Camera face recognition intelligent chat reply method and electronic equipment

Abstract

The invention relates to the technical field of AI data processing, in particular to a camera face recognition intelligent chat reply method and electronic equipment. The method comprises the steps of camera initialization, video interaction, image data acquisition and preprocessing, transmission of the image data to an identification module to acquire expression information, and acquisition of user information and output of cleaned chat information data by a chat module. The data are input into an AI processing system, and language rules are captured to generate reply suggestions. The user may determine whether to send or modify the suggestions. The technology can automatically analyze the psychological state and generate personalized replies, is especially suitable for people with communication difficulty, and provides an automatic communication channel. Meanwhile, a new thought of emotion recognition and automatic communication is provided for an intelligent interaction system, and the method has a wide application prospect.

Description

Technical Field

[0001] The present invention relates to the field of AI data processing technology, and in particular to a camera face recognition intelligent chat reply method and an electronic device. Background Art

[0002] Emotion recognition technology is an advanced analytical method that accurately infers and identifies an individual's emotional state by deeply studying and interpreting non-verbal communication methods such as human facial expressions, voice intonation, and physiological signals. With the rapid development of artificial intelligence technology, emotion recognition technology has made the leap from single-media recognition, such as text, voice, and image, to multi-modal information fusion recognition. This fusion of technology not only improves the accuracy of recognition, but also greatly expands its scope of application, making it widely used in many fields such as human-computer interaction, social networks, healthcare, education, and entertainment.

[0003] In today's era of booming social software, people use various information and expressions to show their inner world. Common communication methods on the market include voice, text and video. However, for some children and people with disabilities, due to language barriers, they may not be able to express their inner thoughts quickly and accurately. In addition, even for adults, in daily communication, sometimes the information conveyed through text or emoticons may be contrary to their true intentions, leading to misunderstandings and communication barriers.

[0004] At present, with the rapid development of artificial intelligence technology, emotion recognition technology has made significant progress. By capturing facial images with cameras and combining advanced algorithms and models, emotion recognition technology can analyze and identify people's current true emotional state and emotional changes in real time. The application of this technology can not only help people better understand each other's emotions and psychological states, but also provide a new way of expression for people with communication barriers. For example, for children and people with disabilities who have difficulty expressing themselves in language, emotion recognition technology can be used as an auxiliary tool to help them communicate with others more effectively and express their needs and feelings. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention discloses a camera face recognition intelligent chat reply method and electronic device, which aims to embed an intelligent recognition module in the chat tool and then combine it with a large model AI to analyze the chat content, so as to automatically or manually provide quick and accurate expression of thoughts and emotions for one party to the chat.

[0006] The present invention is achieved through the following technical solutions:

[0007] In a first aspect, the present invention provides a camera face recognition intelligent chat reply method, comprising the following steps:

[0008] The camera is initialized and enters the video interaction state. The image data is acquired in real time through the camera and data preprocessing is performed.

[0009] The pre-processed image data is transmitted to the recognition module, the expression in the image data and the expression changes during the time period are obtained, and the expression information data is generated;

[0010] Obtain new user information and context information data through the chat module, and output chat information data after data cleaning;

[0011] Input the expression information data and chat information data into the intelligent reply large model AI processing system, and capture the statistical laws and semantic information of the language based on the pre-training of text data;

[0012] Output the current user's language style and match the most realistic and appropriate reply suggestions. Users can use this to decide whether to send or modify the smart reply suggestions before sending.

[0013] Furthermore, in the method, in the process of generating reply suggestions, a rigorous and scientific method is adopted based on the user's personalized preferences and historical behavior data. By deeply analyzing the user's past chat records and the reply content they choose, the user's unique preferences can be accurately captured and learned. Subsequently, in the subsequent generated reply suggestions, these learned preference factors are fully integrated to ensure that the reply suggestions can be closer to the user's actual needs and expectations.

[0014] Furthermore, in the method, the expression information data and the chat information data are input into the intelligent reply large model AI processing system to perform the following steps:

[0015] Input processing: The system first encodes the input text and expressions, converting the text into a numerical form that the model can understand;

[0016] Contextual understanding: The model uses its internal neural network structure, especially the attention mechanism, to understand the contextual meaning of the input information, including language style, emotional color, and conversation history;

[0017] Generate responses: Based on the understood context, the model generates one or more response suggestions. The model predicts the probability distribution of the next word or expression and generates a coherent response based on it.

