System

Abstract

An object of a system according to an embodiment is to improve the efficiency of a document input operation.SOLUTION: A system according to an embodiment includes a document analysis unit, an information generation unit, and an input unit. The document analysis unit analyzes the type and content of the document. The information generation unit automatically generates necessary information based on the information items analyzed by the document analysis unit. The input unit automatically inputs the information generated by the information generation unit to the designated document.SELECTED DRAWING: Figure 1

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] Conventional techniques have had the problem that document input work is time-consuming and laborious, making it difficult to perform efficiently.

[0005] The system according to the embodiment aims to improve the efficiency of document input work. [Means for solving the problem]

[0006] The system according to the embodiment includes a document analysis unit, an information generation unit, and an input unit. The document analysis unit analyzes the type and content of a document. The information generation unit automatically generates necessary information based on the information items analyzed by the document analysis unit. The input unit automatically inputs the information generated by the information generation unit into a specified document. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of document input work. [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) A document input support system according to an embodiment of the present invention is a system in which a generation AI automatically generates information to be entered by a user, thereby streamlining document input work. As a result, the document input support system can streamline document input work that is tedious for users to fill out.

[0029] A document input support system according to an embodiment includes a document analysis unit, an information generation unit, and an input unit. The document analysis unit analyzes the type and content of a document. For example, the document analysis unit analyzes a document template uploaded by a user and identifies necessary information items. The document analysis unit can also analyze the content of a document using text analysis technology. For example, the document analysis unit analyzes a contract template and identifies necessary information items. The information generation unit automatically generates necessary information based on the information items analyzed by the document analysis unit. For example, the information generation unit automatically generates basic information about a user (such as name, address, and contact information). The information generation unit can also automatically generate detailed information (such as income, expenses, and insurance policy details) required for a specific document. For example, the information generation unit references data previously input by the user to generate appropriate information. The input unit automatically inputs the information generated by the information generation unit into a specified document. For example, the input unit inputs appropriate values ​​into the income and expense fields of a tax return. The input unit can also input insurance policy details and claim amounts into an insurance application form. For example, the input unit automatically inputs the generated information into each item. As a result, the document input support system according to the embodiment can streamline the input work of documents that are tedious for users to fill out. For example, the user can complete the document without any hassle. In addition, the generation AI can learn the content corrected by the user and reflect it in subsequent inputs. This improves the accuracy of the generation AI, enabling more efficient document input.

[0030] The document analysis unit can refer to the user's past document creation history and prioritize analysis of frequently used templates. For example, the generation AI retrieves the user's past document creation history from a database and identifies frequently used templates. For example, it prioritizes analysis of templates used most frequently over the past year. The document analysis unit also analyzes the history of documents created by the user in the past and extracts specific patterns and common items. For example, it prioritizes analysis of reports submitted monthly and contracts that are updated regularly. The document analysis unit also allows the generation AI to learn the user's document creation history and automatically list frequently used templates. For example, it prioritizes analysis of document templates related to specific tasks. This enables efficient document creation by prioritizing the analysis of frequently used templates.

[0031] The document analysis unit learns the user's business operations and industry-specific terminology, enabling more accurate analysis. For example, the document analysis unit has the generation AI learn the user's business operations and reflect industry-specific terminology and phrases in the analysis. For example, a user in the medical industry is trained in medical terminology, and a user in the legal industry is trained in legal terminology. The document analysis unit also retrieves terminology and phrases specific to the user's industry from a database, and the generation AI performs analysis based on this. For example, a user in the financial industry is trained in financial terminology. The document analysis unit also has the generation AI automatically learn terminology and phrases related to a specific industry based on the user's business operations. For example, a user in the IT industry is trained in technical terminology. This improves the accuracy of the analysis by learning business operations and industry-specific terminology.

[0032] The document analysis unit enables the analysis of documents in different languages, supporting international document creation. For example, the document analysis unit introduces a multilingual analysis algorithm so that the generation AI can analyze documents in different languages. For example, it analyzes documents in English, French, Chinese, etc. Furthermore, when a user uploads a document in a different language, the document analysis unit automatically detects the language and performs the appropriate analysis. For example, it uses a language detection function to identify the language of the document. Furthermore, when the generation AI analyzes documents in different languages, the document analysis unit combines the analysis with a translation function. For example, by translating the document first and then analyzing it, accuracy can be improved. This allows the analysis of documents in different languages ​​to support international document creation.

