provide application forms to users quickly and reliably and improve overall satisfaction with the application process.

[0034] The acquisition unit may include a supplementation unit that supplements necessary information by referring to past records. The acquisition unit refers to past records, such as past application forms and user history data. The acquisition unit can automatically supplement necessary information based on past records. For example, the acquisition unit supplements the information required for the current application form based on data from past application forms. The acquisition unit can also supplement necessary information based on user history data. In this way, the acquisition unit can automatically supplement necessary information by referring to past records. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input past records into AI and have AI perform the supplementation of necessary information.

[0035] The generation unit can automatically create application forms from chat conversations. For example, the generation unit creates application forms based on information entered by the user in the chat. The generation unit uses a generation AI to automatically generate application forms from chat conversations. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user enters their address in response to a chat message such as "Please enter your address," the generation unit will automatically reflect that information in the application form. The generation unit can also automatically supplement necessary information from past records. In this way, the generation unit can reduce the effort required from the user by automatically generating application forms based on chat conversations. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input the chat conversation into an AI and have the AI ​​perform the automatic generation of the application form.

[0036] The service provider can provide the generated application form to the user. The service provider can provide the application form by methods such as email, an online portal, or postal mail. The service provider can provide the generated application form to the user quickly. For example, the service provider can send the application form by email so that the user can access it immediately. The service provider can also provide the application form through an online portal so that the user can access it at any time. Furthermore, the service provider can send the application form by postal mail so that the user can receive a paper copy. In this way, the service provider can simplify the application process by providing the generated application form to the user. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the generated application form into an AI and have the AI ​​perform the provision of the application form.

[0037] The reception unit can register information entered by users into a database. For example, the reception unit saves information entered by users to the database. The reception unit needs to clarify the specific methods and criteria for registering information into the database. For example, this includes the data format and the timing of registration. The reception unit can register information entered by users into the database quickly and accurately. This makes it easier for the reception unit to manage information by registering information entered by users into the database. Some or all of the above processes in the reception unit may be performed using AI or not. For example, the reception unit can input information entered by users into an AI and have the AI ​​perform the registration to the database.

[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 information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. In this way, the reception desk can suggest the optimal input method to the user by analyzing their 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 have the AI ​​suggest the optimal input method.

[0039] The reception desk can customize input fields based on the user's current situation and areas of interest when they enter information. For example, if the user is traveling, the reception desk can prioritize displaying input fields related to travel. Similarly, if the user is working, the reception desk can prioritize displaying input fields related to work. Furthermore, if the user is entering information about their hobbies, the reception desk can prioritize displaying input fields related to those hobbies. This allows the reception desk to improve input efficiency by customizing input fields according to the user's situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's current situation and areas of interest into the AI ​​and have the AI ​​perform the customization of input fields.

[0040] The reception desk can prioritize displaying input fields that are highly relevant to the user's geographical location when they are entering information. For example, if the user is in a specific region, the reception desk will prioritize displaying input fields related to that region. Furthermore, if the user is traveling, the reception desk can prioritize displaying input fields related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize displaying input fields related to their home. This allows the reception desk to improve the efficiency of data entry by prioritizing input fields based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into the AI ​​and have the AI ​​display highly relevant input fields.

[0041] The reception desk can analyze the user's social media activity and suggest relevant input fields when information is entered. For example, the reception desk can suggest relevant input fields based on information the user has shared on social media. It can also suggest relevant input fields based on information about accounts the user follows on social media. Furthermore, it can suggest relevant input fields based on information about groups the user participates in on social media. In this way, the reception desk can suggest relevant input fields by analyzing 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 have the AI ​​suggest relevant input fields.

[0042] The data retrieval unit can analyze the user's past database usage history and select the optimal retrieval method. For example, the retrieval unit can prioritize retrieving information from databases that the user has frequently used in the past. It can also prioritize selecting retrieval methods (API, scraping, etc.) that the user has used in the past. Furthermore, the retrieval unit can select the optimal retrieval method for a specific time period based on the user's past database usage history. In this way, the retrieval unit can select the optimal retrieval method by analyzing past database usage history. Some or all of the above processing in the retrieval unit may be performed using AI or not. For example, the retrieval unit can input the user's past database usage history into AI and have the AI ​​select the optimal retrieval method.

[0043] The data acquisition unit can filter information based on the user's current situation and areas of interest when acquiring it. For example, if the user is traveling, the data acquisition unit will prioritize acquiring travel-related information. Similarly, if the user is working, the data acquisition unit can prioritize acquiring work-related information. Furthermore, if the user is acquiring information related to their hobbies, the data acquisition unit can prioritize acquiring information related to those hobbies. This allows the data acquisition unit to acquire highly relevant information by filtering it according to the user's situation and areas of interest. Some or all of the above processing in the data acquisition unit may be performed using AI, or it may be performed without AI. For example, the data acquisition unit can input the user's current situation and areas of interest into the AI ​​and have the AI ​​perform the information filtering.

[0044] The data acquisition unit can prioritize acquiring highly relevant information based on the user's geographical location information when acquiring information. For example, if the user is in a specific region, the data acquisition unit will prioritize acquiring information related to that region. Furthermore, if the user is traveling, the data acquisition unit can prioritize acquiring information related to the travel destination. Additionally, if the user is at home, the data acquisition unit can prioritize acquiring information related to their home. In this way, the data acquisition unit can acquire highly relevant information by prioritizing information acquisition based on the user's geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI, or it may be performed without AI. For example, the data acquisition unit can input the user's geographical location information into AI and have the AI ​​perform the acquisition of highly relevant information.

[0045] The acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring information. For example, the acquisition unit can acquire relevant information based on information shared by the user on social media. It can also acquire relevant information based on information of accounts followed by the user on social media. Furthermore, the acquisition unit can acquire relevant information based on information of groups the user participates in on social media. In this way, the acquisition unit can acquire relevant information by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's social media activity into AI and have the AI ​​perform the acquisition of relevant information.

