System

Abstract

To provide a system that automatically converts expressions in documents into appropriate honorific.SOLUTION: A system according to the embodiment comprises a reception unit, a conversion unit, and a provision unit. The reception unit receives a document input by a user. The conversion unit converts expressions in the document accepted by the reception unit into at least one of polite language, humble language, and honorific language. The provision unit provides the document converted by the conversion unit.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] In conventional techniques, converting expressions in documents into appropriate honorific language is time-consuming and difficult to do efficiently.

[0005] The system according to the embodiment aims to automatically convert expressions in a document into appropriate honorific language. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a conversion unit, and a provision unit. The reception unit receives a document input by a user. The conversion unit converts an expression in the document received by the reception unit into at least one of polite language, humble language, and honorific language. The provision unit provides the document converted by the conversion unit. Effect of the Invention

[0007] The system according to the embodiment can automatically convert expressions in a document into appropriate honorific language. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The document conversion system according to the embodiment of the present invention is a system that automatically converts a document input by a user into polite language, humble language, honorific language, etc. In this document conversion system, a user inputs a document in his / her own words, and an AI analyzes the document and converts it into a selected expression format (polite language, humble language, honorific language, etc.). The converted document is provided to the user. This mechanism allows a user to easily create a document in an appropriate expression format. For example, a user inputs a document in his / her own words. For example, the user inputs "Please tell me the time of the meeting." This document is input to the AI. Next, the AI ​​analyzes the input document. The AI ​​understands the content of the document and converts it into the selected expression format. For example, when converting a document "Please tell me the time of the meeting" into polite language, it becomes "Please tell me the time of the meeting." The converted document is provided to the user. The user can check the converted document and correct it as necessary. For example, the user checks the document "Please tell me the time of the meeting" and confirms that it is an appropriate expression. This mechanism allows a user to easily create a document in an appropriate expression format. For example, polite language can be used for colleagues within the company and honorific language can be used for business partners outside the company. In addition, the user can use humble language when speaking to a superior. This allows the user to select an appropriate expression format for the document, improving the quality of communication. This allows the document conversion system to automatically convert the document entered by the user into an appropriate expression format and provide it to the user.

[0029] The document conversion system according to the embodiment includes a reception unit, a conversion unit, and a provision unit. The reception unit accepts a document input by a user. The document input by a user includes, for example, a business email, a private message, and the like, but is not limited to such examples. The reception unit accepts, for example, the document input by the user in a text format. The reception unit can also accept a document using voice input. For example, the content input by the user is converted into text using a voice recognition technology and accepted. The conversion unit converts an expression in the document accepted by the reception unit into polite language, humble language, or honorific language using a generation AI. The conversion is performed, for example, based on the content and context of the document, but is not limited to such examples. For example, the generation AI converts the document into an appropriate expression format using a text generation AI (for example, LLM). The conversion unit can also convert the content of the document using a multimodal generation AI. For example, the generation AI analyzes the content of the document and converts it into an appropriate expression format. The provision unit provides the document converted by the conversion unit. The provision includes, for example, a method of displaying on a user's device, a method of sending by e-mail, and the like, but is not limited to such examples. For example, the provision unit displays the converted document on the user's smartphone. The provision unit can also send the converted document by e-mail. As a result, the document conversion system according to the embodiment can automatically convert the document input by the user into an appropriate expression format and provide it. A part or all of the above-mentioned processing in the conversion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the conversion unit inputs the document input by the user to the generation AI, which analyzes the document and converts it into an appropriate expression format. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the provision unit inputs the converted document to the generation AI, and the generation AI determines how to provide the document.

[0030] The reception unit receives a document input by a user. Examples of documents input by a user include, but are not limited to, business emails and private messages. The reception unit receives, for example, a document input by a user in text format. Specifically, the reception unit can directly receive text data input by a user using a keyboard. The reception unit can also receive a document using voice input. For example, the content input by the user using voice is converted into text using voice recognition technology and then accepted. For the voice recognition technology, a voice recognition model using deep learning is used, and the user's speech can be converted into text with high accuracy. Furthermore, the reception unit can also receive handwritten input, and characters handwritten using a tablet or stylus pen can be converted into text using optical character recognition (OCR) technology. This allows the user to select various input methods, improving convenience. The reception unit temporarily stores the received document and prepares it for transmission to the conversion unit. For example, the reception unit checks the format and content of the document and performs preprocessing as necessary. The preprocessing includes adjusting the format of the document, deleting unnecessary spaces, and checking grammar. This enables the conversion unit to process the document efficiently.

[0031] The conversion unit converts expressions in the document accepted by the acceptance unit into polite language, humble language, or honorific language using the generation AI. The conversion is performed, for example, based on the contents and context of the document, but is not limited to such examples. The generation AI converts the document into an appropriate expression format using a text generation AI (for example, LLM). Specifically, the generation AI analyzes the contents of the document, understands the context, and converts the document into an appropriate expression format. For example, in the case of a business email, the generation AI analyzes the contents of the document and converts the document using an appropriate honorific expression. In addition, in the case of a private message, the generation AI analyzes the contents of the document and converts the document using an appropriate polite language or humble language. The conversion unit can also convert the contents of the document using a multimodal generation AI. For example, the generation AI analyzes the contents of the document and converts it into an appropriate expression format. The multimodal generation AI can process multiple modal data such as images and voices as well as text at the same time. This makes it possible to more accurately understand the contents of the document and convert it into an appropriate expression format. The conversion unit temporarily stores the converted document and prepares it for transmission to the provision unit. For example, the format and content of the converted document are checked, and post-processing is performed as necessary, such as formatting the document, checking grammar, checking spelling, etc., to enable the document provider to provide the document efficiently.