[0033] The document analysis unit can also analyze image and audio data, extracting necessary information from visual and audio information. For example, the generation AI analyzes image data contained in a document and extracts necessary information from the visual information. For example, it analyzes scanned handwritten notes and diagrams. The document analysis unit also analyzes audio data contained in a document and extracts necessary information from audio information. For example, it analyzes audio memos and meeting recordings and converts them into text. The generation AI also combines image recognition technology and audio recognition technology to simultaneously analyze the visual and audio information of a document. For example, it extracts information from both images and audio. This allows for the extraction of necessary information from visual and audio information, making it possible to create a wider variety of documents.

[0034] The information generation unit can improve accuracy by referencing the user's past input data and learning individual patterns. For example, the information generation unit uses a generation AI to retrieve the user's past input data from a database and learn individual patterns. For example, the information generation unit automatically generates necessary information based on past input history. The information generation unit also analyzes data previously entered by the user and extracts specific patterns and common items. For example, it learns monthly income and expenditure patterns. The information generation unit also uses a generation AI to learn individual patterns based on the user's past input data and improve input accuracy from the next time onwards. For example, it automatically generates information required for a specific document. In this way, the accuracy of information generation is improved by referencing past input data and learning individual patterns.

[0035] The information generation unit can obtain information from external databases and generate more accurate information. For example, the generation AI of the information generation unit obtains information from external databases and automatically generates the necessary information. For example, it obtains the latest tax rates and regulatory information from public government databases. The information generation unit also obtains information from a company's internal database and automatically generates the necessary information. For example, it generates the necessary information based on a company's financial data and customer information. The information generation unit also has the generation AI work with external databases to obtain and automatically generate the necessary information in real time. For example, it generates information based on the latest market data and economic indicators. This allows more accurate information to be generated by obtaining information from external databases.

[0036] The information generation unit generates information suitable for different industries and applications, supporting the creation of a wide range of documents. For example, the information generation unit references industry-specific databases so that the generation AI can generate information suitable for different industries and applications. For example, it uses databases for the medical industry and the legal industry. Furthermore, when a user creates a document suitable for a different industry or application, the information generation unit allows the generation AI to automatically generate appropriate information. For example, it automatically generates information for medical reports and contracts. Furthermore, the information generation unit learns industry-specific terminology and phrases so that the generation AI can generate information suitable for different industries and applications. For example, it learns financial industry terminology and technology industry phrases. This allows the generation AI to generate information suitable for different industries and applications, supporting the creation of a wide range of documents.

[0037] The information generation unit enables a user to provide information by voice input and can utilize voice recognition technology. For example, the generation AI in the information generation unit uses voice recognition technology to enable a user to provide information by voice input. For example, a name or address is input by voice. Furthermore, when a user provides information by voice input, the generation AI converts the voice into text in real time and automatically generates the necessary information. For example, income and expense information is input by voice. Furthermore, the information generation unit enables the generation AI to utilize voice recognition technology to improve the accuracy when a user provides information by voice input. For example, to improve the accuracy of voice recognition, the generation AI learns the user's pronunciation and accent. This allows information to be provided by voice input, thereby improving the efficiency of input work.

[0038] The input unit can learn the user's past revision history and improve input accuracy from the next time onwards. For example, the input unit uses a generation AI to retrieve the user's past revision history from a database and improve input accuracy from the next time onwards. For example, the input unit improves the accuracy of automatic input based on the content of past revisions. The input unit also analyzes the content of revisions made by the user in the past and extracts specific patterns and common items. For example, it identifies items that are frequently revised and improves the input accuracy of those parts. The input unit also uses a generation AI to learn the user's revision history and automatically reflect the revisions when inputting from the next time onwards. For example, it learns revision patterns for a specific document and uses them for the next input. In this way, by learning the past revision history, input accuracy from the next time onwards is improved.