[0046] The generation unit can adjust the level of detail in an application form based on the importance of the information when generating the application form. For example, the generation unit can prioritize and describe important information in detail, while describing other information concisely. Alternatively, the generation unit can describe all information in equal detail. Furthermore, the generation unit can omit less important information and include only important information. In this way, the generation unit can describe important information in detail by adjusting the level of detail in the application form based on the importance of the information. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the importance of the information into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the application form.

[0047] The generation unit can apply different generation algorithms depending on the information category when generating application forms. For example, the generation unit can apply a security-focused generation algorithm to categories related to personal information. It can also apply a detailed generation algorithm to categories related to corporate information. Furthermore, it can apply a concise generation algorithm to categories related to general information. This allows the generation unit to generate appropriate application forms by applying different generation algorithms depending on the information category. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input the information category into the AI ​​and have the AI ​​execute the application of the generation algorithm.

[0048] The generation unit can determine the priority of application forms based on the submission timing of the information when generating the application form. For example, the generation unit can prioritize information with an approaching deadline in the application form. It can also postpone information with a distant submission deadline. Furthermore, the generation unit can briefly describe information with ample time before submission and add details later. In this way, the generation unit can prioritize the inclusion of important information by determining the priority of application forms based on the submission timing of the information. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the submission timing of the information into the AI ​​and have the AI ​​perform the determination of the application form priority.

[0049] The generation unit can adjust the order of information in an application form based on the relevance of the information during the application form generation process. For example, the generation unit can list important information first and other information later. It can also group highly relevant information together. Furthermore, it can omit less relevant information and include only important information. This allows the generation unit to prioritize important information by adjusting the order of the application form based on the relevance of the information. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input the relevance of the information into the AI ​​and have the AI ​​perform the adjustment of the application form's order.

[0050] The service provider can select the optimal service method by referring to the user's past application usage history when submitting an application. For example, the service provider may prioritize using service methods previously used by the user. It can also select the most efficient service method based on the user's past application usage history. Furthermore, the service provider may prioritize suggesting service methods that the user has preferred to use in the past. This allows the service provider to select the optimal service method by referring to past application usage history. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's past application usage history into an AI and have the AI ​​select the optimal service method.

[0051] The service provider can customize the delivery method based on the user's current situation when an application is submitted. For example, if the user is on the go, the service provider can use a delivery method optimized for mobile devices. If the user is in the office, the service provider can also use a delivery method optimized for desktop devices. Furthermore, if the user is at home, the service provider can use a delivery method optimized for home devices. This allows the service provider to select the optimal delivery method by customizing it based on the user's current situation. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's current situation into the AI ​​and have the AI ​​perform the customization of the delivery method.

[0052] The service provider can select the most suitable service delivery method based on the user's geographical location information when an application is submitted. For example, if the user is in a specific region, the service provider will prioritize using a service delivery method related to that region. Furthermore, if the user is traveling, the service provider can prioritize using a service delivery method related to their travel destination. Additionally, if the user is at home, the service provider can prioritize using a service delivery method related to their home. This allows the service provider to select a highly relevant service delivery method based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's geographical location information into an AI and have the AI ​​select the most suitable service delivery method.

[0053] The service provider can analyze the user's social media activity and propose service delivery methods when an application is submitted. For example, the service provider can propose relevant service delivery methods based on information shared by the user on social media. It can also propose relevant service delivery methods based on information about accounts the user follows on social media. Furthermore, it can propose relevant service delivery methods based on information about groups the user participates in on social media. In this way, the service provider can propose relevant service delivery methods by analyzing 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 have the AI ​​propose service delivery methods.

[0054] The interpolation unit can optimize the interpolation algorithm by referring to past interpolation data during interpolation. For example, the interpolation unit can select the optimal interpolation algorithm based on past interpolation data. It can also extract specific patterns from past interpolation data and optimize the interpolation algorithm accordingly. Furthermore, the interpolation unit can analyze past interpolation data to improve the accuracy of the interpolation algorithm. Thus, the interpolation unit can optimize the interpolation algorithm by referring to past interpolation data. Some or all of the above processes in the interpolation unit may be performed using AI or not. For example, the interpolation unit can input past interpolation data into AI and have the AI ​​optimize the interpolation algorithm.

[0055] The completion unit can apply different completion methods to each category of information during completion. For example, the completion unit can apply a security-focused completion method to categories related to personal information. It can also apply a completion method that includes detailed information to categories related to corporate information. Furthermore, it can apply a concise completion method to categories related to general information. This allows the completion unit to perform appropriate completion by applying different completion methods to each category of information. Some or all of the above processing in the completion unit may be performed using AI, or not. For example, the completion unit can input the information categories into the AI ​​and have the AI ​​perform the application of the completion methods.

[0056] The completion unit can weight the completed data based on the submission date of the information. For example, the completion unit can prioritize completing information with an approaching deadline. It can also postpone completing information with a distant submission date. Furthermore, it can briefly complete information with ample time before the submission deadline and add details later. In this way, the completion unit can prioritize completing important information by weighting the completed data based on the submission date of the information. Some or all of the above processing in the completion unit may be performed using AI or not. For example, the completion unit can input the submission dates of the information into the AI ​​and have the AI ​​perform the weighting of the completed data.

[0057] The completion unit can improve the accuracy of completion by referring to relevant literature during completion. For example, the completion unit can improve the accuracy of the information being completed based on relevant literature. The completion unit can also improve the accuracy of completion by extracting specific patterns from relevant literature. Furthermore, the completion unit can analyze relevant literature to improve the accuracy of the completion algorithm. In this way, the completion unit can improve the accuracy of completion by referring to relevant literature. Some or all of the above processes in the completion unit may be performed using AI or not. For example, the completion unit can input relevant literature into AI and have the AI ​​perform the task of improving the accuracy of completion.