[0032] The providing unit provides the document converted by the converting unit. The providing includes, for example, a method of displaying the document on the user's device, a method of sending the document by e-mail, and the like, but is not limited to such examples. Specifically, the providing unit displays the converted document on the user's smartphone. The converted document is displayed in real time through an application on the smartphone so that the user can check it. The providing unit can also send the converted document by e-mail. The e-mail is sent by using the SMTP protocol to send the converted document to an e-mail address specified by the user. Furthermore, the providing unit can also use a cloud storage service to store the converted document so that the user can access it at any time. For example, the converted document is uploaded to cloud storage and a sharing link is provided to the user. This allows the user to access the converted document from any device connected to the Internet. When providing the converted document, the providing unit takes security measures to protect the user's privacy. For example, encryption technology is used to protect data when sending or saving the document, preventing unauthorized access. The providing unit can also collect user feedback and use it to improve the providing method. For example, a function is provided for users to rate and comment on the provided documents, and the system is improved based on this feedback. This allows the providing unit to provide converted documents to users quickly and reliably, thereby improving user satisfaction.

[0033] The conversion unit can convert expressions in the document accepted by the acceptance unit into expressions based on the destination of the document. For example, in the case of a business email, the conversion unit converts the text into polite language or honorific language for business use. In addition, in the case of a message to a friend, the conversion unit can also convert the text into casual polite language. For example, in the case of a business email addressed to a superior, the conversion unit converts the text into a humble language. In addition, in the case of a business email addressed to a colleague, the conversion unit can also convert the text into a polite language. This allows conversion into an appropriate expression format according to the destination. Part or all of the above-mentioned processing in the conversion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the conversion unit inputs destination information into a generation AI, which analyzes the document and converts it into an appropriate expression format.

[0034] The reception unit can analyze the user's past document input history and select an appropriate reception method. For example, the reception unit automatically recognizes a document format that the user has frequently used in the past and prompts the user to input in a similar format. The reception unit can also preferentially suggest an input method (such as voice or text) that the user has used in the past. For example, the reception unit predicts and suggests a document format to be used in a specific time period from the user's past document input history. This makes it possible to select an optimal reception method based on the user's past document input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the user's past document input history data into the generation AI, which selects the optimal reception method.

[0035] The reception unit can filter documents based on the user's current project and field of interest when receiving the documents. For example, the reception unit preferentially receives only documents related to a project that the user is currently working on. The reception unit can also filter and receive documents that are highly relevant based on the user's field of interest. For example, the reception unit adjusts the reception of documents based on topics in which the user has shown interest in the past. This makes it possible to filter documents based on the user's current project and field of interest. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the user's project data and field of interest data to the generation AI, and the generation AI filters the documents.

[0036] The reception unit can preferentially receive documents with high relevance based on the geographical location information of the user when receiving the document. For example, when the user is in a specific area, the reception unit preferentially receives documents related to the area. In addition, when the user is moving, the reception unit can also receive documents with high relevance based on the current location. For example, when the user is in a specific location, the reception unit preferentially receives documents related to the location. This makes it possible to preferentially receive documents with high relevance based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the geographical location information of the user to the generation AI, which identifies documents with high relevance and preferentially receives them.

[0037] The reception unit can analyze the user's social media activity and receive related documents when receiving the document. For example, the reception unit preferentially receives documents related to topics mentioned by the user on social media. The reception unit can also analyze the user's social media activity history and receive highly relevant documents. For example, the reception unit preferentially receives documents related to accounts the user follows on social media. This makes it possible to receive related documents based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the user's social media activity data into the generation AI, which identifies and receives related documents.

[0038] The conversion unit can adjust the level of detail of the conversion based on the importance of the document during conversion. For example, the conversion unit converts a document with high importance into detailed polite language or honorific language. The conversion unit can also convert a document with low importance into concise polite language. For example, the conversion unit converts into an appropriate level of expression depending on the importance. This makes it possible to adjust the level of detail of the conversion depending on the importance of the document. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit inputs the importance data of the document to the generation AI, and the generation AI adjusts the level of detail of the conversion.

[0039] The conversion unit can apply different conversion algorithms depending on the category of the document during conversion. For example, the conversion unit applies polite language or honorific language for business use to a business document. The conversion unit can also apply appropriate polite language to a casual document. For example, the conversion unit applies strict honorific language to an official document. This makes it possible to apply an appropriate conversion algorithm depending on the category of the document. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit inputs document category data to the generation AI, which applies an appropriate conversion algorithm.

[0040] The conversion unit can determine the priority of conversion based on the submission time of the document during conversion. For example, the conversion unit preferentially converts documents whose deadline is approaching. The conversion unit can also postpone documents whose submission time is far away. For example, the conversion unit adjusts the priority of conversion according to the submission time. This makes it possible to determine the priority of conversion according to the submission time of the document. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit inputs document submission time data to a generation AI, and the generation AI determines the priority of conversion.

