System

Abstract

A system is provided.SOLUTION: A system comprising: terminal means for inputting text; server means for receiving text data input from the terminal means; means for analyzing the text data by the server means; generation model means for generating a document based on the analyzed text data by the server means; format application means for applying an organization-specific format to the generated document; and return means for returning the document to which the format application means has been applied to the terminal means.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] Creating documents takes up a lot of time, which can lead to a decrease in the efficiency of the entire organization. Furthermore, if the excellent ideas of employees who are not good at creating documents are not properly evaluated, it can result in significant losses for the organization. Furthermore, if documents are required to be created in different formats for each organization, there is a lack of consistency, making evaluation difficult. [Means for solving the problem]

[0005] The present invention provides a system including a terminal means for inputting text, a server means for receiving text data input from the terminal means, a means for the server means to analyze the text data, a generative model means for the server means to generate a document based on the analyzed text data, a format application means for applying an organization-specific format to the generated document, and a return means for returning the document to the terminal means. This significantly reduces the time required to create documents, allowing employees to focus on their essential work. Furthermore, using a standardized format across the organization makes it easier to evaluate documents, which is expected to improve work efficiency.

[0006] "Terminal means" refers to a computer device that a user uses to input text and send data to a server.

[0007] The term "server means" refers to a computer device that receives text data sent from the terminal means and operates the analysis and generative model means.

[0008] The "analyzing means" refers to the process by which the server means interprets the received text data and extracts information for generating an appropriate document.

[0009] "Generative model means" refers to the function of generating documents using AI technology based on input text data.

[0010] The "format application means" refers to a function that applies a predefined organization-specific format to the generated document.

[0011] "Returning means" refers to the process of transmitting the formatted document from the server means to the terminal means. [Brief explanation of the drawings]

[0012] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] 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, a 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), and an APU (Accelerated Processing Unit).

[0016] 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.

[0017] 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.

[0018] 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), Bluetooth (registered trademark), etc.

[0019] 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."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 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.

[0023] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] 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.

[0025] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0027] 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.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0032] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0033] The present invention provides a system that significantly reduces the time required for users to create documents and realizes document generation in a unified format. This system includes a terminal means, a server means, an analyzing means, a generative model means, a format applying means, and a returning means.

[0034] First, the user inputs text that will be the content of the document using the terminal means. For example, the user can input the text, "Please prepare a report on the features and market analysis of a new product." The input text is then sent to the server means by the terminal means.

[0035] The server means receives the text data sent from the terminal means. The received data is analyzed by the analysis means, and basic data for document generation is extracted. Natural language processing technology is used in the analysis to clarify the intent of the text and the main points of the information.

[0036] The analyzed data is passed to a generative modeling tool, which uses AI technology (such as GPT-4) to generate detailed documents based on the analysis results. This organizes the information based on the user's intended content and creates specific reports and presentations.

[0037] The generated documents are then formatted using a predefined organizational formatting mechanism, including uniform standards for color, slide layout, font size, etc., ensuring consistent and attractive presentation.

[0038] Finally, the formatted document is sent back to the terminal means via the return means. The user can receive the document generated on the terminal means, check it, and use it.

[0039] For example, if a user types "Please create a document about the market analysis of a new product," the server analyzes the text, extracts key points of the market analysis, and generates a detailed market analysis report using a generative model. The generated report is formatted according to the organization's format and provided to the user with a unified design and layout. This process significantly reduces the time and effort required to create the document while providing high-quality documents.

[0040] The processing flow will be explained below.

[0041] Step 1:

[0042] The user uses the terminal to input the text that will become the content of the document. For example, they might input, "Please prepare a report on the features and market analysis of a new product." This input becomes the basic data for the document.

[0043] Step 2:

[0044] The text entered by the user is sent to the server by the terminal means. This transmission is performed using an HTTP request, and the data is sent in JSON format.

[0045] Step 3:

[0046] The server receives the text data sent from the terminal, and then starts a process to analyze the text data.

[0047] Step 4:

[0048] The server uses analytical tools to interpret the received text and extract information for generating appropriate documents. It uses natural language processing techniques to understand the intent and main points of the text.

[0049] Step 5:

[0050] The server inputs the analysis results into a generative model means, which uses AI techniques to generate detailed documents based on the analyzed data.

[0051] Step 6:

[0052] The generated document is passed to a formatting mechanism within the server that applies organization-specific formatting to the generated document, including brand colors and slide layouts.

[0053] Step 7:

[0054] The formatted document is sent from the server to the terminal via the return means. The sent data is also in JSON format.

[0055] Step 8:

[0056] The user receives the final document on their device. They can review the document and download or print it as needed. The generated document is already formatted, so the user can use it as is.

[0057] Example 1

[0058] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0059] The traditional document creation process requires users to create documents manually, which is time-consuming, labor-intensive, and inefficient. Furthermore, formats may not be standardized, potentially affecting the overall appearance and quality of the organization. Therefore, there is a need for a system that can reduce the burden on users and quickly create high-quality documents with a consistent look.

[0060] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0061] In this invention, the server includes a computer means for receiving text data, a means for analyzing the text data, and a generating artificial intelligence model means for generating documents based on the analyzed text data. This automates the entire process of document creation, enabling efficient, consistent, and high-quality document creation.

[0062] "Information processing device means" refers to a device or system that allows a user to input text, such as a personal computer or tablet.

[0063] The term "computer means" refers to a computer system such as a server or cloud service that receives and processes text data sent from the information processing means.

[0064] "Analyzing means" refers to software or algorithms that are used by the computing means to analyze the received text data and extract the underlying data.

[0065] "Generative artificial intelligence model means" refers to AI techniques or machine learning models used to generate documents based on analyzed data, including, for example, natural language processing models such as GPT-4.

[0066] "Format application means" refers to software or programs that apply an organization's proprietary format to the generated documents.

[0067] "Returning means" refers to a communication means or protocol for sending the processed document back to the information processing means.

[0068] The present invention provides a system that significantly reduces the time required for users to create documents and generates documents in a unified format. Specific embodiments of this system will be described below.

[0069] First, a user inputs text that will be the content of a document using an information processing device. Information processing devices include devices such as personal computers and tablets. For example, a user can input text such as "Please prepare a report on the features and market analysis of a new product." The input text data is transmitted from the information processing device to the computer.

[0070] The computing means refers to a server on a cloud service or a similar computing system, and is responsible for receiving text data sent from the information processing means. The received data is processed by the analyzing means, where natural language processing techniques are used to clarify the intent and main points of the text. This analysis process includes steps such as tokenization, keyword extraction, and intent recognition.

[0071] The analyzed data is passed to a generative AI modeling means, which may include a powerful natural language processing model such as GPT-4. This model generates a document based on the analyzed data. The generated document organizes the information intended by the user and is structured into a specific report or presentation.

[0072] Next, a formatting application unit operates on the generated document. The formatting application unit applies a predefined, organization-specific format to the document. This formatting includes color usage, slide layout, font size, etc., and applies the unified standards established by the organization. This results in the creation of consistent, attractive-looking documents.

[0073] Finally, the formatted document is sent back to the information processing device via the return means. The user receives the document generated by the information processing device and can check and use it.

[0074] As a specific example, if a user inputs "Please create a document about the market analysis of a new product," the computer means will analyze the text and extract key points of the market analysis. The analysis results will be passed to the generative artificial intelligence model means, which will generate a detailed market analysis report. The generated report will be formatted in the organization's format and provided to the user with a unified design and layout. This process allows the user to significantly reduce the time and effort required to create documents and quickly obtain high-quality documents.

[0075] An example of a prompt sentence is "Please prepare a document about the market analysis of a new product."

[0076] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0077] Step 1:

[0078] The user uses the information processing device to input text that will become the content of the document. At this time, the input data is a prompt sentence such as "Please prepare a report on the features and market analysis of a new product." The input text data is sent to the computer device as an HTTP request.

[0079] Step 2:

[0080] The terminal sends the input text data to the server. Specifically, a POST request is made from the terminal to the computer means, and a prompt sentence is sent to the computer means. At this point, the input is the prompt sentence, and the output is the execution of data transfer to the server.

[0081] Step 3:

[0082] The server receives the prompt sent from the device. This reception process receives an HTTP request using an API endpoint. The input at this point is the prompt sent from the device, and the output is the received text data.

[0083] Step 4:

[0084] The server analyzes the text data using a method for analyzing received data. This analysis uses natural language processing (NLP) technology to extract the intent and main points of the text. Specifically, the text is tokenized, and keywords and intent recognition are performed. The input at this point is the prompt received by the server, and the output becomes the basic data after analysis.

[0085] Step 5:

[0086] The analyzed data is sent to a generative AI modeling means, which generates documents based on the analysis results using, for example, GPT-4. At this point, the input is the analyzed basic data, and the output is the generated detailed document.

[0087] Step 6:

[0088] The server then applies the organization's proprietary formatting to the generated document using a formatting application mechanism, using predefined templates to set color usage, slide layout, font size, etc. At this point, the input is the generated document, and the output is the formatted document.

[0089] Step 7:

[0090] The formatted document is then sent back to the terminal using the return means. Specifically, the generated PDF or PPT file is provided to the terminal as a download link from the server. At this point, the input is the formatted document, and the output is the document sent to the information processing means.

[0091] Step 8:

[0092] The user receives the returned document via the information processing device, and then checks and uses it. The user clicks on the provided link to download the file and check the contents. The input at this point is the download link, and the output is the formatted document that the user has obtained.

[0093] (Application example 1)

[0094] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0095] In recent years, creating documents for new product introductions and market analysis in brick-and-mortar stores has become a complex and time-consuming task, placing a heavy burden on store managers. Furthermore, while creating documents requires generating them in a standardized format, doing this manually leaves issues with quality and efficiency. Furthermore, consistency and prompt delivery of the generated documents are required. A system to solve these issues is needed.

[0096] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0097] In this invention, the server includes a terminal means for inputting text, a server means for receiving text data input from the terminal means, a means for the server means to analyze the text data, a generative model means for the server means to generate a document based on the analyzed text data, a format application means for applying a format exclusive to the organization to the generated document, a return means for returning the document to which the format application means has been applied to the terminal means, and a means for applying the document generation system to applications in the field of brick-and-mortar stores. This enables documents such as new product introduction materials and market analysis reports to be generated quickly and in a unified format for brick-and-mortar stores.

[0098] "Terminal means" refers to a device that allows a user to input text.

[0099] "Server means" refers to a central processing unit that receives and processes text data input from terminal means.

[0100] The "analyzing means" refers to a device or program having the function of analyzing the text data received by the server means and extracting the main points of the information.

[0101] "Generative model means" refers to a system that uses artificial intelligence or other techniques to generate documents based on analyzed textual data.

[0102] "Format application means" refers to a device or program for applying a predefined, organization-specific format to the generated document.

[0103] The "returning means" refers to a communication means for returning the document to which the formatting means has been applied to the terminal means.

[0104] "Means for applying to applications in the field of brick-and-mortar stores" refers to methods for applying the document generation system to the creation of new product introduction materials and market analysis reports in brick-and-mortar stores.

[0105] The present invention relates to a system that significantly reduces the time it takes for users to create documents and generates documents in a unified format. The system includes a terminal, a server, an analyzing unit, a generative model, a format application unit, and a return unit. The system is characterized by its application in the brick-and-mortar retail field.

[0106] First, the user inputs the text that will be the content of the document using a terminal. For example, the user can input, "Please create a document about the features of the new product and the target market." The user inputs this content using a terminal such as a smartphone.

[0107] Next, the input text data is sent by the terminal means to the server means. The server means receives the text data and analyzes its contents using the analysis means. This analysis utilizes natural language processing technology. Specifically, keywords are extracted and the meaning of the text is clarified.

[0108] The analyzed data is passed to a generative modeling means, which uses a generative AI model (such as GPT-4) to generate detailed documents based on the analysis results. This allows information to be organized based on the user's intended content, and specific reports and materials can be created.

[0109] The generated documents are then formatted using a predefined, organization-specific formatting mechanism, including uniform standards for color, layout, font size, etc., ensuring consistent, aesthetically pleasing documents.

[0110] Finally, the formatted document is sent back to the terminal means via the return means, and the user can receive, check, and use the document generated at the terminal means.

[0111] As a specific example, if a user inputs "Please create a document about the features and target market of a new product," the server analyzes the text, extracts key points of market analysis, and generates a detailed market analysis report using a generative model. The generated report is formatted according to the organization's specifications and provided to the user with a unified design and layout. This process significantly reduces the time and effort required to create materials for introducing new products in physical stores, and makes it possible to provide high-quality materials.

[0112] This allows users to create high-quality materials in a short amount of time, improving the operational efficiency of physical stores.

[0113] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0114] Step 1:

[0115] The user uses the terminal means to input text that will become the content of the document. For example, the user might input, "Please prepare a document about the features of the new product and its target market." The input data here is text data entered by the user.

[0116] Step 2:

[0117] The terminal means transmits the input text data to the server means. This transmitted data is text data input by the user.

[0118] Step 3:

[0119] The server means passes the text data received from the terminal means to the analyzing means. The input data here is the text data received from the terminal means. The analyzing means analyzes the text data using natural language processing technology, extracts keywords, and clarifies the meaning of the text. Analysis data is generated by this process.

[0120] Step 4:

[0121] The analyzed data is passed to a generative modeling means, which uses a generative AI model (e.g., GPT-4) to generate a detailed document based on the analysis results. The input data for this process is the analyzed data, and the output data is the generated document data.

[0122] Step 5:

[0123] The generated document is passed to a format application means, which applies a predefined, organization-specific format to the generated document. The input data for this process is the generated document data, and the output data is the formatted document data.

[0124] Step 6:

[0125] Finally, the formatted document is sent back to the terminal via the return means. The input data of this process is the formatted document data, and the output data is the document displayed by the user's terminal. The user receives the document generated by the terminal and can check and use it.

[0126] This series of processes makes it possible to quickly create new product introduction materials and market analysis reports for brick-and-mortar stores in a unified format, simplifying the work involved and improving the quality of the materials.