[0058] The completion unit can complete the necessary information by referring to the user's past records during completion. For example, the completion unit completes the necessary information based on the user's past records. The completion unit can also extract specific patterns from the user's past records and complete the necessary information. Furthermore, the completion unit can analyze the user's past records and complete the necessary information. As a result, the completion unit can automatically complete the necessary information by referring to the user's past records. Some or all of the above processing in the completion unit may be performed using AI or not. For example, the completion unit can input the user's past records into AI and have the AI ​​perform the completion of the necessary information.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The reception desk can analyze user input in real time and provide appropriate feedback based on that input. For example, when a user enters an address, if the input is incomplete, the reception desk will automatically suggest completion options. It can also immediately display an error message and prompt correction if there are errors in the information entered by the user. Furthermore, the reception desk can check the consistency of the input before the user completes the input and request additional information if necessary. This allows the reception desk to improve the accuracy and efficiency of user input by analyzing it in real time and providing appropriate feedback.

[0061] The generation unit can adjust the level of detail in an application form based on the importance of the information when generating the application form. For example, the generation unit can prioritize and describe important information in detail, while describing other information concisely. Alternatively, the generation unit can describe all information in equal detail. Furthermore, the generation unit can omit less important information and include only important information. In this way, the generation unit can describe important information in detail by adjusting the level of detail in the application form based on the importance of the information. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the importance of the information into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the application form.

[0062] 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 information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. In this way, the reception desk can suggest the optimal input method to the user by analyzing their 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 have the AI ​​suggest the optimal input method.

[0063] The data acquisition unit can filter information based on the user's current situation and areas of interest when acquiring it. For example, if the user is traveling, the data acquisition unit will prioritize acquiring travel-related information. Similarly, if the user is working, the data acquisition unit can prioritize acquiring work-related information. Furthermore, if the user is acquiring information related to their hobbies, the data acquisition unit can prioritize acquiring information related to those hobbies. This allows the data acquisition unit to acquire highly relevant information by filtering it according to the user's situation and areas of interest. Some or all of the above processing in the data acquisition unit may be performed using AI, or it may be performed without AI. For example, the data acquisition unit can input the user's current situation and areas of interest into the AI ​​and have the AI ​​perform the information filtering.

[0064] The generation unit can apply different generation algorithms depending on the information category when generating application forms. For example, the generation unit can apply a security-focused generation algorithm to categories related to personal information. It can also apply a detailed generation algorithm to categories related to corporate information. Furthermore, it can apply a concise generation algorithm to categories related to general information. This allows the generation unit to generate appropriate application forms by applying different generation algorithms depending on the information category. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input the information category into the AI ​​and have the AI ​​execute the application of the generation algorithm.

[0065] The service provider can select the optimal service method by referring to the user's past application usage history when submitting an application. For example, the service provider may prioritize using service methods previously used by the user. It can also select the most efficient service method based on the user's past application usage history. Furthermore, the service provider may prioritize suggesting service methods that the user has preferred to use in the past. This allows the service provider to select the optimal service method by referring to past application usage history. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's past application usage history into an AI and have the AI ​​select the optimal service method.

[0066] The completion unit can weight the completed data based on the submission date of the information. For example, the completion unit can prioritize completing information with an approaching deadline. It can also postpone completing information with a distant submission date. Furthermore, it can briefly complete information with ample time before the submission deadline and add details later. In this way, the completion unit can prioritize completing important information by weighting the completed data based on the submission date of the information. Some or all of the above processing in the completion unit may be performed using AI or not. For example, the completion unit can input the submission dates of the information into the AI ​​and have the AI ​​perform the weighting of the completed data.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk receives information input from the user. The reception desk can accept information using methods such as text input, voice input, and image input. The reception desk can also register the information entered by the user into a database. Step 2: The retrieval unit retrieves the information received by the reception unit from the database. The retrieval unit can retrieve information from databases of various types and structures, such as relational databases and NoSQL databases. The retrieval unit may also include a supplementary unit that supplements the information by referring to past records. Step 3: The generation unit creates the application form based on the information acquired by the acquisition unit. The generation unit can automatically create the application form from the chat exchange, and uses a generation AI to create the application form. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 4: The provisioning department provides the application form created by the generation department to the user. The provisioning department can provide the application form via email, online portal, postal mail, etc.

[0069] (Example of form 2) The application form creation platform according to an embodiment of the present invention is a system for reducing the effort required to create application forms and fill out forms. This system solves the problems of current handwritten application forms and users not knowing what information to include by providing a mechanism that allows application forms to be automatically created through chat communication. Specifically, it consists of the following steps: First, each company registers the content to be entered in the application form and each item in advance. Next, the user enters the necessary information through chat. This information is automatically retrieved from a database (DB) where the user's information is registered and from the chat communication. Finally, the application form is automatically created based on the retrieved information. For example, if a user enters their address in response to a chat message such as "Please enter your address," that information is automatically reflected in the application form. In addition, necessary information is automatically supplemented from past records, so users can complete the application form without much effort. This mechanism significantly reduces the effort required to create application forms, allowing users to easily create them. Furthermore, each company can provide a highly convenient service to users by placing this platform on their own page. As a result, the application form creation platform significantly reduces the effort required for users to create application forms and enables them to create them efficiently.

[0070] The application form creation platform according to this embodiment comprises a reception unit, an acquisition unit, a generation unit, and a provision unit. The reception unit receives information input from the user. The reception unit can receive information by methods such as text input, voice input, and image input. The reception unit can also register the information entered by the user in a database. The acquisition unit retrieves the information received by the reception unit from the database. The acquisition unit can retrieve information from databases of various types and structures, such as relational databases and NoSQL databases. The acquisition unit may also include a supplementation unit that supplements necessary information by referring to past records. The generation unit creates an application form based on the information acquired by the acquisition unit. The generation unit can automatically create an application form from chat exchanges, for example. The generation unit creates the application form using a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. The provision unit provides the application form created by the generation unit to the user. The provision unit can provide the application form by methods such as email, an online portal, or postal mail. As a result, the application form creation platform according to this embodiment can automate everything from user information input to the creation and submission of the application form, thereby reducing the effort involved.

