System

Abstract

An object of a system according to an embodiment is to quickly and accurately extract an issue and an expected value from a document and improve work efficiency.SOLUTION: A system includes a document analysis part, an issue extraction part, an expected value extraction part, and a display part. The document analysis unit analyzes a document. The discussion point extraction part extracts a discussion point from the document analyzed by the document analysis part. The expected value extraction unit extracts an expected value from the discussion point extracted by the discussion point extraction unit. The display unit displays the discussion points and the expected values extracted by the expected value extraction 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to quickly and accurately extract the key points and expectations from documents, posing challenges to improving work efficiency.

[0005] The system according to the embodiment aims to quickly and accurately extract issues and expectations from documents, thereby improving business efficiency. [Means for solving the problem]

[0006] The system according to the embodiment includes a document analysis unit, an issue extraction unit, an expected value extraction unit, and a display unit. The document analysis unit analyzes a document. The issue extraction unit extracts issues from the document analyzed by the document analysis unit. The expected value extraction unit extracts expected values ​​from the issues extracted by the issue extraction unit. The display unit displays the issues and expected values ​​extracted by the expected value extraction unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately extract issues and expectations from documents, thereby improving business efficiency. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The business efficiency improvement system according to the embodiment of the present invention analyzes documents, and a generation AI extracts key points and expected values, which are then automatically processed and displayed using a no-code tool. This allows the business efficiency system to instantly extract key points and expected values ​​from documents, supporting rapid decision-making.

[0029] A business efficiency improvement system according to an embodiment includes a document analysis unit, an issue extraction unit, an expected value extraction unit, and a display unit. The document analysis unit analyzes documents. For example, the document analysis unit analyzes documents such as meeting minutes and reports to extract important information. The document analysis unit analyzes documents using a generation AI to extract important information. The issue extraction unit extracts issues from documents analyzed by the document analysis unit. For example, the issue extraction unit extracts main topics and themes from documents using a generation AI. The issue extraction unit analyzes the content of documents using natural language processing technology to identify important issues. The expected value extraction unit extracts expected values ​​from the issues extracted by the issue extraction unit. For example, the expected value extraction unit extracts expected results and goals from documents using a generation AI. The expected value extraction unit analyzes the content of documents using a machine learning algorithm to identify expected values. The display unit displays the issues and expected values ​​extracted by the expected value extraction unit. For example, the display unit displays information extracted using a no-code tool on a dashboard. By visually displaying information, the display unit allows users to grasp important information at a glance. The display unit automatically processes and displays information extracted by the generation AI. As a result, the business efficiency system according to the embodiment can achieve business efficiency by quickly extracting and displaying key points and expectations from documents. For example, by analyzing meeting minutes, extracting key points and expectations, and displaying them on a dashboard, the content of the meeting can be quickly understood and the next action can be decided. In addition, by analyzing the content of a report, extracting and displaying expected results, the progress of a project can be efficiently managed.

[0030] The document analysis unit learns the document author's past writing style and tone, enabling more accurate extraction of talking points and expectations. For example, the document analysis unit allows the generative AI to learn the author's past writing style and analyze the document based on that style. For example, it identifies the phrases and expressions the author frequently uses and extracts talking points and expectations based on those. The document analysis unit also analyzes the tone of the document to more accurately understand the author's intention. For example, it distinguishes between formal and casual tones and extracts appropriate talking points and expectations for each. The document analysis unit also stores the author's past documents in a database, and the generative AI learns the writing style based on that. For example, it analyzes past minutes and reports to understand the author's characteristics. This allows for more accurate analysis by learning the author's style and tone.

[0031] The document analysis unit can extract issues and expected values ​​based on the document's background information, taking into account hidden metadata within the document. For example, the document analysis unit analyzes the creation date and time of the document to extract issues and expected values ​​by taking into account the situation and background at that time. For example, it reflects important events and trends related to a specific period. The document analysis unit also analyzes the editing history to extract issues and expected values ​​based on changes and revisions to the document. For example, it focuses on analyzing frequently revised parts. The document analysis unit also analyzes metadata within the document to more accurately understand the intent of the author or editor. For example, it performs analysis taking into account document version information and comments. This allows for more accurate extraction of issues and expected values ​​by taking into account the document's background information.