[0127] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] The present invention is a system that significantly reduces the time it takes for users to create documents and generates documents in a unified format, and also adds a function to recognize the user's emotions and adjust the tone and content of the document. This system includes a terminal means, a server means, an analyzing means, a generative model means, a format applying means, a return means, and an emotion engine.

[0129] First, the user uses the terminal to input the text that will be the content of the document. For example, the user can input the text, "Please prepare a report on the features and market analysis of a new product." This input becomes the basic data for the document.

[0130] The text entered by the user is sent to the server by the terminal means. This transmission is performed using an HTTP request, and the data is sent in JSON format.

[0131] The server receives the text data sent from the terminal. The received data is analyzed by an analysis means to extract basic data for document generation. Natural language processing technology is used for the analysis to clarify the intent of the text and the main points of the information.

[0132] The analyzed data is passed to a generative modeling tool, which uses AI technology (such as GPT-4) to generate detailed documents based on the analysis results. This organizes the information based on the user's intended content and creates specific reports and presentations.

[0133] The generated document is then passed to a formatting processor, which applies the organization's proprietary formatting to the generated document, including brand colors, slide layouts, font sizes, etc., resulting in a consistent, attractive presentation.

[0134] A distinctive feature of the present invention is the incorporation of an emotion engine. The emotion engine analyzes emotions from text data entered by the user. The analyzed emotion data is fed back to the generative model means, which adjusts the tone and content of the generated document. This adjustment allows the generated document to reflect the user's emotions.

[0135] As a concrete example, if a user inputs "Please create a document about the market analysis of a new product in an optimistic tone," the server analyzes the text, extracts key points of the market analysis, and uses an emotion engine to recognize the emotion "optimistic." A market analysis report with an optimistic tone is then generated using a generative model. The generated report is formatted according to the organization's format and provided to the user with a unified design and layout. This process significantly reduces the time and effort required to create documents while providing high-quality documents. Furthermore, the quality of the content is improved because documents are generated according to the user's emotions.

[0136] The processing flow will be explained below.

[0137] Step 1:

[0138] The user uses the terminal to input the text that will become the content of the document. For example, they might input, "Please write a report on the features and market analysis of a new product in an optimistic tone." This input becomes the basic data for the document.

[0139] Step 2:

[0140] The text entered by the user is sent to the server by the terminal means. This transmission is performed using an HTTP request, and the data is sent in JSON format.

[0141] Step 3:

[0142] The server receives the text data sent from the terminal. The received data is analyzed by an analysis means, and the information necessary for document generation is extracted. Natural language processing technology is used to determine the intent and main points of the text.

[0143] Step 4:

[0144] The server passes the parsed text data to the emotion engine, which analyzes the emotions from the user's input text and generates emotion labels such as optimistic, pessimistic, or neutral.

[0145] Step 5:

[0146] The emotion data analyzed by the emotion engine is fed back to the generative modeling means, which then adjusts the tone and content of the document based on the analyzed emotion data. For example, if the tone is optimistic, positive and optimistic expressions are used in the document.

[0147] Step 6:

[0148] The generative modeling means generates detailed documents based on the analyzed data and sentiment data, and this generation process uses AI technology (e.g., GPT-4).

[0149] Step 7:

[0150] The generated document is passed to a formatting applicator, which arranges the document content based on a predefined, organization-specific template, which may include brand colors, slide layouts, font sizes, etc.

[0151] Step 8:

[0152] The formatted document is then sent back to the terminal via the return means. The sent data is in JSON format.

[0153] Step 9:

[0154] The user receives the final document on their device. They can review it and download or print it as needed. The document is ready to use, as it already has formatting and reflects the specified emotional tone.

[0155] Example 2

[0156] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0157] Conventional document generation systems require a lot of time and effort to create documents, and the generated documents have inconsistent formats and tones. Furthermore, they lack the functionality to reflect the user's emotions in the document, making it difficult to accurately reflect the user's intended tone and content.

[0158] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0159] In this invention, the server includes an analysis means for analyzing text data, a generative model means for generating documents based on the analyzed text data, and an emotion analysis means for analyzing emotions from the input text data. This not only enables accurate analysis of the intent and main points of the text data input by the user, but also enables the generation of documents with a tone and content that reflects the user's emotions. Furthermore, by applying an organization-specific format to the generated documents, consistent materials can be provided, significantly reducing the time and effort required to create documents.

[0160] The "terminal means" is a device for a user to input text.

[0161] The "server means" is a computer system that receives and processes text data sent from the terminal means.

[0162] The "analysis means" is a device or software that has the function of analyzing received text data and extracting basic data for document generation.

[0163] A "generative model means" is a device or software that uses AI techniques (e.g., natural language processing models) to generate documents based on analyzed text data.

[0164] The "format application means" is a device or software that has the function of applying an organization-specific format to the generated document.

[0165] The "returning means" is a device or software that has the function of returning the document to which the formatting means has been applied to the terminal means.

[0166] "Emotion analysis means" refers to a device or software that has the function of analyzing emotions from input text data.

[0167] A "natural language processing model" is a technology that refers to a machine learning model for understanding and generating human language.

[0168] A "template" is a predefined layout or design pattern for arranging document content.

[0169] This invention is a system that significantly reduces the time it takes for users to create documents and generates documents in a unified format. It also has the function of recognizing the user's emotions and adjusting the tone and content of the document. This system includes a terminal unit, a server unit, an analysis unit, a generative model unit, a format application unit, a return unit, and an emotion analysis unit.

[0170] First, the user uses a terminal to input the text that will be the content of the document. For example, the user can input the text, "Please prepare a report on the features and market analysis of a new product." The user can also specify the tone of the document, such as "optimistic tone." This input becomes the basic data for the document.

[0171] The terminal means transmits the input text data and emotion data to the server means using an HTTP request. The data is transmitted in JSON format.

[0172] The server means analyzes the received data using the analysis means. The analysis means uses natural language processing technology to clarify the intent of the text and the main points of the information. The analyzed data is passed to the generative model means. This generative model means uses a natural language processing model such as GPT-4. The generative model means generates a detailed document based on the analysis results.

[0173] The generated document is then passed to a formatting mechanism on the server, which applies the organization's proprietary formatting to the generated document, including brand colors, slide layouts, font sizes, etc. This ensures consistent, attractive presentation.

[0174] The sentiment analysis means also plays an important role. It analyzes emotions from the text data entered by the user and feeds the analyzed emotional data back to the generative model means. The generative model means then adjusts the tone and content of the generated document based on this information. This adjustment allows the generated document to better reflect the user's emotions.

[0175] As a concrete example, if a user inputs "Please create a document about the market analysis of a new product in an optimistic tone," the server analyzes the text, extracts key points of the market analysis, and recognizes the sentiment "optimistic" using a sentiment analysis means. A market analysis report with an optimistic tone is then generated using a generative model means. The generated report is formatted according to the organization's format and provided to the user with a unified design and layout. This process significantly reduces the time and effort required to create documents while providing high-quality documents. Furthermore, the quality of the content is improved because documents are generated according to the user's sentiment.

[0176] An example prompt is:

[0177] "Write your new product market analysis in an optimistic tone."

[0178] By feeding this prompt into a generative AI model, documents with the desired tone and content can be easily generated.

[0179] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0180] Step 1:

[0181] The user uses a terminal to input document content and emotions. Specifically, the user inputs a sentence such as "Please write a report on the features and market analysis of a new product" into the input field of the terminal. The user also specifies the tone of the document, such as "optimistic tone." This input data becomes the basic data of the document. Input: Document content and emotion data. Output: Data stored in the input field of the terminal.

[0182] Step 2:

[0183] The device converts the input text content and emotion data into JSON format and sends it to the server using an HTTP request. Specifically, the device serializes the input data into JSON format and sends it to the specified server URL. Input: Data stored in the device's input field. Output: HTTP request containing JSON format data.

[0184] Step 3:

[0185] The server receives an HTTP request and passes the JSON-formatted data to the analysis means. The analysis means analyzes the received data and uses natural language processing technology to extract the main points and intent of the text. Specifically, it uses a text analysis algorithm to clarify the user's intent. Input: JSON-formatted data. Output: Analyzed data with the main points and intent extracted.

[0186] Step 4:

[0187] The server passes the analyzed data to the generative modeling means. The generative modeling means (e.g., GPT-4) generates detailed documents based on this analyzed data. The generative modeling means determines the structure and content of the text based on a generative AI model. Input: analyzed data. Output: generated document.

[0188] Step 5:

[0189] The formatting mechanism in the server receives the generated document and applies the organization's specific formatting, such as document layout, font size, brand color, etc. according to a pre-defined template. Input: Generated document. Output: Formatted document.

[0190] Step 6:

[0191] The server returns the formatted document to the user's device. Specifically, it converts the formatted document into JSON format and sends it to the user's device as an HTTP response. Input: Formatted document. Output: Document data sent to the user's device.

[0192] Step 7:

[0193] The sentiment analysis means analyzes sentiment from the user's input text data and feeds this sentiment data back to the generative model means. Specifically, the sentiment data is analyzed using a sentiment analysis algorithm and re-input into the generative model means, adjusting the tone and content of the document. Input: User's sentiment data. Output: Adjusted document.

[0194] Through these steps, users can efficiently generate high-quality, uniformly formatted documents that reflect the user's emotions, allowing for better adjustment of the tone and content of the document.

[0195] (Application example 2)

[0196] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0197] Modern business requires the rapid creation of consistent, high-quality documents, but this requires a lot of time and effort. While resolving this issue, it is necessary to improve usability and the quality of communication by providing documents that are in tune with users' emotions. Online shopping sites, in particular, require the creation of user reviews and the quality and consistency of product descriptions. This will increase users' purchasing motivation and ensure the provision of reliable information.

[0198] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0199] In this invention, the server includes a terminal means for inputting text, a means for receiving text data input from the terminal means, and a generative model means for generating documents based on the analyzed text data. This makes it possible to analyze user emotions and automatically generate documents with a tone based on the emotions. The generated documents are formatted according to the organization's specifications, allowing for the rapid provision of consistent, high-quality documents. Furthermore, on online shopping sites, documents such as user reviews and product descriptions can be optimized based on emotions, which is expected to improve user satisfaction and reliability.

[0200] "Terminal means for inputting text" refers to a device for transmitting text data input by a user to a server.

[0201] The term "server means" refers to a server that processes text data received from a user and manages the analyzed and generated documents.

[0202] "Means for analyzing text data" refers to the process of analyzing received text data using natural language processing technology to clarify its intent and key points.

[0203] "Generative model means" refers to AI technology, specifically generative AI models, that generate detailed documents based on analyzed text data.

[0204] An "emotion engine" is an engine that analyzes emotions from text data entered by a user and adjusts the tone and content of the document based on those emotions.

[0205] "Format application means" refers to a process for applying an organization-specific format to the generated document to create a consistent document.

[0206] "Return means" refers to a mechanism for returning a document formatted by the format application means to the user's terminal means.

[0207] The present invention begins with a terminal means for inputting text. A user inputs text using this terminal means. This terminal means refers to devices such as a personal computer, smartphone, or tablet. For example, a user can input a sentence such as "This product was very useful." The input text data is transmitted to a server means via an HTTP request. The data is generally transmitted in JSON format.

[0208] The server means analyzes the received text data using natural language processing (NLP) techniques, such as a generative AI model such as OpenAI's GPT-4. This analysis process clarifies the intent and key information of the text. The server means passes the analyzed data to the generative model means, which generates a detailed document.

[0209] A distinctive feature of the present invention is the incorporation of an emotion engine. The emotion engine analyzes emotions from text data entered by the user. The analysis results are fed back to the generative model means, which adjusts the tone and content of the generated document. For example, if a user enters "I am satisfied with this product" and specifies the emotion as "positive," the generated document will have a positive tone.

[0210] In the next step, the generated document is passed to a format application means. The format application means in the server applies the organization's proprietary format to the generated document. This includes brand colors, slide layouts, font sizes, etc., to create a consistent and attractive document. The formatted document is finally returned to the user's terminal means by a return means.

[0211] As a concrete example, consider the case where a user enters a review on an online shopping site. The user enters a review such as "This product was very useful" along with a positive sentiment. The server means receives this and uses the analysis and generative model means to generate a detailed review with an appropriate tone. The format application means then applies a unified format to the review, which is then sent back to the terminal means. This process improves the consistency and quality of reviews, and increases user satisfaction.

[0212] Prompt Sentence Examples

[0213] A specific example of a prompt sentence entered in text format is as follows:

[0214] User Review: This product was very useful.

[0215] Emotion: Positive

[0216] Generate reviews with the right tone.

[0217] The present invention enables high quality document generation based on user-entered data, greatly improving the efficiency and consistency of document creation for specific applications.

[0218] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0219] Step 1:

[0220] The user inputs a sentence using a terminal. Specifically, the user inputs a sentence such as "This product was very useful" into an input form in a browser or application. This input data is sent to the server in JSON format. The input data also includes the user's sentiment (e.g., positive).

[0221] Step 2:

[0222] The server receives the text data sent from the terminal device. The server receives the data via an HTTP request. This data is in JSON format and includes the text content and sentiment. The server prepares to move on to the next step of analyzing this data.

[0223] Step 3:

[0224] The server analyzes the received text data using natural language processing (NLP) technology, such as a generative AI model like GPT-4. During this process, the server extracts the intent and key points of the text and uses an emotion engine to analyze the user's emotions. The input is JSON-formatted text data, and the output is the analyzed text data and emotion information.

[0225] Step 4:

[0226] The server generates documents based on the analyzed data. The generative model generates detailed documents using, for example, OpenAI's GPT-4. In this process, the generative AI model generates documents based on the user's intent and adjusts the tone and content of the documents to match the user's emotions. The input is the analyzed text data and emotional information, and the output is the generated document.

[0227] Step 5:

[0228] The server applies the organization's proprietary formatting to the generated document. The formatting method uses predefined templates to apply brand colors, slide layouts, font sizes, etc. to the document, resulting in consistent and attractive documents. The input is the generated document and the output is the formatted document.