[0071] The reception desk receives information input from users. The reception desk can accept information through methods such as text input, voice input, and image input. Specifically, for text input, it provides an interface for users to input necessary information using a keyboard. For voice input, it uses speech recognition technology to convert the user's speech into text data and accepts it as input information. For image input, users upload photos they have taken or scanned documents, and image recognition technology extracts the necessary information. These input methods enhance user convenience and provide diverse information input options, allowing the system to cater to a wide range of users. Furthermore, the reception desk can also register the information entered by users into a database. Database registration is important for maintaining consistency and integrity of input information and enables efficient data access in subsequent processing. For example, information entered by users can be saved to the database in real time, allowing other departments to access it immediately. This allows the reception desk to efficiently and accurately receive information input from users and play a role in ensuring the smooth operation of the entire system.

[0072] The acquisition unit retrieves information received by the reception unit from the database. The acquisition unit can retrieve information from databases of various types and structures, such as relational databases and NoSQL databases. In relational databases, necessary information can be efficiently searched and retrieved using SQL queries. On the other hand, NoSQL databases allow for flexible and scalable data retrieval by utilizing document-oriented and key-value data structures. The acquisition unit can also be equipped with a supplementary unit that supplements necessary information by referring to past records. The supplementary unit has the function of automatically supplementing the information necessary for creating the current application by referring to past application forms and related data. For example, it can refer to information of users who have made similar applications in the past, saving the effort of re-entering data. As a result, the acquisition unit can quickly and accurately retrieve necessary information from the database, significantly improving the efficiency of application form creation. Furthermore, the acquisition unit also plays a role in appropriately managing database updates and synchronization to maintain data integrity and consistency. As a result, the acquisition unit can streamline data management throughout the system and provide highly reliable information.

[0073] The generation unit creates application forms based on information acquired by the acquisition unit. For example, the generation unit can automatically create application forms from chat conversations. The generation unit uses generation AI to create application forms. Generation AI includes text generation AI (e.g., LLM) and multimodal generation AI. Specifically, text generation AI automatically generates each item of the application form using natural language processing technology based on information entered by the user and data obtained from the acquisition unit. For example, it can create an application form with appropriate wording and format based on basic information provided by the user and past application content. Multimodal generation AI can generate application forms by integrating various data such as images and audio, not just text. This allows the generation unit to make the most of user input information and create application forms accurately and efficiently. Furthermore, the generation unit can also have a function to automatically check the content of the generated application form to confirm that there are no errors or deficiencies. This allows the generation unit to support users in creating high-quality application forms without effort, and improve the efficiency and accuracy of the entire application process.

[0074] The provisioning department provides the application forms created by the generation department to the users. The provisioning department can provide the application forms by methods such as email, online portal, or postal mail. Specifically, it can attach a PDF file of the application form to an email and send it to the user. An online portal provides an interface where users can log in and download the application form. In the case of postal mail, a service can be provided to print the generated application form and send it to the specified address. This allows the provisioning department to implement flexible delivery methods that meet user needs and ensure smooth receipt of application forms. Furthermore, the provisioning department can also have functions to confirm receipt of the provided application forms and collect feedback. For example, it can confirm that users have received the application forms by tracking whether they have been opened via email or downloaded from the online portal. It can also collect feedback from users and use it to improve the content and delivery method of the application forms. This allows the provisioning department to provide application forms to users quickly and reliably and improve overall satisfaction with the application process.

[0075] The acquisition unit may include a supplementation unit that supplements necessary information by referring to past records. The acquisition unit refers to past records, such as past application forms and user history data. The acquisition unit can automatically supplement necessary information based on past records. For example, the acquisition unit supplements the information required for the current application form based on data from past application forms. The acquisition unit can also supplement necessary information based on user history data. In this way, the acquisition unit can automatically supplement necessary information by referring to past records. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input past records into AI and have AI perform the supplementation of necessary information.

[0076] The generation unit can automatically create application forms from chat conversations. For example, the generation unit creates application forms based on information entered by the user in the chat. The generation unit uses a generation AI to automatically generate application forms from chat conversations. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user enters their address in response to a chat message such as "Please enter your address," the generation unit will automatically reflect that information in the application form. The generation unit can also automatically supplement necessary information from past records. In this way, the generation unit can reduce the effort required from the user by automatically generating application forms based on chat conversations. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input the chat conversation into an AI and have the AI ​​perform the automatic generation of the application form.

[0077] The service provider can provide the generated application form to the user. The service provider can provide the application form by methods such as email, an online portal, or postal mail. The service provider can provide the generated application form to the user quickly. For example, the service provider can send the application form by email so that the user can access it immediately. The service provider can also provide the application form through an online portal so that the user can access it at any time. Furthermore, the service provider can send the application form by postal mail so that the user can receive a paper copy. In this way, the service provider can simplify the application process by providing the generated application form to the user. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the generated application form into an AI and have the AI ​​perform the provision of the application form.

[0078] The reception unit can register information entered by users into a database. For example, the reception unit saves information entered by users to the database. The reception unit needs to clarify the specific methods and criteria for registering information into the database. For example, this includes the data format and the timing of registration. The reception unit can register information entered by users into the database quickly and accurately. This makes it easier for the reception unit to manage information by registering information entered by users into the database. Some or all of the above processes in the reception unit may be performed using AI or not. For example, the reception unit can input information entered by users into an AI and have the AI ​​perform the registration to the database.

