An intelligent question-answering system

Abstract

The application discloses an intelligent question and answer system, and relates to the technical field of intelligent question and answer, which comprises a question input module, an answer presentation module and a feedback collection module arranged in a front-end user interface, a question analysis module, an answer generation module and a knowledge base arranged in a back-end server, the question input module is used for realizing question input, the question analysis module can accurately analyze the meaning of a question, identify the key points and implied requirements of the question, the answer generation module retrieves the knowledge base to generate an answer based on the analysis result of the question analysis module, the answer presentation module is used for presenting the answer, the feedback collection module is used for collecting the evaluation and feedback of the user on the answer and generating feedback information, and the knowledge base is used for storing field knowledge and feedback information, so that the user experience is improved, the question and answer efficiency is improved, the development of the intelligent question and answer system is promoted, and the application scenarios can be adapted to diversification.

Description

Technical Field

[0001] This invention relates to the field of intelligent question-answering technology, and more specifically to an intelligent question-answering system. Background Technology

[0002] The statements in this section are provided only as background information in connection with this disclosure and may not constitute prior art.

[0003] With the continuous development of artificial intelligence technology, intelligent question-answering systems have been widely used in various fields. However, currently, different intelligent question-answering systems vary significantly in their interaction processes, resulting in inconsistent user experiences and difficulty in guaranteeing question-answering efficiency. The lack of a unified interaction process standard has somewhat limited the development of intelligent question-answering systems.

[0004] The differences are particularly pronounced in the question input stage. Some intelligent question-answering systems only support traditional text input, which undoubtedly increases the difficulty for users accustomed to voice input. For example, in busy work scenarios, users may have their hands occupied and unable to input text. If the system does not support voice input, it will greatly affect the efficiency of users obtaining information. Other systems, while supporting voice input, have low accuracy in voice recognition, requiring users to repeatedly input for the system to correctly understand the question, wasting a significant amount of time and effort. Furthermore, the initial validity standards for input content vary. Some systems are overly strict, deeming slightly unusual expressions invalid, while others are too lenient, readily accepting ambiguous or even incorrect input. This introduces considerable uncertainty into subsequent question analysis and answer generation.

[0005] Entering the problem analysis phase, the natural language processing technologies employed by different intelligent question-answering systems vary considerably. Some systems have significant deficiencies in semantic understanding, failing to accurately interpret the true meaning of complex question statements. For example, when a user poses a question containing multiple clauses and modifiers, some systems may misinterpret the question's focus, thus extracting incorrect keywords. Regarding keyword extraction, the algorithms and standards used by different systems also differ. Some systems overemphasize the extraction of high-frequency words while neglecting low-frequency words with specific meanings, resulting in an inability to fully grasp user needs. Other systems are susceptible to noise interference during keyword extraction, extracting words irrelevant to the question and affecting the accuracy of problem analysis.

[0006] The differences are equally pronounced in the answer generation stage. Some intelligent question-answering systems, due to incomplete knowledge bases that fail to cover a wide range of knowledge domains, cannot retrieve relevant information when users ask specialized or niche questions, thus failing to generate accurate answers. Other systems, while possessing richer knowledge bases, suffer from less advanced intelligent algorithms, lacking logic and coherence in answer generation. Their answers are either overly lengthy and complex, making them difficult for users to understand, or too brief and vague, failing to meet user needs. Furthermore, significant differences exist between different systems in terms of the accuracy and reliability of their answers.

[0007] In terms of answer presentation, the lack of variety and consistency in formats also causes inconvenience for users. Some systems only present answers in text format, failing to effectively express data or concepts that require intuitive visualization through charts or other formats. While some systems support multiple presentation formats, switching and selecting them is not convenient enough, requiring users to spend a significant amount of time searching for the appropriate presentation method. Furthermore, different systems vary in the layout and formatting of answer presentations, with some being overly cluttered and negatively impacting the user's reading experience.

[0008] As for the feedback collection phase, the performance of different intelligent question-answering systems varies greatly. Many systems either lack clear feedback channels, leaving users with nowhere to express their dissatisfaction even if they are dissatisfied with the answer, or they do not pay enough attention to user feedback, failing to process and optimize the system promptly even when feedback is received. Some systems lack effective incentive mechanisms during the feedback collection process, resulting in users not being motivated to provide feedback and thus hindering continuous system improvement.