[0032] The document analysis unit can handle different input formats, such as voice input or handwritten input, and perform multimodal analysis. For example, the document analysis unit analyzes voice input and extracts key points and expected values ​​from the voice data. For example, it can analyze recorded meeting data to identify important topics and expected outcomes. The document analysis unit can also analyze handwritten input and recognize handwritten characters to extract key points and expected values. For example, it can analyze handwritten notes and notebooks to extract important information. The document analysis unit can also integrate voice input and handwritten input to perform multimodal analysis. For example, it can simultaneously analyze voice data and handwritten notes to extract information related to each other. This allows for multimodal analysis by handling different input formats.

[0033] The document analysis unit can simultaneously analyze documents in different languages ​​and extract issues and expectations in multiple languages. For example, the document analysis unit can simultaneously analyze documents in different languages ​​and extract issues and expectations in multiple languages. For example, it can simultaneously analyze documents in English and Japanese and identify common issues. The document analysis unit can also analyze documents in different languages ​​using multilingual generative AI and extract issues and expectations taking into account differences between the languages. For example, it can perform analysis taking cultural background into account. The document analysis unit can also translate documents in different languages ​​and perform analysis based on the translation results. For example, it can translate an English document into Japanese and extract issues and expectations based on the translation results. This makes it possible to extract issues and expectations in multiple languages ​​by analyzing documents in different languages.

[0034] The display unit can learn the user's past operation history and automatically suggest the optimal display format. For example, the display unit analyzes the user's past operation history and suggests the optimal display format based on that data. For example, it suggests a new display format based on the layout of a dashboard used in the past. The display unit also builds a system that learns the operation history and automatically selects the display format preferred by the user. For example, it prioritizes displaying widgets and graphs that the user uses frequently. The display unit also suggests a customized display format based on the user's operation history. For example, it automatically generates a layout that highlights specific data points. In this way, it can automatically suggest the optimal display format by learning the user's operation history.

[0035] The display unit can provide a display optimized for different devices. For example, the display unit builds a system that automatically selects a display format optimized for different devices. For example, it provides a compact layout for smartphones and a wide layout for tablets. The display unit also detects the type of device and automatically applies a display format appropriate for that device. For example, it provides different display formats for desktops and mobile devices. The display unit also employs a responsive design that supports different devices, allowing users to comfortably view information on any device. For example, it adjusts the layout according to the screen size. This allows displays optimized for different devices to allow users to comfortably view information on any device.

[0036] The display unit can also link with other business tools to achieve centralized information management. For example, the display unit can link the issues and expected values ​​extracted by the generation AI to a project management tool to centrally manage the project's progress. For example, it can automatically update task progress and deadlines. The display unit can also link with a calendar app to reflect the extracted issues and expected values ​​in the schedule. For example, it can automatically add meeting agendas and expected results to the calendar. The display unit can also link with other business tools to build a system that achieves centralized information management. For example, it can link with a CRM system or ERP system to integrate customer information and inventory information. This makes it possible to centrally manage information by linking with other business tools.

[0037] The display unit provides templates that can be customized by the user, allowing for displays tailored to individual needs. The display unit, for example, builds a system that provides templates that can be customized by the user, allowing for displays tailored to individual needs. For example, it provides templates whose layout and design the user can freely change. The display unit also uses customizable templates to highlight information that the user needs. For example, it positions specific data points or graphs in a prominent position. The display unit also develops a system that automatically customizes templates according to the user's needs. For example, it suggests the optimal template based on past operation history. As a result, by providing templates that can be customized by the user, it becomes possible to display information tailored to individual needs.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The business efficiency improvement system can further include a display unit that learns the user's past operation history and automatically suggests the optimal display format. The display unit, for example, analyzes the user's past operation history and suggests the optimal display format based on that data. For example, it suggests a new display format based on the layout of a dashboard used in the past. The display unit also builds a system that learns the operation history and automatically selects the display format preferred by the user. For example, it prioritizes displaying widgets and graphs that the user uses frequently. The display unit also suggests a customized display format based on the user's operation history. For example, it automatically generates a layout that highlights specific data points. In this way, it is possible to automatically suggest the optimal display format by learning the user's operation history.

[0040] The business efficiency improvement system can further include a display unit that performs display optimized for different devices. The display unit, for example, constructs a system that automatically selects a display format optimized for different devices. For example, it provides a compact layout for smartphones and a wide layout for tablets. The display unit also detects the type of device and automatically applies a display format appropriate for that device. For example, it provides different display formats for desktops and mobile devices. The display unit also employs a responsive design that supports different devices, allowing users to comfortably view information on any device. For example, it adjusts the layout according to the screen size. This allows displays optimized for different devices, allowing users to comfortably view information on any device.

