system

Abstract

The system according to the embodiment aims to automatically input necessary items based on a document format from a government office and submit the document. [Solution] A system according to an embodiment includes an acquisition unit, an input unit, and a submission unit. The acquisition unit acquires document formats from government offices. The input unit automatically inputs required items based on the formats acquired by the acquisition unit. The submission unit submits the documents input by the input unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there was a problem in that document formats from government offices differed, which meant that it was time-consuming to manually enter the necessary information.

[0005] The system according to the embodiment aims to automatically input necessary items based on a document format from a government office and submit the document. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an input unit, and a submission unit. The acquisition unit acquires document formats from government offices. The input unit automatically inputs required items based on the formats acquired by the acquisition unit. The submission unit submits the documents input by the input unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically input and submit required items based on the document format from the government office. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An automatic document entry system according to an embodiment of the present invention automatically obtains document formats from government offices, has a generation AI input the necessary fields, and submits the documents. The automatic document entry system obtains document formats from government offices and has a generation AI automatically input the necessary fields based on the formats, allowing documents to be created without human intervention. For example, the automatic document entry system can download document formats from government offices' websites. The generation AI then automatically inputs the necessary fields based on the obtained formats. The generation AI inputs appropriate information for each field based on data it has previously learned. For example, it can automatically input basic information such as name, address, and phone number. It is also possible to change the input content based on specific conditions. For example, for documents to be submitted to a specific government office, the input content can be adjusted to match a specific format. This significantly reduces the effort required for document creation and enables efficient document submission. For example, when a government office requests submission, the generation AI can automatically create and submit the documents. This promotes paperless operations and improves the efficiency of document creation. This automatically obtains document formats from government offices, inputs the necessary fields, and submits the documents. For example, this significantly reduces the effort required for document creation and enables efficient document submission. This will lead to a paperless system and improve the efficiency of document creation.

[0029] An automatic document input system according to an embodiment includes an acquisition unit, an input unit, and a submission unit. The acquisition unit acquires a document format from a government office. The acquisition unit can acquire the document format, for example, via email, download from a website, or using an API. For example, the acquisition unit downloads the document format from a government office website. The acquisition unit can also receive the document format sent via email. The acquisition unit can also automatically acquire the document format using an API. For example, the acquisition unit downloads the document format from a government office website and acquires the format. The input unit uses a generation AI to automatically enter required fields based on the format acquired by the acquisition unit. The input unit enters appropriate information for each field based on, for example, data learned in advance. For example, the generation AI uses a text generation AI (e.g., LLM) to automatically enter basic information such as name, address, and phone number. The input unit can also change the input content based on specific conditions. For example, for documents to be submitted to a specific government office, the input content can be adjusted to match a specific format. For example, the generation AI can change the input content based on specific conditions. The submission unit submits the document input by the input unit. The submission unit submits, for example, a document automatically created by the generation AI. For example, the submission unit submits the document by email. The submission unit can also submit the document via a website. Furthermore, the submission unit can submit the document using an API. For example, the submission unit submits the document automatically created by the generation AI by email. In this way, the automatic document input system according to the embodiment can automatically obtain a document format from a government office, enter the required items, and submit the document. Some or all of the above-described processing in the submission unit may be performed, for example, using AI, or may be performed without using AI. For example, the submission unit submits the document automatically created by the generation AI by email.

[0030] The acquisition unit can acquire the document format via email, download from a website, or using an API. The acquisition unit, for example, acquires the document format via email. For example, the acquisition unit receives an email sent from a government office and acquires the document format. The acquisition unit can also download the document format from a website. For example, the acquisition unit downloads the document format from the government office's website. The acquisition unit can also acquire the document format using an API. For example, the acquisition unit automatically acquires the document format using the government office's API. This makes it possible to acquire the document format through a variety of means. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit automatically acquires the document format using the government office's API.

[0031] The input unit can input accurate information into each item based on pre-learned data. The input unit inputs appropriate information into each item based on, for example, pre-learned data. For example, the input unit uses a generation AI to automatically input basic information such as name, address, and phone number. The input unit can also change the input content based on specific conditions. For example, for documents to be submitted to a specific government office, the input content can be adjusted to fit a specific format. This allows accurate information to be input based on pre-learned data. Some or all of the above-mentioned processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit uses a generation AI to automatically input basic information such as name, address, and phone number.

[0032] The input unit can change the input content based on specific conditions. The input unit changes the input content based on, for example, specific conditions. For example, in the case of documents to be submitted to a specific government office, the input unit can adjust the input content to fit a specific format. The input unit can also change the input content based on specific conditions. For example, the input unit can change the input content based on specific conditions. This allows the input content to be flexibly changed in accordance with specific conditions. Some or all of the above-described processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit can change the input content based on specific conditions using a generation AI.

[0033] The submission unit can submit documents automatically created by the generation AI. The submission unit, for example, submits documents automatically created by the generation AI. For example, the submission unit submits documents by email. The submission unit can also submit documents through a website. Furthermore, the submission unit can submit documents using an API. For example, the submission unit submits documents automatically created by the generation AI by email. This allows documents automatically created by the generation AI to be submitted quickly. Some or all of the above-mentioned processing in the submission unit may be performed using AI, or may be performed without using AI, for example. For example, the submission unit submits documents automatically created by the generation AI by email.