[0039] The input unit can accommodate documents of different formats, enabling flexible input. The input unit, for example, allows the generation AI to analyze documents of different formats and support automatic input. For example, it supports different formats such as PDF, Word, and Excel. Furthermore, when a user uploads a document of a different format, the input unit automatically recognizes the format and performs appropriate input. For example, input is performed by combining a format conversion function. Furthermore, the input unit learns input rules for each format so that the generation AI can accommodate documents of different formats. For example, it learns input patterns for specific formats and achieves flexible input. This allows for flexible input by supporting documents of different formats.

[0040] The input unit can also accommodate input from different devices. For example, the input unit provides an input interface for each device so that the generation AI can accommodate input from different devices. For example, it provides an input screen optimized for smartphones and tablets. Furthermore, when a user uploads a document from a different device, the input unit automatically recognizes the device and performs appropriate input. For example, it provides an input method according to the characteristics of the device. Furthermore, the input unit learns input rules for each device so that the generation AI can accommodate input from different devices. For example, it learns the input patterns of smartphones and tablets to achieve flexible input. This enables flexible input by accommodating input from different devices.

[0041] The input unit can be added with a function to scan and automatically input information handwritten by the user. The input unit provides, for example, a function in which the generation AI scans handwritten information and automatically converts it into text data. For example, handwritten notes and signatures are scanned and automatically input. The input unit also scans information handwritten by the user, and the generation AI automatically analyzes and inputs the information into a document. For example, handwritten income and expense information is scanned and automatically input. The input unit also uses handwriting recognition technology to analyze handwritten information with high accuracy and automatically input it. For example, handwritten letters and numbers are accurately recognized and input. This improves the efficiency of input work by scanning and automatically inputting handwritten information.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The document analysis unit can refer to the user's work schedule and suggest the optimal timing for document creation. For example, the document analysis unit can link with the user's calendar app to send reminders to encourage document creation before important meetings or deadlines. The document analysis unit can also identify the most efficient time periods for document creation based on the user's past document creation history and suggest document creation during those times. Furthermore, the document analysis unit can support efficient work execution by analyzing the user's workload and suggesting document creation during times when the workload is light.

[0044] The information generation unit can refer to the user's health data and generate information according to the user's health condition. For example, the information generation unit can obtain data from the user's fitness tracker or health app and generate information based on the user's health condition. The information generation unit can also provide information useful for reducing stress and maintaining health based on the user's health data. Furthermore, the information generation unit can make suggestions to reduce the burden of document creation according to the user's health condition. This makes it possible to generate information that takes the user's health condition into consideration.

[0045] The input unit can analyze the user's input speed and provide an optimal input interface. For example, the input unit can analyze the user's typing speed and frequency of typos to customize the input interface. The input unit can also adjust the size and layout of input fields according to the user's input speed. Furthermore, the input unit can provide an input assistance function based on the user's input speed to support efficient input. This allows the input interface to be optimally tailored to the user's input speed.

[0046] The information generation unit can generate personalized information based on the user's hobbies and interests. For example, the information generation unit can analyze the user's social media accounts and browsing history to provide information based on the user's hobbies and interests. The information generation unit can also generate related information based on topics in which the user has shown interest in the past. Furthermore, the information generation unit can support more personalized document creation by providing information that is useful when creating documents according to the user's hobbies and interests. This makes it possible to generate information based on the user's hobbies and interests.

[0047] The information generation unit can predict future input based on the user's past input data and generate information in advance. For example, the information generation unit can analyze the user's past input patterns and predict the next input content. The information generation unit can also generate information necessary for future input in advance based on the user's past input data. Furthermore, the information generation unit can support efficient document creation by making suggestions for future input based on the user's past input data. This makes it possible to efficiently generate information by predicting future input based on the user's past input data.