[0041] The conversion unit can adjust the order of conversion based on the relevance of documents during conversion. For example, the conversion unit preferentially converts documents related to important projects. The conversion unit can also postpone documents with low relevance. For example, the conversion unit adjusts the order of conversion according to the relevance of documents. This makes it possible to adjust the order of conversion according to the relevance of documents. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit inputs document relevance data to the generation AI, and the generation AI adjusts the order of conversion.

[0042] The providing unit can select the optimal display method based on the user's past document confirmation history at the time of providing. For example, the providing unit preferentially provides a display method that the user has used in the past. The providing unit can also suggest the optimal display method from the user's past document confirmation history. For example, the providing unit provides a customized display method based on the display method that the user has used in the past. This makes it possible to select the optimal display method based on the user's past document confirmation history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit inputs the user's past document confirmation history data into the generation AI, and the generation AI selects the optimal display method.

[0043] The providing unit can adjust the level of detail of the provided document based on the importance of the document when the document is provided. For example, the providing unit provides a display method including detailed information for a document with high importance. The providing unit can also provide a simple display method for a document with low importance. For example, the providing unit provides a display method of an appropriate level according to the importance. This makes it possible to adjust the level of detail of the provided document according to the importance of the document. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit inputs the importance data of the document to the generation AI, and the generation AI adjusts the level of detail of the provided document.

[0044] The providing unit can select the optimal display method based on the device information of the user at the time of providing. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, when the user is using a smart watch, the providing unit provides a simple and highly visible display method. This makes it possible to select the optimal display method based on the device information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit inputs the device information of the user to the generation AI, and the generation AI selects the optimal display method.

[0045] The providing unit can analyze the user's social media activity at the time of providing and provide related documents. For example, the providing unit preferentially provides documents related to topics mentioned by the user on social media. The providing unit can also analyze the user's social media activity history and provide highly relevant documents. For example, the providing unit preferentially provides documents related to accounts that the user follows on social media. This makes it possible to provide related documents based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the providing unit inputs the user's social media activity data into a generation AI, which identifies and provides related documents.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reception unit can automatically determine the category of the document based on the content of the document input by the user and select an appropriate conversion algorithm. For example, the reception unit can distinguish between business documents and private messages and apply an appropriate conversion algorithm for each. The reception unit can also distinguish between technical documents and general communication documents and appropriately convert technical terms. Furthermore, the reception unit can select related expressions based on the topic of the document input by the user. This enables optimal conversion according to the content of the document.

[0048] The conversion unit can adjust the level of detail of the conversion based on the length of the user's input document. For example, a short document can be converted into concise and clear expressions. A long document can also be converted into polite expressions including detailed explanations. Furthermore, the conversion unit can apply different conversion algorithms to each paragraph of the document to provide optimal expressions according to the contents of each paragraph. This enables appropriate conversion according to the length of the document.

[0049] The reception unit can analyze the user's past document input history and select an appropriate conversion algorithm. For example, it can automatically recognize expression forms that the user has frequently used in the past and prompt the user to convert using similar forms. The reception unit can also suggest related expressions based on the topics of documents the user has used in the past. Furthermore, the reception unit can predict and suggest expression forms to be used in a specific time period from the user's past document input history. This makes it possible to perform optimal conversion based on the user's past document input history.

[0050] The reception unit can select a conversion algorithm based on the user's current project or field of interest when receiving the document. For example, only documents related to the project the user is currently working on are preferentially converted. The reception unit can also suggest relevant expressions based on the user's field of interest. Furthermore, the reception unit can adjust the document conversion based on topics in which the user has shown interest in the past. This allows for optimal conversion based on the user's current project or field of interest.

[0051] The reception unit can select a document conversion algorithm based on the geographical location information of the user. For example, when the user is in a specific area, an expression suitable for the culture and customs of that area is selected. In addition, when the user is moving, the reception unit can also suggest an expression with high relevance based on the current location. Furthermore, when the user is in a specific location, the reception unit can also preferentially select an expression related to that location. This enables optimal document conversion based on the geographical location information of the user.

[0052] The reception unit can analyze the user's social media activity and select a relevant document conversion algorithm. For example, it preferentially selects expressions related to topics mentioned by the user on social media. The reception unit can also analyze the user's social media activity history and suggest highly relevant expressions. Furthermore, the reception unit can also preferentially select expressions related to accounts the user follows on social media. This enables optimal document conversion based on the user's social media activity.

[0053] During conversion, the conversion unit can adjust the accuracy of conversion based on the importance of the document. For example, documents with high importance are converted to polite or honorific language with higher accuracy. Also, documents with low importance can be converted simply and quickly. Furthermore, the conversion unit can select an appropriate level of expression depending on the importance of the document. This enables optimal conversion according to the importance of the document.

[0054] The process flow of the first embodiment will be briefly described below.