[0229] Step 6:

[0230] The server returns the formatted document to the terminal means. The return means uses an HTTP response to send the document data back to the terminal means. The user can check the returned document and save or share it as needed. The input is the formatted document, and the output is the returned document.

[0231] 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.

[0232] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[0233] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0234] [Second embodiment]

[0235] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0236] 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.

[0237] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0238] 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.

[0239] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0240] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0241] 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.

[0242] 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.

[0243] 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 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.

[0244] 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.

[0245] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0246] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0247] The present invention provides a system that significantly reduces the time required for users to create documents and realizes document generation in a unified format. This system includes a terminal means, a server means, an analyzing means, a generative model means, a format applying means, and a returning means.

[0248] First, the user inputs text that will be the content of the document using the terminal means. For example, the user can input the text, "Please prepare a report on the features and market analysis of a new product." The input text is then sent to the server means by the terminal means.

[0249] The server means receives the text data sent from the terminal means. The received data is analyzed by the analysis means, and basic data for document generation is extracted. Natural language processing technology is used in the analysis to clarify the intent of the text and the main points of the information.

[0250] The analyzed data is passed to a generative modeling tool, which uses AI technology (such as GPT-4) to generate detailed documents based on the analysis results. This organizes the information based on the user's intended content and creates specific reports and presentations.

[0251] The generated documents are then formatted using a predefined organizational formatting mechanism, including uniform standards for color, slide layout, font size, etc., ensuring consistent and attractive presentation.

[0252] Finally, the formatted document is sent back to the terminal means via the return means. The user can receive the document generated on the terminal means, check it, and use it.

[0253] For example, if a user types "Please create a document about the market analysis of a new product," the server analyzes the text, extracts key points of the market analysis, and generates a detailed market analysis report using a generative model. The generated report is formatted according to the organization's format and provided to the user with a unified design and layout. This process significantly reduces the time and effort required to create the document while providing high-quality documents.

[0254] The processing flow will be explained below.

[0255] Step 1:

[0256] The user uses the terminal to input the text that will become the content of the document. For example, they might input, "Please prepare a report on the features and market analysis of a new product." This input becomes the basic data for the document.

[0257] Step 2:

[0258] The text entered by the user is sent to the server by the terminal means. This transmission is performed using an HTTP request, and the data is sent in JSON format.

[0259] Step 3:

[0260] The server receives the text data sent from the terminal, and then starts a process to analyze the text data.

[0261] Step 4:

[0262] The server uses analytical tools to interpret the received text and extract information for generating appropriate documents. It uses natural language processing techniques to understand the intent and main points of the text.

[0263] Step 5:

[0264] The server inputs the analysis results into a generative model means, which uses AI techniques to generate detailed documents based on the analyzed data.

[0265] Step 6:

[0266] The generated document is passed to a formatting mechanism within the server that applies organization-specific formatting to the generated document, including brand colors and slide layouts.

[0267] Step 7:

[0268] The formatted document is sent from the server to the terminal via the return means. The sent data is also in JSON format.

[0269] Step 8:

[0270] The user receives the final document on their device. They can review the document and download or print it as needed. The generated document is already formatted, so the user can use it as is.

[0271] Example 1

[0272] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0273] The traditional document creation process requires users to create documents manually, which is time-consuming, labor-intensive, and inefficient. Furthermore, formats may not be standardized, potentially affecting the overall appearance and quality of the organization. Therefore, there is a need for a system that can reduce the burden on users and quickly create high-quality documents with a consistent look.

[0274] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0275] In this invention, the server includes a computer means for receiving text data, a means for analyzing the text data, and a generating artificial intelligence model means for generating documents based on the analyzed text data. This automates the entire process of document creation, enabling efficient, consistent, and high-quality document creation.

[0276] "Information processing device means" refers to a device or system that allows a user to input text, such as a personal computer or tablet.

[0277] The term "computer means" refers to a computer system such as a server or cloud service that receives and processes text data sent from the information processing means.

[0278] "Analyzing means" refers to software or algorithms that are used by the computing means to analyze the received text data and extract the underlying data.

[0279] "Generative artificial intelligence model means" refers to AI techniques or machine learning models used to generate documents based on analyzed data, including, for example, natural language processing models such as GPT-4.

[0280] "Format application means" refers to software or programs that apply an organization's proprietary format to the generated documents.

[0281] "Returning means" refers to a communication means or protocol for sending the processed document back to the information processing means.

[0282] The present invention provides a system that significantly reduces the time required for users to create documents and generates documents in a unified format. Specific embodiments of this system will be described below.

[0283] First, a user inputs text that will be the content of a document using an information processing device. Information processing devices include devices such as personal computers and tablets. For example, a user can input text such as "Please prepare a report on the features and market analysis of a new product." The input text data is transmitted from the information processing device to the computer.

[0284] The computing means refers to a server on a cloud service or a similar computing system, and is responsible for receiving text data sent from the information processing means. The received data is processed by the analyzing means, where natural language processing techniques are used to clarify the intent and main points of the text. This analysis process includes steps such as tokenization, keyword extraction, and intent recognition.

[0285] The analyzed data is passed to a generative AI modeling means, which may include a powerful natural language processing model such as GPT-4. This model generates a document based on the analyzed data. The generated document organizes the information intended by the user and is structured into a specific report or presentation.

[0286] Next, a formatting application unit operates on the generated document. The formatting application unit applies a predefined, organization-specific format to the document. This formatting includes color usage, slide layout, font size, etc., and applies the unified standards established by the organization. This results in the creation of consistent, attractive-looking documents.

[0287] Finally, the formatted document is sent back to the information processing device via the return means. The user receives the document generated by the information processing device and can check and use it.

[0288] As a specific example, if a user inputs "Please create a document about the market analysis of a new product," the computer means will analyze the text and extract key points of the market analysis. The analysis results will be passed to the generative artificial intelligence model means, which will generate a detailed market analysis report. The generated report will be formatted in the organization's format and provided to the user with a unified design and layout. This process allows the user to significantly reduce the time and effort required to create documents and quickly obtain high-quality documents.

[0289] An example of a prompt sentence is "Please prepare a document about the market analysis of a new product."

[0290] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0291] Step 1:

[0292] The user uses the information processing device to input text that will become the content of the document. At this time, the input data is a prompt sentence such as "Please prepare a report on the features and market analysis of a new product." The input text data is sent to the computer device as an HTTP request.

[0293] Step 2:

[0294] The terminal sends the input text data to the server. Specifically, a POST request is made from the terminal to the computer means, and a prompt sentence is sent to the computer means. At this point, the input is the prompt sentence, and the output is the execution of data transfer to the server.

[0295] Step 3:

[0296] The server receives the prompt sent from the device. This reception process receives an HTTP request using an API endpoint. The input at this point is the prompt sent from the device, and the output is the received text data.

[0297] Step 4:

[0298] The server analyzes the text data using a method for analyzing received data. This analysis uses natural language processing (NLP) technology to extract the intent and main points of the text. Specifically, the text is tokenized, and keywords and intent recognition are performed. The input at this point is the prompt received by the server, and the output becomes the basic data after analysis.

[0299] Step 5:

[0300] The analyzed data is sent to a generative AI modeling means, which generates documents based on the analysis results using, for example, GPT-4. At this point, the input is the analyzed basic data, and the output is the generated detailed document.

[0301] Step 6:

[0302] The server then applies the organization's proprietary formatting to the generated document using a formatting application mechanism, using predefined templates to set color usage, slide layout, font size, etc. At this point, the input is the generated document, and the output is the formatted document.

[0303] Step 7:

[0304] The formatted document is then sent back to the terminal using the return means. Specifically, the generated PDF or PPT file is provided to the terminal as a download link from the server. At this point, the input is the formatted document, and the output is the document sent to the information processing means.

[0305] Step 8:

[0306] The user receives the returned document via the information processing device, and then checks and uses it. The user clicks on the provided link to download the file and check the contents. The input at this point is the download link, and the output is the formatted document that the user has obtained.

[0307] (Application example 1)

[0308] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0309] In recent years, creating documents for new product introductions and market analysis in brick-and-mortar stores has become a complex and time-consuming task, placing a heavy burden on store managers. Furthermore, while creating documents requires generating them in a standardized format, doing this manually leaves issues with quality and efficiency. Furthermore, consistency and prompt delivery of the generated documents are required. A system to solve these issues is needed.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0311] In this invention, the server includes a terminal means for inputting text, a server means for receiving text data input from the terminal means, a means for the server means to analyze the text data, a generative model means for the server means to generate a document based on the analyzed text data, a format application means for applying a format exclusive to the organization to the generated document, a return means for returning the document to which the format application means has been applied to the terminal means, and a means for applying the document generation system to applications in the field of brick-and-mortar stores. This enables documents such as new product introduction materials and market analysis reports to be generated quickly and in a unified format for brick-and-mortar stores.

[0312] "Terminal means" refers to a device that allows a user to input text.

[0313] "Server means" refers to a central processing unit that receives and processes text data input from terminal means.

[0314] The "analyzing means" refers to a device or program having the function of analyzing the text data received by the server means and extracting the main points of the information.

[0315] "Generative model means" refers to a system that uses artificial intelligence or other techniques to generate documents based on analyzed textual data.

[0316] "Format application means" refers to a device or program for applying a predefined, organization-specific format to the generated document.

[0317] The "returning means" refers to a communication means for returning the document to which the formatting means has been applied to the terminal means.

[0318] "Means for applying to applications in the field of brick-and-mortar stores" refers to methods for applying the document generation system to the creation of new product introduction materials and market analysis reports in brick-and-mortar stores.

[0319] The present invention relates to a system that significantly reduces the time it takes for users to create documents and generates documents in a unified format. The system includes a terminal, a server, an analyzing unit, a generative model, a format application unit, and a return unit. The system is characterized by its application in the brick-and-mortar retail field.

[0320] First, the user inputs the text that will be the content of the document using a terminal. For example, the user can input, "Please create a document about the features of the new product and the target market." The user inputs this content using a terminal such as a smartphone.

[0321] Next, the input text data is sent by the terminal means to the server means. The server means receives the text data and analyzes its contents using the analysis means. This analysis utilizes natural language processing technology. Specifically, keywords are extracted and the meaning of the text is clarified.

[0322] The analyzed data is passed to a generative modeling means, which uses a generative AI model (such as GPT-4) to generate detailed documents based on the analysis results. This allows information to be organized based on the user's intended content, and specific reports and materials can be created.

[0323] The generated documents are then formatted using a predefined, organization-specific formatting mechanism, including uniform standards for color, layout, font size, etc., ensuring consistent, aesthetically pleasing documents.

[0324] Finally, the formatted document is sent back to the terminal means via the return means, and the user can receive, check, and use the document generated at the terminal means.

[0325] As a specific example, if a user inputs "Please create a document about the features and target market of a new product," the server analyzes the text, extracts key points of market analysis, and generates a detailed market analysis report using a generative model. The generated report is formatted according to the organization's specifications and provided to the user with a unified design and layout. This process significantly reduces the time and effort required to create materials for introducing new products in physical stores, and makes it possible to provide high-quality materials.

[0326] This allows users to create high-quality materials in a short amount of time, improving the operational efficiency of physical stores.

[0327] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0328] Step 1:

[0329] The user uses the terminal means to input text that will become the content of the document. For example, the user might input, "Please prepare a document about the features of the new product and its target market." The input data here is text data entered by the user.

[0330] Step 2:

[0331] The terminal means transmits the input text data to the server means. This transmitted data is text data input by the user.

[0332] Step 3:

[0333] The server means passes the text data received from the terminal means to the analyzing means. The input data here is the text data received from the terminal means. The analyzing means analyzes the text data using natural language processing technology, extracts keywords, and clarifies the meaning of the text. Analysis data is generated by this process.

[0334] Step 4:

[0335] The analyzed data is passed to a generative modeling means, which uses a generative AI model (e.g., GPT-4) to generate a detailed document based on the analysis results. The input data for this process is the analyzed data, and the output data is the generated document data.

[0336] Step 5:

[0337] The generated document is passed to a format application means, which applies a predefined, organization-specific format to the generated document. The input data for this process is the generated document data, and the output data is the formatted document data.

[0338] Step 6:

[0339] Finally, the formatted document is sent back to the terminal via the return means. The input data of this process is the formatted document data, and the output data is the document displayed by the user's terminal. The user receives the document generated by the terminal and can check and use it.

[0340] This series of processes makes it possible to quickly create new product introduction materials and market analysis reports for brick-and-mortar stores in a unified format, simplifying the work involved and improving the quality of the materials.

[0341] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0342] The present invention is a system that significantly reduces the time it takes for users to create documents and generates documents in a unified format, and also adds a function to recognize the user's emotions and adjust the tone and content of the document. This system includes a terminal means, a server means, an analyzing means, a generative model means, a format applying means, a return means, and an emotion engine.

[0343] First, the user uses the terminal to input the text that will be the content of the document. For example, the user can input the text, "Please prepare a report on the features and market analysis of a new product." This input becomes the basic data for the document.

[0344] The text entered by the user is sent to the server by the terminal means. This transmission is performed using an HTTP request, and the data is sent in JSON format.

[0345] The server receives the text data sent from the terminal. The received data is analyzed by an analysis means to extract basic data for document generation. Natural language processing technology is used for the analysis to clarify the intent of the text and the main points of the information.

[0346] The analyzed data is passed to a generative modeling tool, which uses AI technology (such as GPT-4) to generate detailed documents based on the analysis results. This organizes the information based on the user's intended content and creates specific reports and presentations.

[0347] The generated document is then passed to a formatting processor, which applies the organization's proprietary formatting to the generated document, including brand colors, slide layouts, font sizes, etc., resulting in a consistent, attractive presentation.

[0348] A distinctive feature of the present invention is the incorporation of an emotion engine. The emotion engine analyzes emotions from text data entered by the user. The analyzed emotion data is fed back to the generative model means, which adjusts the tone and content of the generated document. This adjustment allows the generated document to reflect the user's emotions.