[0048] The information generation unit can improve accuracy by learning individual patterns based on the user's past input data. For example, the information generation unit retrieves the user's past input data from a database and learns individual patterns. The information generation unit can also analyze data previously input by the user and extract specific patterns or common items. Furthermore, the information generation unit can perform learning based on the user's past input data to improve input accuracy from the next time onwards. In this way, the accuracy of information generation is improved by referring to the past input data and learning individual patterns.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The document analysis unit analyzes the type and content of the document. For example, the document analysis unit analyzes a document template uploaded by a user and identifies the required information items. The document analysis unit can also analyze the content of the document using text analysis technology. For example, the document analysis unit analyzes a contract template and identifies the required information items. Step 2: The information generation unit automatically generates the necessary information based on the information items analyzed by the document analysis unit. For example, the information generation unit automatically generates the user's basic information (such as name, address, and contact information). The information generation unit can also automatically generate detailed information (such as income, expenses, and insurance contract details) required for a specific document. For example, the information generation unit references the user's past input data to generate appropriate information. Step 3: The input unit automatically inputs the information generated by the information generation unit into a specified document. For example, the input unit inputs appropriate figures into the income and expense items of a tax return. The input unit can also input the insurance contract details and claim amount of an insurance application form. For example, the input unit automatically inputs the generated information into each item.

[0051] (Example 2) A document input support system according to an embodiment of the present invention is a system in which a generation AI automatically generates information to be entered by a user, thereby streamlining document input work. As a result, the document input support system can streamline document input work that is tedious for users to fill out.

[0052] A document input support system according to an embodiment includes a document analysis unit, an information generation unit, and an input unit. The document analysis unit analyzes the type and content of a document. For example, the document analysis unit analyzes a document template uploaded by a user and identifies necessary information items. The document analysis unit can also analyze the content of a document using text analysis technology. For example, the document analysis unit analyzes a contract template and identifies necessary information items. The information generation unit automatically generates necessary information based on the information items analyzed by the document analysis unit. For example, the information generation unit automatically generates basic information about a user (such as name, address, and contact information). The information generation unit can also automatically generate detailed information (such as income, expenses, and insurance policy details) required for a specific document. For example, the information generation unit references data previously input by the user to generate appropriate information. The input unit automatically inputs the information generated by the information generation unit into a specified document. For example, the input unit inputs appropriate values ​​into the income and expense fields of a tax return. The input unit can also input insurance policy details and claim amounts into an insurance application form. For example, the input unit automatically inputs the generated information into each item. As a result, the document input support system according to the embodiment can streamline the input work of documents that are tedious for users to fill out. For example, the user can complete the document without any hassle. In addition, the generation AI can learn the content corrected by the user and reflect it in subsequent inputs. This improves the accuracy of the generation AI, enabling more efficient document input.

[0053] The document analysis unit can refer to the user's past document creation history and prioritize analysis of frequently used templates. For example, the generation AI retrieves the user's past document creation history from a database and identifies frequently used templates. For example, it prioritizes analysis of templates used most frequently over the past year. The document analysis unit also analyzes the history of documents created by the user in the past and extracts specific patterns and common items. For example, it prioritizes analysis of reports submitted monthly and contracts that are updated regularly. The document analysis unit also allows the generation AI to learn the user's document creation history and automatically list frequently used templates. For example, it prioritizes analysis of document templates related to specific tasks. This enables efficient document creation by prioritizing the analysis of frequently used templates.

[0054] The document analysis unit learns the user's business operations and industry-specific terminology, enabling more accurate analysis. For example, the document analysis unit has the generation AI learn the user's business operations and reflect industry-specific terminology and phrases in the analysis. For example, a user in the medical industry is trained in medical terminology, and a user in the legal industry is trained in legal terminology. The document analysis unit also retrieves terminology and phrases specific to the user's industry from a database, and the generation AI performs analysis based on this. For example, a user in the financial industry is trained in financial terminology. The document analysis unit also has the generation AI automatically learn terminology and phrases related to a specific industry based on the user's business operations. For example, a user in the IT industry is trained in technical terminology. This improves the accuracy of the analysis by learning business operations and industry-specific terminology.

[0055] The document analysis unit can use the emotion estimation function to identify which parts of a document the user is feeling stressed about and focus analysis on those parts. The document analysis unit can, for example, use the emotion estimation function to identify parts of a document where the user is feeling stressed while entering documents. For example, it can analyze facial expressions and voice tones and focus analysis on parts that are stressful. The document analysis unit can also detect parts of a document where the user is feeling stressed about while entering documents in real time and strengthen analysis of those parts. For example, it can identify stress points based on input speed and error frequency. The document analysis unit can also use the emotion estimation function to retrieve parts of a document where the user has felt stressed in the past from a database and prioritize analysis of those parts. For example, it can identify stress points based on past input history. This allows for focused analysis of parts where the user is feeling stressed, thereby enabling efficient document creation.