[0055] Step 1: The reception unit accepts documents entered by the user. Documents entered by the user include business emails and private messages. The reception unit accepts input in text format, and can also accept documents using voice input. In the case of voice input, voice recognition technology is used to convert the voice input into text. Step 2: The conversion unit converts the expressions in the document accepted by the acceptance unit into polite, humble, or honorific language. The conversion is based on the content and context of the document. Generative AI can be used to convert the document into an appropriate expression format. For example, text generation AI (LLM) or multimodal generation AI can be used to analyze the content of the document and convert it into an appropriate expression format. Step 3: The providing unit provides the document converted by the converting unit. The providing method includes a method of displaying the document on the user's device, a method of sending the document by email, etc. For example, the providing unit can display the converted document on the user's smartphone or send it by email.

[0056] (Example 2) The document conversion system according to the embodiment of the present invention is a system that automatically converts a document input by a user into polite language, humble language, honorific language, etc. In this document conversion system, a user inputs a document in his / her own words, and an AI analyzes the document and converts it into a selected expression format (polite language, humble language, honorific language, etc.). The converted document is provided to the user. This mechanism allows a user to easily create a document in an appropriate expression format. For example, a user inputs a document in his / her own words. For example, the user inputs "Please tell me the time of the meeting." This document is input to the AI. Next, the AI ​​analyzes the input document. The AI ​​understands the content of the document and converts it into the selected expression format. For example, when converting a document "Please tell me the time of the meeting" into polite language, it becomes "Please tell me the time of the meeting." The converted document is provided to the user. The user can check the converted document and correct it as necessary. For example, the user checks the document "Please tell me the time of the meeting" and confirms that it is an appropriate expression. This mechanism allows a user to easily create a document in an appropriate expression format. For example, polite language can be used for colleagues within the company and honorific language can be used for business partners outside the company. In addition, the user can use humble language when speaking to a superior. This allows the user to select an appropriate expression format for the document, improving the quality of communication. This allows the document conversion system to automatically convert the document entered by the user into an appropriate expression format and provide it to the user.

[0057] The document conversion system according to the embodiment includes a reception unit, a conversion unit, and a provision unit. The reception unit accepts a document input by a user. The document input by a user includes, for example, a business email, a private message, and the like, but is not limited to such examples. The reception unit accepts, for example, the document input by the user in a text format. The reception unit can also accept a document using voice input. For example, the content input by the user is converted into text using a voice recognition technology and accepted. The conversion unit converts an expression in the document accepted by the reception unit into polite language, humble language, or honorific language using a generation AI. The conversion is performed, for example, based on the content and context of the document, but is not limited to such examples. For example, the generation AI converts the document into an appropriate expression format using a text generation AI (for example, LLM). The conversion unit can also convert the content of the document using a multimodal generation AI. For example, the generation AI analyzes the content of the document and converts it into an appropriate expression format. The provision unit provides the document converted by the conversion unit. The provision includes, for example, a method of displaying on a user's device, a method of sending by e-mail, and the like, but is not limited to such examples. For example, the provision unit displays the converted document on the user's smartphone. The provision unit can also send the converted document by e-mail. As a result, the document conversion system according to the embodiment can automatically convert the document input by the user into an appropriate expression format and provide it. A part or all of the above-mentioned processing in the conversion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the conversion unit inputs the document input by the user to the generation AI, which analyzes the document and converts it into an appropriate expression format. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the provision unit inputs the converted document to the generation AI, and the generation AI determines how to provide the document.

[0058] The reception unit receives a document input by a user. Examples of documents input by a user include, but are not limited to, business emails and private messages. The reception unit receives, for example, a document input by a user in text format. Specifically, the reception unit can directly receive text data input by a user using a keyboard. The reception unit can also receive a document using voice input. For example, the content input by the user using voice is converted into text using voice recognition technology and then accepted. For the voice recognition technology, a voice recognition model using deep learning is used, and the user's speech can be converted into text with high accuracy. Furthermore, the reception unit can also receive handwritten input, and characters handwritten using a tablet or stylus pen can be converted into text using optical character recognition (OCR) technology. This allows the user to select various input methods, improving convenience. The reception unit temporarily stores the received document and prepares it for transmission to the conversion unit. For example, the reception unit checks the format and content of the document and performs preprocessing as necessary. The preprocessing includes adjusting the format of the document, deleting unnecessary spaces, and checking grammar. This enables the conversion unit to process the document efficiently.

[0059] The conversion unit converts expressions in the document accepted by the acceptance unit into polite language, humble language, or honorific language using the generation AI. The conversion is performed, for example, based on the contents and context of the document, but is not limited to such examples. The generation AI converts the document into an appropriate expression format using a text generation AI (for example, LLM). Specifically, the generation AI analyzes the contents of the document, understands the context, and converts the document into an appropriate expression format. For example, in the case of a business email, the generation AI analyzes the contents of the document and converts the document using an appropriate honorific expression. In addition, in the case of a private message, the generation AI analyzes the contents of the document and converts the document using an appropriate polite language or humble language. The conversion unit can also convert the contents of the document using a multimodal generation AI. For example, the generation AI analyzes the contents of the document and converts it into an appropriate expression format. The multimodal generation AI can process multiple modal data such as images and voices as well as text at the same time. This makes it possible to more accurately understand the contents of the document and convert it into an appropriate expression format. The conversion unit temporarily stores the converted document and prepares it for transmission to the provision unit. For example, the format and content of the converted document are checked, and post-processing is performed as necessary, such as formatting the document, checking grammar, checking spelling, etc., to enable the document provider to provide the document efficiently.