[0349] As a concrete example, if a user inputs "Please create a document about the market analysis of a new product in an optimistic tone," the server analyzes the text, extracts key points of the market analysis, and uses an emotion engine to recognize the emotion "optimistic." A market analysis report with an optimistic tone is then generated using a generative model. The generated report is formatted according to the organization's format and provided to the user with a unified design and layout. This process significantly reduces the time and effort required to create documents while providing high-quality documents. Furthermore, the quality of the content is improved because documents are generated according to the user's emotions.

[0350] The processing flow will be explained below.

[0351] Step 1:

[0352] The user uses the terminal to input the text that will become the content of the document. For example, they might input, "Please write a report on the features and market analysis of a new product in an optimistic tone." This input becomes the basic data for the document.

[0353] Step 2:

[0354] The text entered by the user is sent to the server by the terminal means. This transmission is performed using an HTTP request, and the data is sent in JSON format.

[0355] Step 3:

[0356] The server receives the text data sent from the terminal. The received data is analyzed by an analysis means, and the information necessary for document generation is extracted. Natural language processing technology is used to determine the intent and main points of the text.

[0357] Step 4:

[0358] The server passes the parsed text data to the emotion engine, which analyzes the emotions from the user's input text and generates emotion labels such as optimistic, pessimistic, or neutral.

[0359] Step 5:

[0360] The emotion data analyzed by the emotion engine is fed back to the generative modeling means, which then adjusts the tone and content of the document based on the analyzed emotion data. For example, if the tone is optimistic, positive and optimistic expressions are used in the document.

[0361] Step 6:

[0362] The generative modeling means generates detailed documents based on the analyzed data and sentiment data, and this generation process uses AI technology (e.g., GPT-4).

[0363] Step 7:

[0364] The generated document is passed to a formatting applicator, which arranges the document content based on a predefined, organization-specific template, which may include brand colors, slide layouts, font sizes, etc.

[0365] Step 8:

[0366] The formatted document is then sent back to the terminal via the return means. The sent data is in JSON format.

[0367] Step 9:

[0368] The user receives the final document on their device. They can review it and download or print it as needed. The document is ready to use, as it already has formatting and reflects the specified emotional tone.

[0369] Example 2

[0370] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0371] Conventional document generation systems require a lot of time and effort to create documents, and the generated documents have inconsistent formats and tones. Furthermore, they lack the functionality to reflect the user's emotions in the document, making it difficult to accurately reflect the user's intended tone and content.

[0372] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0373] In this invention, the server includes an analysis means for analyzing text data, a generative model means for generating documents based on the analyzed text data, and an emotion analysis means for analyzing emotions from the input text data. This not only enables accurate analysis of the intent and main points of the text data input by the user, but also enables the generation of documents with a tone and content that reflects the user's emotions. Furthermore, by applying an organization-specific format to the generated documents, consistent materials can be provided, significantly reducing the time and effort required to create documents.

[0374] The "terminal means" is a device for a user to input text.

[0375] The "server means" is a computer system that receives and processes text data sent from the terminal means.

[0376] The "analysis means" is a device or software that has the function of analyzing received text data and extracting basic data for document generation.

[0377] A "generative model means" is a device or software that uses AI techniques (e.g., natural language processing models) to generate documents based on analyzed text data.

[0378] The "format application means" is a device or software that has the function of applying an organization-specific format to the generated document.

[0379] The "returning means" is a device or software that has the function of returning the document to which the formatting means has been applied to the terminal means.

[0380] "Emotion analysis means" refers to a device or software that has the function of analyzing emotions from input text data.

[0381] A "natural language processing model" is a technology that refers to a machine learning model for understanding and generating human language.

[0382] A "template" is a predefined layout or design pattern for arranging document content.

[0383] This invention is a system that significantly reduces the time it takes for users to create documents and generates documents in a unified format. It also has the function of recognizing the user's emotions and adjusting the tone and content of the document. This system includes a terminal unit, a server unit, an analysis unit, a generative model unit, a format application unit, a return unit, and an emotion analysis unit.

[0384] First, the user uses a terminal to input the text that will be the content of the document. For example, the user can input the text, "Please prepare a report on the features and market analysis of a new product." The user can also specify the tone of the document, such as "optimistic tone." This input becomes the basic data for the document.

[0385] The terminal means transmits the input text data and emotion data to the server means using an HTTP request. The data is transmitted in JSON format.

[0386] The server means analyzes the received data using the analysis means. The analysis means uses natural language processing technology to clarify the intent of the text and the main points of the information. The analyzed data is passed to the generative model means. This generative model means uses a natural language processing model such as GPT-4. The generative model means generates a detailed document based on the analysis results.

[0387] The generated document is then passed to a formatting mechanism on the server, which applies the organization's proprietary formatting to the generated document, including brand colors, slide layouts, font sizes, etc. This ensures consistent, attractive presentation.

[0388] The sentiment analysis means also plays an important role. It analyzes emotions from the text data entered by the user and feeds the analyzed emotional data back to the generative model means. The generative model means then adjusts the tone and content of the generated document based on this information. This adjustment allows the generated document to better reflect the user's emotions.

[0389] As a concrete example, if a user inputs "Please create a document about the market analysis of a new product in an optimistic tone," the server analyzes the text, extracts key points of the market analysis, and recognizes the sentiment "optimistic" using a sentiment analysis means. A market analysis report with an optimistic tone is then generated using a generative model means. The generated report is formatted according to the organization's format and provided to the user with a unified design and layout. This process significantly reduces the time and effort required to create documents while providing high-quality documents. Furthermore, the quality of the content is improved because documents are generated according to the user's sentiment.

[0390] An example prompt is:

[0391] "Write your new product market analysis in an optimistic tone."

[0392] By feeding this prompt into a generative AI model, documents with the desired tone and content can be easily generated.

[0393] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0394] Step 1:

[0395] The user uses a terminal to input document content and emotions. Specifically, the user inputs a sentence such as "Please write a report on the features and market analysis of a new product" into the input field of the terminal. The user also specifies the tone of the document, such as "optimistic tone." This input data becomes the basic data of the document. Input: Document content and emotion data. Output: Data stored in the input field of the terminal.

[0396] Step 2:

[0397] The device converts the input text content and emotion data into JSON format and sends it to the server using an HTTP request. Specifically, the device serializes the input data into JSON format and sends it to the specified server URL. Input: Data stored in the device's input field. Output: HTTP request containing JSON format data.

[0398] Step 3:

[0399] The server receives an HTTP request and passes the JSON-formatted data to the analysis means. The analysis means analyzes the received data and uses natural language processing technology to extract the main points and intent of the text. Specifically, it uses a text analysis algorithm to clarify the user's intent. Input: JSON-formatted data. Output: Analyzed data with the main points and intent extracted.

[0400] Step 4:

[0401] The server passes the analyzed data to the generative modeling means. The generative modeling means (e.g., GPT-4) generates detailed documents based on this analyzed data. The generative modeling means determines the structure and content of the text based on a generative AI model. Input: analyzed data. Output: generated document.

[0402] Step 5:

[0403] The formatting mechanism in the server receives the generated document and applies the organization's specific formatting, such as document layout, font size, brand color, etc. according to a pre-defined template. Input: Generated document. Output: Formatted document.

[0404] Step 6:

[0405] The server returns the formatted document to the user's device. Specifically, it converts the formatted document into JSON format and sends it to the user's device as an HTTP response. Input: Formatted document. Output: Document data sent to the user's device.

[0406] Step 7:

[0407] The sentiment analysis means analyzes sentiment from the user's input text data and feeds this sentiment data back to the generative model means. Specifically, the sentiment data is analyzed using a sentiment analysis algorithm and re-input into the generative model means, adjusting the tone and content of the document. Input: User's sentiment data. Output: Adjusted document.

[0408] Through these steps, users can efficiently generate high-quality, uniformly formatted documents that reflect the user's emotions, allowing for better adjustment of the tone and content of the document.

[0409] (Application example 2)

[0410] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0411] Modern business requires the rapid creation of consistent, high-quality documents, but this requires a lot of time and effort. While resolving this issue, it is necessary to improve usability and the quality of communication by providing documents that are in tune with users' emotions. Online shopping sites, in particular, require the creation of user reviews and the quality and consistency of product descriptions. This will increase users' purchasing motivation and ensure the provision of reliable information.

[0412] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0413] In this invention, the server includes a terminal means for inputting text, a means for receiving text data input from the terminal means, and a generative model means for generating documents based on the analyzed text data. This makes it possible to analyze user emotions and automatically generate documents with a tone based on the emotions. The generated documents are formatted according to the organization's specifications, allowing for the rapid provision of consistent, high-quality documents. Furthermore, on online shopping sites, documents such as user reviews and product descriptions can be optimized based on emotions, which is expected to improve user satisfaction and reliability.

[0414] "Terminal means for inputting text" refers to a device for transmitting text data input by a user to a server.

[0415] The term "server means" refers to a server that processes text data received from a user and manages the analyzed and generated documents.

[0416] "Means for analyzing text data" refers to the process of analyzing received text data using natural language processing technology to clarify its intent and key points.

[0417] "Generative model means" refers to AI technology, specifically generative AI models, that generate detailed documents based on analyzed text data.

[0418] An "emotion engine" is an engine that analyzes emotions from text data entered by a user and adjusts the tone and content of the document based on those emotions.

[0419] "Format application means" refers to a process for applying an organization-specific format to the generated document to create a consistent document.

[0420] "Return means" refers to a mechanism for returning a document formatted by the format application means to the user's terminal means.

[0421] The present invention begins with a terminal means for inputting text. A user inputs text using this terminal means. This terminal means refers to devices such as a personal computer, smartphone, or tablet. For example, a user can input a sentence such as "This product was very useful." The input text data is transmitted to a server means via an HTTP request. The data is generally transmitted in JSON format.

[0422] The server means analyzes the received text data using natural language processing (NLP) techniques, such as a generative AI model such as OpenAI's GPT-4. This analysis process clarifies the intent and key information of the text. The server means passes the analyzed data to the generative model means, which generates a detailed document.

[0423] A distinctive feature of the present invention is the incorporation of an emotion engine. The emotion engine analyzes emotions from text data entered by the user. The analysis results are fed back to the generative model means, which adjusts the tone and content of the generated document. For example, if a user enters "I am satisfied with this product" and specifies the emotion as "positive," the generated document will have a positive tone.

[0424] In the next step, the generated document is passed to a format application means. The format application means in the server applies the organization's proprietary format to the generated document. This includes brand colors, slide layouts, font sizes, etc., to create a consistent and attractive document. The formatted document is finally returned to the user's terminal means by a return means.

[0425] As a concrete example, consider the case where a user enters a review on an online shopping site. The user enters a review such as "This product was very useful" along with a positive sentiment. The server means receives this and uses the analysis and generative model means to generate a detailed review with an appropriate tone. The format application means then applies a unified format to the review, which is then sent back to the terminal means. This process improves the consistency and quality of reviews, and increases user satisfaction.

[0426] Prompt Sentence Examples

[0427] A specific example of a prompt sentence entered in text format is as follows:

[0428] User Review: This product was very useful.

[0429] Emotion: Positive

[0430] Generate reviews with the right tone.

[0431] The present invention enables high quality document generation based on user-entered data, greatly improving the efficiency and consistency of document creation for specific applications.

[0432] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0433] Step 1:

[0434] The user inputs a sentence using a terminal. Specifically, the user inputs a sentence such as "This product was very useful" into an input form in a browser or application. This input data is sent to the server in JSON format. The input data also includes the user's sentiment (e.g., positive).

[0435] Step 2:

[0436] The server receives the text data sent from the terminal device. The server receives the data via an HTTP request. This data is in JSON format and includes the text content and sentiment. The server prepares to move on to the next step of analyzing this data.

[0437] Step 3:

[0438] The server analyzes the received text data using natural language processing (NLP) technology, such as a generative AI model like GPT-4. During this process, the server extracts the intent and key points of the text and uses an emotion engine to analyze the user's emotions. The input is JSON-formatted text data, and the output is the analyzed text data and emotion information.

[0439] Step 4:

[0440] The server generates documents based on the analyzed data. The generative model generates detailed documents using, for example, OpenAI's GPT-4. In this process, the generative AI model generates documents based on the user's intent and adjusts the tone and content of the documents to match the user's emotions. The input is the analyzed text data and emotional information, and the output is the generated document.

[0441] Step 5:

[0442] The server applies the organization's proprietary formatting to the generated document. The formatting method uses predefined templates to apply brand colors, slide layouts, font sizes, etc. to the document, resulting in consistent and attractive documents. The input is the generated document and the output is the formatted document.

[0443] Step 6:

[0444] The server returns the formatted document to the terminal means. The return means uses an HTTP response to send the document data back to the terminal means. The user can check the returned document and save or share it as needed. The input is the formatted document, and the output is the returned document.

[0445] 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.

[0446] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[0447] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0448] [Third embodiment]

[0449] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0450] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0451] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0452] 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.

[0453] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0454] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0455] 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.

[0456] 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.

[0457] 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 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.

[0458] 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.

[0459] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0460] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0461] The present invention provides a system that significantly reduces the time required for users to create documents and realizes document generation in a unified format. This system includes a terminal means, a server means, an analyzing means, a generative model means, a format applying means, and a returning means.

[0462] First, the user inputs text that will be the content of the document using the terminal means. For example, the user can input the text, "Please prepare a report on the features and market analysis of a new product." The input text is then sent to the server means by the terminal means.

[0463] The server means receives the text data sent from the terminal means. The received data is analyzed by the analysis means, and basic data for document generation is extracted. Natural language processing technology is used in the analysis to clarify the intent of the text and the main points of the information.

[0464] The analyzed data is passed to a generative modeling tool, which uses AI technology (such as GPT-4) to generate detailed documents based on the analysis results. This organizes the information based on the user's intended content and creates specific reports and presentations.

[0465] The generated documents are then formatted using a predefined organizational formatting mechanism, including uniform standards for color, slide layout, font size, etc., ensuring consistent and attractive presentation.

[0466] Finally, the formatted document is sent back to the terminal means via the return means. The user can receive the document generated on the terminal means, check it, and use it.