[0056] The document analysis unit enables the analysis of documents in different languages, supporting international document creation. For example, the document analysis unit introduces a multilingual analysis algorithm so that the generation AI can analyze documents in different languages. For example, it analyzes documents in English, French, Chinese, etc. Furthermore, when a user uploads a document in a different language, the document analysis unit automatically detects the language and performs the appropriate analysis. For example, it uses a language detection function to identify the language of the document. Furthermore, when the generation AI analyzes documents in different languages, the document analysis unit combines the analysis with a translation function. For example, by translating the document first and then analyzing it, accuracy can be improved. This allows the analysis of documents in different languages ​​to support international document creation.

[0057] The document analysis unit can also analyze image and audio data, extracting necessary information from visual and audio information. For example, the generation AI analyzes image data contained in a document and extracts necessary information from the visual information. For example, it analyzes scanned handwritten notes and diagrams. The document analysis unit also analyzes audio data contained in a document and extracts necessary information from audio information. For example, it analyzes audio memos and meeting recordings and converts them into text. The generation AI also combines image recognition technology and audio recognition technology to simultaneously analyze the visual and audio information of a document. For example, it extracts information from both images and audio. This allows for the extraction of necessary information from visual and audio information, making it possible to create a wider variety of documents.

[0058] The document analysis unit uses the emotion estimation function to analyze the emotions of a user when uploading a document in real time and make suggestions to elicit positive emotions. The document analysis unit, for example, uses the emotion estimation function to analyze the emotions of a user when uploading a document in real time. For example, it analyzes facial expressions and voice tone and calculates an emotion score. The document analysis unit also makes suggestions to elicit positive emotions when the user uploads a document. For example, it presents encouraging messages and success stories. The document analysis unit also uses the emotion estimation function to provide feedback on the emotions of a user when uploading a document in real time and provides an interface that elicits positive emotions. For example, it makes appropriate suggestions based on the emotion score. In this way, the stress of creating documents can be reduced by analyzing the user's emotions in real time and eliciting positive emotions.

[0059] The information generation unit can improve accuracy by referencing the user's past input data and learning individual patterns. For example, the information generation unit uses a generation AI to retrieve the user's past input data from a database and learn individual patterns. For example, the information generation unit automatically generates necessary information based on past input history. The information generation unit also analyzes data previously entered by the user and extracts specific patterns and common items. For example, it learns monthly income and expenditure patterns. The information generation unit also uses a generation AI to learn individual patterns based on the user's past input data and improve input accuracy from the next time onwards. For example, it automatically generates information required for a specific document. In this way, the accuracy of information generation is improved by referencing past input data and learning individual patterns.

[0060] The information generation unit can obtain information from external databases and generate more accurate information. For example, the generation AI of the information generation unit obtains information from external databases and automatically generates the necessary information. For example, it obtains the latest tax rates and regulatory information from public government databases. The information generation unit also obtains information from a company's internal database and automatically generates the necessary information. For example, it generates the necessary information based on a company's financial data and customer information. The information generation unit also has the generation AI work with external databases to obtain and automatically generate the necessary information in real time. For example, it generates information based on the latest market data and economic indicators. This allows more accurate information to be generated by obtaining information from external databases.

[0061] The information generation unit can use the emotion estimation function to analyze emotions regarding information input by the user and generate information that elicits positive emotions. The information generation unit, for example, uses the emotion estimation function to analyze emotions regarding information input by the user in real time. For example, it analyzes facial expressions and vocal tone and calculates an emotion score. The information generation unit also generates information to elicit positive emotions regarding the information input by the user. For example, it presents encouraging messages and success stories. The information generation unit also uses the emotion estimation function to provide feedback regarding emotions regarding the information input by the user in real time and provides an interface that elicits positive emotions. For example, it generates appropriate information according to the emotion score. In this way, the stress of input work can be reduced by analyzing the user's emotions and generating information that elicits positive emotions.