[0060] The providing unit provides the document converted by the converting unit. The providing includes, for example, a method of displaying the document on the user's device, a method of sending the document by e-mail, and the like, but is not limited to such examples. Specifically, the providing unit displays the converted document on the user's smartphone. The converted document is displayed in real time through an application on the smartphone so that the user can check it. The providing unit can also send the converted document by e-mail. The e-mail is sent by using the SMTP protocol to send the converted document to an e-mail address specified by the user. Furthermore, the providing unit can also use a cloud storage service to store the converted document so that the user can access it at any time. For example, the converted document is uploaded to cloud storage and a sharing link is provided to the user. This allows the user to access the converted document from any device connected to the Internet. When providing the converted document, the providing unit takes security measures to protect the user's privacy. For example, encryption technology is used to protect data when sending or saving the document, preventing unauthorized access. The providing unit can also collect user feedback and use it to improve the providing method. For example, a function is provided for users to rate and comment on the provided documents, and the system is improved based on this feedback. This allows the providing unit to provide converted documents to users quickly and reliably, thereby improving user satisfaction.

[0061] The conversion unit can convert expressions in the document accepted by the acceptance unit into expressions based on the destination of the document. For example, in the case of a business email, the conversion unit converts the text into polite language or honorific language for business use. In addition, in the case of a message to a friend, the conversion unit can also convert the text into casual polite language. For example, in the case of a business email addressed to a superior, the conversion unit converts the text into a humble language. In addition, in the case of a business email addressed to a colleague, the conversion unit can also convert the text into a polite language. This allows conversion into an appropriate expression format according to the destination. Part or all of the above-mentioned processing in the conversion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the conversion unit inputs destination information into a generation AI, which analyzes the document and converts it into an appropriate expression format.

[0062] The reception unit can estimate the user's emotion and adjust the timing of receiving the document based on the estimated user's emotion. For example, when the user is stressed, the reception unit delays the timing of receiving the document so that the user can input in a relaxed state. In addition, when the user is relaxed, the reception unit can immediately receive the document and smoothly start the conversion process. For example, when the user is in a hurry, the reception unit quickly receives the document and prioritizes the conversion process. This makes it possible to adjust the timing of receiving the document according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the user's facial expression data to the generation AI, which estimates the user's emotion and adjusts the timing of receiving the document.

[0063] The reception unit can analyze the user's past document input history and select an appropriate reception method. For example, the reception unit automatically recognizes a document format that the user has frequently used in the past and prompts the user to input in a similar format. The reception unit can also preferentially suggest an input method (such as voice or text) that the user has used in the past. For example, the reception unit predicts and suggests a document format to be used in a specific time period from the user's past document input history. This makes it possible to select an optimal reception method based on the user's past document input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the user's past document input history data into the generation AI, which selects the optimal reception method.

[0064] The reception unit can filter documents based on the user's current project and field of interest when receiving the documents. For example, the reception unit preferentially receives only documents related to a project that the user is currently working on. The reception unit can also filter and receive documents that are highly relevant based on the user's field of interest. For example, the reception unit adjusts the reception of documents based on topics in which the user has shown interest in the past. This makes it possible to filter documents based on the user's current project and field of interest. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the user's project data and field of interest data to the generation AI, and the generation AI filters the documents.

[0065] The reception unit can estimate the user's emotion and determine the priority of documents to be received based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit postpones documents with low importance and preferentially accepts documents with high importance. In addition, when the user is relaxed, the reception unit can equally accept all documents. For example, when the user is in a hurry, the reception unit preferentially accepts documents with high urgency. This makes it possible to determine the priority of documents according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the user's facial expression data to the generation AI, which then estimates the user's emotion and determines the priority of documents.

[0066] The reception unit can preferentially receive documents with high relevance based on the geographical location information of the user when receiving the document. For example, when the user is in a specific area, the reception unit preferentially receives documents related to the area. In addition, when the user is moving, the reception unit can also receive documents with high relevance based on the current location. For example, when the user is in a specific location, the reception unit preferentially receives documents related to the location. This makes it possible to preferentially receive documents with high relevance based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the geographical location information of the user to the generation AI, which identifies documents with high relevance and preferentially receives them.

[0067] The reception unit can analyze the user's social media activity and receive related documents when receiving the document. For example, the reception unit preferentially receives documents related to topics mentioned by the user on social media. The reception unit can also analyze the user's social media activity history and receive highly relevant documents. For example, the reception unit preferentially receives documents related to accounts the user follows on social media. This makes it possible to receive related documents based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the user's social media activity data into the generation AI, which identifies and receives related documents.

[0068] The conversion unit can estimate the user's emotion and adjust the expression method of the conversion based on the estimated user's emotion. For example, when the user is relaxed, the conversion unit converts the expression into a polite and soft expression. In addition, when the user is nervous, the conversion unit can also convert into a concise and clear expression. For example, when the user is in a hurry, the conversion unit converts into an expression that can be quickly understood. This makes it possible to adjust the expression method of the conversion according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the conversion unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the expression method of the conversion.