[0041] The business efficiency system can also be equipped with a display unit that links with other business tools and realizes centralized information management. For example, the display unit links the issues and expectations extracted by the generation AI to a project management tool to centrally manage the project's progress. For example, it automatically updates task progress and deadlines. The display unit also links with a calendar app to reflect the extracted issues and expectations in the schedule. For example, it automatically adds meeting agendas and expected results to the calendar. The display unit also links with other business tools to build a system that realizes centralized information management. For example, it links with a CRM system or ERP system to integrate customer information and inventory information. This allows for centralized information management by linking with other business tools.

[0042] The business efficiency system can further include a display unit that provides user-customizable templates and displays information according to individual needs. The display unit, for example, provides user-customizable templates and builds a system that displays information according to individual needs. For example, it provides templates whose layout and design the user can freely change. The display unit also uses the customizable templates to highlight information the user needs. For example, it positions specific data points or graphs in a prominent position. The display unit also develops a system that automatically customizes templates according to the user's needs. For example, it suggests the optimal template based on past operation history. In this way, by providing user-customizable templates, it becomes possible to display information according to individual needs.

[0043] The business efficiency system can also be equipped with a document analysis unit that learns the user's past writing style and tone to extract key points and expectations with greater accuracy. For example, the document analysis unit uses a generation AI to learn the author's past writing style and analyze the document based on that style. For example, it identifies the phrases and expressions the author frequently uses and extracts key points and expectations based on those. The document analysis unit also analyzes the tone of the document to more accurately understand the author's intention. For example, it distinguishes between formal and casual tones and extracts key points and expectations appropriate for each. The document analysis unit also stores the author's past documents in a database, and the generation AI learns the writing style based on that. For example, it analyzes past minutes and reports to understand the author's characteristics. This allows for more accurate analysis by learning the author's style and tone.

[0044] The business efficiency improvement system can further include a document analysis unit that takes into account hidden metadata within a document and extracts issues and expected values ​​based on the document's background information. The document analysis unit, for example, analyzes the creation date and time of a document and extracts issues and expected values ​​by taking into account the situation and background at that time. For example, it reflects important events and trends related to a specific period. The document analysis unit also analyzes the editing history and extracts issues and expected values ​​based on changes and revisions to the document. For example, it focuses its analysis on frequently revised parts. The document analysis unit also analyzes metadata within a document to more accurately grasp the intent of the author or editor. For example, it performs analysis taking into account document version information and comments. This allows for more accurate extraction of issues and expected values ​​by taking into account the document's background information.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The document analysis unit analyzes the document. For example, the document analysis unit analyzes documents such as meeting minutes or reports and extracts important information. The document analysis unit uses generative AI to analyze the document and extract important information. Step 2: The issue extraction unit extracts issues from the documents analyzed by the document analysis unit. For example, the issue extraction unit uses generative AI to extract key topics and themes from the documents. The issue extraction unit uses natural language processing technology to analyze the content of the documents and identify important issues. Step 3: The expected value extraction unit extracts expected values ​​from the issues extracted by the issue extraction unit. For example, the expected value extraction unit uses generative AI to extract expected outcomes and goals from the document. The expected value extraction unit uses a machine learning algorithm to analyze the content of the document and identify expected values. Step 4: The display unit displays the issues and expectations extracted by the expectation extraction unit. For example, the display unit displays information extracted using a no-code tool on a dashboard. By visually displaying the information, the display unit allows users to grasp important information at a glance. The display unit automatically processes and displays the information extracted by the generation AI.

[0047] (Example 2) The business efficiency improvement system according to the embodiment of the present invention analyzes documents, and a generation AI extracts key points and expected values, which are then automatically processed and displayed using a no-code tool. This allows the business efficiency system to instantly extract key points and expected values ​​from documents, supporting rapid decision-making.