[0467] For example, if a user types "Please create a document about the market analysis of a new product," the server analyzes the text, extracts key points of the market analysis, and generates a detailed market analysis report using a generative model. The generated report is formatted according to the organization's format and provided to the user with a unified design and layout. This process significantly reduces the time and effort required to create the document while providing high-quality documents.

[0468] The processing flow will be explained below.

[0469] Step 1:

[0470] The user uses the terminal to input the text that will become the content of the document. For example, they might input, "Please prepare a report on the features and market analysis of a new product." This input becomes the basic data for the document.

[0471] Step 2:

[0472] The text entered by the user is sent to the server by the terminal means. This transmission is performed using an HTTP request, and the data is sent in JSON format.

[0473] Step 3:

[0474] The server receives the text data sent from the terminal, and then starts a process to analyze the text data.

[0475] Step 4:

[0476] The server uses analytical tools to interpret the received text and extract information for generating appropriate documents. It uses natural language processing techniques to understand the intent and main points of the text.

[0477] Step 5:

[0478] The server inputs the analysis results into a generative model means, which uses AI techniques to generate detailed documents based on the analyzed data.

[0479] Step 6:

[0480] The generated document is passed to a formatting mechanism within the server that applies organization-specific formatting to the generated document, including brand colors and slide layouts.

[0481] Step 7:

[0482] The formatted document is sent from the server to the terminal via the return means. The sent data is also in JSON format.

[0483] Step 8:

[0484] The user receives the final document on their device. They can review the document and download or print it as needed. The generated document is already formatted, so the user can use it as is.

[0485] Example 1

[0486] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0487] The traditional document creation process requires users to create documents manually, which is time-consuming, labor-intensive, and inefficient. Furthermore, formats may not be standardized, potentially affecting the overall appearance and quality of the organization. Therefore, there is a need for a system that can reduce the burden on users and quickly create high-quality documents with a consistent look.

[0488] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0489] In this invention, the server includes a computer means for receiving text data, a means for analyzing the text data, and a generating artificial intelligence model means for generating documents based on the analyzed text data. This automates the entire process of document creation, enabling efficient, consistent, and high-quality document creation.

[0490] "Information processing device means" refers to a device or system that allows a user to input text, such as a personal computer or tablet.

[0491] The term "computer means" refers to a computer system such as a server or cloud service that receives and processes text data sent from the information processing means.

[0492] "Analyzing means" refers to software or algorithms that are used by the computing means to analyze the received text data and extract the underlying data.

[0493] "Generative artificial intelligence model means" refers to AI techniques or machine learning models used to generate documents based on analyzed data, including, for example, natural language processing models such as GPT-4.

[0494] "Format application means" refers to software or programs that apply an organization's proprietary format to the generated documents.

[0495] "Returning means" refers to a communication means or protocol for sending the processed document back to the information processing means.

[0496] The present invention provides a system that significantly reduces the time required for users to create documents and generates documents in a unified format. Specific embodiments of this system will be described below.

[0497] First, a user inputs text that will be the content of a document using an information processing device. Information processing devices include devices such as personal computers and tablets. For example, a user can input text such as "Please prepare a report on the features and market analysis of a new product." The input text data is transmitted from the information processing device to the computer.

[0498] The computing means refers to a server on a cloud service or a similar computing system, and is responsible for receiving text data sent from the information processing means. The received data is processed by the analyzing means, where natural language processing techniques are used to clarify the intent and main points of the text. This analysis process includes steps such as tokenization, keyword extraction, and intent recognition.

[0499] The analyzed data is passed to a generative AI modeling means, which may include a powerful natural language processing model such as GPT-4. This model generates a document based on the analyzed data. The generated document organizes the information intended by the user and is structured into a specific report or presentation.

[0500] Next, a formatting application unit operates on the generated document. The formatting application unit applies a predefined, organization-specific format to the document. This formatting includes color usage, slide layout, font size, etc., and applies the unified standards established by the organization. This results in the creation of consistent, attractive-looking documents.

[0501] Finally, the formatted document is sent back to the information processing device via the return means. The user receives the document generated by the information processing device and can check and use it.

[0502] As a specific example, if a user inputs "Please create a document about the market analysis of a new product," the computer means will analyze the text and extract key points of the market analysis. The analysis results will be passed to the generative artificial intelligence model means, which will generate a detailed market analysis report. The generated report will be formatted in the organization's format and provided to the user with a unified design and layout. This process allows the user to significantly reduce the time and effort required to create documents and quickly obtain high-quality documents.

[0503] An example of a prompt sentence is "Please prepare a document about the market analysis of a new product."

[0504] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0505] Step 1:

[0506] The user uses the information processing device to input text that will become the content of the document. At this time, the input data is a prompt sentence such as "Please prepare a report on the features and market analysis of a new product." The input text data is sent to the computer device as an HTTP request.

[0507] Step 2:

[0508] The terminal sends the input text data to the server. Specifically, a POST request is made from the terminal to the computer means, and a prompt sentence is sent to the computer means. At this point, the input is the prompt sentence, and the output is the execution of data transfer to the server.

[0509] Step 3:

[0510] The server receives the prompt sent from the device. This reception process receives an HTTP request using an API endpoint. The input at this point is the prompt sent from the device, and the output is the received text data.

[0511] Step 4:

[0512] The server analyzes the text data using a method for analyzing received data. This analysis uses natural language processing (NLP) technology to extract the intent and main points of the text. Specifically, the text is tokenized, and keywords and intent recognition are performed. The input at this point is the prompt received by the server, and the output becomes the basic data after analysis.

[0513] Step 5:

[0514] The analyzed data is sent to a generative AI modeling means, which generates documents based on the analysis results using, for example, GPT-4. At this point, the input is the analyzed basic data, and the output is the generated detailed document.

[0515] Step 6:

[0516] The server then applies the organization's proprietary formatting to the generated document using a formatting application mechanism, using predefined templates to set color usage, slide layout, font size, etc. At this point, the input is the generated document, and the output is the formatted document.

[0517] Step 7:

[0518] The formatted document is then sent back to the terminal using the return means. Specifically, the generated PDF or PPT file is provided to the terminal as a download link from the server. At this point, the input is the formatted document, and the output is the document sent to the information processing means.

[0519] Step 8:

[0520] The user receives the returned document via the information processing device, and then checks and uses it. The user clicks on the provided link to download the file and check the contents. The input at this point is the download link, and the output is the formatted document that the user has obtained.

[0521] (Application example 1)

[0522] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0523] In recent years, creating documents for new product introductions and market analysis in brick-and-mortar stores has become a complex and time-consuming task, placing a heavy burden on store managers. Furthermore, while creating documents requires generating them in a standardized format, doing this manually leaves issues with quality and efficiency. Furthermore, consistency and prompt delivery of the generated documents are required. A system to solve these issues is needed.

[0524] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0525] In this invention, the server includes a terminal means for inputting text, a server means for receiving text data input from the terminal means, a means for the server means to analyze the text data, a generative model means for the server means to generate a document based on the analyzed text data, a format application means for applying a format exclusive to the organization to the generated document, a return means for returning the document to which the format application means has been applied to the terminal means, and a means for applying the document generation system to applications in the field of brick-and-mortar stores. This enables documents such as new product introduction materials and market analysis reports to be generated quickly and in a unified format for brick-and-mortar stores.

[0526] "Terminal means" refers to a device that allows a user to input text.

[0527] "Server means" refers to a central processing unit that receives and processes text data input from terminal means.

[0528] The "analyzing means" refers to a device or program having the function of analyzing the text data received by the server means and extracting the main points of the information.

[0529] "Generative model means" refers to a system that uses artificial intelligence or other techniques to generate documents based on analyzed textual data.

[0530] "Format application means" refers to a device or program for applying a predefined, organization-specific format to the generated document.

[0531] The "returning means" refers to a communication means for returning the document to which the formatting means has been applied to the terminal means.

[0532] "Means for applying to applications in the field of brick-and-mortar stores" refers to methods for applying the document generation system to the creation of new product introduction materials and market analysis reports in brick-and-mortar stores.

[0533] The present invention relates to a system that significantly reduces the time it takes for users to create documents and generates documents in a unified format. The system includes a terminal, a server, an analyzing unit, a generative model, a format application unit, and a return unit. The system is characterized by its application in the brick-and-mortar retail field.

[0534] First, the user inputs the text that will be the content of the document using a terminal. For example, the user can input, "Please create a document about the features of the new product and the target market." The user inputs this content using a terminal such as a smartphone.

[0535] Next, the input text data is sent by the terminal means to the server means. The server means receives the text data and analyzes its contents using the analysis means. This analysis utilizes natural language processing technology. Specifically, keywords are extracted and the meaning of the text is clarified.

[0536] The analyzed data is passed to a generative modeling means, which uses a generative AI model (such as GPT-4) to generate detailed documents based on the analysis results. This allows information to be organized based on the user's intended content, and specific reports and materials can be created.

[0537] The generated documents are then formatted using a predefined, organization-specific formatting mechanism, including uniform standards for color, layout, font size, etc., ensuring consistent, aesthetically pleasing documents.

[0538] Finally, the formatted document is sent back to the terminal means via the return means, and the user can receive, check, and use the document generated at the terminal means.

[0539] As a specific example, if a user inputs "Please create a document about the features and target market of a new product," the server analyzes the text, extracts key points of market analysis, and generates a detailed market analysis report using a generative model. The generated report is formatted according to the organization's specifications and provided to the user with a unified design and layout. This process significantly reduces the time and effort required to create materials for introducing new products in physical stores, and makes it possible to provide high-quality materials.

[0540] This allows users to create high-quality materials in a short amount of time, improving the operational efficiency of physical stores.

[0541] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0542] Step 1:

[0543] The user uses the terminal means to input text that will become the content of the document. For example, the user might input, "Please prepare a document about the features of the new product and its target market." The input data here is text data entered by the user.

[0544] Step 2:

[0545] The terminal means transmits the input text data to the server means. This transmitted data is text data input by the user.

[0546] Step 3:

[0547] The server means passes the text data received from the terminal means to the analyzing means. The input data here is the text data received from the terminal means. The analyzing means analyzes the text data using natural language processing technology, extracts keywords, and clarifies the meaning of the text. Analysis data is generated by this process.

[0548] Step 4:

[0549] The analyzed data is passed to a generative modeling means, which uses a generative AI model (e.g., GPT-4) to generate a detailed document based on the analysis results. The input data for this process is the analyzed data, and the output data is the generated document data.

[0550] Step 5:

[0551] The generated document is passed to a format application means, which applies a predefined, organization-specific format to the generated document. The input data for this process is the generated document data, and the output data is the formatted document data.

[0552] Step 6:

[0553] Finally, the formatted document is sent back to the terminal via the return means. The input data of this process is the formatted document data, and the output data is the document displayed by the user's terminal. The user receives the document generated by the terminal and can check and use it.

[0554] This series of processes makes it possible to quickly create new product introduction materials and market analysis reports for brick-and-mortar stores in a unified format, simplifying the work involved and improving the quality of the materials.

[0555] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0556] The present invention is a system that significantly reduces the time it takes for users to create documents and generates documents in a unified format, and also adds a function to recognize the user's emotions and adjust the tone and content of the document. This system includes a terminal means, a server means, an analyzing means, a generative model means, a format applying means, a return means, and an emotion engine.

[0557] First, the user uses the terminal to input the text that will be the content of the document. For example, the user can input the text, "Please prepare a report on the features and market analysis of a new product." This input becomes the basic data for the document.

[0558] The text entered by the user is sent to the server by the terminal means. This transmission is performed using an HTTP request, and the data is sent in JSON format.

[0559] The server receives the text data sent from the terminal. The received data is analyzed by an analysis means to extract basic data for document generation. Natural language processing technology is used for the analysis to clarify the intent of the text and the main points of the information.

[0560] The analyzed data is passed to a generative modeling tool, which uses AI technology (such as GPT-4) to generate detailed documents based on the analysis results. This organizes the information based on the user's intended content and creates specific reports and presentations.

[0561] The generated document is then passed to a formatting processor, which applies the organization's proprietary formatting to the generated document, including brand colors, slide layouts, font sizes, etc., resulting in a consistent, attractive presentation.

[0562] A distinctive feature of the present invention is the incorporation of an emotion engine. The emotion engine analyzes emotions from text data entered by the user. The analyzed emotion data is fed back to the generative model means, which adjusts the tone and content of the generated document. This adjustment allows the generated document to reflect the user's emotions.

[0563] As a concrete example, if a user inputs "Please create a document about the market analysis of a new product in an optimistic tone," the server analyzes the text, extracts key points of the market analysis, and uses an emotion engine to recognize the emotion "optimistic." A market analysis report with an optimistic tone is then generated using a generative model. The generated report is formatted according to the organization's format and provided to the user with a unified design and layout. This process significantly reduces the time and effort required to create documents while providing high-quality documents. Furthermore, the quality of the content is improved because documents are generated according to the user's emotions.

[0564] The processing flow will be explained below.

[0565] Step 1:

[0566] The user uses the terminal to input the text that will become the content of the document. For example, they might input, "Please write a report on the features and market analysis of a new product in an optimistic tone." This input becomes the basic data for the document.

[0567] Step 2:

[0568] The text entered by the user is sent to the server by the terminal means. This transmission is performed using an HTTP request, and the data is sent in JSON format.

[0569] Step 3:

[0570] The server receives the text data sent from the terminal. The received data is analyzed by an analysis means, and the information necessary for document generation is extracted. Natural language processing technology is used to determine the intent and main points of the text.

[0571] Step 4:

[0572] The server passes the parsed text data to the emotion engine, which analyzes the emotions from the user's input text and generates emotion labels such as optimistic, pessimistic, or neutral.

[0573] Step 5:

[0574] The emotion data analyzed by the emotion engine is fed back to the generative modeling means, which then adjusts the tone and content of the document based on the analyzed emotion data. For example, if the tone is optimistic, positive and optimistic expressions are used in the document.

[0575] Step 6:

[0576] The generative modeling means generates detailed documents based on the analyzed data and sentiment data, and this generation process uses AI technology (e.g., GPT-4).

[0577] Step 7:

[0578] The generated document is passed to a formatting applicator, which arranges the document content based on a predefined, organization-specific template, which may include brand colors, slide layouts, font sizes, etc.