[0069] The conversion unit can adjust the level of detail of the conversion based on the importance of the document during conversion. For example, the conversion unit converts a document with high importance into detailed polite language or honorific language. The conversion unit can also convert a document with low importance into concise polite language. For example, the conversion unit converts into an appropriate level of expression depending on the importance. This makes it possible to adjust the level of detail of the conversion depending on the importance of the document. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit inputs the importance data of the document to the generation AI, and the generation AI adjusts the level of detail of the conversion.

[0070] The conversion unit can apply different conversion algorithms depending on the category of the document during conversion. For example, the conversion unit applies polite language or honorific language for business use to a business document. The conversion unit can also apply appropriate polite language to a casual document. For example, the conversion unit applies strict honorific language to an official document. This makes it possible to apply an appropriate conversion algorithm depending on the category of the document. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit inputs document category data to the generation AI, which applies an appropriate conversion algorithm.

[0071] The conversion unit can estimate the user's emotion and adjust the length of the conversion based on the estimated user's emotion. For example, when the user is in a hurry, the conversion unit converts the expression into a short, to-the-point expression. Also, when the user is relaxed, the conversion unit can convert into a longer expression including detailed explanations. For example, when the user is excited, the conversion unit converts into an expression with a visually stimulating effect. This allows the length of the conversion to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the conversion unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the length of the conversion.

[0072] The conversion unit can determine the priority of conversion based on the submission time of the document during conversion. For example, the conversion unit preferentially converts documents whose deadline is approaching. The conversion unit can also postpone documents whose submission time is far away. For example, the conversion unit adjusts the priority of conversion according to the submission time. This makes it possible to determine the priority of conversion according to the submission time of the document. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit inputs document submission time data to a generation AI, and the generation AI determines the priority of conversion.

[0073] The conversion unit can adjust the order of conversion based on the relevance of documents during conversion. For example, the conversion unit preferentially converts documents related to important projects. The conversion unit can also postpone documents with low relevance. For example, the conversion unit adjusts the order of conversion according to the relevance of documents. This makes it possible to adjust the order of conversion according to the relevance of documents. A part or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit inputs document relevance data to the generation AI, and the generation AI adjusts the order of conversion.

[0074] The providing unit can estimate the user's emotion and adjust the display method of the document to be provided based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides a simple and highly visible display method. In addition, when the user is relaxed, the providing unit can also provide a display method including detailed information. For example, when the user is in a hurry, the providing unit provides a display method that focuses on the main points. This makes it possible to adjust the display method of the document according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the display method of the document.

[0075] The providing unit can select the optimal display method based on the user's past document confirmation history at the time of providing. For example, the providing unit preferentially provides a display method that the user has used in the past. The providing unit can also suggest the optimal display method from the user's past document confirmation history. For example, the providing unit provides a customized display method based on the display method that the user has used in the past. This makes it possible to select the optimal display method based on the user's past document confirmation history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit inputs the user's past document confirmation history data into the generation AI, and the generation AI selects the optimal display method.

[0076] The providing unit can adjust the level of detail of the provided document based on the importance of the document when the document is provided. For example, the providing unit provides a display method including detailed information for a document with high importance. The providing unit can also provide a simple display method for a document with low importance. For example, the providing unit provides a display method of an appropriate level according to the importance. This makes it possible to adjust the level of detail of the provided document according to the importance of the document. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit inputs the importance data of the document to the generation AI, and the generation AI adjusts the level of detail of the provided document.

[0077] The providing unit can estimate the user's emotion and determine the priority order of documents to be provided based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit postpones documents with low importance and provides documents with high importance preferentially. In addition, when the user is relaxed, the providing unit can provide all documents equally. For example, when the user is in a hurry, the providing unit provides documents with high urgency preferentially. This makes it possible to determine the priority order of documents according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit inputs the user's emotion data into the generation AI, and the generation AI determines the priority order of documents.

[0078] The providing unit can select the optimal display method based on the device information of the user at the time of providing. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, when the user is using a smart watch, the providing unit provides a simple and highly visible display method. This makes it possible to select the optimal display method based on the device information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit inputs the device information of the user to the generation AI, and the generation AI selects the optimal display method.

[0079] The providing unit can analyze the user's social media activity at the time of providing and provide related documents. For example, the providing unit preferentially provides documents related to topics mentioned by the user on social media. The providing unit can also analyze the user's social media activity history and provide highly relevant documents. For example, the providing unit preferentially provides documents related to accounts that the user follows on social media. This makes it possible to provide related documents based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the providing unit inputs the user's social media activity data into a generation AI, which identifies and provides related documents.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The reception unit can automatically determine the category of the document based on the content of the document input by the user and select an appropriate conversion algorithm. For example, the reception unit can distinguish between business documents and private messages and apply an appropriate conversion algorithm for each. The reception unit can also distinguish between technical documents and general communication documents and appropriately convert technical terms. Furthermore, the reception unit can select related expressions based on the topic of the document input by the user. This enables optimal conversion according to the content of the document.

[0082] The conversion unit can adjust the level of detail of the conversion based on the length of the user's input document. For example, a short document can be converted into concise and clear expressions. A long document can also be converted into polite expressions including detailed explanations. Furthermore, the conversion unit can apply different conversion algorithms to each paragraph of the document to provide optimal expressions according to the contents of each paragraph. This enables appropriate conversion according to the length of the document.