[0034] The acquisition unit can analyze the government's past document format change history and select the optimal acquisition method. For example, the acquisition unit can prioritize acquisition of government formats that are frequently changed based on the past change history. The acquisition unit can also analyze the change history and periodically acquire government formats that are less changed. Furthermore, the acquisition unit can also acquire government formats that are frequently changed at specific times based on the change history, according to that time. This makes it possible to select the optimal acquisition method based on the past change history. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the past change history into a generation AI and select the optimal acquisition method.

[0035] When acquiring a document format, the acquisition unit can adjust the acquisition timing taking into account the business hours and busy periods of the government office. For example, the acquisition unit may perform acquisition outside of business hours of the government office to reduce the load during business hours. The acquisition unit may also perform acquisition during periods with relatively less business, avoiding busy periods of the government office. Furthermore, the acquisition unit may select a time period with less business even during business hours of the government office to perform acquisition. This allows the document format to be acquired at the optimal timing taking into account the business hours and busy periods of the government office. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input data on the business hours and busy periods of the government office into the generation AI to adjust the acquisition timing.

[0036] When acquiring a document format, the acquisition unit can analyze the update frequency of the government office's website and determine the optimal acquisition timing. For example, the acquisition unit can analyze the update frequency of the government office's website and acquire the format immediately after an update. The acquisition unit can also acquire formats periodically if the update frequency is low. Furthermore, the acquisition unit can acquire formats frequently and maintain the latest information if the update frequency is high. This allows document formats to be acquired at the optimal timing based on the update frequency of the government office's website. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input update frequency data of the government office's website into the generation AI and determine the optimal acquisition timing.

[0037] When acquiring document formats, the acquisition unit can prioritize acquiring highly relevant formats by taking into consideration the geographical location information of government offices. The acquisition unit, for example, prioritizes acquiring document formats of nearby government offices. The acquisition unit can also prioritize acquiring document formats of government offices that the user frequently uses. Furthermore, the acquisition unit can also prioritize acquiring document formats of government offices that are highly geographically relevant. This makes it possible to prioritize acquiring document formats of government offices that are highly geographically relevant. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input geographical location information of government offices into the generation AI and prioritize acquiring highly relevant formats.

[0038] When acquiring document formats, the acquisition unit can analyze the social media activity of the government office and acquire related formats. The acquisition unit can acquire related document formats based on, for example, the content of announcements made by the government office on social media. The acquisition unit can also analyze the frequency of social media activity of the government office and prioritize acquiring formats from government offices that are frequently active. Furthermore, the acquisition unit can acquire related document formats by referring to feedback on social media from the government office. This makes it possible to acquire related document formats based on the social media activity of the government office. Some or all of the above-mentioned processing in the acquisition unit can be performed, for example, using AI, or can be performed without using AI. For example, the acquisition unit can input social media activity data of the government office into a generation AI to acquire related formats.

[0039] When acquiring a document format, the acquisition unit can adjust the acquisition method by reflecting past feedback from the government office. The acquisition unit can optimize the acquisition method based on, for example, past feedback from the government office. The acquisition unit can also adjust the acquisition frequency by reflecting feedback from the government office. Furthermore, the acquisition unit can select the type of document format to acquire based on feedback from the government office. This makes it possible to optimize the acquisition method based on past feedback from the government office. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input past feedback data from the government office into the generation AI and adjust the acquisition method.

[0040] The input unit can adjust the level of detail of the input based on the importance of the document during input. For example, the input unit inputs detailed information for a document of high importance. The input unit can also input only basic information for a document of low importance. Furthermore, the input unit can adjust the level of detail to complete input quickly for a document with an approaching submission deadline. This makes it possible to adjust the level of detail of the input depending on the importance of the document. Some or all of the above-mentioned processing in the input unit may be performed using, or without, a generation AI. For example, the input unit can input document importance data into a generation AI and adjust the level of detail of the input.

[0041] The input unit can apply different input algorithms depending on the document category during input. For example, in the case of application documents, the input unit performs input based on a specific format. In addition, in the case of report documents, the input unit can also apply an algorithm for inputting detailed data. Furthermore, in the case of permission documents, the input unit can also apply an algorithm for automatically inputting required items. This makes it possible to apply the optimal input algorithm depending on the document category. Some or all of the above-mentioned processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit can input document category data into a generation AI and apply different input algorithms.

[0042] The input unit can improve the accuracy of input by referring to the user's past input results. For example, the input unit automatically inputs similar items based on information previously input by the user. The input unit can also analyze the user's past input results and apply an algorithm to reduce input errors. Furthermore, the input unit can also suggest an optimal input method by referring to the user's past input patterns. This makes it possible to improve the accuracy of input based on the user's past input results. Some or all of the above-mentioned processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit can input the user's past input result data into the generation AI to improve the accuracy of input.

[0043] The input unit can determine the priority of input based on the submission time of the document at the time of input. For example, the input unit prioritizes input of documents with an upcoming submission deadline. The input unit can also postpone documents with a distant submission deadline. Furthermore, when documents have the same submission deadline, the input unit can also prioritize input of documents with higher importance. This makes it possible to determine the priority of input based on the submission time of the document. Some or all of the above-mentioned processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit can input document submission time data into a generation AI to determine the priority of input.