[0079] The reception desk can estimate the user's emotions and adjust the information input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick information entry. In this way, the reception desk can reduce user stress by adjusting the interface according 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 user emotion data into AI and have the AI ​​perform the interface adjustments.

[0080] 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 information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. In this way, the reception desk can suggest the optimal input method to the user by analyzing their 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 have the AI ​​suggest the optimal input method.

[0081] The reception desk can customize input fields based on the user's current situation and areas of interest when they enter information. For example, if the user is traveling, the reception desk can prioritize displaying input fields related to travel. Similarly, if the user is working, the reception desk can prioritize displaying input fields related to work. Furthermore, if the user is entering information about their hobbies, the reception desk can prioritize displaying input fields related to those hobbies. This allows the reception desk to improve input efficiency by customizing input fields according to the user's situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's current situation and areas of interest into the AI ​​and have the AI ​​perform the customization of input fields.

[0082] The reception desk can estimate the user's emotions and prioritize input fields based on those emotions. For example, if the user is stressed, the reception desk can prioritize displaying important input fields and postpone other fields. If the user is relaxed, the reception desk can display all input fields equally. Furthermore, if the user is in a hurry, the reception desk can display only the most important input fields. This allows the reception desk to prioritize input fields according to the user's emotions, enabling them to input important information preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI and have the AI ​​determine the priority of input fields.

[0083] The reception desk can prioritize displaying input fields that are highly relevant to the user's geographical location when they are entering information. For example, if the user is in a specific region, the reception desk will prioritize displaying input fields related to that region. Furthermore, if the user is traveling, the reception desk can prioritize displaying input fields related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize displaying input fields related to their home. This allows the reception desk to improve the efficiency of data entry by prioritizing input fields based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into the AI ​​and have the AI ​​display highly relevant input fields.

[0084] The reception desk can analyze the user's social media activity and suggest relevant input fields when information is entered. For example, the reception desk can suggest relevant input fields based on information the user has shared on social media. It can also suggest relevant input fields based on information about accounts the user follows on social media. Furthermore, it can suggest relevant input fields based on information about groups the user participates in on social media. In this way, the reception desk can suggest relevant input fields by analyzing 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 have the AI ​​suggest relevant input fields.

[0085] The data acquisition unit can estimate the user's emotions and adjust the timing of information acquisition from the database based on the estimated emotions. For example, if the user is stressed, the data acquisition unit will acquire information quickly. Conversely, if the user is relaxed, the data acquisition unit can acquire information slowly. Furthermore, if the user is in a hurry, the data acquisition unit can prioritize acquiring the most important information. In this way, the data acquisition unit can efficiently acquire information by adjusting the timing of information acquisition 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 data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input user emotion data into an AI and have the AI ​​adjust the timing of information acquisition.

[0086] The data retrieval unit can analyze the user's past database usage history and select the optimal retrieval method. For example, the retrieval unit can prioritize retrieving information from databases that the user has frequently used in the past. It can also prioritize selecting retrieval methods (API, scraping, etc.) that the user has used in the past. Furthermore, the retrieval unit can select the optimal retrieval method for a specific time period based on the user's past database usage history. In this way, the retrieval unit can select the optimal retrieval method by analyzing past database usage history. Some or all of the above processing in the retrieval unit may be performed using AI or not. For example, the retrieval unit can input the user's past database usage history into AI and have the AI ​​select the optimal retrieval method.

[0087] The data acquisition unit can filter information based on the user's current situation and areas of interest when acquiring it. For example, if the user is traveling, the data acquisition unit will prioritize acquiring travel-related information. Similarly, if the user is working, the data acquisition unit can prioritize acquiring work-related information. Furthermore, if the user is acquiring information related to their hobbies, the data acquisition unit can prioritize acquiring information related to those hobbies. This allows the data acquisition unit to acquire highly relevant information by filtering it according to the user's situation and areas of interest. Some or all of the above processing in the data acquisition unit may be performed using AI, or it may be performed without AI. For example, the data acquisition unit can input the user's current situation and areas of interest into the AI ​​and have the AI ​​perform the information filtering.

[0088] The data acquisition unit can estimate the user's emotions and determine the priority of information to acquire based on the estimated emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring important information. If the user is relaxed, the data acquisition unit can acquire all information equally. Furthermore, if the user is in a hurry, the data acquisition unit can acquire only the most important information. In this way, the data acquisition unit can prioritize the acquisition of important information by determining the priority of information 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 data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input user emotion data into an AI and have the AI ​​perform the determination of information priority.

[0089] The data acquisition unit can prioritize acquiring highly relevant information based on the user's geographical location information when acquiring information. For example, if the user is in a specific region, the data acquisition unit will prioritize acquiring information related to that region. Furthermore, if the user is traveling, the data acquisition unit can prioritize acquiring information related to the travel destination. Additionally, if the user is at home, the data acquisition unit can prioritize acquiring information related to their home. In this way, the data acquisition unit can acquire highly relevant information by prioritizing information acquisition based on the user's geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI, or it may be performed without AI. For example, the data acquisition unit can input the user's geographical location information into AI and have the AI ​​perform the acquisition of highly relevant information.

[0090] The acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring information. For example, the acquisition unit can acquire relevant information based on information shared by the user on social media. It can also acquire relevant information based on information of accounts followed by the user on social media. Furthermore, the acquisition unit can acquire relevant information based on information of groups the user participates in on social media. In this way, the acquisition unit can acquire relevant information by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's social media activity into AI and have the AI ​​perform the acquisition of relevant information.

[0091] The generation unit can estimate the user's emotions and adjust the wording of the application form based on the estimated emotions. For example, if the user is stressed, the generation unit will use a simple and easy-to-understand wording. If the user is relaxed, the generation unit may also use a wording that includes detailed information. Furthermore, if the user is in a hurry, the generation unit may use a wording that gets straight to the point. In this way, the generation unit can generate an application form that is easy for the user to understand by adjusting the wording of the application form according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the wording of the application form.