[0009] This lack of uniformity in interaction processes forces users to constantly adapt to new operating methods and experiences when using different intelligent question-answering systems, increasing the learning cost and difficulty of use. Furthermore, the lack of unified standards makes effective comparison and evaluation between various intelligent question-answering systems difficult, thus limiting the development of the entire industry. Summary of the Invention

[0010] The purpose of this invention is to provide an intelligent question-and-answer system that addresses the problems existing in the prior art.

[0011] The technical solution of the present invention is as follows:

[0012] An intelligent question-answering system includes: a question input module, an answer presentation module, and a feedback collection module set in the front-end user interface; and a question analysis module, an answer generation module, and a knowledge base set in the back-end server.

[0013] The question input module is used to input questions; the question analysis module can accurately analyze the meaning of questions, identify the key points and implicit needs of questions; the answer generation module generates answers based on the analysis results of the question analysis module and retrieves knowledge base; the answer presentation module is used to present answers; the feedback collection module is used to collect user evaluations and feedback on answers and generate feedback information; the knowledge base is used to store domain knowledge and feedback information.

[0014] Furthermore, the question input module provides a variety of input methods to meet the needs of different users in various scenarios;

[0015] The question input module has a strict and intelligent validity verification mechanism to verify the validity of the input questions.

[0016] Furthermore, the input methods include: text input, voice input, and image input;

[0017] The input question is validated for validity, including:

[0018] For text input, it is necessary not only to check for grammatical errors, but also to judge the rationality and clarity of the question; for voice input, advanced speech recognition technology is used to improve accuracy, while the legality of the recognized text is verified; for image input, key information is extracted through image recognition technology, and it is determined whether it meets the input requirements.

[0019] Furthermore, the problem analysis module employs advanced natural language processing technology, combined with deep learning algorithms, to improve the accuracy of semantic understanding; for complex problem statements, it can accurately parse their meaning and identify the key points and implicit needs of the problem.

[0020] Furthermore, the problem analysis module accurately extracts keywords by analyzing sentence structure, semantic relationships, and contextual information.

[0021] Furthermore, the problem analysis module incorporates a keyword extraction algorithm that comprehensively considers the frequency, importance, and relevance of words to specific fields; at the same time, the problem analysis module employs noise reduction technology to eliminate interference from irrelevant words.

[0022] Furthermore, the answer generation module has advanced intelligent algorithms, which make the generated answers logical and organized;

[0023] The answer generation module retrieves relevant information from the knowledge base based on the analysis results of the question analysis module and generates the answer.

[0024] Furthermore, the answer presentation module provides multiple answer presentation methods, including: text, charts, voice, and video; it automatically selects the most suitable presentation method based on the type of question and the user's needs.

[0025] Furthermore, the feedback collection module has multiple feedback channels, including: online evaluation, feedback forms, and email feedback;

[0026] The feedback collection module can also analyze and process user feedback in a timely manner based on a preset feedback processing mechanism, and optimize and improve the system based on the feedback results.

[0027] Furthermore, the knowledge base covers knowledge from multiple fields and is continuously updated and expanded.

[0028] Compared with existing technologies, the advantages of this invention are:

[0029] 1. Improve user experience: Through standardized interaction processes and a wide variety of functions, users can more easily use the intelligent question-and-answer system to obtain accurate, timely, and diverse answers, greatly improving user satisfaction.

[0030] 2. Improved Question-Answering Efficiency: Standardized interaction processes help reduce unnecessary operations and waiting time, improving the response speed of intelligent question-answering systems. Simultaneously, advanced algorithms and technologies can quickly and accurately analyze questions and generate answers, further enhancing question-answering efficiency.

[0031] 3. Promote the development of intelligent question-answering systems: A unified interaction process specification provides a common standard for different intelligent question-answering systems, which is conducive to promoting industry development and innovation. Each system can continuously upgrade its technology and expand its functions while adhering to the specification, providing better services to users.

[0032] 4. Adaptability to diverse application scenarios: Standardized interaction processes ensure that intelligent question-answering systems across different fields have similar operation methods and user experiences, facilitating user switching between different scenarios. A unified interaction process standard allows intelligent question-answering systems to better adapt to the characteristics of different devices and user habits, providing a consistent service experience. Attached Figure Description

[0033] Figure 1 This is a block diagram of an intelligent question-answering system;

[0034] Figure 2 This is a flowchart illustrating a standardized interactive process for intelligent question answering. Detailed Implementation

[0035] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0036] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0037] Example 1

[0038] Please see Figure 1 An intelligent question-answering system, specifically including:

[0039] The front-end user interface includes a question input module, an answer presentation module, and a feedback collection module; while the back-end server includes a question analysis module, an answer generation module, and a knowledge base.