[0044] The input unit can adjust the order of input based on the relevance of documents during input. For example, the input unit prioritizes input of highly relevant documents. The input unit can also postpone input of less relevant documents. Furthermore, the input unit can input related documents together. This makes it possible to adjust the order of input based on the relevance of documents. Some or all of the above-mentioned processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit can input document relevance data into a generation AI and adjust the order of input.

[0045] The input unit can adjust the use of technical terms in the input depending on the user's level of expertise during input. For example, the input unit uses technical terms for input by a user with high level of expertise. The input unit can also use easy-to-understand language for a user with low level of expertise. Furthermore, the input unit can adjust the frequency of use of technical terms depending on the user's level of expertise. This allows the most appropriate technical terms to be used depending on the user's level of expertise. Some or all of the above-described processing in the input unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the input unit can input the user's level of expertise data into the generation AI to adjust the use of technical terms.

[0046] At the time of submission, the submission unit can analyze the government office's past submission history and select the optimal submission method. For example, the submission unit selects the submission method with the highest success rate based on the past submission history. The submission unit can also analyze the submission history and prioritize methods that result in smooth submission. Furthermore, the submission unit can select the optimal submission method for a specific government office based on the submission history. This makes it possible to select the optimal submission method based on the government office's past submission history. Some or all of the above-described processing in the submission unit may be performed using, for example, AI, or may be performed without using AI. For example, the submission unit can input the government office's past submission history data into a generation AI to select the optimal submission method.

[0047] The submission unit can optimize the timing of submission by taking into account the business hours and busy periods of the government office. For example, the submission unit can submit during business hours of the government office to promote quick processing. The submission unit can also submit during periods when there is relatively little business, avoiding busy periods of the government office. Furthermore, the submission unit can submit outside business hours of the government office so that the application is processed on the next business day. This allows submission at the optimal time by taking into account the business hours and busy periods of the government office. Some or all of the above-mentioned processing in the submission unit may be performed using, for example, AI, or may be performed without using AI. For example, the submission unit can input data on the business hours and busy periods of the government office into the generation AI to optimize the timing of submission.

[0048] At the time of submission, the submission unit can analyze the update frequency of the government office website and determine the optimal submission timing. For example, the submission unit can analyze the update frequency of the government office website and submit immediately after an update. The submission unit can also submit periodically if the update frequency is low. Furthermore, the submission unit can submit frequently if the update frequency is high to maintain the latest information. This allows submission at the optimal timing based on the update frequency of the government office website. Some or all of the above-mentioned processing in the submission unit may be performed using, for example, AI, or may be performed without using AI. For example, the submission unit can input update frequency data of the government office website into a generation AI to determine the optimal submission timing.

[0049] The submission unit can prioritize submitting highly relevant documents by taking into consideration the geographical location information of government offices when submitting documents. For example, the submission unit can prioritize submitting documents to be submitted to nearby government offices. The submission unit can also prioritize submitting documents to government offices that the user frequently uses. Furthermore, the submission unit can also prioritize submitting documents to government offices that are highly geographically relevant. This allows documents to be submitted to government offices that are highly geographically relevant to be submitted preferentially. Some or all of the above-described processing in the submission unit may be performed using AI, for example, or may be performed without using AI. For example, the submission unit can input geographical location information of government offices into the generation AI and prioritize submitting highly relevant documents.

[0050] The submission unit can analyze the social media activity of the government office and submit relevant documents at the time of submission. The submission unit can submit relevant documents based on, for example, the content of the government office's social media announcements. The submission unit can also analyze the frequency of the government office's social media activity and prioritize documents to be submitted to government offices that are frequently active. Furthermore, the submission unit can also submit relevant documents based on feedback on the government office's social media. This makes it possible to submit relevant documents based on the government office's social media activity. Some or all of the above-mentioned processing in the submission unit can be performed, for example, using AI, or can be performed without using AI. For example, the submission unit can input the government office's social media activity data into a generation AI and submit relevant documents.

[0051] At the time of submission, the submission unit can customize the submission method by reflecting past feedback from the government office. The submission unit can, for example, optimize the submission method based on past feedback from the government office. The submission unit can also adjust the frequency of submission by reflecting feedback from the government office. Furthermore, the submission unit can select the type of document to be submitted based on feedback from the government office. This makes it possible to optimize the submission method based on past feedback from the government office. Some or all of the above-mentioned processing in the submission unit may be performed, for example, using AI, or may be performed without using AI. For example, the submission unit can input past feedback data from the government office into the generation AI to customize the submission method.

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

[0053] When acquiring document formats from government offices, the acquisition unit can monitor the access status of the government office's website in real time and acquire them during times of low access. For example, the acquisition unit downloads document formats during times of low access, such as at night or early in the morning. The acquisition unit can also analyze the access status of the government office's website and determine the optimal acquisition timing. Furthermore, the acquisition unit can temporarily delay acquisition if there is heavy access. This reduces the load on the government office's website and enables document formats to be acquired efficiently.

[0054] The input unit can automatically complete input content based on the user's past input history. For example, the input unit automatically inputs information such as a name or address that was previously input. The input unit can also analyze past input history and input frequently used information preferentially. Furthermore, the input unit can learn from past input errors and display alerts to prevent similar errors. This can improve the efficiency of the user's input work and reduce input errors.