[0018] Style adaptation: using mechanisms including analysis of the user's historical conversation data or using specific style transfer techniques to ensure that the response style matches the user's style;

[0019] Response selection: Choose the most appropriate response based on multiple response suggestions, including confidence scoring, sentiment analysis, or conversational fluency;

[0020] Output reply: Output one or more most appropriate reply suggestions for the user to choose or send directly to the user.

[0021] Furthermore, in the method, user behavior analysis is a rigorous and rational task, which aims to gain a deeper understanding of the user's emotional state and potential intentions by carefully observing the user's behavior and expressions in front of the camera and combining them with real-time chat content. In this process, if the system detects that the user shows happy emotional characteristics, it will tend to generate more positive and encouraging reply suggestions in a steady and appropriate manner to better meet the user's communication needs.

[0022] Furthermore, in the method, in the process of parsing contextual information, it is necessary to comprehensively consider the current conversation content and simultaneously integrate additional factors such as the user's personal profile, geographic location, and current time, so as to ensure that the reply suggestions provided are both personalized and closely fit the context.

[0023] Furthermore, in the method, an intelligent learning mechanism is set up, which can continuously improve the quality and accuracy of reply suggestions based on user feedback and choices. At the same time, when processing user data, we strictly follow the privacy protection principle to ensure the absolute security of user personal information.

[0024] Furthermore, in the described method system, the intelligent reply large model AI processing system relies on deep learning algorithms to systematically train massive conversation data, aiming to improve the accuracy and natural fluency of replies. The system can proactively predict users' potential needs based on the analysis of conversation history records and user behavior patterns, and generate corresponding reply suggestions accordingly. At the same time, the system also has the ability to dynamically adjust and optimize reply strategies based on user feedback, so as to continuously enhance user experience satisfaction.

[0025] Furthermore, in the method, through the carefully designed cross-platform compatibility settings, it is possible to ensure that the service is seamlessly integrated into various devices and operating systems. With the help of a unified background service architecture, users can enjoy consistent and efficient intelligent chat reply services regardless of the device environment, thereby truly achieving an excellent experience of cross-platform intelligent interaction.

[0026] Furthermore, in the method, the intelligent reply system has a multi-language interaction function, and the system has a built-in multi-language processing module, which can accurately identify and deeply understand user input from different language environments, and then generate accurate and appropriate replies.

[0027] In a second aspect, the present invention provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the memory is coupled to the processor, and when the processor executes the computer program, the camera face recognition intelligent chat reply method described in the first aspect is implemented.

[0028] The beneficial effects of the present invention are:

[0029] Compared with existing chat software, the present invention has significant innovations. It can automatically capture and analyze the real psychological state of the chat partner, and generate personalized automatic reply suggestions based on this. This function is particularly suitable for people who have difficulty communicating smoothly due to various reasons (such as physical inconvenience, lack of input conditions, etc.), providing them with an efficient and automated communication channel. In addition, the implementation principle and technical solution of the present invention also provide new ideas for emotion recognition and automatic communication for other intelligent interactive systems, which has broad application prospects and far-reaching significance. The present invention also has the following advantages:

[0030] 1. Improve user experience: Through camera face recognition technology, the system can identify the user's identity and provide personalized intelligent chat reply services. Users can get replies that match their personal preferences and historical behaviors without tedious input operations, which greatly improves the convenience of interaction and personalized experience.

[0031] 2. Improve interaction efficiency: The intelligent reply system can quickly respond to user input, reduce waiting time, and make the conversation smooth and natural. At the same time, the system can predict user needs by learning user behavior patterns and prepare corresponding reply options in advance, further improving interaction efficiency.

[0032] 3. Enhanced security: Using facial recognition technology, the system can ensure that only authorized users can access specific smart chat services, thereby effectively preventing unauthorized access and information leakage, and protecting user privacy and data security.

[0033] 4. Reduce operating costs: The intelligent response system automatically handles a large number of common problems, reducing the workload of manual customer service, thereby reducing the operating costs of the enterprise. At the same time, the system can continue to learn and optimize, improve the accuracy of problem solving, and further reduce labor costs.