[0040] The question input module is used to input questions; the question analysis module can accurately analyze the meaning of questions, identify the key points and implicit needs of questions; the answer generation module generates answers based on the analysis results of the question analysis module and retrieves knowledge base; the answer presentation module is used to present answers; the feedback collection module is used to collect user evaluations and feedback on answers and generate feedback information; the knowledge base is used to store domain knowledge and feedback information.

[0041] It should be noted that the front-end user interface should provide a friendly and intuitive interactive environment, making it convenient for users to input questions and view answers. The interface design should be simple, aesthetically pleasing, and easy to operate. It should adopt a responsive design to adapt to different screen sizes and resolutions of various devices.

[0042] Backend server: Responsible for core tasks such as problem analysis, answer generation, and knowledge base management. It employs a high-performance server architecture and advanced technology to ensure system stability and responsiveness.

[0043] Continuously optimize system performance to improve reliability and scalability. Regularly update and upgrade the system, introducing new technologies and features to meet the ever-changing needs of users.

[0044] In this embodiment, specifically, the question input module provides a variety of input methods to meet the needs of different users in various scenarios;

[0045] The question input module has a strict and intelligent validity verification mechanism to verify the validity of the input questions.

[0046] In this embodiment, the specific input methods include: text input, voice input, and image input;

[0047] The input question is validated for validity, including:

[0048] For text input, it is necessary not only to check for grammatical errors, but also to judge the rationality and clarity of the question; for voice input, advanced speech recognition technology is used to improve accuracy, while the legality of the recognized text is verified; for image input, key information is extracted through image recognition technology, and it is determined whether it meets the input requirements.

[0049] In this embodiment, specifically, the problem analysis module employs advanced natural language processing technology combined with deep learning algorithms to improve the accuracy of semantic understanding; for complex problem statements, it can accurately parse their meaning and identify the key points and implicit needs of the problem.

[0050] In this embodiment, specifically, the problem analysis module accurately extracts keywords by analyzing sentence structure, semantic relationships, and contextual information.

[0051] In this embodiment, the problem analysis module incorporates a keyword extraction algorithm that comprehensively considers the frequency, importance, and relevance of words to a specific domain. Simultaneously, the problem analysis module employs noise reduction technology to eliminate interference from irrelevant words.

[0052] In this embodiment, specifically, the answer generation module has an advanced intelligent algorithm that makes the generated answer logical and coherent;

[0053] The answer generation module retrieves relevant information from the knowledge base based on the analysis results of the question analysis module and generates the answer.

[0054] In this embodiment, the answer presentation module provides multiple answer presentation methods, including: text, charts, voice, and video; and automatically selects the most suitable presentation method according to the type of question and the user's needs.

[0055] In this embodiment, specifically, the feedback collection module has multiple feedback channels, including: online evaluation, feedback form, and email feedback;

[0056] The feedback collection module can also analyze and process user feedback in a timely manner based on a preset feedback processing mechanism, and optimize and improve the system based on the feedback results.

[0057] In this embodiment, specifically, the knowledge base covers knowledge from multiple fields and is continuously updated and expanded.

[0058] In this embodiment, it should also be noted that the above-mentioned intelligent question-answering system is designed and developed based on an intelligent question-answering standard interaction process specification method.

[0059] In this embodiment, for details, please refer to... Figure 2 A standardized interactive process specification method for intelligent question answering, including:

[0060] Step S1: Design of the interactive process of the intelligent question-and-answer system;

[0061] The interaction process of an intelligent question-answering system is divided into five main stages: question input, question analysis, answer generation, answer presentation, and feedback collection.

[0062] The problem input phase includes:

[0063] It offers a variety of input methods, including text input, voice input, and image input, to meet the needs of different users in various scenarios. For example, users can use voice input to ask questions while driving, or use image input to ask questions related to images when viewing image materials;

[0064] Specifically, for text input, advanced text input box technology is used, supporting functions such as automatic error correction and intelligent suggestions. For example, when a user makes a spelling mistake during input, the system automatically provides correct spelling suggestions. At the same time, input length limits are set to prevent users from inputting too much or too little text.