[0062] The information generation unit generates information suitable for different industries and applications, supporting the creation of a wide range of documents. For example, the information generation unit references industry-specific databases so that the generation AI can generate information suitable for different industries and applications. For example, it uses databases for the medical industry and the legal industry. Furthermore, when a user creates a document suitable for a different industry or application, the information generation unit allows the generation AI to automatically generate appropriate information. For example, it automatically generates information for medical reports and contracts. Furthermore, the information generation unit learns industry-specific terminology and phrases so that the generation AI can generate information suitable for different industries and applications. For example, it learns financial industry terminology and technology industry phrases. This allows the generation AI to generate information suitable for different industries and applications, supporting the creation of a wide range of documents.

[0063] The information generation unit enables a user to provide information by voice input and can utilize voice recognition technology. For example, the generation AI in the information generation unit uses voice recognition technology to enable a user to provide information by voice input. For example, a name or address is input by voice. Furthermore, when a user provides information by voice input, the generation AI converts the voice into text in real time and automatically generates the necessary information. For example, income and expense information is input by voice. Furthermore, the information generation unit enables the generation AI to utilize voice recognition technology to improve the accuracy when a user provides information by voice input. For example, to improve the accuracy of voice recognition, the generation AI learns the user's pronunciation and accent. This allows information to be provided by voice input, thereby improving the efficiency of input work.

[0064] The information generation unit can use the emotion estimation function to analyze emotions regarding information input by the user in real time and generate information that elicits positive emotions. The information generation unit, for example, uses the emotion estimation function to analyze emotions regarding information input by the user in real time. For example, it analyzes facial expressions and vocal tone and calculates an emotion score. The information generation unit also generates information to elicit positive emotions regarding the information input by the user. For example, it presents encouraging messages and success stories. The information generation unit also uses the emotion estimation function to provide feedback regarding emotions regarding the information input by the user in real time and provides an interface that elicits positive emotions. For example, it generates appropriate information according to the emotion score. In this way, the user's emotions can be analyzed in real time and information that elicits positive emotions can be generated, thereby reducing the stress of input work.

[0065] The input unit can learn the user's past revision history and improve input accuracy from the next time onwards. For example, the input unit uses a generation AI to retrieve the user's past revision history from a database and improve input accuracy from the next time onwards. For example, the input unit improves the accuracy of automatic input based on the content of past revisions. The input unit also analyzes the content of revisions made by the user in the past and extracts specific patterns and common items. For example, it identifies items that are frequently revised and improves the input accuracy of those parts. The input unit also uses a generation AI to learn the user's revision history and automatically reflect the revisions when inputting from the next time onwards. For example, it learns revision patterns for a specific document and uses them for the next input. In this way, by learning the past revision history, input accuracy from the next time onwards is improved.

[0066] The input unit can accommodate documents of different formats, enabling flexible input. The input unit, for example, allows the generation AI to analyze documents of different formats and support automatic input. For example, it supports different formats such as PDF, Word, and Excel. Furthermore, when a user uploads a document of a different format, the input unit automatically recognizes the format and performs appropriate input. For example, input is performed by combining a format conversion function. Furthermore, the input unit learns input rules for each format so that the generation AI can accommodate documents of different formats. For example, it learns input patterns for specific formats and achieves flexible input. This allows for flexible input by supporting documents of different formats.

[0067] The input unit can use the emotion estimation function to analyze the emotion of the user when checking the input content and make suggestions to reduce stress. The input unit, for example, uses the emotion estimation function to analyze the emotion of the user when checking the input content in real time. For example, it analyzes facial expressions and voice tone and calculates an emotion score. The input unit also makes suggestions to reduce stress when the user checks the input content. For example, it presents encouraging messages and relaxation techniques. The input unit also uses the emotion estimation function to provide feedback on the emotion of the user when checking the input content in real time and provide an interface to reduce stress. For example, it makes appropriate suggestions based on the emotion score. In this way, the burden of input work can be reduced by analyzing the user's emotion and making suggestions to reduce stress.

[0068] The input unit can also accommodate input from different devices. For example, the input unit provides an input interface for each device so that the generation AI can accommodate input from different devices. For example, it provides an input screen optimized for smartphones and tablets. Furthermore, when a user uploads a document from a different device, the input unit automatically recognizes the device and performs appropriate input. For example, it provides an input method according to the characteristics of the device. Furthermore, the input unit learns input rules for each device so that the generation AI can accommodate input from different devices. For example, it learns the input patterns of smartphones and tablets to achieve flexible input. This enables flexible input by accommodating input from different devices.