[0579] Step 8:

[0580] The formatted document is then sent back to the terminal via the return means. The sent data is in JSON format.

[0581] Step 9:

[0582] The user receives the final document on their device. They can review it and download or print it as needed. The document is ready to use, as it already has formatting and reflects the specified emotional tone.

[0583] Example 2

[0584] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0585] Conventional document generation systems require a lot of time and effort to create documents, and the generated documents have inconsistent formats and tones. Furthermore, they lack the functionality to reflect the user's emotions in the document, making it difficult to accurately reflect the user's intended tone and content.

[0586] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0587] In this invention, the server includes an analysis means for analyzing text data, a generative model means for generating documents based on the analyzed text data, and an emotion analysis means for analyzing emotions from the input text data. This not only enables accurate analysis of the intent and main points of the text data input by the user, but also enables the generation of documents with a tone and content that reflects the user's emotions. Furthermore, by applying an organization-specific format to the generated documents, consistent materials can be provided, significantly reducing the time and effort required to create documents.

[0588] The "terminal means" is a device for a user to input text.

[0589] The "server means" is a computer system that receives and processes text data sent from the terminal means.

[0590] The "analysis means" is a device or software that has the function of analyzing received text data and extracting basic data for document generation.

[0591] A "generative model means" is a device or software that uses AI techniques (e.g., natural language processing models) to generate documents based on analyzed text data.

[0592] The "format application means" is a device or software that has the function of applying an organization-specific format to the generated document.

[0593] The "returning means" is a device or software that has the function of returning the document to which the formatting means has been applied to the terminal means.

[0594] "Emotion analysis means" refers to a device or software that has the function of analyzing emotions from input text data.

[0595] A "natural language processing model" is a technology that refers to a machine learning model for understanding and generating human language.

[0596] A "template" is a predefined layout or design pattern for arranging document content.

[0597] This invention is a system that significantly reduces the time it takes for users to create documents and generates documents in a unified format. It also has the function of recognizing the user's emotions and adjusting the tone and content of the document. This system includes a terminal unit, a server unit, an analysis unit, a generative model unit, a format application unit, a return unit, and an emotion analysis unit.

[0598] First, the user uses a terminal to input the text that will be the content of the document. For example, the user can input the text, "Please prepare a report on the features and market analysis of a new product." The user can also specify the tone of the document, such as "optimistic tone." This input becomes the basic data for the document.

[0599] The terminal means transmits the input text data and emotion data to the server means using an HTTP request. The data is transmitted in JSON format.

[0600] The server means analyzes the received data using the analysis means. The analysis means uses natural language processing technology to clarify the intent of the text and the main points of the information. The analyzed data is passed to the generative model means. This generative model means uses a natural language processing model such as GPT-4. The generative model means generates a detailed document based on the analysis results.

[0601] The generated document is then passed to a formatting mechanism on the server, which applies the organization's proprietary formatting to the generated document, including brand colors, slide layouts, font sizes, etc. This ensures consistent, attractive presentation.

[0602] The sentiment analysis means also plays an important role. It analyzes emotions from the text data entered by the user and feeds the analyzed emotional data back to the generative model means. The generative model means then adjusts the tone and content of the generated document based on this information. This adjustment allows the generated document to better reflect the user's emotions.

[0603] As a concrete example, if a user inputs "Please create a document about the market analysis of a new product in an optimistic tone," the server analyzes the text, extracts key points of the market analysis, and recognizes the sentiment "optimistic" using a sentiment analysis means. A market analysis report with an optimistic tone is then generated using a generative model means. The generated report is formatted according to the organization's format and provided to the user with a unified design and layout. This process significantly reduces the time and effort required to create documents while providing high-quality documents. Furthermore, the quality of the content is improved because documents are generated according to the user's sentiment.

[0604] An example prompt is:

[0605] "Write your new product market analysis in an optimistic tone."

[0606] By feeding this prompt into a generative AI model, documents with the desired tone and content can be easily generated.

[0607] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0608] Step 1:

[0609] The user uses a terminal to input document content and emotions. Specifically, the user inputs a sentence such as "Please write a report on the features and market analysis of a new product" into the input field of the terminal. The user also specifies the tone of the document, such as "optimistic tone." This input data becomes the basic data of the document. Input: Document content and emotion data. Output: Data stored in the input field of the terminal.

[0610] Step 2:

[0611] The device converts the input text content and emotion data into JSON format and sends it to the server using an HTTP request. Specifically, the device serializes the input data into JSON format and sends it to the specified server URL. Input: Data stored in the device's input field. Output: HTTP request containing JSON format data.

[0612] Step 3:

[0613] The server receives an HTTP request and passes the JSON-formatted data to the analysis means. The analysis means analyzes the received data and uses natural language processing technology to extract the main points and intent of the text. Specifically, it uses a text analysis algorithm to clarify the user's intent. Input: JSON-formatted data. Output: Analyzed data with the main points and intent extracted.

[0614] Step 4:

[0615] The server passes the analyzed data to the generative modeling means. The generative modeling means (e.g., GPT-4) generates detailed documents based on this analyzed data. The generative modeling means determines the structure and content of the text based on a generative AI model. Input: analyzed data. Output: generated document.

[0616] Step 5:

[0617] The formatting mechanism in the server receives the generated document and applies the organization's specific formatting, such as document layout, font size, brand color, etc. according to a pre-defined template. Input: Generated document. Output: Formatted document.

[0618] Step 6:

[0619] The server returns the formatted document to the user's device. Specifically, it converts the formatted document into JSON format and sends it to the user's device as an HTTP response. Input: Formatted document. Output: Document data sent to the user's device.

[0620] Step 7:

[0621] The sentiment analysis means analyzes sentiment from the user's input text data and feeds this sentiment data back to the generative model means. Specifically, the sentiment data is analyzed using a sentiment analysis algorithm and re-input into the generative model means, adjusting the tone and content of the document. Input: User's sentiment data. Output: Adjusted document.

[0622] Through these steps, users can efficiently generate high-quality, uniformly formatted documents that reflect the user's emotions, allowing for better adjustment of the tone and content of the document.

[0623] (Application example 2)

[0624] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0625] Modern business requires the rapid creation of consistent, high-quality documents, but this requires a lot of time and effort. While resolving this issue, it is necessary to improve usability and the quality of communication by providing documents that are in tune with users' emotions. Online shopping sites, in particular, require the creation of user reviews and the quality and consistency of product descriptions. This will increase users' purchasing motivation and ensure the provision of reliable information.

[0626] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0627] In this invention, the server includes a terminal means for inputting text, a means for receiving text data input from the terminal means, and a generative model means for generating documents based on the analyzed text data. This makes it possible to analyze user emotions and automatically generate documents with a tone based on the emotions. The generated documents are formatted according to the organization's specifications, allowing for the rapid provision of consistent, high-quality documents. Furthermore, on online shopping sites, documents such as user reviews and product descriptions can be optimized based on emotions, which is expected to improve user satisfaction and reliability.

[0628] "Terminal means for inputting text" refers to a device for transmitting text data input by a user to a server.

[0629] The term "server means" refers to a server that processes text data received from a user and manages the analyzed and generated documents.

[0630] "Means for analyzing text data" refers to the process of analyzing received text data using natural language processing technology to clarify its intent and key points.

[0631] "Generative model means" refers to AI technology, specifically generative AI models, that generate detailed documents based on analyzed text data.

[0632] An "emotion engine" is an engine that analyzes emotions from text data entered by a user and adjusts the tone and content of the document based on those emotions.

[0633] "Format application means" refers to a process for applying an organization-specific format to the generated document to create a consistent document.

[0634] "Return means" refers to a mechanism for returning a document formatted by the format application means to the user's terminal means.

[0635] The present invention begins with a terminal means for inputting text. A user inputs text using this terminal means. This terminal means refers to devices such as a personal computer, smartphone, or tablet. For example, a user can input a sentence such as "This product was very useful." The input text data is transmitted to a server means via an HTTP request. The data is generally transmitted in JSON format.

[0636] The server means analyzes the received text data using natural language processing (NLP) techniques, such as a generative AI model such as OpenAI's GPT-4. This analysis process clarifies the intent and key information of the text. The server means passes the analyzed data to the generative model means, which generates a detailed document.

[0637] A distinctive feature of the present invention is the incorporation of an emotion engine. The emotion engine analyzes emotions from text data entered by the user. The analysis results are fed back to the generative model means, which adjusts the tone and content of the generated document. For example, if a user enters "I am satisfied with this product" and specifies the emotion as "positive," the generated document will have a positive tone.

[0638] In the next step, the generated document is passed to a format application means. The format application means in the server applies the organization's proprietary format to the generated document. This includes brand colors, slide layouts, font sizes, etc., to create a consistent and attractive document. The formatted document is finally returned to the user's terminal means by a return means.

[0639] As a concrete example, consider the case where a user enters a review on an online shopping site. The user enters a review such as "This product was very useful" along with a positive sentiment. The server means receives this and uses the analysis and generative model means to generate a detailed review with an appropriate tone. The format application means then applies a unified format to the review, which is then sent back to the terminal means. This process improves the consistency and quality of reviews, and increases user satisfaction.

[0640] Prompt Sentence Examples

[0641] A specific example of a prompt sentence entered in text format is as follows:

[0642] User Review: This product was very useful.

[0643] Emotion: Positive

[0644] Generate reviews with the right tone.

[0645] The present invention enables high quality document generation based on user-entered data, greatly improving the efficiency and consistency of document creation for specific applications.

[0646] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0647] Step 1:

[0648] The user inputs a sentence using a terminal. Specifically, the user inputs a sentence such as "This product was very useful" into an input form in a browser or application. This input data is sent to the server in JSON format. The input data also includes the user's sentiment (e.g., positive).

[0649] Step 2:

[0650] The server receives the text data sent from the terminal device. The server receives the data via an HTTP request. This data is in JSON format and includes the text content and sentiment. The server prepares to move on to the next step of analyzing this data.

[0651] Step 3:

[0652] The server analyzes the received text data using natural language processing (NLP) technology, such as a generative AI model like GPT-4. During this process, the server extracts the intent and key points of the text and uses an emotion engine to analyze the user's emotions. The input is JSON-formatted text data, and the output is the analyzed text data and emotion information.

[0653] Step 4:

[0654] The server generates documents based on the analyzed data. The generative model generates detailed documents using, for example, OpenAI's GPT-4. In this process, the generative AI model generates documents based on the user's intent and adjusts the tone and content of the documents to match the user's emotions. The input is the analyzed text data and emotional information, and the output is the generated document.

[0655] Step 5:

[0656] The server applies the organization's proprietary formatting to the generated document. The formatting method uses predefined templates to apply brand colors, slide layouts, font sizes, etc. to the document, resulting in consistent and attractive documents. The input is the generated document and the output is the formatted document.

[0657] Step 6:

[0658] The server returns the formatted document to the terminal means. The return means uses an HTTP response to send the document data back to the terminal means. The user can check the returned document and save or share it as needed. The input is the formatted document, and the output is the returned document.

[0659] 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.

[0660] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[0661] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0662] [Fourth embodiment]

[0663] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0664] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0665] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0666] 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.

[0667] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0668] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0669] 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.

[0670] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[0671] 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.

[0672] 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 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.

[0673] 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.

[0674] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0675] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0676] The present invention provides a system that significantly reduces the time required for users to create documents and realizes document generation in a unified format. This system includes a terminal means, a server means, an analyzing means, a generative model means, a format applying means, and a returning means.

[0677] First, the user inputs text that will be the content of the document using the terminal means. For example, the user can input the text, "Please prepare a report on the features and market analysis of a new product." The input text is then sent to the server means by the terminal means.

[0678] The server means receives the text data sent from the terminal means. The received data is analyzed by the analysis means, and basic data for document generation is extracted. Natural language processing technology is used in the analysis to clarify the intent of the text and the main points of the information.

[0679] The analyzed data is passed to a generative modeling tool, which uses AI technology (such as GPT-4) to generate detailed documents based on the analysis results. This organizes the information based on the user's intended content and creates specific reports and presentations.

[0680] The generated documents are then formatted using a predefined organizational formatting mechanism, including uniform standards for color, slide layout, font size, etc., ensuring consistent and attractive presentation.

[0681] Finally, the formatted document is sent back to the terminal means via the return means. The user can receive the document generated on the terminal means, check it, and use it.

[0682] For example, if a user types "Please create a document about the market analysis of a new product," the server analyzes the text, extracts key points of the market analysis, and generates a detailed market analysis report using a generative model. The generated report is formatted according to the organization's format and provided to the user with a unified design and layout. This process significantly reduces the time and effort required to create the document while providing high-quality documents.

[0683] The processing flow will be explained below.

[0684] Step 1:

[0685] The user uses the terminal to input the text that will become the content of the document. For example, they might input, "Please prepare a report on the features and market analysis of a new product." This input becomes the basic data for the document.

[0686] Step 2:

[0687] The text entered by the user is sent to the server by the terminal means. This transmission is performed using an HTTP request, and the data is sent in JSON format.

[0688] Step 3:

[0689] The server receives the text data sent from the terminal, and then starts a process to analyze the text data.

[0690] Step 4:

[0691] The server uses analytical tools to interpret the received text and extract information for generating appropriate documents. It uses natural language processing techniques to understand the intent and main points of the text.

[0692] Step 5:

[0693] The server inputs the analysis results into a generative model means, which uses AI techniques to generate detailed documents based on the analyzed data.

[0694] Step 6:

[0695] The generated document is passed to a formatting mechanism within the server that applies organization-specific formatting to the generated document, including brand colors and slide layouts.

[0696] Step 7:

[0697] The formatted document is sent from the server to the terminal via the return means. The sent data is also in JSON format.

[0698] Step 8:

[0699] The user receives the final document on their device. They can review the document and download or print it as needed. The generated document is already formatted, so the user can use it as is.

[0700] Example 1

[0701] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0702] The traditional document creation process requires users to create documents manually, which is time-consuming, labor-intensive, and inefficient. Furthermore, formats may not be standardized, potentially affecting the overall appearance and quality of the organization. Therefore, there is a need for a system that can reduce the burden on users and quickly create high-quality documents with a consistent look.