[0065] For voice input, a high-quality speech recognition engine is integrated, capable of adapting to different accents and speaking speeds. The system displays the recognition results in real time as the user inputs voice data for easy confirmation. Furthermore, noise reduction processing is applied to the input speech to improve recognition accuracy.

[0066] For image input, image recognition technology is used to analyze user-uploaded images and extract key information. For example, for images containing text, optical character recognition technology is used to extract the text, which is then analyzed as part of the question.

[0067] Establish a rigorous and intelligent validity verification mechanism. For text input, not only should grammatical errors be checked, but the rationality and clarity of the question should also be assessed. For voice input, advanced speech recognition technology should be used to improve accuracy, while the recognized text should be validated for validity. For image input, key information should be extracted using image recognition technology, and its compliance with input requirements should be determined.

[0068] Problem analysis phase:

[0069] This system utilizes lexical, syntactic, and semantic analysis techniques from natural language processing to achieve a deep understanding of the input questions. It extracts keywords, themes, and intents from the questions, providing accurate information for subsequent answer generation. Advanced natural language processing techniques, combined with deep learning algorithms such as large language models, are employed to improve the accuracy of semantic understanding. For complex question statements, it can accurately parse their meaning and identify the key points and implicit needs of the questions. For example, by analyzing sentence structure, semantic relationships, and contextual information, keywords are accurately extracted.

[0070] Establish a problem classification system to categorize different types of problems so that different processing strategies can be adopted. For example, for factual problems, answers can be retrieved directly from a knowledge base; for reasoning problems, logical reasoning algorithms need to be used to solve them.

[0071] The keyword extraction algorithm has been optimized, comprehensively considering the frequency, importance, and relevance of words to specific fields. It not only focuses on high-frequency words but also considers low-frequency but crucial words. Simultaneously, noise reduction technology is employed to eliminate interference from irrelevant words, ensuring that the extracted keywords accurately reflect user needs.

[0072] Answer generation stage:

[0073] Build a rich and comprehensive knowledge base, including structured data (such as tabular data in a database) and unstructured data (such as text files, web page content, etc.); covering knowledge from multiple fields, and continuously updating and expanding it. Ensure that when users ask specialized or niche questions, the system can retrieve relevant information from the knowledge base; use knowledge graph technology to associate and integrate knowledge in the knowledge base for faster retrieval of relevant information;

[0074] Based on the problem analysis results, relevant knowledge fragments are retrieved from the knowledge base, and intelligent algorithms (such as the GPT series models) are used to generate and optimize the answer. This ensures that the generated answer is accurate, concise, and easy to understand.

[0075] Develop advanced intelligent algorithms to ensure that generated answers are logical and well-organized. Answers should be neither too lengthy and complex, nor too brief and vague; they must accurately and concisely answer the user's question. During the answer generation process, rigorous verification and screening are conducted to ensure the accuracy and reliability of the answers.

[0076] Answer presentation stage:

[0077] It offers multiple answer presentation formats, including text, charts, audio, and video. Based on the question type and user needs, it automatically selects the most suitable presentation method; for example, simple questions can be presented directly as text; data-related questions can be visually displayed using charts; and complex questions can be presented using charts, images, or animated videos to enhance the intuitiveness and readability of the answers.

[0078] The text is presented using clear and legible fonts and layouts. Important content can be bolded or highlighted to attract the user's attention. Hyperlinks and quotes are also supported to facilitate further understanding of the content.

[0079] The appropriate chart format should be selected based on the type of question, such as bar charts, line charts, pie charts, etc. Data visualization techniques should be used to present complex data to users in an intuitive way.

[0080] The voice presentation uses natural and fluent speech synthesis technology, supporting multiple languages ​​and voice options. Users can adjust the speech rate and volume according to their preferences.

[0081] Optimize the layout and typography of the presentation interface to make the answers easier to read and understand. Include a convenient button to switch between different presentation modes, allowing users to easily navigate between them. Also, provide relevant links and references to help users further understand the background and related knowledge of the questions.

[0082] Feedback collection phase:

[0083] Establish multiple feedback channels, such as online evaluation, feedback forms, and email feedback, to ensure users can easily evaluate and provide feedback on the answers; for example, provide a feedback button or evaluation area after the answer is presented to encourage users to evaluate and provide feedback. Feedback can include aspects such as the accuracy, completeness, and comprehensibility of the answer;

[0084] We highly value user feedback and have established a dedicated feedback processing mechanism. We promptly analyze and process user feedback, and optimize and improve the system based on the feedback results. Incorrect answers are corrected and optimized in a timely manner and added to the knowledge base. New questions raised by users are promptly added to the knowledge base to continuously improve the intelligent question-and-answer system.