[0069] The input unit can be added with a function to scan and automatically input information handwritten by the user. The input unit provides, for example, a function in which the generation AI scans handwritten information and automatically converts it into text data. For example, handwritten notes and signatures are scanned and automatically input. The input unit also scans information handwritten by the user, and the generation AI automatically analyzes and inputs the information into a document. For example, handwritten income and expense information is scanned and automatically input. The input unit also uses handwriting recognition technology to analyze handwritten information with high accuracy and automatically input it. For example, handwritten letters and numbers are accurately recognized and input. This improves the efficiency of input work by scanning and automatically inputting handwritten information.

[0070] The input unit can use the emotion estimation function to analyze the emotions of the user when confirming the input content in real time and make suggestions to elicit positive emotions. The input unit, for example, uses the emotion estimation function to analyze the emotions of the user when confirming the input content in real time. For example, it analyzes facial expressions and voice tone and calculates an emotion score. The input unit also makes suggestions to elicit positive emotions when the user confirms the input content. For example, it presents encouraging messages and success stories. The input unit also uses the emotion estimation function to provide feedback on the emotions of the user when confirming the input content in real time and provides an interface that elicits positive emotions. For example, it makes appropriate suggestions based on the emotion score. In this way, the stress of the input work can be reduced by analyzing the user's emotions in real time and making suggestions to elicit positive emotions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The document analysis unit can refer to the user's work schedule and suggest the optimal timing for document creation. For example, the document analysis unit can link with the user's calendar app to send reminders to encourage document creation before important meetings or deadlines. The document analysis unit can also identify the most efficient time periods for document creation based on the user's past document creation history and suggest document creation during those times. Furthermore, the document analysis unit can support efficient work execution by analyzing the user's workload and suggesting document creation during times when the workload is light.

[0073] The information generation unit can refer to the user's health data and generate information according to the user's health condition. For example, the information generation unit can obtain data from the user's fitness tracker or health app and generate information based on the user's health condition. The information generation unit can also provide information useful for reducing stress and maintaining health based on the user's health data. Furthermore, the information generation unit can make suggestions to reduce the burden of document creation according to the user's health condition. This makes it possible to generate information that takes the user's health condition into consideration.

[0074] The input unit can analyze the user's input speed and provide an optimal input interface. For example, the input unit can analyze the user's typing speed and frequency of typos to customize the input interface. The input unit can also adjust the size and layout of input fields according to the user's input speed. Furthermore, the input unit can provide an input assistance function based on the user's input speed to support efficient input. This allows the input interface to be optimally tailored to the user's input speed.

[0075] The document analysis unit can use the emotion estimation function to identify which parts of a document the user is interested in and focus analysis on those parts. For example, the document analysis unit can analyze the user's eye tracking data and click data to identify parts of interest. The document analysis unit can also detect parts of a document that the user is interested in in real time while entering data, and enhance analysis of those parts. Furthermore, the document analysis unit can use the emotion estimation function to retrieve parts of a document that the user has previously been interested in from a database and prioritize analysis of those parts. This enables efficient document creation by focusing analysis on parts of the document that the user is interested in.

[0076] The information generation unit can generate personalized information based on the user's hobbies and interests. For example, the information generation unit can analyze the user's social media accounts and browsing history to provide information based on the user's hobbies and interests. The information generation unit can also generate related information based on topics in which the user has shown interest in the past. Furthermore, the information generation unit can support more personalized document creation by providing information that is useful when creating documents according to the user's hobbies and interests. This makes it possible to generate information based on the user's hobbies and interests.

[0077] The document analysis unit can use the emotion estimation function to identify which parts of a document the user is feeling anxious about and focus analysis on those parts. For example, the document analysis unit can analyze the user's heart rate and galvanic skin response to identify parts that cause high anxiety. The document analysis unit can also detect parts of a document where the user is feeling anxious in real time while filling it out and strengthen analysis of those parts. Furthermore, the document analysis unit can use the emotion estimation function to retrieve parts of a document where the user has felt anxious in the past from a database and prioritize analysis of those parts. This allows for efficient document creation by focusing analysis on parts that cause the user anxiety.