[0092] The generation unit can adjust the level of detail in an application form based on the importance of the information when generating the application form. For example, the generation unit can prioritize and describe important information in detail, while describing other information concisely. Alternatively, the generation unit can describe all information in equal detail. Furthermore, the generation unit can omit less important information and include only important information. In this way, the generation unit can describe important information in detail by adjusting the level of detail in the application form based on the importance of the information. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the importance of the information into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the application form.

[0093] The generation unit can apply different generation algorithms depending on the information category when generating application forms. For example, the generation unit can apply a security-focused generation algorithm to categories related to personal information. It can also apply a detailed generation algorithm to categories related to corporate information. Furthermore, it can apply a concise generation algorithm to categories related to general information. This allows the generation unit to generate appropriate application forms by applying different generation algorithms depending on the information category. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input the information category into the AI ​​and have the AI ​​execute the application of the generation algorithm.

[0094] The generation unit can estimate the user's emotions and adjust the length of the application form based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a short, concise application form. If the user is relaxed, the generation unit can also generate a longer application form with detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate a concise and quick-to-complete application form. In this way, the generation unit can generate an application form of an appropriate length for the user by adjusting the length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the length of the application form.

[0095] The generation unit can determine the priority of application forms based on the submission timing of the information when generating the application form. For example, the generation unit can prioritize information with an approaching deadline in the application form. It can also postpone information with a distant submission deadline. Furthermore, the generation unit can briefly describe information with ample time before submission and add details later. In this way, the generation unit can prioritize the inclusion of important information by determining the priority of application forms based on the submission timing of the information. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the submission timing of the information into the AI ​​and have the AI ​​perform the determination of the application form priority.

[0096] The generation unit can adjust the order of information in an application form based on the relevance of the information during the application form generation process. For example, the generation unit can list important information first and other information later. It can also group highly relevant information together. Furthermore, it can omit less relevant information and include only important information. This allows the generation unit to prioritize important information by adjusting the order of the application form based on the relevance of the information. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input the relevance of the information into the AI ​​and have the AI ​​perform the adjustment of the application form's order.

[0097] The service provider can estimate the user's emotions and adjust the method of providing the application form based on the estimated emotions. For example, if the user is stressed, the service provider can use a simple and easy-to-understand method of providing the application form. If the user is relaxed, the service provider can also use a method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can use a quick and concise method of providing the application form. In this way, the service provider can select a method of providing the application form that is easy for the user to understand by adjusting the method of providing the application form 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 service provider may be performed using AI or not. For example, the service provider can input user emotion data into AI and have the AI ​​adjust the method of providing the application form.

[0098] The service provider can select the optimal service method by referring to the user's past application usage history when submitting an application. For example, the service provider may prioritize using service methods previously used by the user. It can also select the most efficient service method based on the user's past application usage history. Furthermore, the service provider may prioritize suggesting service methods that the user has preferred to use in the past. This allows the service provider to select the optimal service method by referring to past application usage history. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's past application usage history into an AI and have the AI ​​select the optimal service method.

[0099] The service provider can customize the delivery method based on the user's current situation when an application is submitted. For example, if the user is on the go, the service provider can use a delivery method optimized for mobile devices. If the user is in the office, the service provider can also use a delivery method optimized for desktop devices. Furthermore, if the user is at home, the service provider can use a delivery method optimized for home devices. This allows the service provider to select the optimal delivery method by customizing it based on the user's current situation. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's current situation into the AI ​​and have the AI ​​perform the customization of the delivery method.

[0100] The service provider can estimate the user's emotions and determine the priority of application delivery based on the estimated emotions. For example, if the user is stressed, the service provider may prioritize providing important applications. If the user is relaxed, the service provider may also provide all applications equally. Furthermore, if the user is in a hurry, the service provider may provide only the most important applications. In this way, the service provider can prioritize providing important applications by determining the priority of application delivery 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 service provider may be performed using AI or not. For example, the service provider can input user emotion data into an AI and have the AI ​​perform the determination of the priority of application delivery.

[0101] The service provider can select the most suitable service delivery method based on the user's geographical location information when an application is submitted. For example, if the user is in a specific region, the service provider will prioritize using a service delivery method related to that region. Furthermore, if the user is traveling, the service provider can prioritize using a service delivery method related to their travel destination. Additionally, if the user is at home, the service provider can prioritize using a service delivery method related to their home. This allows the service provider to select a highly relevant service delivery method based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's geographical location information into an AI and have the AI ​​select the most suitable service delivery method.

[0102] The service provider can analyze the user's social media activity and propose service delivery methods when an application is submitted. For example, the service provider can propose relevant service delivery methods based on information shared by the user on social media. It can also propose relevant service delivery methods based on information about accounts the user follows on social media. Furthermore, it can propose relevant service delivery methods based on information about groups the user participates in on social media. In this way, the service provider can propose relevant service delivery methods by analyzing 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 have the AI ​​propose service delivery methods.

[0103] The supplementary unit can estimate the user's emotions and select information to supplement based on the estimated emotions. For example, if the user is stressed, the supplementary unit will prioritize supplementing important information. If the user is relaxed, the supplementary unit can also supplement all information equally. Furthermore, if the user is in a hurry, the supplementary unit can supplement only the most important information. In this way, the supplementary unit can prioritize supplementing important information by selecting information to supplement 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 supplementary unit may be performed using AI or not. For example, the supplementary unit can input user emotion data into an AI and have the AI ​​perform the selection of information to supplement.

[0104] The interpolation unit can optimize the interpolation algorithm by referring to past interpolation data during interpolation. For example, the interpolation unit can select the optimal interpolation algorithm based on past interpolation data. It can also extract specific patterns from past interpolation data and optimize the interpolation algorithm accordingly. Furthermore, the interpolation unit can analyze past interpolation data to improve the accuracy of the interpolation algorithm. Thus, the interpolation unit can optimize the interpolation algorithm by referring to past interpolation data. Some or all of the above processes in the interpolation unit may be performed using AI or not. For example, the interpolation unit can input past interpolation data into AI and have the AI ​​optimize the interpolation algorithm.