[0055] The submission department can suggest the optimal submission method based on the content of the document to be submitted. For example, if submission by email is appropriate, the submission department can suggest that method. In addition, if submission via a website is quicker, the submission department can also suggest that method. Furthermore, if submission using an API is more efficient, the submission department can also suggest that method. This makes it possible to select the optimal submission method depending on the content of the document to be submitted.

[0056] When acquiring document formats from a government office, the acquisition unit can analyze the business processes of the government office and determine the optimal acquisition timing. For example, the acquisition unit can analyze the business processes of the government office and predict when document formats will be updated. The acquisition unit can also adjust the frequency of acquiring document formats based on the business processes of the government office. Furthermore, the acquisition unit can optimize the acquisition timing by taking into account the business processes of the government office. This allows document formats to be acquired at the optimal timing according to the business processes of the government office.

[0057] The input unit can dynamically adjust the input interface according to the user's input speed. For example, if the user is typing quickly, the input unit automatically enlarges the input field to make input easier. Also, if the user is typing slowly, the input unit can reduce the input field to reduce visual strain. Furthermore, the input unit can display an input guide according to the user's input speed. This makes it possible to provide an optimal input interface according to the user's input speed.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The acquisition unit acquires the document format from the government office. The acquisition unit can acquire the document format, for example, by email, downloading from a website, or using an API. Specifically, the acquisition unit can download the document format from the government office's website, receive the document format sent by email, or automatically acquire the document format using an API. Step 2: The input unit uses a generation AI to automatically enter the required items based on the format acquired by the acquisition unit. The input unit enters appropriate information into each item based on data it has learned in advance. For example, it uses a text generation AI (e.g., LLM) to automatically enter basic information such as name, address, and phone number. It can also change the input content based on specific conditions. Step 3: The submission unit submits the documents entered by the input unit. The submission unit submits the documents automatically created by the generation AI. Specifically, documents can be submitted by email, via a website, or using an API.

[0060] (Example 2) An automatic document entry system according to an embodiment of the present invention automatically obtains document formats from government offices, has a generation AI input the necessary fields, and submits the documents. The automatic document entry system obtains document formats from government offices and has a generation AI automatically input the necessary fields based on the formats, allowing documents to be created without human intervention. For example, the automatic document entry system can download document formats from government offices' websites. The generation AI then automatically inputs the necessary fields based on the obtained formats. The generation AI inputs appropriate information for each field based on data it has previously learned. For example, it can automatically input basic information such as name, address, and phone number. It is also possible to change the input content based on specific conditions. For example, for documents to be submitted to a specific government office, the input content can be adjusted to match a specific format. This significantly reduces the effort required for document creation and enables efficient document submission. For example, when a government office requests submission, the generation AI can automatically create and submit the documents. This promotes paperless operations and improves the efficiency of document creation. This automatically obtains document formats from government offices, inputs the necessary fields, and submits the documents. For example, this significantly reduces the effort required for document creation and enables efficient document submission. This will lead to a paperless system and improve the efficiency of document creation.

[0061] An automatic document input system according to an embodiment includes an acquisition unit, an input unit, and a submission unit. The acquisition unit acquires a document format from a government office. The acquisition unit can acquire the document format, for example, via email, download from a website, or using an API. For example, the acquisition unit downloads the document format from a government office website. The acquisition unit can also receive the document format sent via email. The acquisition unit can also automatically acquire the document format using an API. For example, the acquisition unit downloads the document format from a government office website and acquires the format. The input unit uses a generation AI to automatically enter required fields based on the format acquired by the acquisition unit. The input unit enters appropriate information for each field based on, for example, data learned in advance. For example, the generation AI uses a text generation AI (e.g., LLM) to automatically enter basic information such as name, address, and phone number. The input unit can also change the input content based on specific conditions. For example, for documents to be submitted to a specific government office, the input content can be adjusted to match a specific format. For example, the generation AI can change the input content based on specific conditions. The submission unit submits the document input by the input unit. The submission unit submits, for example, a document automatically created by the generation AI. For example, the submission unit submits the document by email. The submission unit can also submit the document via a website. Furthermore, the submission unit can submit the document using an API. For example, the submission unit submits the document automatically created by the generation AI by email. In this way, the automatic document input system according to the embodiment can automatically obtain a document format from a government office, enter the required items, and submit the document. Some or all of the above-described processing in the submission unit may be performed, for example, using AI, or may be performed without using AI. For example, the submission unit submits the document automatically created by the generation AI by email.

[0062] The acquisition unit can acquire the document format via email, download from a website, or using an API. The acquisition unit, for example, acquires the document format via email. For example, the acquisition unit receives an email sent from a government office and acquires the document format. The acquisition unit can also download the document format from a website. For example, the acquisition unit downloads the document format from the government office's website. The acquisition unit can also acquire the document format using an API. For example, the acquisition unit automatically acquires the document format using the government office's API. This makes it possible to acquire the document format through a variety of means. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit automatically acquires the document format using the government office's API.