[0078] The information generation unit can predict future input based on the user's past input data and generate information in advance. For example, the information generation unit can analyze the user's past input patterns and predict the next input content. The information generation unit can also generate information necessary for future input in advance based on the user's past input data. Furthermore, the information generation unit can support efficient document creation by making suggestions for future input based on the user's past input data. This makes it possible to efficiently generate information by predicting future input based on the user's past input data.

[0079] The input unit can use the emotion estimation function to analyze the emotions of the user when confirming the input content in real time and make suggestions to elicit positive emotions. For example, the input unit can analyze the user's facial expressions and voice tone and calculate an emotion score. The input unit can also make suggestions to elicit positive emotions when the user confirms the input content. For example, the input unit can present encouraging messages or success stories. Furthermore, the input unit can use the emotion estimation function to provide feedback on the emotions of the user when confirming the input content in real time and provide an interface that elicits positive emotions. In this way, by analyzing the user's emotions in real time and making suggestions to elicit positive emotions, the stress of the input work can be reduced.

[0080] The information generation unit can improve accuracy by learning individual patterns based on the user's past input data. For example, the information generation unit retrieves the user's past input data from a database and learns individual patterns. The information generation unit can also analyze data previously input by the user and extract specific patterns or common items. Furthermore, the information generation unit can perform learning based on the user's past input data to improve input accuracy from the next time onwards. In this way, the accuracy of information generation is improved by referring to the past input data and learning individual patterns.

[0081] The information generation unit can use the emotion estimation function to analyze emotions regarding information entered by the user in real time and generate information that elicits positive emotions. For example, the information generation unit analyzes the user's facial expressions and vocal tone and calculates an emotion score. The information generation unit also generates information to elicit positive emotions regarding the information entered by the user. For example, encouraging messages and success stories can be presented. Furthermore, the information generation unit can use the emotion estimation function to provide feedback regarding emotions regarding information entered by the user in real time and provide an interface that elicits positive emotions. In this way, by analyzing the user's emotions in real time and generating information that elicits positive emotions, the stress of input work can be reduced.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The document analysis unit analyzes the type and content of the document. For example, the document analysis unit analyzes a document template uploaded by a user and identifies the required information items. The document analysis unit can also analyze the content of the document using text analysis technology. For example, the document analysis unit analyzes a contract template and identifies the required information items. Step 2: The information generation unit automatically generates the necessary information based on the information items analyzed by the document analysis unit. For example, the information generation unit automatically generates the user's basic information (such as name, address, and contact information). The information generation unit can also automatically generate detailed information (such as income, expenses, and insurance contract details) required for a specific document. For example, the information generation unit references the user's past input data to generate appropriate information. Step 3: The input unit automatically inputs the information generated by the information generation unit into a specified document. For example, the input unit inputs appropriate figures into the income and expense items of a tax return. The input unit can also input the insurance contract details and claim amount of an insurance application form. For example, the input unit automatically inputs the generated information into each item.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] In the robot 414, 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 robot 414 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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. [Explanation of symbols]

[0151] 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. a document analysis unit that analyzes the type and content of a document; an information generation unit that automatically generates necessary information based on the information items analyzed by the document analysis unit; an input unit that automatically inputs the information generated by the information generation unit into a designated document; A system characterized by:

2. The document analysis unit Refer to the user's document creation history and prioritize frequently used templates.

2. The system of claim 1.

3. The document analysis unit Learns the user's business operations and industry-specific terminology to perform more accurate analysis 2. The system of claim 1.

4. The document analysis unit Identify which parts of a document cause stress to users and focus analysis on those parts 2. The system of claim 1.

5. The document analysis unit The documents in different languages ​​can also be analyzed to support international document creation.

2. The system of claim 1.

6. The document analysis unit Analyze image and audio data to extract necessary information from visual and auditory information.

2. The system of claim 1.

7. The document analysis unit Analyzes users' emotions in real time when they upload documents and makes suggestions that elicit positive emotions 2. The system of claim 1.

8. The information generation unit Refers to the user's past input data and learns individual patterns to improve accuracy 2. The system of claim 1.

Images