[0105] The completion unit can apply different completion methods to each category of information during completion. For example, the completion unit can apply a security-focused completion method to categories related to personal information. It can also apply a completion method that includes detailed information to categories related to corporate information. Furthermore, it can apply a concise completion method to categories related to general information. This allows the completion unit to perform appropriate completion by applying different completion methods to each category of information. Some or all of the above processing in the completion unit may be performed using AI, or not. For example, the completion unit can input the information categories into the AI ​​and have the AI ​​perform the application of the completion methods.

[0106] The completion unit can estimate the user's emotions and determine the priority of completion based on the estimated emotions. For example, if the user is stressed, the completion unit will prioritize completing important information. If the user is relaxed, the completion unit can complete all information evenly. Furthermore, if the user is in a hurry, the completion unit can complete only the most important information. In this way, the completion unit can prioritize completing important information by determining the priority of completion 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 completion unit may be performed using AI or not. For example, the completion unit can input user emotion data into an AI and have the AI ​​perform the determination of completion priorities.

[0107] The completion unit can weight the completed data based on the submission date of the information. For example, the completion unit can prioritize completing information with an approaching deadline. It can also postpone completing information with a distant submission date. Furthermore, it can briefly complete information with ample time before the submission deadline and add details later. In this way, the completion unit can prioritize completing important information by weighting the completed data based on the submission date of the information. Some or all of the above processing in the completion unit may be performed using AI or not. For example, the completion unit can input the submission dates of the information into the AI ​​and have the AI ​​perform the weighting of the completed data.

[0108] The completion unit can improve the accuracy of completion by referring to relevant literature during completion. For example, the completion unit can improve the accuracy of the information being completed based on relevant literature. The completion unit can also improve the accuracy of completion by extracting specific patterns from relevant literature. Furthermore, the completion unit can analyze relevant literature to improve the accuracy of the completion algorithm. In this way, the completion unit can improve the accuracy of completion by referring to relevant literature. Some or all of the above processes in the completion unit may be performed using AI or not. For example, the completion unit can input relevant literature into AI and have the AI ​​perform the task of improving the accuracy of completion.

[0109] The completion unit can complete the necessary information by referring to the user's past records during completion. For example, the completion unit completes the necessary information based on the user's past records. The completion unit can also extract specific patterns from the user's past records and complete the necessary information. Furthermore, the completion unit can analyze the user's past records and complete the necessary information. As a result, the completion unit can automatically complete the necessary information by referring to the user's past records. Some or all of the above processing in the completion unit may be performed using AI or not. For example, the completion unit can input the user's past records into AI and have the AI ​​perform the completion of the necessary information.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception desk can analyze user input in real time and provide appropriate feedback based on that input. For example, when a user enters an address, if the input is incomplete, the reception desk will automatically suggest completion options. It can also immediately display an error message and prompt correction if there are errors in the information entered by the user. Furthermore, the reception desk can check the consistency of the input before the user completes the input and request additional information if necessary. This allows the reception desk to improve the accuracy and efficiency of user input by analyzing it in real time and providing appropriate feedback.

[0112] The data acquisition unit can estimate the user's emotions and adjust the timing of information acquisition from the database based on the estimated emotions. For example, if the user is stressed, information can be acquired quickly. If the user is relaxed, information can be acquired slowly. Furthermore, if the user is in a hurry, the most important information can be prioritized. In this way, the data acquisition unit can efficiently acquire information by adjusting the timing of information acquisition 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 data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input user emotion data into AI and have the AI ​​adjust the timing of information acquisition.

[0113] The generation unit can adjust the level of detail in an application form based on the importance of the information when generating the application form. For example, the generation unit can prioritize and describe important information in detail, while describing other information concisely. Alternatively, the generation unit can describe all information in equal detail. Furthermore, the generation unit can omit less important information and include only important information. In this way, the generation unit can describe important information in detail by adjusting the level of detail in the application form based on the importance of the information. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the importance of the information into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the application form.

[0114] The service provider can estimate the user's emotions and adjust the method of providing the application form based on the estimated emotions. For example, if the user is stressed, the service provider can use a simple and easy-to-understand method of providing the application form. If the user is relaxed, the service provider can also use a method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can use a quick and concise method of providing the application form. In this way, the service provider can select a method of providing the application form that is easy for the user to understand by adjusting the method of providing the application form 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 service provider may be performed using AI or not. For example, the service provider can input user emotion data into AI and have the AI ​​adjust the method of providing the application form.

[0115] The supplementary unit can estimate the user's emotions and select information to supplement based on the estimated emotions. For example, if the user is stressed, the supplementary unit will prioritize supplementing important information. If the user is relaxed, the supplementary unit can also supplement all information equally. Furthermore, if the user is in a hurry, the supplementary unit can supplement only the most important information. In this way, the supplementary unit can prioritize supplementing important information by selecting information to supplement 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 supplementary unit may be performed using AI or not. For example, the supplementary unit can input user emotion data into an AI and have the AI ​​perform the selection of information to supplement.

[0116] 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 information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. In this way, the reception desk can suggest the optimal input method to the user by analyzing their 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 have the AI ​​suggest the optimal input method.

[0117] The data acquisition unit can filter information based on the user's current situation and areas of interest when acquiring it. For example, if the user is traveling, the data acquisition unit will prioritize acquiring travel-related information. Similarly, if the user is working, the data acquisition unit can prioritize acquiring work-related information. Furthermore, if the user is acquiring information related to their hobbies, the data acquisition unit can prioritize acquiring information related to those hobbies. This allows the data acquisition unit to acquire highly relevant information by filtering it according to the user's situation and areas of interest. Some or all of the above processing in the data acquisition unit may be performed using AI, or it may be performed without AI. For example, the data acquisition unit can input the user's current situation and areas of interest into the AI ​​and have the AI ​​perform the information filtering.