[0048] A business efficiency improvement system according to an embodiment includes a document analysis unit, an issue extraction unit, an expected value extraction unit, and a display unit. The document analysis unit analyzes documents. For example, the document analysis unit analyzes documents such as meeting minutes and reports to extract important information. The document analysis unit analyzes documents using a generation AI to extract important information. The issue extraction unit extracts issues from documents analyzed by the document analysis unit. For example, the issue extraction unit extracts main topics and themes from documents using a generation AI. The issue extraction unit analyzes the content of documents using natural language processing technology to identify important issues. The expected value extraction unit extracts expected values ​​from the issues extracted by the issue extraction unit. For example, the expected value extraction unit extracts expected results and goals from documents using a generation AI. The expected value extraction unit analyzes the content of documents using a machine learning algorithm to identify expected values. The display unit displays the issues and expected values ​​extracted by the expected value extraction unit. For example, the display unit displays information extracted using a no-code tool on a dashboard. By visually displaying information, the display unit allows users to grasp important information at a glance. The display unit automatically processes and displays information extracted by the generation AI. As a result, the business efficiency system according to the embodiment can achieve business efficiency by quickly extracting and displaying key points and expectations from documents. For example, by analyzing meeting minutes, extracting key points and expectations, and displaying them on a dashboard, the content of the meeting can be quickly understood and the next action can be decided. In addition, by analyzing the content of a report, extracting and displaying expected results, the progress of a project can be efficiently managed.

[0049] The document analysis unit learns the document author's past writing style and tone, enabling more accurate extraction of talking points and expectations. For example, the document analysis unit allows the generative AI to learn the author's past writing style and analyze the document based on that style. For example, it identifies the phrases and expressions the author frequently uses and extracts talking points and expectations based on those. The document analysis unit also analyzes the tone of the document to more accurately understand the author's intention. For example, it distinguishes between formal and casual tones and extracts appropriate talking points and expectations for each. The document analysis unit also stores the author's past documents in a database, and the generative AI learns the writing style based on that. For example, it analyzes past minutes and reports to understand the author's characteristics. This allows for more accurate analysis by learning the author's style and tone.

[0050] The document analysis unit can extract issues and expected values ​​based on the document's background information, taking into account hidden metadata within the document. For example, the document analysis unit analyzes the creation date and time of the document to extract issues and expected values ​​by taking into account the situation and background at that time. For example, it reflects important events and trends related to a specific period. The document analysis unit also analyzes the editing history to extract issues and expected values ​​based on changes and revisions to the document. For example, it focuses on analyzing frequently revised parts. The document analysis unit also analyzes metadata within the document to more accurately understand the intent of the author or editor. For example, it performs analysis taking into account document version information and comments. This allows for more accurate extraction of issues and expected values ​​by taking into account the document's background information.

[0051] The document analysis unit can use the emotion estimation function to analyze the emotional nuances in a document and extract emotionally important issues and expectations. The document analysis unit, for example, analyzes emotional expressions in a document and calculates an emotion score. For example, it identifies positive and negative expressions and extracts issues and expectations based on them. The document analysis unit also uses the emotion estimation function to analyze the emotional nuances in a document and emphasizes emotionally important parts. For example, it prioritizes analysis of parts with high emotional intensity. The document analysis unit also analyzes the emotional tone of a document and extracts issues and expectations that are likely to resonate emotionally. For example, it identifies expressions that elicit emotional resonation and performs analysis based on those. In this way, by analyzing emotional nuances, it is possible to extract emotionally important issues and expectations.

[0052] The document analysis unit can handle different input formats, such as voice input or handwritten input, and perform multimodal analysis. For example, the document analysis unit analyzes voice input and extracts key points and expected values ​​from the voice data. For example, it can analyze recorded meeting data to identify important topics and expected outcomes. The document analysis unit can also analyze handwritten input and recognize handwritten characters to extract key points and expected values. For example, it can analyze handwritten notes and notebooks to extract important information. The document analysis unit can also integrate voice input and handwritten input to perform multimodal analysis. For example, it can simultaneously analyze voice data and handwritten notes to extract information related to each other. This allows for multimodal analysis by handling different input formats.

[0053] The document analysis unit can simultaneously analyze documents in different languages ​​and extract issues and expectations in multiple languages. For example, the document analysis unit can simultaneously analyze documents in different languages ​​and extract issues and expectations in multiple languages. For example, it can simultaneously analyze documents in English and Japanese and identify common issues. The document analysis unit can also analyze documents in different languages ​​using multilingual generative AI and extract issues and expectations taking into account differences between the languages. For example, it can perform analysis taking cultural background into account. The document analysis unit can also translate documents in different languages ​​and perform analysis based on the translation results. For example, it can translate an English document into Japanese and extract issues and expectations based on the translation results. This makes it possible to extract issues and expectations in multiple languages ​​by analyzing documents in different languages.