[0034] 5. Strong scalability: Since the system adopts a unified background service architecture, new functional modules can be easily expanded, such as adding new language support or integrating new service interfaces, so that the system can quickly adapt to market changes and technological developments. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 This is a principle and step diagram of the camera face recognition intelligent chat reply method of the present invention;

[0037] Figure 2 This is a principle step diagram of an intelligent chat reply method in an embodiment of the present invention;

[0038] Figure 3 It is a schematic diagram of the execution steps of the intelligent reply large model AI processing system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] In one embodiment, referring to Figure 1 As shown, a camera face recognition intelligent chat reply method is provided, comprising the following steps:

[0041] The camera is initialized and enters the video interaction state. The image data is acquired in real time through the camera and data preprocessing is performed.

[0042] The pre-processed image data is transmitted to the recognition module, the expression in the image data and the expression changes during the time period are obtained, and the expression information data is generated;

[0043] Obtain new user information and context information data through the chat module, and output chat information data after data cleaning;

[0044] Input the expression information data and chat information data into the intelligent reply large model AI processing system, and capture the statistical laws and semantic information of the language based on the pre-training of text data;

[0045] Output the current user's language style and match the most realistic and appropriate reply suggestions. Users can use this to decide whether to send or modify the smart reply suggestions before sending.

[0046] In this embodiment, in the process of generating reply suggestions, the system will comprehensively consider the user's personal preferences and historical behavior data. By deeply analyzing the user's past chat records, including the reply content and reply method they chose, the system can gradually learn and understand the user's preferences.

[0047] In this embodiment, the learning process is dynamic, which allows the system to continuously optimize its reply strategy over time to better adapt to the specific needs of users. When generating reply suggestions in the future, the system will incorporate these learned preferences into them, thereby providing more personalized and user-friendly reply options. Such a mechanism not only improves the user experience, but also enhances the interaction efficiency between users and the system, making communication more smooth and natural.

[0048] In one embodiment, when performing a simple application, such as Figure 2 As shown, an intelligent chat reply method is provided, and the specific steps are as follows

[0049] i. Activate the front camera of your phone when chatting.

[0050] ii. Read camera image data in real time and transmit it to the intelligent recognition module to obtain the current expression and expression changes over time

[0051] iii. The chat module obtains new information from the other party and context information.

[0052] iv. Input chat information and emoticon information into the intelligent reply large model AI processing system to obtain the most realistic and appropriate reply suggestions at the moment based on the user's language style.

[0053] V. Users can decide whether to send the message or modify part of the content before sending it based on the smart reply opinions.

[0054] Compared with existing chat software, this embodiment can automatically obtain the real inner thoughts of the chatter and give automatic reply suggestions. It provides a fast and automatic communication channel for people who are inconvenient to output and lack input conditions. It also provides a solution for other intelligent interactive systems to recognize emotions and communicate automatically.

[0055] In one embodiment, referring to Figure 3 As shown, the expression information data and chat information data are input into the intelligent reply large model AI processing system to perform the following steps:

[0056] Input processing: The system first encodes the input text and expressions, converting the text into a numerical form that the model can understand;

[0057] Contextual understanding: The model uses its internal neural network structure, especially the attention mechanism, to understand the contextual meaning of the input information, including language style, emotional color, and conversation history;

[0058] Generate responses: Based on the understood context, the model generates one or more response suggestions. The model predicts the probability distribution of the next word or expression and generates a coherent response based on it.

[0059] Style adaptation: using mechanisms including analysis of the user's historical conversation data or using specific style transfer techniques to ensure that the response style matches the user's style;

[0060] Response selection: Choose the most appropriate response based on multiple response suggestions, including confidence scoring, sentiment analysis, or conversational fluency;

[0061] Output reply: Output one or more most appropriate reply suggestions for the user to choose or send directly to the user.

[0062] In one embodiment, user behavior analysis is a complex and detailed process that involves in-depth observation and interpretation of the user's behavior and facial expressions in front of the camera. Through this observation, the system can capture the user's emotional changes and combine the content expressed by the user in the conversation to more accurately understand the user's emotional state and potential intentions. For example, when the system detects that the user appears happy and excited, it tends to generate more positive and optimistic reply suggestions to cater to the user's emotions and enhance the pleasure of communication.