[0063] The input unit can input accurate information into each item based on pre-learned data. The input unit inputs appropriate information into each item based on, for example, pre-learned data. For example, the input unit uses a generation AI to automatically input basic information such as name, address, and phone number. The input unit can also change the input content based on specific conditions. For example, for documents to be submitted to a specific government office, the input content can be adjusted to fit a specific format. This allows accurate information to be input based on pre-learned data. Some or all of the above-mentioned processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit uses a generation AI to automatically input basic information such as name, address, and phone number.

[0064] The input unit can change the input content based on specific conditions. The input unit changes the input content based on, for example, specific conditions. For example, in the case of documents to be submitted to a specific government office, the input unit can adjust the input content to fit a specific format. The input unit can also change the input content based on specific conditions. For example, the input unit can change the input content based on specific conditions. This allows the input content to be flexibly changed in accordance with specific conditions. Some or all of the above-described processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit can change the input content based on specific conditions using a generation AI.

[0065] The submission unit can submit documents automatically created by the generation AI. The submission unit, for example, submits documents automatically created by the generation AI. For example, the submission unit submits documents by email. The submission unit can also submit documents through a website. Furthermore, the submission unit can submit documents using an API. For example, the submission unit submits documents automatically created by the generation AI by email. This allows documents automatically created by the generation AI to be submitted quickly. Some or all of the above-mentioned processing in the submission unit may be performed using AI, or may be performed without using AI, for example. For example, the submission unit submits documents automatically created by the generation AI by email.

[0066] The acquisition unit can estimate the user's emotions and adjust the timing of document format acquisition based on the emotion data. For example, if the user is feeling stressed, the acquisition unit can delay the acquisition timing and acquire the document format when the user is relaxed. Furthermore, if the user is in a hurry, the acquisition unit can immediately acquire the document format and quickly start processing. Furthermore, if the user is relaxed, the acquisition unit can adjust the acquisition timing to acquire the document format at a time when the user can process it most efficiently. This allows the document format to be acquired at the optimal timing depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's emotion data into the generation AI and adjust the acquisition timing based on the emotion.

[0067] The acquisition unit can analyze the government's past document format change history and select the optimal acquisition method. For example, the acquisition unit can prioritize acquisition of government formats that are frequently changed based on the past change history. The acquisition unit can also analyze the change history and periodically acquire government formats that are less changed. Furthermore, the acquisition unit can also acquire government formats that are frequently changed at specific times based on the change history, according to that time. This makes it possible to select the optimal acquisition method based on the past change history. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the past change history into a generation AI and select the optimal acquisition method.

[0068] When acquiring a document format, the acquisition unit can adjust the acquisition timing taking into account the business hours and busy periods of the government office. For example, the acquisition unit may perform acquisition outside of business hours of the government office to reduce the load during business hours. The acquisition unit may also perform acquisition during periods with relatively less business, avoiding busy periods of the government office. Furthermore, the acquisition unit may select a time period with less business even during business hours of the government office to perform acquisition. This allows the document format to be acquired at the optimal timing taking into account the business hours and busy periods of the government office. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input data on the business hours and busy periods of the government office into the generation AI to adjust the acquisition timing.

[0069] When acquiring a document format, the acquisition unit can analyze the update frequency of the government office's website and determine the optimal acquisition timing. For example, the acquisition unit can analyze the update frequency of the government office's website and acquire the format immediately after an update. The acquisition unit can also acquire formats periodically if the update frequency is low. Furthermore, the acquisition unit can acquire formats frequently and maintain the latest information if the update frequency is high. This allows document formats to be acquired at the optimal timing based on the update frequency of the government office's website. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input update frequency data of the government office's website into the generation AI and determine the optimal acquisition timing.

[0070] The acquisition unit can estimate the user's emotions and determine the priority of document formats to be acquired based on the emotion data. For example, when the user is feeling stressed, the acquisition unit postpones document formats of lower importance. Furthermore, when the user is relaxed, the acquisition unit can also prioritize acquiring document formats of higher importance. Furthermore, when the user is in a hurry, the acquisition unit can also prioritize acquiring document formats with upcoming submission deadlines. This allows the priority of document formats to be acquired to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and determine the priority of document formats to be acquired.

[0071] When acquiring document formats, the acquisition unit can prioritize acquiring highly relevant formats by taking into consideration the geographical location information of government offices. The acquisition unit, for example, prioritizes acquiring document formats of nearby government offices. The acquisition unit can also prioritize acquiring document formats of government offices that the user frequently uses. Furthermore, the acquisition unit can also prioritize acquiring document formats of government offices that are highly geographically relevant. This makes it possible to prioritize acquiring document formats of government offices that are highly geographically relevant. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input geographical location information of government offices into the generation AI and prioritize acquiring highly relevant formats.

[0072] When acquiring document formats, the acquisition unit can analyze the social media activity of the government office and acquire related formats. The acquisition unit can acquire related document formats based on, for example, the content of announcements made by the government office on social media. The acquisition unit can also analyze the frequency of social media activity of the government office and prioritize acquiring formats from government offices that are frequently active. Furthermore, the acquisition unit can acquire related document formats by referring to feedback on social media from the government office. This makes it possible to acquire related document formats based on the social media activity of the government office. Some or all of the above-mentioned processing in the acquisition unit can be performed, for example, using AI, or can be performed without using AI. For example, the acquisition unit can input social media activity data of the government office into a generation AI to acquire related formats.