[0054] The document analysis unit can analyze the emotions of a user when entering a document in real time and provide feedback to elicit positive emotions. For example, the document analysis unit uses an emotion estimation function to analyze emotions in real time when the user is entering a document. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The document analysis unit also provides feedback that encourages the user to have positive emotions based on the emotion estimation data. For example, it displays encouraging messages or positive comments. The document analysis unit also monitors the user's emotional responses in real time and builds a system that provides emotionally positive feedback. For example, it makes positive suggestions when the user's emotions are negative. In this way, the user's emotions are analyzed in real time and positive feedback is provided, thereby improving user satisfaction.

[0055] The display unit can learn the user's past operation history and automatically suggest the optimal display format. For example, the display unit analyzes the user's past operation history and suggests the optimal display format based on that data. For example, it suggests a new display format based on the layout of a dashboard used in the past. The display unit also builds a system that learns the operation history and automatically selects the display format preferred by the user. For example, it prioritizes displaying widgets and graphs that the user uses frequently. The display unit also suggests a customized display format based on the user's operation history. For example, it automatically generates a layout that highlights specific data points. In this way, it can automatically suggest the optimal display format by learning the user's operation history.

[0056] The display unit can provide a display optimized for different devices. For example, the display unit builds a system that automatically selects a display format optimized for different devices. For example, it provides a compact layout for smartphones and a wide layout for tablets. The display unit also detects the type of device and automatically applies a display format appropriate for that device. For example, it provides different display formats for desktops and mobile devices. The display unit also employs a responsive design that supports different devices, allowing users to comfortably view information on any device. For example, it adjusts the layout according to the screen size. This allows displays optimized for different devices to allow users to comfortably view information on any device.

[0057] The display unit can use the emotion estimation function to analyze the emotions a user feels toward the displayed information and propose a display format that is likely to resonate emotionally. The display unit, for example, analyzes the emotions a user feels toward the displayed information in real time and proposes a display format that is likely to resonate emotionally based on the data. For example, it selects colors and designs that evoke positive emotions. The display unit also builds a system that automatically selects a display format that is likely to resonate emotionally with the user based on the emotion estimation data. For example, it prioritizes displaying designs with high emotion scores. The display unit also analyzes the user's emotional response and proposes a display format that is likely to resonate emotionally. For example, it provides a layout that highlights parts with high emotional intensity. In this way, by analyzing the user's emotions and proposing a display format that is likely to resonate emotionally, user satisfaction is improved.

[0058] The display unit can also link with other business tools to achieve centralized information management. For example, the display unit can link the issues and expected values ​​extracted by the generation AI to a project management tool to centrally manage the project's progress. For example, it can automatically update task progress and deadlines. The display unit can also link with a calendar app to reflect the extracted issues and expected values ​​in the schedule. For example, it can automatically add meeting agendas and expected results to the calendar. The display unit can also link with other business tools to build a system that achieves centralized information management. For example, it can link with a CRM system or ERP system to integrate customer information and inventory information. This makes it possible to centrally manage information by linking with other business tools.

[0059] The display unit provides templates that can be customized by the user, allowing for displays tailored to individual needs. The display unit, for example, builds a system that provides templates that can be customized by the user, allowing for displays tailored to individual needs. For example, it provides templates whose layout and design the user can freely change. The display unit also uses customizable templates to highlight information that the user needs. For example, it positions specific data points or graphs in a prominent position. The display unit also develops a system that automatically customizes templates according to the user's needs. For example, it suggests the optimal template based on past operation history. As a result, by providing templates that can be customized by the user, it becomes possible to display information tailored to individual needs.

[0060] The display unit can use the emotion estimation function to monitor in real time the emotions a user feels toward the displayed information and provide interactive feedback to elicit positive emotions. The display unit, for example, monitors in real time the emotions a user feels toward the displayed information and provides feedback to elicit positive emotions based on the data. For example, an encouraging message is displayed if the user's emotion score is low. The display unit also builds a system that provides interactive feedback to encourage a user to feel positive emotions based on the emotion estimation data. For example, the display content is dynamically changed depending on the user's emotions. The display unit also analyzes the user's emotional response in real time and provides feedback to elicit positive emotions. For example, positive comments or advice is displayed if the emotion score is high. In this way, the user's emotions are monitored in real time and positive feedback is provided, thereby improving user satisfaction.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The business efficiency improvement system can further include a display unit that learns the user's past operation history and automatically suggests the optimal display format. The display unit, for example, analyzes the user's past operation history and suggests the optimal display format based on that data. For example, it suggests a new display format based on the layout of a dashboard used in the past. The display unit also builds a system that learns the operation history and automatically selects the display format preferred by the user. For example, it prioritizes displaying widgets and graphs that the user uses frequently. The display unit also suggests a customized display format based on the user's operation history. For example, it automatically generates a layout that highlights specific data points. In this way, it is possible to automatically suggest the optimal display format by learning the user's operation history.