[0085] Step S2: Standardize and define the intelligent question-and-answer interaction process.

[0086] In this embodiment, specifically, step S2 includes:

[0087] Step S21: Develop detailed and rigorous standardized specifications for each stage of the interaction process;

[0088] Specifically, the input format requirements are as follows: clearly define the format standards for different input methods, such as character limits for text input, duration and sound quality requirements for voice input, and resolution and format requirements for image input, etc.

[0089] Problem analysis algorithm standard: specifies the specific algorithms and technical indicators for semantic understanding and keyword extraction to ensure consistency and accuracy in problem analysis across different systems;

[0090] Answer generation rules: Establish principles and standards for answer generation, including requirements for accuracy, completeness, and logical coherence, as well as guidelines for answer length and language style;

[0091] Answer presentation format: Determine the specific format requirements for various presentation methods, such as the font, font size, and color specifications for text, the type and style standards for charts, and the requirements for speech rate and timbre, etc.

[0092] Step S22: Ensure that different intelligent question-answering systems can achieve consistency and efficiency in their interaction processes while adhering to these specifications. The development of standardized specifications enables effective comparison and evaluation between different systems, promoting the healthy development of the entire industry.

[0093] The embodiments described above merely illustrate specific implementation methods of this application, and while the descriptions are detailed and specific, they should not be construed as limiting the scope of protection of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the technical solution of this application, and these modifications and improvements all fall within the scope of protection of this application.

[0094] This background section is provided to generally present the context of the invention. The work of the currently named inventors, the work to the extent described in this background section, and aspects of this section that did not constitute prior art at the time of application are neither expressly nor impliedly acknowledged as prior art to the invention.

Claims

1. An intelligent question answering system, characterized in that, Comprise: A question input module, an answer presentation module and a feedback collection module set in the front-end user interface; And a question analysis module, an answer generation module and a knowledge base set in the back-end server; The question input module is used to realize question input; the question analysis module can accurately analyze the meaning of the question and identify the key points and implied needs of the question; the answer generation module retrieves the knowledge base based on the analysis results of the question analysis module to generate answers; the answer presentation module is used for the presentation of answers; the feedback collection module is used to collect user evaluations and feedback on the answers and generate feedback information; The knowledge base is used to store domain knowledge and feedback information; The question input module provides a variety of input methods to meet the needs of different users in various scenarios; The question input module has a strict and intelligent legality verification mechanism for legality verification of input questions; Input methods include: text input, voice input, image input; Legality verification of input questions includes: For text input, not only syntax errors but also the reasonableness and definiteness of the question are checked; for voice input, advanced speech recognition technology is used to improve accuracy, and the recognized text is also checked for legality; for image input, key information is extracted through image recognition technology, and whether it meets the input requirements is judged.

2. The intelligent question answering system of claim 1, wherein, The question analysis module uses advanced natural language processing technology combined with deep learning algorithms to improve the accuracy of semantic understanding; for complex question sentences, it can accurately analyze their meaning and identify the key points and implied needs of the question.

3. The intelligent question answering system of claim 2, wherein, The question analysis module accurately extracts keywords by analyzing sentence structure, semantic relationship and context information.

4. The intelligent question answering system of claim 3, wherein, The question analysis module has a built-in keyword extraction algorithm that considers the frequency, importance and relevance of specific domain of words; at the same time, the question analysis module uses noise reduction technology to exclude irrelevant word interference.

5. The intelligent question answering system of claim 4, wherein, The answer generation module has advanced intelligent algorithms to make the generated answers logical and organized; The answer generation module retrieves relevant information from the knowledge base based on the analysis results of the question analysis module and generates answers.

6. The intelligent question answering system of claim 5, wherein, The answer presentation module provides multiple forms of answer presentation methods, including: text, chart, voice, video; according to the type of question and user needs, the most appropriate presentation method is automatically selected.

7. The intelligent question answering system of claim 6, wherein, The feedback collection module has multiple feedback channels, including: online evaluation, opinion feedback form, email feedback; The feedback collection module can also analyze and process user feedback in a timely manner based on the preset feedback processing mechanism, and optimize and improve the system according to the feedback results.

8. The intelligent question answering system of claim 7, wherein, The knowledge base covers knowledge in multiple fields and is constantly updated and expanded.

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