[0073] When acquiring a document format, the acquisition unit can adjust the acquisition method by reflecting past feedback from the government office. The acquisition unit can optimize the acquisition method based on, for example, past feedback from the government office. The acquisition unit can also adjust the acquisition frequency by reflecting feedback from the government office. Furthermore, the acquisition unit can select the type of document format to acquire based on feedback from the government office. This makes it possible to optimize the acquisition method based on past feedback from the government office. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input past feedback data from the government office into the generation AI and adjust the acquisition method.

[0074] The input unit can estimate the user's emotions and adjust the input expression method based on the emotion data. For example, when the user is stressed, the input unit can provide a simple interface and minimize input steps. Furthermore, when the user is relaxed, the input unit can provide detailed input options and suggest customizable input methods. Furthermore, when the user is in a hurry, the input unit can prioritize voice input and enable the user to quickly enter necessary items. This makes it possible to provide the optimal input expression method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the input unit can input the user's emotion data into the generation AI and adjust the input expression method.

[0075] The input unit can adjust the level of detail of the input based on the importance of the document during input. For example, the input unit inputs detailed information for a document of high importance. The input unit can also input only basic information for a document of low importance. Furthermore, the input unit can adjust the level of detail to complete input quickly for a document with an approaching submission deadline. This makes it possible to adjust the level of detail of the input depending on the importance of the document. Some or all of the above-mentioned processing in the input unit may be performed using, or without, a generation AI. For example, the input unit can input document importance data into a generation AI and adjust the level of detail of the input.

[0076] The input unit can apply different input algorithms depending on the document category during input. For example, in the case of application documents, the input unit performs input based on a specific format. In addition, in the case of report documents, the input unit can also apply an algorithm for inputting detailed data. Furthermore, in the case of permission documents, the input unit can also apply an algorithm for automatically inputting required items. This makes it possible to apply the optimal input algorithm depending on the document category. Some or all of the above-mentioned processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit can input document category data into a generation AI and apply different input algorithms.

[0077] The input unit can improve the accuracy of input by referring to the user's past input results. For example, the input unit automatically inputs similar items based on information previously input by the user. The input unit can also analyze the user's past input results and apply an algorithm to reduce input errors. Furthermore, the input unit can also suggest an optimal input method by referring to the user's past input patterns. This makes it possible to improve the accuracy of input based on the user's past input results. Some or all of the above-mentioned processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit can input the user's past input result data into the generation AI to improve the accuracy of input.

[0078] The input unit can estimate the user's emotions and adjust the length of the input based on the emotion data. For example, the input unit can minimize the number of input items when the user is stressed. The input unit can also provide detailed input items when the user is relaxed. Furthermore, the input unit can input only the minimum amount of information necessary when the user is in a hurry. This allows the length of the input to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the input unit can input the user's emotion data into the generation AI and adjust the length of the input.

[0079] The input unit can determine the priority of input based on the submission time of the document at the time of input. For example, the input unit prioritizes input of documents with an upcoming submission deadline. The input unit can also postpone documents with a distant submission deadline. Furthermore, when documents have the same submission deadline, the input unit can also prioritize input of documents with higher importance. This makes it possible to determine the priority of input based on the submission time of the document. Some or all of the above-mentioned processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit can input document submission time data into a generation AI to determine the priority of input.

[0080] The input unit can adjust the order of input based on the relevance of documents during input. For example, the input unit prioritizes input of highly relevant documents. The input unit can also postpone input of less relevant documents. Furthermore, the input unit can input related documents together. This makes it possible to adjust the order of input based on the relevance of documents. Some or all of the above-mentioned processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit can input document relevance data into a generation AI and adjust the order of input.

[0081] The input unit can adjust the use of technical terms in the input depending on the user's level of expertise during input. For example, the input unit uses technical terms for input by a user with high level of expertise. The input unit can also use easy-to-understand language for a user with low level of expertise. Furthermore, the input unit can adjust the frequency of use of technical terms depending on the user's level of expertise. This allows the most appropriate technical terms to be used depending on the user's level of expertise. Some or all of the above-described processing in the input unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the input unit can input the user's level of expertise data into the generation AI to adjust the use of technical terms.

[0082] The submission unit can estimate the user's emotions and adjust the submission method based on the emotion data. For example, if the user is feeling stressed, the submission unit can provide a simple submission method. Furthermore, if the user is relaxed, the submission unit can provide detailed submission options. Furthermore, if the user is in a hurry, the submission unit can provide a quick submission method. This makes it possible to provide the optimal submission method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the submission unit can be performed using, for example, AI, or without AI. For example, the submission unit can input the user's emotion data into the generation AI and adjust the submission method.

[0083] At the time of submission, the submission unit can analyze the government office's past submission history and select the optimal submission method. For example, the submission unit selects the submission method with the highest success rate based on the past submission history. The submission unit can also analyze the submission history and prioritize methods that result in smooth submission. Furthermore, the submission unit can select the optimal submission method for a specific government office based on the submission history. This makes it possible to select the optimal submission method based on the government office's past submission history. Some or all of the above-described processing in the submission unit may be performed using, for example, AI, or may be performed without using AI. For example, the submission unit can input the government office's past submission history data into a generation AI to select the optimal submission method.