[0063] The business efficiency improvement system can further include a display unit that performs display optimized for different devices. The display unit, for example, constructs a system that automatically selects a display format optimized for different devices. For example, it provides a compact layout for smartphones and a wide layout for tablets. The display unit also detects the type of device and automatically applies a display format appropriate for that device. For example, it provides different display formats for desktops and mobile devices. The display unit also employs a responsive design that supports different devices, allowing users to comfortably view information on any device. For example, it adjusts the layout according to the screen size. This allows displays optimized for different devices, allowing users to comfortably view information on any device.

[0064] The business efficiency improvement system can further include a display unit that uses an emotion estimation function to analyze the emotions a user feels toward displayed information and proposes a display format that is likely to resonate emotionally. The display unit, for example, analyzes the emotions a user feels toward displayed information in real time and proposes a display format that is likely to resonate emotionally based on the data. For example, it selects colors and designs that evoke positive emotions. The display unit also builds a system that automatically selects a display format that is likely to resonate emotionally with the user based on the emotion estimation data. For example, it prioritizes displaying designs with high emotion scores. The display unit also analyzes the user's emotional response and proposes a display format that is likely to resonate emotionally. For example, it provides a layout that highlights parts with high emotional intensity. In this way, by analyzing the user's emotions and proposing a display format that is likely to resonate emotionally, user satisfaction is improved.

[0065] The business efficiency system can also be equipped with a display unit that links with other business tools and realizes centralized information management. For example, the display unit links the issues and expectations extracted by the generation AI to a project management tool to centrally manage the project's progress. For example, it automatically updates task progress and deadlines. The display unit also links with a calendar app to reflect the extracted issues and expectations in the schedule. For example, it automatically adds meeting agendas and expected results to the calendar. The display unit also links with other business tools to build a system that realizes centralized information management. For example, it links with a CRM system or ERP system to integrate customer information and inventory information. This allows for centralized information management by linking with other business tools.

[0066] The business efficiency system can further include a display unit that provides user-customizable templates and displays information according to individual needs. The display unit, for example, provides user-customizable templates and builds a system that displays information according to individual needs. For example, it provides templates whose layout and design the user can freely change. The display unit also uses the customizable templates to highlight information the user needs. For example, it positions specific data points or graphs in a prominent position. The display unit also develops a system that automatically customizes templates according to the user's needs. For example, it suggests the optimal template based on past operation history. In this way, by providing user-customizable templates, it becomes possible to display information according to individual needs.

[0067] The business efficiency improvement system can further include a display unit that uses an emotion estimation function to monitor in real time the emotions a user feels toward displayed information and provide interactive feedback to elicit positive emotions. The display unit, for example, monitors in real time the emotions a user feels toward displayed information and provides feedback to elicit positive emotions based on the data. For example, an encouraging message is displayed when the user's emotion score is low. The display unit also constructs a system that provides interactive feedback to encourage a user to feel positive emotions based on the emotion estimation data. For example, the display content is dynamically changed depending on the user's emotions. The display unit also analyzes the user's emotional response in real time and provides feedback to elicit positive emotions. For example, positive comments or advice is displayed when the emotion score is high. In this way, by monitoring the user's emotions in real time and providing positive feedback, user satisfaction is improved.

[0068] The business efficiency system can also be equipped with a document analysis unit that learns the user's past writing style and tone to extract key points and expectations with greater accuracy. For example, the document analysis unit uses a generation AI to learn the author's past writing style and analyze the document based on that style. For example, it identifies the phrases and expressions the author frequently uses and extracts key points and expectations based on those. The document analysis unit also analyzes the tone of the document to more accurately understand the author's intention. For example, it distinguishes between formal and casual tones and extracts key points and expectations appropriate for each. The document analysis unit also stores the author's past documents in a database, and the generation AI learns the writing style based on that. For example, it analyzes past minutes and reports to understand the author's characteristics. This allows for more accurate analysis by learning the author's style and tone.