[0063] In addition, in the process of understanding contextual information, the system is not limited to the current conversation content, but also takes into account additional information such as the user's profile, geographic location, current time, etc. This information helps the system build a more comprehensive user portrait, thereby providing more personalized and contextual response suggestions. For example, if a user expresses interest in a topic within a certain time period, the system can provide a response that is more in line with the user's current needs and situation based on this time point and the user's interests. Such personalized services can not only enhance the user experience, but also make communication more natural and smooth.

[0064] This embodiment is provided with an intelligent learning mechanism, which continuously optimizes the quality and accuracy of reply suggestions based on user feedback and choices, and abides by the privacy protection principles when processing user data to ensure the security of user personal information.

[0065] In actual applications, the intelligent chat reply system also has the ability to self-learn and adapt. Through deep learning algorithms, the system can analyze the nuances of user feedback and identify which reply suggestions are more popular with users and which need to be improved. Over time, the system will accumulate a large amount of user interaction data. After analysis, this data can help the system more accurately predict user needs and provide more personalized reply suggestions.

[0066] In addition, in order to protect the privacy of users, the system is designed with privacy protection as one of its core principles. All user data will be encrypted and will only be used with the user's authorization. The system also provides transparent privacy setting options, allowing users to view and manage their own data at any time, and choose whether to participate in the data collection and analysis process.

[0067] In terms of technical implementation, the system uses advanced camera face recognition technology to accurately identify the user's identity and combine the user's personal information and historical behavior data to provide users with a more personalized chat experience. At the same time, the system can also provide context-related reply suggestions based on the user's geographic location and current time, making the chat content more in line with the user's actual life scenarios.

[0068] In one embodiment, the intelligent reply large model AI processing system uses deep learning algorithms to conduct in-depth training and analysis on massive amounts of conversation data, thereby significantly improving the accuracy and naturalness of replies. This system can carefully analyze conversation history and user behavior patterns, predict users' potential needs, and provide corresponding reply suggestions based on these needs. In addition, the system can also adjust and optimize reply strategies in real time based on user feedback to ensure continuous improvement of user experience.

[0069] Through the carefully designed cross-platform compatibility, the intelligent reply large model AI processing system of this embodiment can be seamlessly integrated into a variety of different devices and operating systems. Whether the user is using a smartphone, tablet or personal computer, the system can provide users with consistent intelligent chat reply services through a unified background service. The realization of this seamless integration and unified service enables users to enjoy a truly consistent and smooth cross-platform intelligent interaction experience on different devices.

[0070] The intelligent reply large model AI processing system of this embodiment also has powerful multilingual interaction capabilities. The system has built-in advanced multilingual processing modules that can accurately identify and understand input information from users of different languages. Whether the user uses English, Chinese, Spanish or any other language, the system can provide accurate and natural responses. This function greatly expands the scope of application of the system, enabling it to serve users around the world and meet their diverse language needs.

[0071] In one embodiment, an electronic device is provided, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the memory is coupled to the processor, and when the processor executes the computer program, a camera face recognition intelligent chat reply method is implemented.

[0072] In summary, compared with existing chat software, the present invention has significant innovations. It can automatically capture and analyze the real psychological state of the chat partner, and generate personalized automatic reply suggestions based on this. This function is particularly suitable for people who have difficulty communicating smoothly due to various reasons (such as physical inconvenience, lack of input conditions, etc.), providing them with an efficient and automated communication channel. In addition, the implementation principle and technical solution of the present invention also provide new ideas for emotion recognition and automatic communication for other intelligent interactive systems, which has broad application prospects and far-reaching significance.

[0073] The present invention also has the following advantages:

[0074] Improve user experience: Through camera face recognition technology, the system can identify the user's identity and provide personalized intelligent chat reply services. Users can get replies that match their personal preferences and historical behaviors without tedious input operations, which greatly improves the convenience of interaction and personalized experience.

[0075] Improve interaction efficiency: The intelligent reply system can quickly respond to user input, reduce waiting time, and make the conversation smooth and natural. At the same time, by learning the user's behavior patterns, the system can predict user needs and prepare corresponding reply options in advance, further improving interaction efficiency.

[0076] Enhanced security: Using facial recognition technology, the system can ensure that only authorized users can access specific smart chat services, thereby effectively preventing unauthorized access and information leakage, and protecting user privacy and data security.