[0084] The submission unit can optimize the timing of submission by taking into account the business hours and busy periods of the government office. For example, the submission unit can submit during business hours of the government office to promote quick processing. The submission unit can also submit during periods when there is relatively little business, avoiding busy periods of the government office. Furthermore, the submission unit can submit outside business hours of the government office so that the application is processed on the next business day. This allows submission at the optimal time by taking into account the business hours and busy periods of the government office. Some or all of the above-mentioned processing in the submission unit may be performed using, for example, AI, or may be performed without using AI. For example, the submission unit can input data on the business hours and busy periods of the government office into the generation AI to optimize the timing of submission.

[0085] At the time of submission, the submission unit can analyze the update frequency of the government office website and determine the optimal submission timing. For example, the submission unit can analyze the update frequency of the government office website and submit immediately after an update. The submission unit can also submit periodically if the update frequency is low. Furthermore, the submission unit can submit frequently if the update frequency is high to maintain the latest information. This allows submission at the optimal timing based on the update frequency of the government office website. Some or all of the above-mentioned processing in the submission unit may be performed using, for example, AI, or may be performed without using AI. For example, the submission unit can input update frequency data of the government office website into a generation AI to determine the optimal submission timing.

[0086] The submission unit can estimate the user's emotions and determine the priority of documents to be submitted based on the emotion data. For example, if the user is feeling stressed, the submission unit can postpone documents of lower importance. Furthermore, if the user is relaxed, the submission unit can also prioritize documents of higher importance. Furthermore, if the user is in a hurry, the submission unit can also prioritize documents with upcoming deadlines. This allows the priority of documents to be submitted to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the submission unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the submission unit can input the user's emotion data into the generation AI to determine the priority of documents to be submitted.

[0087] The submission unit can prioritize submitting highly relevant documents by taking into consideration the geographical location information of government offices when submitting documents. For example, the submission unit can prioritize submitting documents to be submitted to nearby government offices. The submission unit can also prioritize submitting documents to government offices that the user frequently uses. Furthermore, the submission unit can also prioritize submitting documents to government offices that are highly geographically relevant. This allows documents to be submitted to government offices that are highly geographically relevant to be submitted preferentially. Some or all of the above-described processing in the submission unit may be performed using AI, for example, or may be performed without using AI. For example, the submission unit can input geographical location information of government offices into the generation AI and prioritize submitting highly relevant documents.

[0088] The submission unit can analyze the social media activity of the government office and submit relevant documents at the time of submission. The submission unit can submit relevant documents based on, for example, the content of the government office's social media announcements. The submission unit can also analyze the frequency of the government office's social media activity and prioritize documents to be submitted to government offices that are frequently active. Furthermore, the submission unit can also submit relevant documents based on feedback on the government office's social media. This makes it possible to submit relevant documents based on the government office's social media activity. Some or all of the above-mentioned processing in the submission unit can be performed, for example, using AI, or can be performed without using AI. For example, the submission unit can input the government office's social media activity data into a generation AI and submit relevant documents.

[0089] At the time of submission, the submission unit can customize the submission method by reflecting past feedback from the government office. The submission unit can, for example, optimize the submission method based on past feedback from the government office. The submission unit can also adjust the frequency of submission by reflecting feedback from the government office. Furthermore, the submission unit can select the type of document to be submitted based on feedback from the government office. This makes it possible to optimize the submission method based on past feedback from the government office. Some or all of the above-mentioned processing in the submission unit may be performed, for example, using AI, or may be performed without using AI. For example, the submission unit can input past feedback data from the government office into the generation AI to customize the submission method. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, input unit, and submission unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit can download a document format from a government website using the communication I / F 44 of the smart device 14. The input unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and inputs appropriate information into each field based on data previously learned by the generation AI. The submission unit can submit a document by email, for example, using the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, input unit, and submission unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit can download a document format from a government website using the communication I / F 44 of the smart glasses 214. The input unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and inputs appropriate information into each field based on data previously learned by the generation AI. The submission unit can submit a document by email, for example, using the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, input unit, and submission unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit can download a document format from a government website using the communication I / F 44 of the headset terminal 314. The input unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and inputs appropriate information into each item based on data previously learned by the generation AI. The submission unit can submit a document by email, for example, using the communication I / F 44 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned acquisition unit, input unit, and submission unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit can download a document format from a government website using the communication I / F 44 of the robot 414. The input unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and inputs appropriate information into each item based on data previously learned by the generation AI. The submission unit can submit a document by email, for example, using the communication I / F 44 of the robot 414.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] When acquiring document formats from government offices, the acquisition unit can monitor the access status of the government office's website in real time and acquire them during times of low access. For example, the acquisition unit downloads document formats during times of low access, such as at night or early in the morning. The acquisition unit can also analyze the access status of the government office's website and determine the optimal acquisition timing. Furthermore, the acquisition unit can temporarily delay acquisition if there is heavy access. This reduces the load on the government office's website and enables document formats to be acquired efficiently.

[0092] The input unit can automatically complete input content based on the user's past input history. For example, the input unit automatically inputs information such as a name or address that was previously input. The input unit can also analyze past input history and input frequently used information preferentially. Furthermore, the input unit can learn from past input errors and display alerts to prevent similar errors. This can improve the efficiency of the user's input work and reduce input errors.