[0703] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0704] In this invention, the server includes a computer means for receiving text data, a means for analyzing the text data, and a generating artificial intelligence model means for generating documents based on the analyzed text data. This automates the entire process of document creation, enabling efficient, consistent, and high-quality document creation.

[0705] "Information processing device means" refers to a device or system that allows a user to input text, such as a personal computer or tablet.

[0706] The term "computer means" refers to a computer system such as a server or cloud service that receives and processes text data sent from the information processing means.

[0707] "Analyzing means" refers to software or algorithms that are used by the computing means to analyze the received text data and extract the underlying data.

[0708] "Generative artificial intelligence model means" refers to AI techniques or machine learning models used to generate documents based on analyzed data, including, for example, natural language processing models such as GPT-4.

[0709] "Format application means" refers to software or programs that apply an organization's proprietary format to the generated documents.

[0710] "Returning means" refers to a communication means or protocol for sending the processed document back to the information processing means.

[0711] The present invention provides a system that significantly reduces the time required for users to create documents and generates documents in a unified format. Specific embodiments of this system will be described below.

[0712] First, a user inputs text that will be the content of a document using an information processing device. Information processing devices include devices such as personal computers and tablets. For example, a user can input text such as "Please prepare a report on the features and market analysis of a new product." The input text data is transmitted from the information processing device to the computer.

[0713] The computing means refers to a server on a cloud service or a similar computing system, and is responsible for receiving text data sent from the information processing means. The received data is processed by the analyzing means, where natural language processing techniques are used to clarify the intent and main points of the text. This analysis process includes steps such as tokenization, keyword extraction, and intent recognition.

[0714] The analyzed data is passed to a generative AI modeling means, which may include a powerful natural language processing model such as GPT-4. This model generates a document based on the analyzed data. The generated document organizes the information intended by the user and is structured into a specific report or presentation.

[0715] Next, a formatting application unit operates on the generated document. The formatting application unit applies a predefined, organization-specific format to the document. This formatting includes color usage, slide layout, font size, etc., and applies the unified standards established by the organization. This results in the creation of consistent, attractive-looking documents.

[0716] Finally, the formatted document is sent back to the information processing device via the return means. The user receives the document generated by the information processing device and can check and use it.

[0717] As a specific example, if a user inputs "Please create a document about the market analysis of a new product," the computer means will analyze the text and extract key points of the market analysis. The analysis results will be passed to the generative artificial intelligence model means, which will generate a detailed market analysis report. The generated report will be formatted in the organization's format and provided to the user with a unified design and layout. This process allows the user to significantly reduce the time and effort required to create documents and quickly obtain high-quality documents.

[0718] An example of a prompt sentence is "Please prepare a document about the market analysis of a new product."

[0719] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0720] Step 1:

[0721] The user uses the information processing device to input text that will become the content of the document. At this time, the input data is a prompt sentence such as "Please prepare a report on the features and market analysis of a new product." The input text data is sent to the computer device as an HTTP request.

[0722] Step 2:

[0723] The terminal sends the input text data to the server. Specifically, a POST request is made from the terminal to the computer means, and a prompt sentence is sent to the computer means. At this point, the input is the prompt sentence, and the output is the execution of data transfer to the server.

[0724] Step 3:

[0725] The server receives the prompt sent from the device. This reception process receives an HTTP request using an API endpoint. The input at this point is the prompt sent from the device, and the output is the received text data.

[0726] Step 4:

[0727] The server analyzes the text data using a method for analyzing received data. This analysis uses natural language processing (NLP) technology to extract the intent and main points of the text. Specifically, the text is tokenized, and keywords and intent recognition are performed. The input at this point is the prompt received by the server, and the output becomes the basic data after analysis.

[0728] Step 5:

[0729] The analyzed data is sent to a generative AI modeling means, which generates documents based on the analysis results using, for example, GPT-4. At this point, the input is the analyzed basic data, and the output is the generated detailed document.

[0730] Step 6:

[0731] The server then applies the organization's proprietary formatting to the generated document using a formatting application mechanism, using predefined templates to set color usage, slide layout, font size, etc. At this point, the input is the generated document, and the output is the formatted document.

[0732] Step 7:

[0733] The formatted document is then sent back to the terminal using the return means. Specifically, the generated PDF or PPT file is provided to the terminal as a download link from the server. At this point, the input is the formatted document, and the output is the document sent to the information processing means.

[0734] Step 8:

[0735] The user receives the returned document via the information processing device, and then checks and uses it. The user clicks on the provided link to download the file and check the contents. The input at this point is the download link, and the output is the formatted document that the user has obtained.

[0736] (Application example 1)

[0737] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0738] In recent years, creating documents for new product introductions and market analysis in brick-and-mortar stores has become a complex and time-consuming task, placing a heavy burden on store managers. Furthermore, while creating documents requires generating them in a standardized format, doing this manually leaves issues with quality and efficiency. Furthermore, consistency and prompt delivery of the generated documents are required. A system to solve these issues is needed.

[0739] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0740] In this invention, the server includes a terminal means for inputting text, a server means for receiving text data input from the terminal means, a means for the server means to analyze the text data, a generative model means for the server means to generate a document based on the analyzed text data, a format application means for applying a format exclusive to the organization to the generated document, a return means for returning the document to which the format application means has been applied to the terminal means, and a means for applying the document generation system to applications in the field of brick-and-mortar stores. This enables documents such as new product introduction materials and market analysis reports to be generated quickly and in a unified format for brick-and-mortar stores.

[0741] "Terminal means" refers to a device that allows a user to input text.

[0742] "Server means" refers to a central processing unit that receives and processes text data input from terminal means.

[0743] The "analyzing means" refers to a device or program having the function of analyzing the text data received by the server means and extracting the main points of the information.

[0744] "Generative model means" refers to a system that uses artificial intelligence or other techniques to generate documents based on analyzed textual data.

[0745] "Format application means" refers to a device or program for applying a predefined, organization-specific format to the generated document.

[0746] The "returning means" refers to a communication means for returning the document to which the formatting means has been applied to the terminal means.

[0747] "Means for applying to applications in the field of brick-and-mortar stores" refers to methods for applying the document generation system to the creation of new product introduction materials and market analysis reports in brick-and-mortar stores.

[0748] The present invention relates to a system that significantly reduces the time it takes for users to create documents and generates documents in a unified format. The system includes a terminal, a server, an analyzing unit, a generative model, a format application unit, and a return unit. The system is characterized by its application in the brick-and-mortar retail field.

[0749] First, the user inputs the text that will be the content of the document using a terminal. For example, the user can input, "Please create a document about the features of the new product and the target market." The user inputs this content using a terminal such as a smartphone.

[0750] Next, the input text data is sent by the terminal means to the server means. The server means receives the text data and analyzes its contents using the analysis means. This analysis utilizes natural language processing technology. Specifically, keywords are extracted and the meaning of the text is clarified.

[0751] The analyzed data is passed to a generative modeling means, which uses a generative AI model (such as GPT-4) to generate detailed documents based on the analysis results. This allows information to be organized based on the user's intended content, and specific reports and materials can be created.

[0752] The generated documents are then formatted using a predefined, organization-specific formatting mechanism, including uniform standards for color, layout, font size, etc., ensuring consistent, aesthetically pleasing documents.

[0753] Finally, the formatted document is sent back to the terminal means via the return means, and the user can receive, check, and use the document generated at the terminal means.

[0754] As a specific example, if a user inputs "Please create a document about the features and target market of a new product," the server analyzes the text, extracts key points of market analysis, and generates a detailed market analysis report using a generative model. The generated report is formatted according to the organization's specifications and provided to the user with a unified design and layout. This process significantly reduces the time and effort required to create materials for introducing new products in physical stores, and makes it possible to provide high-quality materials.

[0755] This allows users to create high-quality materials in a short amount of time, improving the operational efficiency of physical stores.

[0756] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0757] Step 1:

[0758] The user uses the terminal means to input text that will become the content of the document. For example, the user might input, "Please prepare a document about the features of the new product and its target market." The input data here is text data entered by the user.

[0759] Step 2:

[0760] The terminal means transmits the input text data to the server means. This transmitted data is text data input by the user.

[0761] Step 3:

[0762] The server means passes the text data received from the terminal means to the analyzing means. The input data here is the text data received from the terminal means. The analyzing means analyzes the text data using natural language processing technology, extracts keywords, and clarifies the meaning of the text. Analysis data is generated by this process.

[0763] Step 4:

[0764] The analyzed data is passed to a generative modeling means, which uses a generative AI model (e.g., GPT-4) to generate a detailed document based on the analysis results. The input data for this process is the analyzed data, and the output data is the generated document data.

[0765] Step 5:

[0766] The generated document is passed to a format application means, which applies a predefined, organization-specific format to the generated document. The input data for this process is the generated document data, and the output data is the formatted document data.

[0767] Step 6:

[0768] Finally, the formatted document is sent back to the terminal via the return means. The input data of this process is the formatted document data, and the output data is the document displayed by the user's terminal. The user receives the document generated by the terminal and can check and use it.

[0769] This series of processes makes it possible to quickly create new product introduction materials and market analysis reports for brick-and-mortar stores in a unified format, simplifying the work involved and improving the quality of the materials.

[0770] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0771] The present invention is a system that significantly reduces the time it takes for users to create documents and generates documents in a unified format, and also adds a function to recognize the user's emotions and adjust the tone and content of the document. This system includes a terminal means, a server means, an analyzing means, a generative model means, a format applying means, a return means, and an emotion engine.

[0772] First, the user uses the terminal to input the text that will be the content of the document. For example, the user can input the text, "Please prepare a report on the features and market analysis of a new product." This input becomes the basic data for the document.

[0773] The text entered by the user is sent to the server by the terminal means. This transmission is performed using an HTTP request, and the data is sent in JSON format.

[0774] The server receives the text data sent from the terminal. The received data is analyzed by an analysis means to extract basic data for document generation. Natural language processing technology is used for the analysis to clarify the intent of the text and the main points of the information.

[0775] The analyzed data is passed to a generative modeling tool, which uses AI technology (such as GPT-4) to generate detailed documents based on the analysis results. This organizes the information based on the user's intended content and creates specific reports and presentations.

[0776] The generated document is then passed to a formatting processor, which applies the organization's proprietary formatting to the generated document, including brand colors, slide layouts, font sizes, etc., resulting in a consistent, attractive presentation.

[0777] A distinctive feature of the present invention is the incorporation of an emotion engine. The emotion engine analyzes emotions from text data entered by the user. The analyzed emotion data is fed back to the generative model means, which adjusts the tone and content of the generated document. This adjustment allows the generated document to reflect the user's emotions.

[0778] As a concrete example, if a user inputs "Please create a document about the market analysis of a new product in an optimistic tone," the server analyzes the text, extracts key points of the market analysis, and uses an emotion engine to recognize the emotion "optimistic." A market analysis report with an optimistic tone is then generated using a generative model. The generated report is formatted according to the organization's format and provided to the user with a unified design and layout. This process significantly reduces the time and effort required to create documents while providing high-quality documents. Furthermore, the quality of the content is improved because documents are generated according to the user's emotions.

[0779] The processing flow will be explained below.

[0780] Step 1:

[0781] The user uses the terminal to input the text that will become the content of the document. For example, they might input, "Please write a report on the features and market analysis of a new product in an optimistic tone." This input becomes the basic data for the document.

[0782] Step 2:

[0783] The text entered by the user is sent to the server by the terminal means. This transmission is performed using an HTTP request, and the data is sent in JSON format.

[0784] Step 3:

[0785] The server receives the text data sent from the terminal. The received data is analyzed by an analysis means, and the information necessary for document generation is extracted. Natural language processing technology is used to determine the intent and main points of the text.

[0786] Step 4:

[0787] The server passes the parsed text data to the emotion engine, which analyzes the emotions from the user's input text and generates emotion labels such as optimistic, pessimistic, or neutral.

[0788] Step 5:

[0789] The emotion data analyzed by the emotion engine is fed back to the generative modeling means, which then adjusts the tone and content of the document based on the analyzed emotion data. For example, if the tone is optimistic, positive and optimistic expressions are used in the document.

[0790] Step 6:

[0791] The generative modeling means generates detailed documents based on the analyzed data and sentiment data, and this generation process uses AI technology (e.g., GPT-4).

[0792] Step 7:

[0793] The generated document is passed to a formatting applicator, which arranges the document content based on a predefined, organization-specific template, which may include brand colors, slide layouts, font sizes, etc.

[0794] Step 8:

[0795] The formatted document is then sent back to the terminal via the return means. The sent data is in JSON format.

[0796] Step 9:

[0797] The user receives the final document on their device. They can review it and download or print it as needed. The document is ready to use, as it already has formatting and reflects the specified emotional tone.

[0798] Example 2

[0799] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0800] Conventional document generation systems require a lot of time and effort to create documents, and the generated documents have inconsistent formats and tones. Furthermore, they lack the functionality to reflect the user's emotions in the document, making it difficult to accurately reflect the user's intended tone and content.

[0801] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0802] In this invention, the server includes an analysis means for analyzing text data, a generative model means for generating documents based on the analyzed text data, and an emotion analysis means for analyzing emotions from the input text data. This not only enables accurate analysis of the intent and main points of the text data input by the user, but also enables the generation of documents with a tone and content that reflects the user's emotions. Furthermore, by applying an organization-specific format to the generated documents, consistent materials can be provided, significantly reducing the time and effort required to create documents.

[0803] The "terminal means" is a device for a user to input text.

[0804] The "server means" is a computer system that receives and processes text data sent from the terminal means.

[0805] The "analysis means" is a device or software that has the function of analyzing received text data and extracting basic data for document generation.

[0806] A "generative model means" is a device or software that uses AI techniques (e.g., natural language processing models) to generate documents based on analyzed text data.

[0807] The "format application means" is a device or software that has the function of applying an organization-specific format to the generated document.

[0808] The "returning means" is a device or software that has the function of returning the document to which the formatting means has been applied to the terminal means.