[0077] Reduce operating costs: The intelligent response system automatically handles a large number of common problems, reducing the workload of manual customer service, thereby reducing the operating costs of the enterprise. At the same time, the system can continue to learn and optimize, improve the accuracy of problem solving, and further reduce labor costs.

[0078] Strong scalability: Since the system adopts a unified background service architecture, new functional modules can be easily expanded, such as adding new language support or integrating new service interfaces, so that the system can quickly adapt to market changes and technological developments.

[0079] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A camera face recognition intelligent chat reply method, characterized in that: The following steps are involved: The camera is initialized and enters the video interaction state. The image data is acquired in real time through the camera and data preprocessing is performed. The pre-processed image data is transmitted to the recognition module, the expression in the image data and the expression changes during the time period are obtained, and the expression information data is generated; Obtain new user information and context information data through the chat module, and output chat information data after data cleaning; Input the expression information data and chat information data into the intelligent reply large model AI processing system, and capture the statistical laws and semantic information of the language based on the pre-training of text data; Output the current user's language style and match the most realistic and appropriate reply suggestions. Users can use this to decide whether to send or modify the smart reply suggestions before sending.

2. The camera face recognition intelligent chat reply method according to claim 1, characterized in that: In the method, when generating reply suggestions, based on the user's personal preferences and historical behavior data, by analyzing the user's previous chat records and selected replies, the user's preferences can be learned and reflected in future reply suggestions.

3. The camera face recognition intelligent chat reply method according to claim 1, characterized in that: In the method, the expression information data and the chat information data are input into the intelligent reply large model AI processing system to perform the following steps: Input processing: The system first encodes the input text and expressions, converting the text into a numerical form that the model can understand; Contextual understanding: The model uses its internal neural network structure to understand the contextual meaning of the input information, including language style, emotional color, and conversation history; Generate responses: Based on the understood context, the model generates one or more response suggestions. The model predicts the probability distribution of the next word or expression and generates a coherent response based on it. Style adaptation: using mechanisms including analysis of the user's historical conversation data or using specific style transfer techniques to ensure that the response style matches the user's style; Response selection: Choose the most appropriate response based on multiple response suggestions, including confidence scoring, sentiment analysis, or conversational fluency; Output reply: Output one or more most appropriate reply suggestions for the user to choose or send directly to the user.

4. The camera face recognition intelligent chat reply method according to claim 1, characterized in that: In the method, user behavior analysis further understands the user's emotional state and intention by analyzing the user's behavior and expressions in front of the camera and combining them with the chat content. When the user appears happy, the system tends to generate more positive reply suggestions.

5. The camera face recognition intelligent chat reply method according to claim 1, characterized in that: In the method, when understanding contextual information, the current conversation content is taken into consideration, and additional information such as the user's profile, geographic location, current time, etc. is combined to provide more personalized and context-related reply suggestions.

6. The camera face recognition intelligent chat reply method according to claim 1, characterized in that: In the method, an intelligent learning mechanism is provided to continuously optimize the quality and accuracy of reply suggestions based on user feedback and choices. When processing user data, the privacy protection principle is followed to ensure the security of user personal information.

7. The camera face recognition intelligent chat reply method according to claim 1, characterized in that: In the method, the intelligent reply large model AI processing system uses a deep learning algorithm to train a large amount of conversation data to improve the accuracy and naturalness of the reply. It can predict the user's potential needs based on the conversation history and user behavior patterns, and provide corresponding reply suggestions. It can also adjust and optimize the reply strategy based on user feedback, thereby continuously improving the user experience.

8. The camera face recognition intelligent chat reply method according to claim 1, characterized in that: In the method, by setting cross-platform compatibility, it can be seamlessly integrated into different devices and operating systems. Through a unified background service, users can enjoy consistent intelligent chat reply services on different devices, achieving a truly cross-platform intelligent interactive experience.

9. The camera face recognition intelligent chat reply method according to claim 1, characterized in that: In the method, intelligent reply supports multi-language interaction, and the system has a built-in multi-language processing module that can recognize and understand user input in different languages ​​and provide accurate replies.

10. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. The memory is coupled to the processor, and when the processor executes the computer program, the camera face recognition intelligent chat reply method as described in any one of claims 1 to 9 is implemented.

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