[0069] The business efficiency improvement system can further include a document analysis unit that takes into account hidden metadata within a document and extracts issues and expected values ​​based on the document's background information. The document analysis unit, for example, analyzes the creation date and time of a document and extracts issues and expected values ​​by taking into account the situation and background at that time. For example, it reflects important events and trends related to a specific period. The document analysis unit also analyzes the editing history and extracts issues and expected values ​​based on changes and revisions to the document. For example, it focuses its analysis on frequently revised parts. The document analysis unit also analyzes metadata within a document to more accurately grasp the intent of the author or editor. For example, it performs analysis taking into account document version information and comments. This allows for more accurate extraction of issues and expected values ​​by taking into account the document's background information.

[0070] The business efficiency improvement system can further include a document analysis unit that uses an emotion estimation function to analyze emotional nuances in a document and extract emotionally important issues and expectations. The document analysis unit, for example, analyzes emotional expressions in a document and calculates an emotion score. For example, it identifies positive and negative expressions and extracts issues and expectations based on them. The document analysis unit also uses the emotion estimation function to analyze emotional nuances in a document and emphasizes emotionally important parts. For example, it prioritizes analysis of parts with high emotional intensity. The document analysis unit also analyzes the emotional tone of a document and extracts issues and expectations that are likely to resonate emotionally. For example, it identifies expressions that elicit emotional resonation and performs analysis based on those. In this way, emotionally important issues and expectations can be extracted by analyzing emotional nuances.

[0071] The business efficiency improvement system can further include a document analysis unit that analyzes a user's emotions in real time when they are entering documents and provides feedback to elicit positive emotions. The document analysis unit, for example, uses an emotion estimation function to analyze emotions in real time when a user is entering documents. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The document analysis unit also provides feedback that encourages the user to have positive emotions based on the emotion estimation data. For example, it displays encouraging messages or positive comments. The document analysis unit also monitors the user's emotional reactions in real time and builds a system that provides emotionally positive feedback. For example, it makes positive suggestions when the user's emotions are negative. In this way, the user's emotions are analyzed in real time and positive feedback is provided, thereby improving user satisfaction.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The document analysis unit analyzes the document. For example, the document analysis unit analyzes documents such as meeting minutes or reports and extracts important information. The document analysis unit uses generative AI to analyze the document and extract important information. Step 2: The issue extraction unit extracts issues from the documents analyzed by the document analysis unit. For example, the issue extraction unit uses generative AI to extract key topics and themes from the documents. The issue extraction unit uses natural language processing technology to analyze the content of the documents and identify important issues. Step 3: The expected value extraction unit extracts expected values ​​from the issues extracted by the issue extraction unit. For example, the expected value extraction unit uses generative AI to extract expected outcomes and goals from the document. The expected value extraction unit uses a machine learning algorithm to analyze the content of the document and identify expected values. Step 4: The display unit displays the issues and expectations extracted by the expectation extraction unit. For example, the display unit displays information extracted using a no-code tool on a dashboard. By visually displaying the information, the display unit allows users to grasp important information at a glance. The display unit automatically processes and displays the information extracted by the generation AI.

[0074] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0075] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0076] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0080] 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.

[0081] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0082] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0083] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0084] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0085] 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.

[0086] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0087] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0088] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0089] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0090] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] 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.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] 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.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0103] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The 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.

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

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

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

[0114] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0124] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0125] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0126] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0127] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0128] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0129] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0130] 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.

[0131] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0132] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0133] 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.

[0134] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0135] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0136] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0137] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0138] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0139] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0140] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0141] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a document analysis unit that analyzes documents; an issue extraction unit that extracts issues from the document analyzed by the document analysis unit; an expected value extraction unit that extracts expected values ​​from the issues extracted by the issue extraction unit; a display unit that displays the issues and expected values ​​extracted by the expected value extraction unit. A system characterized by:

2. The document analysis unit Learns the author's past writing style and tone to extract more accurate points and expectations 2. The system of claim 1.

3. The document analysis unit Considering hidden metadata within documents, extracting issues and expectations based on the document's context 2. The system of claim 1.

4. The document analysis unit Analyzes emotional nuances in documents and extracts emotionally important points and expectations 2. The system of claim 1.

5. The document analysis unit Supports different input formats such as voice input or handwriting input, and performs multimodal analysis 2. The system of claim 1.

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