[0809] "Emotion analysis means" refers to a device or software that has the function of analyzing emotions from input text data.

[0810] A "natural language processing model" is a technology that refers to a machine learning model for understanding and generating human language.

[0811] A "template" is a predefined layout or design pattern for arranging document content.

[0812] This invention is a system that significantly reduces the time it takes for users to create documents and generates documents in a unified format. It also has the function of recognizing the user's emotions and adjusting the tone and content of the document. This system includes a terminal unit, a server unit, an analysis unit, a generative model unit, a format application unit, a return unit, and an emotion analysis unit.

[0813] First, the user uses a terminal to input the text that will be the content of the document. For example, the user can input the text, "Please prepare a report on the features and market analysis of a new product." The user can also specify the tone of the document, such as "optimistic tone." This input becomes the basic data for the document.

[0814] The terminal means transmits the input text data and emotion data to the server means using an HTTP request. The data is transmitted in JSON format.

[0815] The server means analyzes the received data using the analysis means. The analysis means uses natural language processing technology to clarify the intent of the text and the main points of the information. The analyzed data is passed to the generative model means. This generative model means uses a natural language processing model such as GPT-4. The generative model means generates a detailed document based on the analysis results.

[0816] The generated document is then passed to a formatting mechanism on the server, which applies the organization's proprietary formatting to the generated document, including brand colors, slide layouts, font sizes, etc. This ensures consistent, attractive presentation.

[0817] The sentiment analysis means also plays an important role. It analyzes emotions from the text data entered by the user and feeds the analyzed emotional data back to the generative model means. The generative model means then adjusts the tone and content of the generated document based on this information. This adjustment allows the generated document to better reflect the user's emotions.

[0818] As a concrete example, if a user inputs "Please create a document about the market analysis of a new product in an optimistic tone," the server analyzes the text, extracts key points of the market analysis, and recognizes the sentiment "optimistic" using a sentiment analysis means. A market analysis report with an optimistic tone is then generated using a generative model means. The generated report is formatted according to the organization's format and provided to the user with a unified design and layout. This process significantly reduces the time and effort required to create documents while providing high-quality documents. Furthermore, the quality of the content is improved because documents are generated according to the user's sentiment.

[0819] An example prompt is:

[0820] "Write your new product market analysis in an optimistic tone."

[0821] By feeding this prompt into a generative AI model, documents with the desired tone and content can be easily generated.

[0822] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0823] Step 1:

[0824] The user uses a terminal to input document content and emotions. Specifically, the user inputs a sentence such as "Please write a report on the features and market analysis of a new product" into the input field of the terminal. The user also specifies the tone of the document, such as "optimistic tone." This input data becomes the basic data of the document. Input: Document content and emotion data. Output: Data stored in the input field of the terminal.

[0825] Step 2:

[0826] The device converts the input text content and emotion data into JSON format and sends it to the server using an HTTP request. Specifically, the device serializes the input data into JSON format and sends it to the specified server URL. Input: Data stored in the device's input field. Output: HTTP request containing JSON format data.

[0827] Step 3:

[0828] The server receives an HTTP request and passes the JSON-formatted data to the analysis means. The analysis means analyzes the received data and uses natural language processing technology to extract the main points and intent of the text. Specifically, it uses a text analysis algorithm to clarify the user's intent. Input: JSON-formatted data. Output: Analyzed data with the main points and intent extracted.

[0829] Step 4:

[0830] The server passes the analyzed data to the generative modeling means. The generative modeling means (e.g., GPT-4) generates detailed documents based on this analyzed data. The generative modeling means determines the structure and content of the text based on a generative AI model. Input: analyzed data. Output: generated document.

[0831] Step 5:

[0832] The formatting mechanism in the server receives the generated document and applies the organization's specific formatting, such as document layout, font size, brand color, etc. according to a pre-defined template. Input: Generated document. Output: Formatted document.

[0833] Step 6:

[0834] The server returns the formatted document to the user's device. Specifically, it converts the formatted document into JSON format and sends it to the user's device as an HTTP response. Input: Formatted document. Output: Document data sent to the user's device.

[0835] Step 7:

[0836] The sentiment analysis means analyzes sentiment from the user's input text data and feeds this sentiment data back to the generative model means. Specifically, the sentiment data is analyzed using a sentiment analysis algorithm and re-input into the generative model means, adjusting the tone and content of the document. Input: User's sentiment data. Output: Adjusted document.

[0837] Through these steps, users can efficiently generate high-quality, uniformly formatted documents that reflect the user's emotions, allowing for better adjustment of the tone and content of the document.

[0838] (Application example 2)

[0839] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0840] Modern business requires the rapid creation of consistent, high-quality documents, but this requires a lot of time and effort. While resolving this issue, it is necessary to improve usability and the quality of communication by providing documents that are in tune with users' emotions. Online shopping sites, in particular, require the creation of user reviews and the quality and consistency of product descriptions. This will increase users' purchasing motivation and ensure the provision of reliable information.

[0841] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0842] In this invention, the server includes a terminal means for inputting text, a means for receiving text data input from the terminal means, and a generative model means for generating documents based on the analyzed text data. This makes it possible to analyze user emotions and automatically generate documents with a tone based on the emotions. The generated documents are formatted according to the organization's specifications, allowing for the rapid provision of consistent, high-quality documents. Furthermore, on online shopping sites, documents such as user reviews and product descriptions can be optimized based on emotions, which is expected to improve user satisfaction and reliability.

[0843] "Terminal means for inputting text" refers to a device for transmitting text data input by a user to a server.

[0844] The term "server means" refers to a server that processes text data received from a user and manages the analyzed and generated documents.

[0845] "Means for analyzing text data" refers to the process of analyzing received text data using natural language processing technology to clarify its intent and key points.

[0846] "Generative model means" refers to AI technology, specifically generative AI models, that generate detailed documents based on analyzed text data.

[0847] An "emotion engine" is an engine that analyzes emotions from text data entered by a user and adjusts the tone and content of the document based on those emotions.

[0848] "Format application means" refers to a process for applying an organization-specific format to the generated document to create a consistent document.

[0849] "Return means" refers to a mechanism for returning a document formatted by the format application means to the user's terminal means.

[0850] The present invention begins with a terminal means for inputting text. A user inputs text using this terminal means. This terminal means refers to devices such as a personal computer, smartphone, or tablet. For example, a user can input a sentence such as "This product was very useful." The input text data is transmitted to a server means via an HTTP request. The data is generally transmitted in JSON format.

[0851] The server means analyzes the received text data using natural language processing (NLP) techniques, such as a generative AI model such as OpenAI's GPT-4. This analysis process clarifies the intent and key information of the text. The server means passes the analyzed data to the generative model means, which generates a detailed document.

[0852] A distinctive feature of the present invention is the incorporation of an emotion engine. The emotion engine analyzes emotions from text data entered by the user. The analysis results are fed back to the generative model means, which adjusts the tone and content of the generated document. For example, if a user enters "I am satisfied with this product" and specifies the emotion as "positive," the generated document will have a positive tone.

[0853] In the next step, the generated document is passed to a format application means. The format application means in the server applies the organization's proprietary format to the generated document. This includes brand colors, slide layouts, font sizes, etc., to create a consistent and attractive document. The formatted document is finally returned to the user's terminal means by a return means.

[0854] As a concrete example, consider the case where a user enters a review on an online shopping site. The user enters a review such as "This product was very useful" along with a positive sentiment. The server means receives this and uses the analysis and generative model means to generate a detailed review with an appropriate tone. The format application means then applies a unified format to the review, which is then sent back to the terminal means. This process improves the consistency and quality of reviews, and increases user satisfaction.

[0855] Prompt Sentence Examples

[0856] A specific example of a prompt sentence entered in text format is as follows:

[0857] User Review: This product was very useful.

[0858] Emotion: Positive

[0859] Generate reviews with the right tone.

[0860] The present invention enables high quality document generation based on user-entered data, greatly improving the efficiency and consistency of document creation for specific applications.

[0861] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0862] Step 1:

[0863] The user inputs a sentence using a terminal. Specifically, the user inputs a sentence such as "This product was very useful" into an input form in a browser or application. This input data is sent to the server in JSON format. The input data also includes the user's sentiment (e.g., positive).

[0864] Step 2:

[0865] The server receives the text data sent from the terminal device. The server receives the data via an HTTP request. This data is in JSON format and includes the text content and sentiment. The server prepares to move on to the next step of analyzing this data.

[0866] Step 3:

[0867] The server analyzes the received text data using natural language processing (NLP) technology, such as a generative AI model like GPT-4. During this process, the server extracts the intent and key points of the text and uses an emotion engine to analyze the user's emotions. The input is JSON-formatted text data, and the output is the analyzed text data and emotion information.

[0868] Step 4:

[0869] The server generates documents based on the analyzed data. The generative model generates detailed documents using, for example, OpenAI's GPT-4. In this process, the generative AI model generates documents based on the user's intent and adjusts the tone and content of the documents to match the user's emotions. The input is the analyzed text data and emotional information, and the output is the generated document.

[0870] Step 5:

[0871] The server applies the organization's proprietary formatting to the generated document. The formatting method uses predefined templates to apply brand colors, slide layouts, font sizes, etc. to the document, resulting in consistent and attractive documents. The input is the generated document and the output is the formatted document.

[0872] Step 6:

[0873] The server returns the formatted document to the terminal means. The return means uses an HTTP response to send the document data back to the terminal means. The user can check the returned document and save or share it as needed. The input is the formatted document, and the output is the returned document.

[0874] 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.

[0875] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[0876] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[0877] 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.

[0878] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[0879] 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.

[0880] 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).

[0881] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0882] 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."

[0883] 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.

[0884] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[0885] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[0890] The hardware resource that executes the specific processing 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 processing may be a single processor.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] The following is further disclosed regarding the above embodiment.

[0896] (Claim 1)

[0897] a terminal means for inputting text;

[0898] a server means for receiving text data input from the terminal means;

[0899] means for the server means to analyze the text data;

[0900] a generative model means for generating a document based on the analyzed text data by the server means;

[0901] a formatting application means for applying an organization-specific format to the generated document;

[0902] a return means for returning the document to which the format application means has been applied to the terminal means;

[0903] A system including:

[0904] (Claim 2)

[0905] 2. The system of claim 1, wherein the generative model means generates documents using a natural language processing model.

[0906] (Claim 3)

[0907] 10. The system of claim 1, wherein the formatting means arranges document content based on a predefined template.

[0908] "Example 1"

[0909] (Claim 1)

[0910] an information processing device for inputting text;

[0911] a computer means for receiving text data input from the information processing means;

[0912] means for said computer means to analyze said text data;

[0913] a generating artificial intelligence model means for generating a document based on the text data analyzed by the computer means;

[0914] a formatting application means for applying an organization-specific format to the generated document;

[0915] a return means for returning the document to which the format application means has been applied to the information processing means;

[0916] A system including:

[0917] (Claim 2)

[0918] 2. The system of claim 1, wherein the generative artificial intelligence model means generates documents using a natural language processing model.

[0919] (Claim 3)

[0920] 10. The system of claim 1, wherein the formatting means arranges document content based on a predefined template.

[0921] "Application Example 1"

[0922] (Claim 1)

[0923] a terminal means for inputting text;

[0924] a server means for receiving text data input from the terminal means;

[0925] means for the server means to analyze the text data;

[0926] a generative model means for generating a document based on the analyzed text data by the server means;

[0927] a formatting application means for applying an organization-specific format to the generated document;

[0928] a return means for returning the document to which the format application means has been applied to the terminal means;

[0929] A means for applying the document generation system to applications in the field of brick-and-mortar stores;

[0930] A system including:

[0931] (Claim 2)

[0932] 2. The system of claim 1, wherein the generative model means generates documents using a natural language processing model.

[0933] (Claim 3)

[0934] 2. The system of claim 1, wherein the format application means arranges document content based on a predefined template and further applies a specific format for a physical store.

[0935] "Example 2: Combining Emotion Engines"

[0936] (Claim 1)

[0937] a terminal means for inputting text;

[0938] a server means for receiving text data input from the terminal means;

[0939] analysis means for the server means to analyze the text data;

[0940] a generative model means for generating a document based on the analyzed text data by the server means;

[0941] formatting means for applying an organization-specific format to the generated document;

[0942] return means for returning the document to which the format application means has been applied to the terminal means;

[0943] emotion analysis means for analyzing emotions from input text data;

[0944] the emotion analysis means feeds back the analyzed emotion data to the generative model means to adjust the tone and content of the generated document;

[0945] A system including:

[0946] (Claim 2)

[0947] 2. The system of claim 1, wherein the generative model means generates documents using a natural language processing model.

[0948] (Claim 3)

[0949] 10. The system of claim 1, wherein the formatting means arranges document content based on a predefined template.

[0950] "Application example 2 when combining emotion engines"

[0951] (Claim 1)

[0952] a terminal means for inputting text;

[0953] a server means for receiving text data input from the terminal means;

[0954] means for the server means to analyze the text data;

[0955] a generative model means for generating a document based on the analyzed text data by the server means;

[0956] means for analyzing a user's emotion using an emotion engine, and adjusting the tone of the document based on the emotion;

[0957] a formatting application means for applying an organization-specific format to the generated document;

[0958] a return means for returning the document to which the format application means has been applied to the terminal means;

[0959] A system including:

[0960] (Claim 2)

[0961] 2. The system of claim 1, wherein the generative model means generates documents using a natural language processing model.

[0962] (Claim 3)

[0963] 10. The system of claim 1, wherein the formatting means arranges document content based on a predefined template. [Explanation of symbols]

[0964] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a terminal means for inputting text; a server means for receiving text data input from the terminal means; means for the server means to analyze the text data; a generative model means for generating a document based on the analyzed text data by the server means; a formatting application means for applying an organization-specific format to the generated document; a return means for returning the document to which the format application means has been applied to the terminal means; A system including:

2. 2. The system of claim 1, wherein the generative model means generates documents using a natural language processing model.

3. 2. The system of claim 1, wherein the formatting means arranges document content based on a predefined template.

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