[0083] The reception unit can estimate the user's emotions and adjust the method of receiving documents based on the estimated user's emotions. For example, if the user is stressed, voice input can be given priority. Also, if the user is relaxed, text input can be given priority. Furthermore, the reception unit can change the design of the input interface according to the user's emotions, allowing the user to input documents comfortably. This makes it possible to optimally receive documents according to the user's emotions.

[0084] The reception unit can analyze the user's past document input history and select an appropriate conversion algorithm. For example, it can automatically recognize expression forms that the user has frequently used in the past and prompt the user to convert using similar forms. The reception unit can also suggest related expressions based on the topics of documents the user has used in the past. Furthermore, the reception unit can predict and suggest expression forms to be used in a specific time period from the user's past document input history. This makes it possible to perform optimal conversion based on the user's past document input history.

[0085] The reception unit can select a conversion algorithm based on the user's current project or field of interest when receiving the document. For example, only documents related to the project the user is currently working on are preferentially converted. The reception unit can also suggest relevant expressions based on the user's field of interest. Furthermore, the reception unit can adjust the document conversion based on topics in which the user has shown interest in the past. This allows for optimal conversion based on the user's current project or field of interest.

[0086] The reception unit can estimate the user's emotions and customize the document reception interface based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible interface can be provided. Also, if the user is relaxed, an interface including detailed information can be provided. Furthermore, if the user is in a hurry, the reception unit can provide an interface that allows quick input. This enables an optimal document reception interface according to the user's emotions.

[0087] The reception unit can select a document conversion algorithm based on the geographical location information of the user. For example, when the user is in a specific area, an expression suitable for the culture and customs of that area is selected. In addition, when the user is moving, the reception unit can also suggest an expression with high relevance based on the current location. Furthermore, when the user is in a specific location, the reception unit can also preferentially select an expression related to that location. This enables optimal document conversion based on the geographical location information of the user.

[0088] The reception unit can analyze the user's social media activity and select a relevant document conversion algorithm. For example, it preferentially selects expressions related to topics mentioned by the user on social media. The reception unit can also analyze the user's social media activity history and suggest highly relevant expressions. Furthermore, the reception unit can also preferentially select expressions related to accounts the user follows on social media. This enables optimal document conversion based on the user's social media activity.

[0089] The conversion unit can estimate the user's emotion and adjust the tone of the conversion based on the estimated user's emotion. For example, if the user is relaxed, the tone can be converted to a soft and friendly tone. If the user is nervous, the tone can be converted to a formal and clear tone. Furthermore, if the user is in a hurry, the conversion unit can convert to a concise tone that can be quickly understood. This makes it possible to convert documents in an optimal tone according to the user's emotion.

[0090] During conversion, the conversion unit can adjust the accuracy of conversion based on the importance of the document. For example, documents with high importance are converted to polite or honorific language with higher accuracy. Also, documents with low importance can be converted simply and quickly. Furthermore, the conversion unit can select an appropriate level of expression depending on the importance of the document. This enables optimal conversion according to the importance of the document.

[0091] The process flow of the second embodiment will be briefly described below.

[0092] Step 1: The reception unit accepts documents entered by the user. Documents entered by the user include business emails and private messages. The reception unit accepts input in text format, and can also accept documents using voice input. In the case of voice input, voice recognition technology is used to convert the voice input into text. Step 2: The conversion unit converts the expressions in the document accepted by the acceptance unit into polite, humble, or honorific language. The conversion is based on the content and context of the document. Generative AI can be used to convert the document into an appropriate expression format. For example, text generation AI (LLM) or multimodal generation AI can be used to analyze the content of the document and convert it into an appropriate expression format. Step 3: The providing unit provides the document converted by the converting unit. The providing method includes a method of displaying the document on the user's device, a method of sending the document by email, etc. For example, the providing unit can display the converted document on the user's smartphone or send it by email.

[0093] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0095] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0096] For example, the reception unit is realized by the reception device 38 of the smart device 14. For example, the document input by the user is received in text format. For example, when receiving a document using voice input, the voice is converted into text using the microphone 38B of the smart device 14. The conversion unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and converts the document into an appropriate expression format using a generation AI. The provision unit is realized by, for example, the output device 40 of the smart device 14, and displays the converted document on the user's device. In addition, the provision unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and can also send the converted document by email. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0100] 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.

[0101] 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 the voice according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0103] 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 performed in a secure state.

[0104] Fig. 4 shows an example of 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.

[0105] 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.

[0106] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0109] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0110] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0111] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] For example, the reception unit is realized by the microphone 238 of the smart glasses 214. For example, the reception unit receives a document input by a user in text format. For example, when receiving a document using voice input, the microphone 238 of the smart glasses 214 is used to convert the voice into text. The conversion unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and converts the document into an appropriate expression format using a generation AI. The provision unit is realized by, for example, the speaker 240 of the smart glasses 214, and displays the converted document on the user's device. In addition, the provision unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and can also send the converted document by email. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0116] 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.

[0117] 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 the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] 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 performed in a secure state.

[0120] 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.

[0121] 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.

[0122] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] 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 voice indicating a user input for 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.