[0118] The generation unit can apply different generation algorithms depending on the information category when generating application forms. For example, the generation unit can apply a security-focused generation algorithm to categories related to personal information. It can also apply a detailed generation algorithm to categories related to corporate information. Furthermore, it can apply a concise generation algorithm to categories related to general information. This allows the generation unit to generate appropriate application forms by applying different generation algorithms depending on the information category. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input the information category into the AI ​​and have the AI ​​execute the application of the generation algorithm.

[0119] The service provider can select the optimal service method by referring to the user's past application usage history when submitting an application. For example, the service provider may prioritize using service methods previously used by the user. It can also select the most efficient service method based on the user's past application usage history. Furthermore, the service provider may prioritize suggesting service methods that the user has preferred to use in the past. This allows the service provider to select the optimal service method by referring to past application usage history. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's past application usage history into an AI and have the AI ​​select the optimal service method.

[0120] The completion unit can weight the completed data based on the submission date of the information. For example, the completion unit can prioritize completing information with an approaching deadline. It can also postpone completing information with a distant submission date. Furthermore, it can briefly complete information with ample time before the submission deadline and add details later. In this way, the completion unit can prioritize completing important information by weighting the completed data based on the submission date of the information. Some or all of the above processing in the completion unit may be performed using AI or not. For example, the completion unit can input the submission dates of the information into the AI ​​and have the AI ​​perform the weighting of the completed data.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk receives information input from the user. The reception desk can accept information using methods such as text input, voice input, and image input. The reception desk can also register the information entered by the user into a database. Step 2: The retrieval unit retrieves the information received by the reception unit from the database. The retrieval unit can retrieve information from databases of various types and structures, such as relational databases and NoSQL databases. The retrieval unit may also include a supplementary unit that supplements the information by referring to past records. Step 3: The generation unit creates the application form based on the information acquired by the acquisition unit. The generation unit can automatically create the application form from the chat exchange, and uses a generation AI to create the application form. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 4: The provisioning department provides the application form created by the generation department to the user. The provisioning department can provide the application form via email, online portal, postal mail, etc.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, acquisition unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives information input from the user. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and acquires information from the database 24. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates an application form based on the acquired information. The provision unit is implemented by the output device 40 of the smart device 14 and provides the created application form to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, acquisition unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives voice input from the user. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and acquires information from the database 24. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates an application form based on the acquired information. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the created application form to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the reception unit, acquisition unit, generation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives voice input from the user. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and acquires information from the database 24. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates an application form based on the acquired information. The provision unit is implemented by the display 343 of the headset terminal 314 and provides the created application form to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the reception unit, acquisition unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives voice input from the user. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and acquires information from the database 24. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates an application form based on the acquired information. The provision unit is implemented by the speaker 240 of the robot 414 and provides the created application form to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A reception area that accepts information input from users, An acquisition unit that retrieves information received by the aforementioned reception unit from a database, A generation unit that creates an application form based on the information acquired by the acquisition unit, The system includes a provisioning unit that provides the application form created by the generation unit to the user. A system characterized by the following features. (Note 2) The acquisition unit is, It includes a supplementary unit that supplements necessary information by referring to past records. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Automatically create application forms from chat conversations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide the generated application form to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The information entered by the user is registered in the database. 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 adjusts the information input interface based on the estimated user 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 When entering information, input fields are customized based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users enter information, the system prioritizes displaying the most relevant input fields based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter information, the system analyzes their social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, It estimates the user's emotions and adjusts the timing of information retrieval from the database based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The acquisition unit is, Analyze the user's past database usage history and select the optimal retrieval method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The acquisition unit is, When retrieving information, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The acquisition unit is, When retrieving information, the system prioritizes obtaining highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The acquisition unit is, When acquiring information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is The system estimates the user's emotions and adjusts the wording of the application form based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating an application form, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating application forms, different generation algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is The system estimates the user's emotions and adjusts the length of the application form based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating an application, the priority of the application is determined based on the timing of information submission. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating application forms, the order of application forms is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the application process based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When submitting an application form, the system will refer to the user's past application form usage history to select the most suitable method of submission. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When submitting an application, the method of delivery will be customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and determines the priority of application submissions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When an application is submitted, the optimal delivery method will be selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When submitting an application, we analyze the user's social media activity and propose a method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supplementary unit is, The system estimates the user's emotions and selects supplementary information based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned supplementary unit is, During completion, the completion algorithm is optimized by referring to past completion data. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned supplementary unit is, During the completion process, different completion methods are applied for each category of information. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned supplementary unit is, It estimates the user's emotions and determines the priority of completion based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned supplementary unit is, During the completion process, the completed data is weighted based on when the information was submitted. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned supplementary unit is, When completing the information, refer to relevant literature to improve the accuracy of the completion. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned supplementary unit is, During completion, the system references the user's past records to complete the necessary information. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts information input from users, An acquisition unit that retrieves information received by the aforementioned reception unit from a database, A generation unit that creates an application form based on the information acquired by the acquisition unit, The system includes a provisioning unit that provides the application form created by the generation unit to the user. A system characterized by the following features.

2. The acquisition unit is, It includes a supplementary unit that supplements necessary information by referring to past records. The system according to feature 1.

3. The generating unit is Automatically create application forms from chat conversations. The system according to feature 1.

4. The aforementioned supply unit is, Provide the generated application form to the user. The system according to feature 1.

5. The aforementioned reception unit is The information entered by the user is registered in the database. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the information input interface based on the estimated user 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 When entering information, input fields are customized based on the user's current situation and areas of interest. The system according to feature 1.

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