[0093] The submission department can suggest the optimal submission method based on the content of the document to be submitted. For example, if submission by email is appropriate, the submission department can suggest that method. In addition, if submission via a website is quicker, the submission department can also suggest that method. Furthermore, if submission using an API is more efficient, the submission department can also suggest that method. This makes it possible to select the optimal submission method depending on the content of the document to be submitted.

[0094] When acquiring document formats from a government office, the acquisition unit can analyze the business processes of the government office and determine the optimal acquisition timing. For example, the acquisition unit can analyze the business processes of the government office and predict when document formats will be updated. The acquisition unit can also adjust the frequency of acquiring document formats based on the business processes of the government office. Furthermore, the acquisition unit can optimize the acquisition timing by taking into account the business processes of the government office. This allows document formats to be acquired at the optimal timing according to the business processes of the government office.

[0095] The input unit can dynamically adjust the input interface according to the user's input speed. For example, if the user is typing quickly, the input unit automatically enlarges the input field to make input easier. Also, if the user is typing slowly, the input unit can reduce the input field to reduce visual strain. Furthermore, the input unit can display an input guide according to the user's input speed. This makes it possible to provide an optimal input interface according to the user's input speed.

[0096] The acquisition unit can estimate the user's emotions and adjust the document format acquisition method based on the emotion data. For example, if the user is feeling stressed, the acquisition unit can select a simple acquisition method to reduce the user's burden. If the user is relaxed, the acquisition unit can provide detailed acquisition options for the user to select. Furthermore, if the user is in a hurry, the acquisition unit can select a method that allows for quick acquisition. In this way, the optimal acquisition method can be provided according to the user's emotions.

[0097] The input unit can estimate the user's emotions and adjust the input order based on the emotion data. For example, if the user is feeling stressed, important items can be input first, followed by detailed items later. Alternatively, if the user is relaxed, detailed items can be input first, followed by basic items later. Furthermore, if the user is in a hurry, the minimum necessary items can be input first, followed by additional items later. This makes it possible to provide an optimal input order according to the user's emotions.

[0098] The submission unit can estimate the user's emotions and adjust the submission confirmation process based on the emotion data. For example, if the user is stressed, the confirmation process before submission can be simplified to allow the user to submit quickly. Alternatively, if the user is relaxed, a detailed confirmation process can be provided to allow the user to submit with peace of mind. Furthermore, if the user is in a hurry, a minimal confirmation process can be provided to allow the user to submit quickly. In this way, the submission confirmation process can be optimally provided according to the user's emotions.

[0099] The acquisition unit can estimate the user's emotions and adjust the type of document format to be acquired based on the emotion data. For example, if the user is feeling stressed, document formats with low importance can be postponed. Also, if the user is relaxed, document formats with high importance can be acquired with priority. Furthermore, if the user is in a hurry, document formats with an upcoming submission deadline can be acquired with priority. In this way, the type of document format to be acquired can be adjusted according to the user's emotions.

[0100] The input unit can estimate the user's emotions and adjust input feedback based on the emotion data. For example, if the user is feeling stressed, positive feedback can be provided to increase the user's motivation. If the user is relaxed, detailed feedback can be provided to allow the user to check the input content. Furthermore, if the user is in a hurry, quick feedback can be provided to improve the efficiency of input work. In this way, optimal input feedback can be provided according to the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The acquisition unit acquires the document format from the government office. The acquisition unit can acquire the document format, for example, by email, downloading from a website, or using an API. Specifically, the acquisition unit can download the document format from the government office's website, receive the document format sent by email, or automatically acquire the document format using an API. Step 2: The input unit uses a generation AI to automatically enter the required items based on the format acquired by the acquisition unit. The input unit enters appropriate information into each item based on data it has learned in advance. For example, it uses a text generation AI (e.g., LLM) to automatically enter basic information such as name, address, and phone number. It can also change the input content based on specific conditions. Step 3: The submission unit submits the documents entered by the input unit. The submission unit submits the documents automatically created by the generation AI. Specifically, documents can be submitted by email, via a website, or using an API.

[0103] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a 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.

[0141] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] 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.

[0166] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An acquisition department that acquires document formats from government offices; an input unit that automatically inputs necessary items based on the format acquired by the acquisition unit; a submission unit that submits the document input by the input unit. A system characterized by:

2. The acquisition unit Specify the specific methods for obtaining document formats, such as email, downloading from the website, or using API.

2. The system of claim 1.

3. The input unit Enter accurate information into each field based on pre-learned data 2. The system of claim 1.

4. The input unit Change input based on specific conditions 2. The system of claim 1.

5. The submission unit Submit documents automatically created by the generation AI 2. The system of claim 1.

6. The acquisition unit To estimate a user's emotion and appropriately adjust the timing of obtaining a document format based on the estimated user's emotion.

2. The system of claim 1.

7. The acquisition unit Analyze the history of past changes to government document formats and select the appropriate acquisition method 2. The system of claim 1.

8. The acquisition unit When obtaining document formats, adjust the timing to take into account the business hours and busy periods of government offices.

2. The system of claim 1.

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