[0126] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] For example, the reception unit is realized by the microphone 238 of the headset type terminal 314. For example, the reception unit receives a document input by a user in text format. For example, when receiving a document using voice input, the voice is converted into text using the microphone 238 of the headset type terminal 314. The conversion unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and converts the document into an appropriate expression format using a generation AI. The provision unit is realized by, for example, the display 343 of the headset type terminal 314, and displays the converted document on the user's device. In addition, the provision unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and can also send the converted document by email. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0132] 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.

[0133] 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 the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0135] 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 performed in a secure state.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0137] 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.

[0138] 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.

[0139] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 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.

[0141] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] 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 a voice indicating a user input for 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.

[0143] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0145] For example, the reception unit is realized by the microphone 238 of the robot 414. For example, the reception unit receives a document input by a user in text format. For example, when receiving a document using voice input, the voice is converted into text using the microphone 238 of the robot 414. The conversion unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and converts the document into an appropriate expression format using a generation AI. The provision unit is realized by, for example, the speaker 240 of the robot 414, and displays the converted document on the user's device. In addition, the provision unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and can also send the converted document by email. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0147] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0149] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0151] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0153] 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.

[0154] 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 Universal Serial Bus (USB) 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.

[0155] In addition, 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 upon request from the data processing device 12.

[0156] 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.

[0157] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0158] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0159] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0160] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0162] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0164] (Appendix 1) a reception unit for receiving a document input by a user; a conversion unit that converts an expression in the document accepted by the acceptance unit into at least one of polite language, humble language, and honorific language; a providing unit that provides the document converted by the converting unit. A system characterized by: (Appendix 2) The conversion unit is The expression in the document accepted by the accepting unit is converted into an expression based on the destination of the document. 2. The system of claim 1, (Appendix 3) The reception unit is To estimate a user's emotion and adjust the timing of document acceptance based on the estimated user's emotion. 2. The system of claim 1, (Appendix 4) The reception unit is Analyze the user's past document input history and select the appropriate reception method 2. The system of claim 1, (Appendix 5) The reception unit is Filter documents as they arrive based on the user's current projects and interests 2. The system of claim 1, (Appendix 6) The reception unit is Estimate user sentiment and prioritize documents to be accepted based on the estimated user sentiment 2. The system of claim 1, (Appendix 7) The reception unit is When accepting documents, prioritize acceptance of documents that are highly relevant based on the user's geographic location information 2. The system of claim 1, (Appendix 8) The reception unit is When accepting documents, analyze the user's social media activity and accept related documents. 2. The system of claim 1, (Appendix 9) The conversion unit is Infer user emotions and adjust the way the transformation is expressed based on the estimated user emotions 2. The system of claim 1, (Appendix 10) The conversion unit is During conversion, adjust conversion detail based on the importance of the document 2. The system of claim 1, (Appendix 11) The conversion unit is During conversion, apply different conversion algorithms depending on the document category 2. The system of claim 1, (Appendix 12) The conversion unit is Inferring a user's emotion and adjusting the length of the transformation based on the estimated user's emotion 2. The system of claim 1, (Appendix 13) The conversion unit is During conversion, prioritize conversions based on when documents were submitted 2. The system of claim 1, (Appendix 14) The conversion unit is During conversion, adjust the conversion order based on document relevance 2. The system of claim 1, (Appendix 15) The providing unit is To estimate a user's emotion and adjust a method of displaying a document based on the estimated user's emotion. 2. The system of claim 1, (Appendix 16) The providing unit is When providing the document, the optimal display method is selected based on the user's past document confirmation history. 2. The system of claim 1, (Appendix 17) The providing unit is When delivering, adjust the level of detail of the delivery based on the importance of the document 2. The system of claim 1, (Appendix 18) The providing unit is Inferring user sentiment and prioritizing documents to be provided based on the estimated user sentiment 2. The system of claim 1, (Appendix 19) The providing unit is When providing the content, the optimal display method is selected based on the user's device information. 2. The system of claim 1, (Appendix 20) The providing unit is When provided, analyze your social media activity and provide you with relevant documentation 2. The system of claim 1, [Explanation of symbols]

[0165] 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 reception unit for receiving a document input by a user; a conversion unit that converts an expression in the document accepted by the acceptance unit into at least one of polite language, humble language, and honorific language; a providing unit that provides the document converted by the converting unit. A system characterized by:

2. The conversion unit is The expression in the document accepted by the accepting unit is converted into an expression based on the destination of the document.

2. The system of claim 1 .

3. The reception unit is To estimate a user's emotion and adjust the timing of document acceptance based on the estimated user's emotion.

2. The system of claim 1 .

4. The reception unit is Analyze the user's past document input history and select the appropriate reception method 2. The system of claim 1 .

5. The reception unit is Filter documents as they arrive based on the user's current projects and interests 2. The system of claim 1 .

6. The reception unit is Estimate user sentiment and prioritize documents to be accepted based on the estimated user sentiment 2. The system of claim 1 .

7. The reception unit is When accepting documents, prioritize acceptance of documents that are highly relevant based on the user's geographic location information 2. The system of claim 1 .

8. The reception unit is When accepting documents, analyze the user's social media activity and accept related documents.

2. The system of claim 1 .

9. The conversion unit is Infer user emotions and adjust the way the transformation is expressed based on the estimated user emotions 2. The system of claim 1 .

10. The conversion unit is During conversion, adjust conversion detail based on the importance of the document 2. The system of claim 1 .

Images